first vision code

HiveCoreR0/src/cv_part/src/.cmake-format.py

@@ -0,0 +1,104 @@
+# -----------------------------
+# Options affecting formatting.
+# -----------------------------
+with section("format"):
+
+  # Disable formatting entirely, making cmake-format a no-op
+  disable = False
+
+  # How wide to allow formatted cmake files
+  line_width = 120
+
+  # How many spaces to tab for indent
+  tab_size = 4
+
+  # If true, lines are indented using tab characters (utf-8 0x09) instead of
+  # <tab_size> space characters (utf-8 0x20). In cases where the layout would
+  # require a fractional tab character, the behavior of the  fractional
+  # indentation is governed by <fractional_tab_policy>
+  use_tabchars = False
+
+  # If <use_tabchars> is True, then the value of this variable indicates how
+  # fractional indentions are handled during whitespace replacement. If set to
+  # 'use-space', fractional indentation is left as spaces (utf-8 0x20). If set
+  # to `round-up` fractional indentation is replaced with a single tab character
+  # (utf-8 0x09) effectively shifting the column to the next tabstop
+  fractional_tab_policy = 'use-space'
+
+  # If an argument group contains more than this many sub-groups (parg or kwarg
+  # groups) then force it to a vertical layout.
+  max_subgroups_hwrap = 4
+
+  # If a positional argument group contains more than this many arguments, then
+  # force it to a vertical layout.
+  max_pargs_hwrap = 6
+
+  # If a cmdline positional group consumes more than this many lines without
+  # nesting, then invalidate the layout (and nest)
+  max_rows_cmdline = 2
+
+  # If true, separate flow control names from their parentheses with a space
+  separate_ctrl_name_with_space = False
+
+  # If true, separate function names from parentheses with a space
+  separate_fn_name_with_space = False
+
+  # If a statement is wrapped to more than one line, than dangle the closing
+  # parenthesis on its own line.
+  dangle_parens = True
+
+  # If the trailing parenthesis must be 'dangled' on its on line, then align it
+  # to this reference: `prefix`: the start of the statement,  `prefix-indent`:
+  # the start of the statement, plus one indentation  level, `child`: align to
+  # the column of the arguments
+  dangle_align = 'prefix'
+
+  # If the statement spelling length (including space and parenthesis) is
+  # smaller than this amount, then force reject nested layouts.
+  min_prefix_chars = 4
+
+  # If the statement spelling length (including space and parenthesis) is larger
+  # than the tab width by more than this amount, then force reject un-nested
+  # layouts.
+  max_prefix_chars = 10
+
+  # If a candidate layout is wrapped horizontally but it exceeds this many
+  # lines, then reject the layout.
+  max_lines_hwrap = 2
+
+  # What style line endings to use in the output.
+  line_ending = 'unix'
+
+  # Format command names consistently as 'lower' or 'upper' case
+  command_case = 'canonical'
+
+  # Format keywords consistently as 'lower' or 'upper' case
+  keyword_case = 'unchanged'
+
+  # A list of command names which should always be wrapped
+  always_wrap = []
+
+  # If true, the argument lists which are known to be sortable will be sorted
+  # lexicographicall
+  enable_sort = True
+
+  # If true, the parsers may infer whether or not an argument list is sortable
+  # (without annotation).
+  autosort = False
+
+  # By default, if cmake-format cannot successfully fit everything into the
+  # desired linewidth it will apply the last, most agressive attempt that it
+  # made. If this flag is True, however, cmake-format will print error, exit
+  # with non-zero status code, and write-out nothing
+  require_valid_layout = False
+
+  # A dictionary mapping layout nodes to a list of wrap decisions. See the
+  # documentation for more information.
+  layout_passes = {}
+
+# -----------------------------
+# Options affecting comment formatting (indirectly).
+# -----------------------------
+with section("markup"):
+    # Disabling markup avoids merging consecutive comment lines
+    enable_markup = False

HiveCoreR0/src/cv_part/src/.make_deb.sh

@@ -0,0 +1,13 @@
+#!/usr/bin/bash
+set -e
+CURR_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
+sub_dir=(
+  orbbec_camera_msgs
+  orbbec_camera
+)
+
+for pkg in "${sub_dir[@]}"; do
+  cd "${pkg}"
+  bash .make_deb.sh
+  cd "${CURR_DIR}"
+done

HiveCoreR0/src/cv_part/src/LICENSE

@@ -0,0 +1,201 @@
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+
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+
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HiveCoreR0/src/cv_part/src/detect_part/.idea/.gitignore

@@ -0,0 +1,8 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
+# 基于编辑器的 HTTP 客户端请求
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

HiveCoreR0/src/cv_part/src/detect_part/.idea/cv_part.iml

@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.10" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="py.test" />
+  </component>
+</module>

HiveCoreR0/src/cv_part/src/detect_part/.idea/dictionaries/project.xml

@@ -0,0 +1,7 @@
+<component name="ProjectDictionaryState">
+  <dictionary name="project">
+    <words>
+      <w>rclpy</w>
+    </words>
+  </dictionary>
+</component>

HiveCoreR0/src/cv_part/src/detect_part/.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

HiveCoreR0/src/cv_part/src/detect_part/.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.10" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
+</project>

HiveCoreR0/src/cv_part/src/detect_part/.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/cv_part.iml" filepath="$PROJECT_DIR$/.idea/cv_part.iml" />
+    </modules>
+  </component>
+</project>

HiveCoreR0/src/cv_part/src/detect_part/detect_part/detect.py

@@ -0,0 +1,230 @@
+import os
+import random
+from collections import defaultdict
+
+import cv2
+import numpy as np
+from cv_bridge import CvBridge
+
+import torch
+from detect_part.ultralytics import YOLO
+
+import rclpy
+from rclpy.node import Node
+from message_filters import ApproximateTimeSynchronizer, Subscriber
+
+from sensor_msgs.msg import Image, CameraInfo
+from geometry_msgs.msg import Point
+from interfaces.msg import PointWithID, PointWithIDArray
+
+import detect_part
+
+
+class DetectNode(Node):
+    def __init__(self, name):
+        super().__init__(name)
+        self.depth_image = None
+        self.fx = self.fy = self.cx = self.cy = None
+        self.checkpoint_path = None
+
+        self._init_param()
+        self._init_model()
+        self._init_subscriber()
+
+        self.cv_bridge = CvBridge()
+
+        self.pub_pose_list = self.create_publisher(PointWithIDArray, '/pose/cv_detect_pose', 10)
+        if self.output_detect_image:
+            self.pub_detect_image = self.create_publisher(Image, '/image/detect_image', 10)
+
+    def _init_param(self):
+        pkg_dir = os.path.dirname(detect_part.__file__)
+
+        self.declare_parameter('checkpoint_name', 'yolo11s.pt')
+        checkpoint_name = self.get_parameter('checkpoint_name').value
+        self.checkpoint_path = os.path.join(pkg_dir, 'checkpoint', checkpoint_name)
+
+
+        self.declare_parameter('output_detect_image', False)
+        self.output_detect_image = self.get_parameter('output_detect_image').value
+
+        self.declare_parameter('set_confidence', 0.6)
+        self.set_confidence = self.get_parameter('set_confidence').value
+
+    def _init_model(self):
+        device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        try:
+            self.model = YOLO(self.checkpoint_path).to(device)
+        except Exception as e:
+            self.get_logger().error(f'Failed to load YOLO model: {e}')
+            raise
+        self.get_logger().info(f'Loading checkpoint from: {self.checkpoint_path}')
+
+    def _init_subscriber(self):
+        self.sub_camera_info = self.create_subscription(
+            CameraInfo,
+            '/camera/color/camera_info',
+            self._camera_info_callback,
+            10
+        )
+
+        # sync get color and depth img
+        self.sub_color_image = Subscriber(self, Image, '/camera/color/image_raw')
+        self.sub_depth_image = Subscriber(self, Image, '/camera/depth/image_raw')
+
+        self.sync_subscriber = ApproximateTimeSynchronizer(
+            [self.sub_color_image, self.sub_depth_image],
+            queue_size=10,
+            slop=0.1
+        )
+        self.sync_subscriber.registerCallback(self._sync_callback)
+
+    def _sync_callback(self, color_img_ros, depth_img_ros):
+        if None in (self.fx, self.fy, self.cx, self.cy):
+            self.get_logger().warn('Camera intrinsics not yet received. Waiting...')
+            return
+
+        color_img_cv = self.cv_bridge.imgmsg_to_cv2(color_img_ros, "bgr8")
+        depth_img_cv = self.cv_bridge.imgmsg_to_cv2(depth_img_ros, '16UC1')
+        # self.get_logger().info(f'Color image: {color_img_cv.shape}')
+        # self.get_logger().info(f'Depth image: {depth_img_cv.shape}')
+
+        if self.output_detect_image:
+            detect_img, pose_dict = self._detect_image(color_img_cv, depth_img_cv)
+            self.pub_detect_image.publish(detect_img)
+        else:
+            pose_dict = self._detect_image(color_img_cv)
+
+        pose_list_all = PointWithIDArray()
+        for (class_id, class_name), points in pose_dict.items():
+            pose_list_all.objects.append(
+                PointWithID(
+                    class_name = class_name,
+                    class_id = class_id,
+                    points = points
+                )
+            )
+
+        self.pub_pose_list.publish(pose_list_all)
+
+    def _camera_info_callback(self, msg: CameraInfo):
+        self.fx = msg.k[0]
+        self.fy = msg.k[4]
+        self.cx = msg.k[2]
+        self.cy = msg.k[5]
+
+        self.destroy_subscription(self.sub_camera_info)
+
+    def _detect_image(self, rgb_img: np.ndarray, depth_img: np.ndarray):
+        pose_dict = defaultdict(list)
+
+        img_h, img_w = rgb_img.shape[:2]
+        border_x = img_w * 0.10
+        border_y = img_h * 0.08
+
+        results = self.model(rgb_img)
+        result = results[0]
+
+        boxes = result.boxes.xywh.cpu().numpy()
+        confidences = result.boxes.conf.cpu().numpy()
+        class_ids = result.boxes.cls.cpu().numpy()
+        labels = result.names
+
+        for i, box in enumerate(boxes):
+            if confidences[i] < self.set_confidence:
+                continue
+            else:
+                x_center, y_center, width, height = box[:4]
+
+                if not (border_x <= x_center <= img_w - border_x and border_y <= y_center <= img_h - border_y):
+                    # self.get_logger().info(f'Skipping object near edge at ({x_center},{y_center})')
+                    continue
+
+                x, y, z = self._calculate_coordinate(depth_img, x_center, y_center, width, height)
+                if (x, y, z) != (0.0, 0.0, 0.0):
+                    pose_dict[int(class_ids[i]), labels[class_ids[i]]].append(Point(x=x, y=y, z=z))
+
+        if self.output_detect_image:
+            self._draw_box(rgb_img, boxes, confidences, class_ids, labels)
+            return self.cv_bridge.cv2_to_imgmsg(rgb_img, "bgr8"), pose_dict
+        else:
+            return pose_dict
+
+    def _draw_box(self, rgb_img, boxes, confidences, class_ids, labels):
+        for i, box in enumerate(boxes):
+            if confidences[i] < self.set_confidence:
+                continue
+            else:
+                x_center, y_center, width, height = box[:4]
+
+                p1 = [int((x_center - width / 2)), int((y_center - height / 2))]
+                p2 = [int((x_center + width / 2)), int((y_center + height / 2))]
+                cv2.rectangle(rgb_img, p1, p2, (255, 255, 0), 2)
+
+                cv2.putText(rgb_img, f'{labels[class_ids[i]]}: {confidences[i]*100:.2f}', (p1[0], p1[1] - 10),
+                            cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+                # cv2.putText(rgb_img, f'cs:{x:.2f} {y:.2f} {z:.2f}', (p1[0], p1[1] - 35),
+                #             cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+
+    def _calculate_coordinate(self, depth_img: np.ndarray, u, v, width: int, height: int) -> tuple[float, float, float]:
+        u = int(round(u))
+        v = int(round(v))
+
+        if not (0 <= v < depth_img.shape[0] and 0 <= u < depth_img.shape[1]):
+            self.get_logger().warning(f'Calculate coordinate error: u={u}, v={v}')
+            return 0.0, 0.0, 0.0
+
+        window_size = 39
+        half = window_size // 2
+
+        patch = depth_img[max(0, v - half):v + half + 1, max(0, u - half):u + half + 1:2].flatten()
+        valid_depths = patch[patch > 0]
+
+        if len(valid_depths) == 0:
+            patch = self._get_nine_patch_samples(depth_img, u, v, width, height)
+            valid_depths = patch[patch > 0]
+
+            if len(valid_depths) == 0:
+                self.get_logger().warning(f'No valid depth in window at ({u}, {v})')
+                return 0.0, 0.0, 0.0
+
+        # x = depth_img[v, u] / 1e3
+        depth = np.median(valid_depths) / 1e3
+        x = depth
+
+        y = -(u - self.cx) * x / self.fx
+        z = -(v - self.cy) * x / self.fy
+
+        return x, y, z
+
+    def _get_nine_patch_samples(self, depth_img, u, v, width, height):
+        ws = [int(round(random.uniform(-0.05*width, 0.05*width)-width/4)), 0, int(random.uniform(-0.05*width, 0.05*width)+round(width/4))]
+        hs = [int(round(random.uniform(-0.05*height, 0.05*height)-height/4)), 0, int(round(random.uniform(-0.05*height, 0.05*height)+height/4))]
+
+        window_size = 25
+        half = window_size // 2
+
+        patch = []
+        for w in ws:
+            for h in hs:
+                patch.append(
+                    depth_img[int(max(0, v + h - half)):int(min(v + h + half + 1, depth_img.shape[0])):2,
+                    int(max(0, u + w - half)):int(min(u + w + half + 1, depth_img.shape[1])):2].flatten())
+
+        return np.concatenate(patch)
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = DetectNode('detect')
+    try:
+        rclpy.spin(node)
+    except KeyboardInterrupt:
+        pass
+    finally:
+        node.destroy_node()
+        rclpy.shutdown()
+
+
+if __name__ == '__main__':
+    main()

HiveCoreR0/src/cv_part/src/detect_part/detect_part/sub_pose.py

@@ -0,0 +1,31 @@
+import rclpy
+from rclpy.node import Node
+
+from interfaces.msg import PointWithIDArray
+
+
+class SubPose(Node):
+    def __init__(self, name):
+        super().__init__(name)
+        self.sub = self.create_subscription(PointWithIDArray, '/pose/cv_detect_pose', self.pose_callback, 10)
+
+    def pose_callback(self, msg):
+        pose_dict = {}
+        for object in msg.objects:
+            pose_dict[object.class_id] = {
+                'name' : object.class_name,
+                'points' : object.points,
+            }
+
+        self.get_logger().info(f'{pose_dict}')
+
+
+def main(args=None):
+    rclpy.init(args=args)
+    node = SubPose('pose')
+    rclpy.spin(node)
+    node.destroy_node()
+    rclpy.shutdown()
+
+if __name__ == '__main__':
+    main()

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/__init__.py

@@ -0,0 +1,30 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+__version__ = "8.3.168"
+
+import os
+
+# Set ENV variables (place before imports)
+if not os.environ.get("OMP_NUM_THREADS"):
+    os.environ["OMP_NUM_THREADS"] = "1"  # default for reduced CPU utilization during training
+
+from ultralytics.models import NAS, RTDETR, SAM, YOLO, YOLOE, FastSAM, YOLOWorld
+from ultralytics.utils import ASSETS, SETTINGS
+from ultralytics.utils.checks import check_yolo as checks
+from ultralytics.utils.downloads import download
+
+settings = SETTINGS
+__all__ = (
+    "__version__",
+    "ASSETS",
+    "YOLO",
+    "YOLOWorld",
+    "YOLOE",
+    "NAS",
+    "SAM",
+    "FastSAM",
+    "RTDETR",
+    "checks",
+    "download",
+    "settings",
+)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/Argoverse.yaml

@@ -0,0 +1,77 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Argoverse-HD dataset (ring-front-center camera) https://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
+# Example usage: yolo train data=Argoverse.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── Argoverse ← downloads here (31.5 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: bus
+  5: truck
+  6: traffic_light
+  7: stop_sign
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  from pathlib import Path
+
+  from tqdm import tqdm
+  from ultralytics.utils.downloads import download
+
+  def argoverse2yolo(set):
+      """Convert Argoverse dataset annotations to YOLO format for object detection tasks."""
+      labels = {}
+      a = json.load(open(set, "rb"))
+      for annot in tqdm(a["annotations"], desc=f"Converting {set} to YOLOv5 format..."):
+          img_id = annot["image_id"]
+          img_name = a["images"][img_id]["name"]
+          img_label_name = f"{img_name[:-3]}txt"
+
+          cls = annot["category_id"]  # instance class id
+          x_center, y_center, width, height = annot["bbox"]
+          x_center = (x_center + width / 2) / 1920.0  # offset and scale
+          y_center = (y_center + height / 2) / 1200.0  # offset and scale
+          width /= 1920.0  # scale
+          height /= 1200.0  # scale
+
+          img_dir = set.parents[2] / "Argoverse-1.1" / "labels" / a["seq_dirs"][a["images"][annot["image_id"]]["sid"]]
+          if not img_dir.exists():
+              img_dir.mkdir(parents=True, exist_ok=True)
+
+          k = str(img_dir / img_label_name)
+          if k not in labels:
+              labels[k] = []
+          labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+      for k in labels:
+          with open(k, "w", encoding="utf-8") as f:
+              f.writelines(labels[k])
+
+
+  # Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = ["https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link"]
+  print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
+  print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
+  # download(urls, dir=dir)
+
+  # Convert
+  annotations_dir = "Argoverse-HD/annotations/"
+  (dir / "Argoverse-1.1" / "tracking").rename(dir / "Argoverse-1.1" / "images")  # rename 'tracking' to 'images'
+  for d in "train.json", "val.json":
+      argoverse2yolo(dir / annotations_dir / d)  # convert Argoverse annotations to YOLO labels

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/DOTAv1.5.yaml

@@ -0,0 +1,37 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA 1.5 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.5.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota1.5 ← downloads here (2GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: DOTAv1.5 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.5
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+  15: container crane
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.5.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/DOTAv1.yaml

@@ -0,0 +1,36 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota1 ← downloads here (2GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: DOTAv1 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/GlobalWheat2020.yaml

@@ -0,0 +1,68 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
+# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
+# Example usage: yolo train data=GlobalWheat2020.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── GlobalWheat2020 ← downloads here (7.0 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+  - images/arvalis_1
+  - images/arvalis_2
+  - images/arvalis_3
+  - images/ethz_1
+  - images/rres_1
+  - images/inrae_1
+  - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+  - images/ethz_1
+test: # test images (optional) 1276 images
+  - images/utokyo_1
+  - images/utokyo_2
+  - images/nau_1
+  - images/uq_1
+
+# Classes
+names:
+  0: wheat_head
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from pathlib import Path
+
+  from ultralytics.utils.downloads import download
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [
+      "https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip",
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip",
+  ]
+  download(urls, dir=dir)
+
+  # Make Directories
+  for p in "annotations", "images", "labels":
+      (dir / p).mkdir(parents=True, exist_ok=True)
+
+  # Move
+  for p in (
+      "arvalis_1",
+      "arvalis_2",
+      "arvalis_3",
+      "ethz_1",
+      "rres_1",
+      "inrae_1",
+      "usask_1",
+      "utokyo_1",
+      "utokyo_2",
+      "nau_1",
+      "uq_1",
+  ):
+      (dir / "global-wheat-codalab-official" / p).rename(dir / "images" / p)  # move to /images
+      f = (dir / "global-wheat-codalab-official" / p).with_suffix(".json")  # json file
+      if f.exists():
+          f.rename((dir / "annotations" / p).with_suffix(".json"))  # move to /annotations

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/HomeObjects-3K.yaml

@@ -0,0 +1,32 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# HomeObjects-3K dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/homeobjects-3k/
+# Example usage: yolo train data=HomeObjects-3K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── homeobjects-3K ← downloads here (390 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: homeobjects-3K # dataset root dir
+train: images/train # train images (relative to 'path') 2285 images
+val: images/val # val images (relative to 'path') 404 images
+
+# Classes
+names:
+  0: bed
+  1: sofa
+  2: chair
+  3: table
+  4: lamp
+  5: tv
+  6: laptop
+  7: wardrobe
+  8: window
+  9: door
+  10: potted plant
+  11: photo frame
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/homeobjects-3K.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/Objects365.yaml

@@ -0,0 +1,443 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
+# Example usage: yolo train data=Objects365.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: Person
+  1: Sneakers
+  2: Chair
+  3: Other Shoes
+  4: Hat
+  5: Car
+  6: Lamp
+  7: Glasses
+  8: Bottle
+  9: Desk
+  10: Cup
+  11: Street Lights
+  12: Cabinet/shelf
+  13: Handbag/Satchel
+  14: Bracelet
+  15: Plate
+  16: Picture/Frame
+  17: Helmet
+  18: Book
+  19: Gloves
+  20: Storage box
+  21: Boat
+  22: Leather Shoes
+  23: Flower
+  24: Bench
+  25: Potted Plant
+  26: Bowl/Basin
+  27: Flag
+  28: Pillow
+  29: Boots
+  30: Vase
+  31: Microphone
+  32: Necklace
+  33: Ring
+  34: SUV
+  35: Wine Glass
+  36: Belt
+  37: Monitor/TV
+  38: Backpack
+  39: Umbrella
+  40: Traffic Light
+  41: Speaker
+  42: Watch
+  43: Tie
+  44: Trash bin Can
+  45: Slippers
+  46: Bicycle
+  47: Stool
+  48: Barrel/bucket
+  49: Van
+  50: Couch
+  51: Sandals
+  52: Basket
+  53: Drum
+  54: Pen/Pencil
+  55: Bus
+  56: Wild Bird
+  57: High Heels
+  58: Motorcycle
+  59: Guitar
+  60: Carpet
+  61: Cell Phone
+  62: Bread
+  63: Camera
+  64: Canned
+  65: Truck
+  66: Traffic cone
+  67: Cymbal
+  68: Lifesaver
+  69: Towel
+  70: Stuffed Toy
+  71: Candle
+  72: Sailboat
+  73: Laptop
+  74: Awning
+  75: Bed
+  76: Faucet
+  77: Tent
+  78: Horse
+  79: Mirror
+  80: Power outlet
+  81: Sink
+  82: Apple
+  83: Air Conditioner
+  84: Knife
+  85: Hockey Stick
+  86: Paddle
+  87: Pickup Truck
+  88: Fork
+  89: Traffic Sign
+  90: Balloon
+  91: Tripod
+  92: Dog
+  93: Spoon
+  94: Clock
+  95: Pot
+  96: Cow
+  97: Cake
+  98: Dining Table
+  99: Sheep
+  100: Hanger
+  101: Blackboard/Whiteboard
+  102: Napkin
+  103: Other Fish
+  104: Orange/Tangerine
+  105: Toiletry
+  106: Keyboard
+  107: Tomato
+  108: Lantern
+  109: Machinery Vehicle
+  110: Fan
+  111: Green Vegetables
+  112: Banana
+  113: Baseball Glove
+  114: Airplane
+  115: Mouse
+  116: Train
+  117: Pumpkin
+  118: Soccer
+  119: Skiboard
+  120: Luggage
+  121: Nightstand
+  122: Tea pot
+  123: Telephone
+  124: Trolley
+  125: Head Phone
+  126: Sports Car
+  127: Stop Sign
+  128: Dessert
+  129: Scooter
+  130: Stroller
+  131: Crane
+  132: Remote
+  133: Refrigerator
+  134: Oven
+  135: Lemon
+  136: Duck
+  137: Baseball Bat
+  138: Surveillance Camera
+  139: Cat
+  140: Jug
+  141: Broccoli
+  142: Piano
+  143: Pizza
+  144: Elephant
+  145: Skateboard
+  146: Surfboard
+  147: Gun
+  148: Skating and Skiing shoes
+  149: Gas stove
+  150: Donut
+  151: Bow Tie
+  152: Carrot
+  153: Toilet
+  154: Kite
+  155: Strawberry
+  156: Other Balls
+  157: Shovel
+  158: Pepper
+  159: Computer Box
+  160: Toilet Paper
+  161: Cleaning Products
+  162: Chopsticks
+  163: Microwave
+  164: Pigeon
+  165: Baseball
+  166: Cutting/chopping Board
+  167: Coffee Table
+  168: Side Table
+  169: Scissors
+  170: Marker
+  171: Pie
+  172: Ladder
+  173: Snowboard
+  174: Cookies
+  175: Radiator
+  176: Fire Hydrant
+  177: Basketball
+  178: Zebra
+  179: Grape
+  180: Giraffe
+  181: Potato
+  182: Sausage
+  183: Tricycle
+  184: Violin
+  185: Egg
+  186: Fire Extinguisher
+  187: Candy
+  188: Fire Truck
+  189: Billiards
+  190: Converter
+  191: Bathtub
+  192: Wheelchair
+  193: Golf Club
+  194: Briefcase
+  195: Cucumber
+  196: Cigar/Cigarette
+  197: Paint Brush
+  198: Pear
+  199: Heavy Truck
+  200: Hamburger
+  201: Extractor
+  202: Extension Cord
+  203: Tong
+  204: Tennis Racket
+  205: Folder
+  206: American Football
+  207: earphone
+  208: Mask
+  209: Kettle
+  210: Tennis
+  211: Ship
+  212: Swing
+  213: Coffee Machine
+  214: Slide
+  215: Carriage
+  216: Onion
+  217: Green beans
+  218: Projector
+  219: Frisbee
+  220: Washing Machine/Drying Machine
+  221: Chicken
+  222: Printer
+  223: Watermelon
+  224: Saxophone
+  225: Tissue
+  226: Toothbrush
+  227: Ice cream
+  228: Hot-air balloon
+  229: Cello
+  230: French Fries
+  231: Scale
+  232: Trophy
+  233: Cabbage
+  234: Hot dog
+  235: Blender
+  236: Peach
+  237: Rice
+  238: Wallet/Purse
+  239: Volleyball
+  240: Deer
+  241: Goose
+  242: Tape
+  243: Tablet
+  244: Cosmetics
+  245: Trumpet
+  246: Pineapple
+  247: Golf Ball
+  248: Ambulance
+  249: Parking meter
+  250: Mango
+  251: Key
+  252: Hurdle
+  253: Fishing Rod
+  254: Medal
+  255: Flute
+  256: Brush
+  257: Penguin
+  258: Megaphone
+  259: Corn
+  260: Lettuce
+  261: Garlic
+  262: Swan
+  263: Helicopter
+  264: Green Onion
+  265: Sandwich
+  266: Nuts
+  267: Speed Limit Sign
+  268: Induction Cooker
+  269: Broom
+  270: Trombone
+  271: Plum
+  272: Rickshaw
+  273: Goldfish
+  274: Kiwi fruit
+  275: Router/modem
+  276: Poker Card
+  277: Toaster
+  278: Shrimp
+  279: Sushi
+  280: Cheese
+  281: Notepaper
+  282: Cherry
+  283: Pliers
+  284: CD
+  285: Pasta
+  286: Hammer
+  287: Cue
+  288: Avocado
+  289: Hami melon
+  290: Flask
+  291: Mushroom
+  292: Screwdriver
+  293: Soap
+  294: Recorder
+  295: Bear
+  296: Eggplant
+  297: Board Eraser
+  298: Coconut
+  299: Tape Measure/Ruler
+  300: Pig
+  301: Showerhead
+  302: Globe
+  303: Chips
+  304: Steak
+  305: Crosswalk Sign
+  306: Stapler
+  307: Camel
+  308: Formula 1
+  309: Pomegranate
+  310: Dishwasher
+  311: Crab
+  312: Hoverboard
+  313: Meatball
+  314: Rice Cooker
+  315: Tuba
+  316: Calculator
+  317: Papaya
+  318: Antelope
+  319: Parrot
+  320: Seal
+  321: Butterfly
+  322: Dumbbell
+  323: Donkey
+  324: Lion
+  325: Urinal
+  326: Dolphin
+  327: Electric Drill
+  328: Hair Dryer
+  329: Egg tart
+  330: Jellyfish
+  331: Treadmill
+  332: Lighter
+  333: Grapefruit
+  334: Game board
+  335: Mop
+  336: Radish
+  337: Baozi
+  338: Target
+  339: French
+  340: Spring Rolls
+  341: Monkey
+  342: Rabbit
+  343: Pencil Case
+  344: Yak
+  345: Red Cabbage
+  346: Binoculars
+  347: Asparagus
+  348: Barbell
+  349: Scallop
+  350: Noddles
+  351: Comb
+  352: Dumpling
+  353: Oyster
+  354: Table Tennis paddle
+  355: Cosmetics Brush/Eyeliner Pencil
+  356: Chainsaw
+  357: Eraser
+  358: Lobster
+  359: Durian
+  360: Okra
+  361: Lipstick
+  362: Cosmetics Mirror
+  363: Curling
+  364: Table Tennis
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  from pathlib import Path
+
+  import numpy as np
+  from tqdm import tqdm
+
+  from ultralytics.utils.checks import check_requirements
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywhn
+
+  check_requirements("faster-coco-eval")
+  from faster_coco_eval import COCO
+
+  # Make Directories
+  dir = Path(yaml["path"])  # dataset root dir
+  for p in "images", "labels":
+      (dir / p).mkdir(parents=True, exist_ok=True)
+      for q in "train", "val":
+          (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+  # Train, Val Splits
+  for split, patches in [("train", 50 + 1), ("val", 43 + 1)]:
+      print(f"Processing {split} in {patches} patches ...")
+      images, labels = dir / "images" / split, dir / "labels" / split
+
+      # Download
+      url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+      if split == "train":
+          download([f"{url}zhiyuan_objv2_{split}.tar.gz"], dir=dir)  # annotations json
+          download([f"{url}patch{i}.tar.gz" for i in range(patches)], dir=images, curl=True, threads=8)
+      elif split == "val":
+          download([f"{url}zhiyuan_objv2_{split}.json"], dir=dir)  # annotations json
+          download([f"{url}images/v1/patch{i}.tar.gz" for i in range(15 + 1)], dir=images, curl=True, threads=8)
+          download([f"{url}images/v2/patch{i}.tar.gz" for i in range(16, patches)], dir=images, curl=True, threads=8)
+
+      # Move
+      for f in tqdm(images.rglob("*.jpg"), desc=f"Moving {split} images"):
+          f.rename(images / f.name)  # move to /images/{split}
+
+      # Labels
+      coco = COCO(dir / f"zhiyuan_objv2_{split}.json")
+      names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+      for cid, cat in enumerate(names):
+          catIds = coco.getCatIds(catNms=[cat])
+          imgIds = coco.getImgIds(catIds=catIds)
+          for im in tqdm(coco.loadImgs(imgIds), desc=f"Class {cid + 1}/{len(names)} {cat}"):
+              width, height = im["width"], im["height"]
+              path = Path(im["file_name"])  # image filename
+              try:
+                  with open(labels / path.with_suffix(".txt").name, "a", encoding="utf-8") as file:
+                      annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+                      for a in coco.loadAnns(annIds):
+                          x, y, w, h = a["bbox"]  # bounding box in xywh (xy top-left corner)
+                          xyxy = np.array([x, y, x + w, y + h])[None]  # pixels(1,4)
+                          x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0]  # normalized and clipped
+                          file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+              except Exception as e:
+                  print(e)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/SKU-110K.yaml

@@ -0,0 +1,58 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
+# Example usage: yolo train data=SKU-110K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── SKU-110K ← downloads here (13.6 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+names:
+  0: object
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import shutil
+  from pathlib import Path
+
+  import numpy as np
+  import pandas as pd
+  from tqdm import tqdm
+
+  from ultralytics.utils.downloads import download
+  from ultralytics.utils.ops import xyxy2xywh
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  parent = Path(dir.parent)  # download dir
+  urls = ["http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz"]
+  download(urls, dir=parent)
+
+  # Rename directories
+  if dir.exists():
+      shutil.rmtree(dir)
+  (parent / "SKU110K_fixed").rename(dir)  # rename dir
+  (dir / "labels").mkdir(parents=True, exist_ok=True)  # create labels dir
+
+  # Convert labels
+  names = "image", "x1", "y1", "x2", "y2", "class", "image_width", "image_height"  # column names
+  for d in "annotations_train.csv", "annotations_val.csv", "annotations_test.csv":
+      x = pd.read_csv(dir / "annotations" / d, names=names).values  # annotations
+      images, unique_images = x[:, 0], np.unique(x[:, 0])
+      with open((dir / d).with_suffix(".txt").__str__().replace("annotations_", ""), "w", encoding="utf-8") as f:
+          f.writelines(f"./images/{s}\n" for s in unique_images)
+      for im in tqdm(unique_images, desc=f"Converting {dir / d}"):
+          cls = 0  # single-class dataset
+          with open((dir / "labels" / im).with_suffix(".txt"), "a", encoding="utf-8") as f:
+              for r in x[images == im]:
+                  w, h = r[6], r[7]  # image width, height
+                  xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0]  # instance
+                  f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n")  # write label

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/VOC.yaml

@@ -0,0 +1,106 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Documentation: # Documentation: https://docs.ultralytics.com/datasets/detect/voc/
+# Example usage: yolo train data=VOC.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── VOC ← downloads here (2.8 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: VOC
+train: # train images (relative to 'path') 16551 images
+  - images/train2012
+  - images/train2007
+  - images/val2012
+  - images/val2007
+val: # val images (relative to 'path') 4952 images
+  - images/test2007
+test: # test images (optional)
+  - images/test2007
+
+# Classes
+names:
+  0: aeroplane
+  1: bicycle
+  2: bird
+  3: boat
+  4: bottle
+  5: bus
+  6: car
+  7: cat
+  8: chair
+  9: cow
+  10: diningtable
+  11: dog
+  12: horse
+  13: motorbike
+  14: person
+  15: pottedplant
+  16: sheep
+  17: sofa
+  18: train
+  19: tvmonitor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import xml.etree.ElementTree as ET
+  from pathlib import Path
+
+  from tqdm import tqdm
+
+  from ultralytics.utils.downloads import download
+
+
+  def convert_label(path, lb_path, year, image_id):
+      """Converts XML annotations from VOC format to YOLO format by extracting bounding boxes and class IDs."""
+
+      def convert_box(size, box):
+          dw, dh = 1.0 / size[0], 1.0 / size[1]
+          x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+          return x * dw, y * dh, w * dw, h * dh
+
+      in_file = open(path / f"VOC{year}/Annotations/{image_id}.xml")
+      out_file = open(lb_path, "w")
+      tree = ET.parse(in_file)
+      root = tree.getroot()
+      size = root.find("size")
+      w = int(size.find("width").text)
+      h = int(size.find("height").text)
+
+      names = list(yaml["names"].values())  # names list
+      for obj in root.iter("object"):
+          cls = obj.find("name").text
+          if cls in names and int(obj.find("difficult").text) != 1:
+              xmlbox = obj.find("bndbox")
+              bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ("xmin", "xmax", "ymin", "ymax")])
+              cls_id = names.index(cls)  # class id
+              out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + "\n")
+
+
+  # Download
+  dir = Path(yaml["path"])  # dataset root dir
+  url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+  urls = [
+      f"{url}VOCtrainval_06-Nov-2007.zip",  # 446MB, 5012 images
+      f"{url}VOCtest_06-Nov-2007.zip",  # 438MB, 4953 images
+      f"{url}VOCtrainval_11-May-2012.zip",  # 1.95GB, 17126 images
+  ]
+  download(urls, dir=dir / "images", curl=True, threads=3, exist_ok=True)  # download and unzip over existing (required)
+
+  # Convert
+  path = dir / "images/VOCdevkit"
+  for year, image_set in ("2012", "train"), ("2012", "val"), ("2007", "train"), ("2007", "val"), ("2007", "test"):
+      imgs_path = dir / "images" / f"{image_set}{year}"
+      lbs_path = dir / "labels" / f"{image_set}{year}"
+      imgs_path.mkdir(exist_ok=True, parents=True)
+      lbs_path.mkdir(exist_ok=True, parents=True)
+
+      with open(path / f"VOC{year}/ImageSets/Main/{image_set}.txt") as f:
+          image_ids = f.read().strip().split()
+      for id in tqdm(image_ids, desc=f"{image_set}{year}"):
+          f = path / f"VOC{year}/JPEGImages/{id}.jpg"  # old img path
+          lb_path = (lbs_path / f.name).with_suffix(".txt")  # new label path
+          f.rename(imgs_path / f.name)  # move image
+          convert_label(path, lb_path, year, id)  # convert labels to YOLO format

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/VisDrone.yaml

@@ -0,0 +1,87 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
+# Example usage: yolo train data=VisDrone.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── VisDrone ← downloads here (2.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: VisDrone # dataset root dir
+train: images/train # train images (relative to 'path') 6471 images
+val: images/val # val images (relative to 'path') 548 images
+test: images/test # test-dev images (optional) 1610 images
+
+# Classes
+names:
+  0: pedestrian
+  1: people
+  2: bicycle
+  3: car
+  4: van
+  5: truck
+  6: tricycle
+  7: awning-tricycle
+  8: bus
+  9: motor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import os
+  from pathlib import Path
+  import shutil
+
+  from ultralytics.utils.downloads import download
+
+
+  def visdrone2yolo(dir, split, source_name=None):
+      """Convert VisDrone annotations to YOLO format with images/{split} and labels/{split} structure."""
+      from PIL import Image
+      from tqdm import tqdm
+
+      source_dir = dir / (source_name or f"VisDrone2019-DET-{split}")
+      images_dir = dir / "images" / split
+      labels_dir = dir / "labels" / split
+      labels_dir.mkdir(parents=True, exist_ok=True)
+      
+      # Move images to new structure
+      if (source_images_dir := source_dir / "images").exists():
+          images_dir.mkdir(parents=True, exist_ok=True)
+          for img in source_images_dir.glob("*.jpg"):
+              img.rename(images_dir / img.name)
+      
+      for f in tqdm((source_dir / "annotations").glob("*.txt"), desc=f"Converting {split}"):
+          img_size = Image.open(images_dir / f.with_suffix(".jpg").name).size
+          dw, dh = 1.0 / img_size[0], 1.0 / img_size[1]
+          lines = []
+          
+          with open(f, encoding="utf-8") as file:
+              for row in [x.split(",") for x in file.read().strip().splitlines()]:
+                  if row[4] != "0":  # Skip ignored regions
+                      x, y, w, h = map(int, row[:4])
+                      cls = int(row[5]) - 1
+                      # Convert to YOLO format
+                      x_center, y_center = (x + w / 2) * dw, (y + h / 2) * dh
+                      w_norm, h_norm = w * dw, h * dh
+                      lines.append(f"{cls} {x_center:.6f} {y_center:.6f} {w_norm:.6f} {h_norm:.6f}\n")
+          
+          (labels_dir / f.name).write_text("".join(lines), encoding="utf-8")
+
+
+  # Download (ignores test-challenge split)
+  dir = Path(yaml["path"])  # dataset root dir
+  urls = [
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip",
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip",
+      "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip",
+      # "https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip",
+  ]
+  download(urls, dir=dir, curl=True, threads=4)
+
+  # Convert
+  splits = {"VisDrone2019-DET-train": "train", "VisDrone2019-DET-val": "val", "VisDrone2019-DET-test-dev": "test"}
+  for folder, split in splits.items():
+      visdrone2yolo(dir, split, folder)  # convert VisDrone annotations to YOLO labels
+      shutil.rmtree(dir / folder)  # cleanup original directory

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/african-wildlife.yaml

@@ -0,0 +1,25 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# African-wildlife dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/african-wildlife/
+# Example usage: yolo train data=african-wildlife.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── african-wildlife ← downloads here (100 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: african-wildlife # dataset root dir
+train: images/train # train images (relative to 'path') 1052 images
+val: images/val # val images (relative to 'path') 225 images
+test: images/test # test images (relative to 'path') 227 images
+
+# Classes
+names:
+  0: buffalo
+  1: elephant
+  2: rhino
+  3: zebra
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/african-wildlife.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/brain-tumor.yaml

@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Brain-tumor dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
+# Example usage: yolo train data=brain-tumor.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── brain-tumor ← downloads here (4.21 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: brain-tumor # dataset root dir
+train: images/train # train images (relative to 'path') 893 images
+val: images/val # val images (relative to 'path') 223 images
+
+# Classes
+names:
+  0: negative
+  1: positive
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/carparts-seg.yaml

@@ -0,0 +1,44 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Carparts-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
+# Example usage: yolo train data=carparts-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── carparts-seg ← downloads here (133 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: carparts-seg # dataset root dir
+train: images/train # train images (relative to 'path') 3516 images
+val: images/val # val images (relative to 'path') 276 images
+test: images/test # test images (relative to 'path') 401 images
+
+# Classes
+names:
+  0: back_bumper
+  1: back_door
+  2: back_glass
+  3: back_left_door
+  4: back_left_light
+  5: back_light
+  6: back_right_door
+  7: back_right_light
+  8: front_bumper
+  9: front_door
+  10: front_glass
+  11: front_left_door
+  12: front_left_light
+  13: front_light
+  14: front_right_door
+  15: front_right_light
+  16: hood
+  17: left_mirror
+  18: object
+  19: right_mirror
+  20: tailgate
+  21: trunk
+  22: wheel
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/carparts-seg.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco-pose.yaml

@@ -0,0 +1,42 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 Keypoints dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
+# Example usage: yolo train data=coco-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco-pose ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco-pose # dataset root dir
+train: train2017.txt # train images (relative to 'path') 56599 images
+val: val2017.txt # val images (relative to 'path') 2346 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://codalab.lisn.upsaclay.fr/competitions/7403
+
+# Keypoints
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes
+names:
+  0: person
+
+# Download script/URL (optional)
+download: |
+  from pathlib import Path
+
+  from ultralytics.utils.downloads import download
+
+  # Download labels
+  dir = Path(yaml["path"])  # dataset root dir
+  url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+  urls = [f"{url}coco2017labels-pose.zip"]
+  download(urls, dir=dir.parent)
+  # Download data
+  urls = [
+      "http://images.cocodataset.org/zips/train2017.zip",  # 19G, 118k images
+      "http://images.cocodataset.org/zips/val2017.zip",  # 1G, 5k images
+      "http://images.cocodataset.org/zips/test2017.zip",  # 7G, 41k images (optional)
+  ]
+  download(urls, dir=dir / "images", threads=3)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco.yaml

@@ -0,0 +1,118 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
+# Example usage: yolo train data=coco.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: |
+  from pathlib import Path
+
+  from ultralytics.utils.downloads import download
+
+  # Download labels
+  segments = True  # segment or box labels
+  dir = Path(yaml["path"])  # dataset root dir
+  url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+  urls = [url + ("coco2017labels-segments.zip" if segments else "coco2017labels.zip")]  # labels
+  download(urls, dir=dir.parent)
+  # Download data
+  urls = [
+      "http://images.cocodataset.org/zips/train2017.zip",  # 19G, 118k images
+      "http://images.cocodataset.org/zips/val2017.zip",  # 1G, 5k images
+      "http://images.cocodataset.org/zips/test2017.zip",  # 7G, 41k images (optional)
+  ]
+  download(urls, dir=dir / "images", threads=3)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco128-seg.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
+# Example usage: yolo train data=coco128.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco128-seg ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco128.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
+# Example usage: yolo train data=coco128.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco128 ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco8-grayscale.yaml

@@ -0,0 +1,103 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-Grayscale dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8-grayscale/
+# Example usage: yolo train data=coco8-grayscale.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-grayscale ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-grayscale # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+channels: 1
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-grayscale.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco8-multispectral.yaml

@@ -0,0 +1,104 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-Multispectral dataset (COCO8 images interpolated across 10 channels in the visual spectrum) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8-multispectral/
+# Example usage: yolo train data=coco8-multispectral.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-multispectral ← downloads here (20.2 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-multispectral # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Number of multispectral image channels
+channels: 10
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-multispectral.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco8-pose.yaml

@@ -0,0 +1,26 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose/
+# Example usage: yolo train data=coco8-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-pose ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-pose # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Keypoints
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes
+names:
+  0: person
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-pose.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco8-seg.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
+# Example usage: yolo train data=coco8-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8-seg ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8-seg # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/coco8.yaml

@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
+# Example usage: yolo train data=coco8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── coco8 ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: coco8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: person
+  1: bicycle
+  2: car
+  3: motorcycle
+  4: airplane
+  5: bus
+  6: train
+  7: truck
+  8: boat
+  9: traffic light
+  10: fire hydrant
+  11: stop sign
+  12: parking meter
+  13: bench
+  14: bird
+  15: cat
+  16: dog
+  17: horse
+  18: sheep
+  19: cow
+  20: elephant
+  21: bear
+  22: zebra
+  23: giraffe
+  24: backpack
+  25: umbrella
+  26: handbag
+  27: tie
+  28: suitcase
+  29: frisbee
+  30: skis
+  31: snowboard
+  32: sports ball
+  33: kite
+  34: baseball bat
+  35: baseball glove
+  36: skateboard
+  37: surfboard
+  38: tennis racket
+  39: bottle
+  40: wine glass
+  41: cup
+  42: fork
+  43: knife
+  44: spoon
+  45: bowl
+  46: banana
+  47: apple
+  48: sandwich
+  49: orange
+  50: broccoli
+  51: carrot
+  52: hot dog
+  53: pizza
+  54: donut
+  55: cake
+  56: chair
+  57: couch
+  58: potted plant
+  59: bed
+  60: dining table
+  61: toilet
+  62: tv
+  63: laptop
+  64: mouse
+  65: remote
+  66: keyboard
+  67: cell phone
+  68: microwave
+  69: oven
+  70: toaster
+  71: sink
+  72: refrigerator
+  73: book
+  74: clock
+  75: vase
+  76: scissors
+  77: teddy bear
+  78: hair drier
+  79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/crack-seg.yaml

@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Crack-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
+# Example usage: yolo train data=crack-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── crack-seg ← downloads here (91.6 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: crack-seg # dataset root dir
+train: images/train # train images (relative to 'path') 3717 images
+val: images/val # val images (relative to 'path') 112 images
+test: images/test # test images (relative to 'path') 200 images
+
+# Classes
+names:
+  0: crack
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/dog-pose.yaml

@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Dogs dataset http://vision.stanford.edu/aditya86/ImageNetDogs/ by Stanford
+# Documentation: https://docs.ultralytics.com/datasets/pose/dog-pose/
+# Example usage: yolo train data=dog-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dog-pose ← downloads here (337 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: dog-pose # dataset root dir
+train: images/train # train images (relative to 'path') 6773 images
+val: images/val # val images (relative to 'path') 1703 images
+
+# Keypoints
+kpt_shape: [24, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+
+# Classes
+names:
+  0: dog
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dog-pose.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/dota8-multispectral.yaml

@@ -0,0 +1,38 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA8-Multispectral dataset (DOTA8 interpolated across 10 channels in the visual spectrum) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8-multispectral.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8-multispectral ← downloads here (37.3MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: dota8-multispectral # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+
+# Number of multispectral image channels
+channels: 10
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dota8-multispectral.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/dota8.yaml

@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8 ← downloads here (1MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: dota8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dota8.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/hand-keypoints.yaml

@@ -0,0 +1,26 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hand Keypoints dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/hand-keypoints/
+# Example usage: yolo train data=hand-keypoints.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── hand-keypoints ← downloads here (369 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: hand-keypoints # dataset root dir
+train: images/train # train images (relative to 'path') 18776 images
+val: images/val # val images (relative to 'path') 7992 images
+
+# Keypoints
+kpt_shape: [21, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx:
+  [0, 1, 2, 4, 3, 10, 11, 12, 13, 14, 5, 6, 7, 8, 9, 15, 16, 17, 18, 19, 20]
+
+# Classes
+names:
+  0: hand
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/hand-keypoints.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/medical-pills.yaml

@@ -0,0 +1,21 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Medical-pills dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/medical-pills/
+# Example usage: yolo train data=medical-pills.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── medical-pills ← downloads here (8.19 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: medical-pills # dataset root dir
+train: images/train # train images (relative to 'path') 92 images
+val: images/val # val images (relative to 'path') 23 images
+
+# Classes
+names:
+  0: pill
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/medical-pills.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/open-images-v7.yaml

@@ -0,0 +1,663 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
+# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7/
+# Example usage: yolo train data=open-images-v7.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── open-images-v7 ← downloads here (561 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: open-images-v7 # dataset root dir
+train: images/train # train images (relative to 'path') 1743042 images
+val: images/val # val images (relative to 'path') 41620 images
+test: # test images (optional)
+
+# Classes
+names:
+  0: Accordion
+  1: Adhesive tape
+  2: Aircraft
+  3: Airplane
+  4: Alarm clock
+  5: Alpaca
+  6: Ambulance
+  7: Animal
+  8: Ant
+  9: Antelope
+  10: Apple
+  11: Armadillo
+  12: Artichoke
+  13: Auto part
+  14: Axe
+  15: Backpack
+  16: Bagel
+  17: Baked goods
+  18: Balance beam
+  19: Ball
+  20: Balloon
+  21: Banana
+  22: Band-aid
+  23: Banjo
+  24: Barge
+  25: Barrel
+  26: Baseball bat
+  27: Baseball glove
+  28: Bat (Animal)
+  29: Bathroom accessory
+  30: Bathroom cabinet
+  31: Bathtub
+  32: Beaker
+  33: Bear
+  34: Bed
+  35: Bee
+  36: Beehive
+  37: Beer
+  38: Beetle
+  39: Bell pepper
+  40: Belt
+  41: Bench
+  42: Bicycle
+  43: Bicycle helmet
+  44: Bicycle wheel
+  45: Bidet
+  46: Billboard
+  47: Billiard table
+  48: Binoculars
+  49: Bird
+  50: Blender
+  51: Blue jay
+  52: Boat
+  53: Bomb
+  54: Book
+  55: Bookcase
+  56: Boot
+  57: Bottle
+  58: Bottle opener
+  59: Bow and arrow
+  60: Bowl
+  61: Bowling equipment
+  62: Box
+  63: Boy
+  64: Brassiere
+  65: Bread
+  66: Briefcase
+  67: Broccoli
+  68: Bronze sculpture
+  69: Brown bear
+  70: Building
+  71: Bull
+  72: Burrito
+  73: Bus
+  74: Bust
+  75: Butterfly
+  76: Cabbage
+  77: Cabinetry
+  78: Cake
+  79: Cake stand
+  80: Calculator
+  81: Camel
+  82: Camera
+  83: Can opener
+  84: Canary
+  85: Candle
+  86: Candy
+  87: Cannon
+  88: Canoe
+  89: Cantaloupe
+  90: Car
+  91: Carnivore
+  92: Carrot
+  93: Cart
+  94: Cassette deck
+  95: Castle
+  96: Cat
+  97: Cat furniture
+  98: Caterpillar
+  99: Cattle
+  100: Ceiling fan
+  101: Cello
+  102: Centipede
+  103: Chainsaw
+  104: Chair
+  105: Cheese
+  106: Cheetah
+  107: Chest of drawers
+  108: Chicken
+  109: Chime
+  110: Chisel
+  111: Chopsticks
+  112: Christmas tree
+  113: Clock
+  114: Closet
+  115: Clothing
+  116: Coat
+  117: Cocktail
+  118: Cocktail shaker
+  119: Coconut
+  120: Coffee
+  121: Coffee cup
+  122: Coffee table
+  123: Coffeemaker
+  124: Coin
+  125: Common fig
+  126: Common sunflower
+  127: Computer keyboard
+  128: Computer monitor
+  129: Computer mouse
+  130: Container
+  131: Convenience store
+  132: Cookie
+  133: Cooking spray
+  134: Corded phone
+  135: Cosmetics
+  136: Couch
+  137: Countertop
+  138: Cowboy hat
+  139: Crab
+  140: Cream
+  141: Cricket ball
+  142: Crocodile
+  143: Croissant
+  144: Crown
+  145: Crutch
+  146: Cucumber
+  147: Cupboard
+  148: Curtain
+  149: Cutting board
+  150: Dagger
+  151: Dairy Product
+  152: Deer
+  153: Desk
+  154: Dessert
+  155: Diaper
+  156: Dice
+  157: Digital clock
+  158: Dinosaur
+  159: Dishwasher
+  160: Dog
+  161: Dog bed
+  162: Doll
+  163: Dolphin
+  164: Door
+  165: Door handle
+  166: Doughnut
+  167: Dragonfly
+  168: Drawer
+  169: Dress
+  170: Drill (Tool)
+  171: Drink
+  172: Drinking straw
+  173: Drum
+  174: Duck
+  175: Dumbbell
+  176: Eagle
+  177: Earrings
+  178: Egg (Food)
+  179: Elephant
+  180: Envelope
+  181: Eraser
+  182: Face powder
+  183: Facial tissue holder
+  184: Falcon
+  185: Fashion accessory
+  186: Fast food
+  187: Fax
+  188: Fedora
+  189: Filing cabinet
+  190: Fire hydrant
+  191: Fireplace
+  192: Fish
+  193: Flag
+  194: Flashlight
+  195: Flower
+  196: Flowerpot
+  197: Flute
+  198: Flying disc
+  199: Food
+  200: Food processor
+  201: Football
+  202: Football helmet
+  203: Footwear
+  204: Fork
+  205: Fountain
+  206: Fox
+  207: French fries
+  208: French horn
+  209: Frog
+  210: Fruit
+  211: Frying pan
+  212: Furniture
+  213: Garden Asparagus
+  214: Gas stove
+  215: Giraffe
+  216: Girl
+  217: Glasses
+  218: Glove
+  219: Goat
+  220: Goggles
+  221: Goldfish
+  222: Golf ball
+  223: Golf cart
+  224: Gondola
+  225: Goose
+  226: Grape
+  227: Grapefruit
+  228: Grinder
+  229: Guacamole
+  230: Guitar
+  231: Hair dryer
+  232: Hair spray
+  233: Hamburger
+  234: Hammer
+  235: Hamster
+  236: Hand dryer
+  237: Handbag
+  238: Handgun
+  239: Harbor seal
+  240: Harmonica
+  241: Harp
+  242: Harpsichord
+  243: Hat
+  244: Headphones
+  245: Heater
+  246: Hedgehog
+  247: Helicopter
+  248: Helmet
+  249: High heels
+  250: Hiking equipment
+  251: Hippopotamus
+  252: Home appliance
+  253: Honeycomb
+  254: Horizontal bar
+  255: Horse
+  256: Hot dog
+  257: House
+  258: Houseplant
+  259: Human arm
+  260: Human beard
+  261: Human body
+  262: Human ear
+  263: Human eye
+  264: Human face
+  265: Human foot
+  266: Human hair
+  267: Human hand
+  268: Human head
+  269: Human leg
+  270: Human mouth
+  271: Human nose
+  272: Humidifier
+  273: Ice cream
+  274: Indoor rower
+  275: Infant bed
+  276: Insect
+  277: Invertebrate
+  278: Ipod
+  279: Isopod
+  280: Jacket
+  281: Jacuzzi
+  282: Jaguar (Animal)
+  283: Jeans
+  284: Jellyfish
+  285: Jet ski
+  286: Jug
+  287: Juice
+  288: Kangaroo
+  289: Kettle
+  290: Kitchen & dining room table
+  291: Kitchen appliance
+  292: Kitchen knife
+  293: Kitchen utensil
+  294: Kitchenware
+  295: Kite
+  296: Knife
+  297: Koala
+  298: Ladder
+  299: Ladle
+  300: Ladybug
+  301: Lamp
+  302: Land vehicle
+  303: Lantern
+  304: Laptop
+  305: Lavender (Plant)
+  306: Lemon
+  307: Leopard
+  308: Light bulb
+  309: Light switch
+  310: Lighthouse
+  311: Lily
+  312: Limousine
+  313: Lion
+  314: Lipstick
+  315: Lizard
+  316: Lobster
+  317: Loveseat
+  318: Luggage and bags
+  319: Lynx
+  320: Magpie
+  321: Mammal
+  322: Man
+  323: Mango
+  324: Maple
+  325: Maracas
+  326: Marine invertebrates
+  327: Marine mammal
+  328: Measuring cup
+  329: Mechanical fan
+  330: Medical equipment
+  331: Microphone
+  332: Microwave oven
+  333: Milk
+  334: Miniskirt
+  335: Mirror
+  336: Missile
+  337: Mixer
+  338: Mixing bowl
+  339: Mobile phone
+  340: Monkey
+  341: Moths and butterflies
+  342: Motorcycle
+  343: Mouse
+  344: Muffin
+  345: Mug
+  346: Mule
+  347: Mushroom
+  348: Musical instrument
+  349: Musical keyboard
+  350: Nail (Construction)
+  351: Necklace
+  352: Nightstand
+  353: Oboe
+  354: Office building
+  355: Office supplies
+  356: Orange
+  357: Organ (Musical Instrument)
+  358: Ostrich
+  359: Otter
+  360: Oven
+  361: Owl
+  362: Oyster
+  363: Paddle
+  364: Palm tree
+  365: Pancake
+  366: Panda
+  367: Paper cutter
+  368: Paper towel
+  369: Parachute
+  370: Parking meter
+  371: Parrot
+  372: Pasta
+  373: Pastry
+  374: Peach
+  375: Pear
+  376: Pen
+  377: Pencil case
+  378: Pencil sharpener
+  379: Penguin
+  380: Perfume
+  381: Person
+  382: Personal care
+  383: Personal flotation device
+  384: Piano
+  385: Picnic basket
+  386: Picture frame
+  387: Pig
+  388: Pillow
+  389: Pineapple
+  390: Pitcher (Container)
+  391: Pizza
+  392: Pizza cutter
+  393: Plant
+  394: Plastic bag
+  395: Plate
+  396: Platter
+  397: Plumbing fixture
+  398: Polar bear
+  399: Pomegranate
+  400: Popcorn
+  401: Porch
+  402: Porcupine
+  403: Poster
+  404: Potato
+  405: Power plugs and sockets
+  406: Pressure cooker
+  407: Pretzel
+  408: Printer
+  409: Pumpkin
+  410: Punching bag
+  411: Rabbit
+  412: Raccoon
+  413: Racket
+  414: Radish
+  415: Ratchet (Device)
+  416: Raven
+  417: Rays and skates
+  418: Red panda
+  419: Refrigerator
+  420: Remote control
+  421: Reptile
+  422: Rhinoceros
+  423: Rifle
+  424: Ring binder
+  425: Rocket
+  426: Roller skates
+  427: Rose
+  428: Rugby ball
+  429: Ruler
+  430: Salad
+  431: Salt and pepper shakers
+  432: Sandal
+  433: Sandwich
+  434: Saucer
+  435: Saxophone
+  436: Scale
+  437: Scarf
+  438: Scissors
+  439: Scoreboard
+  440: Scorpion
+  441: Screwdriver
+  442: Sculpture
+  443: Sea lion
+  444: Sea turtle
+  445: Seafood
+  446: Seahorse
+  447: Seat belt
+  448: Segway
+  449: Serving tray
+  450: Sewing machine
+  451: Shark
+  452: Sheep
+  453: Shelf
+  454: Shellfish
+  455: Shirt
+  456: Shorts
+  457: Shotgun
+  458: Shower
+  459: Shrimp
+  460: Sink
+  461: Skateboard
+  462: Ski
+  463: Skirt
+  464: Skull
+  465: Skunk
+  466: Skyscraper
+  467: Slow cooker
+  468: Snack
+  469: Snail
+  470: Snake
+  471: Snowboard
+  472: Snowman
+  473: Snowmobile
+  474: Snowplow
+  475: Soap dispenser
+  476: Sock
+  477: Sofa bed
+  478: Sombrero
+  479: Sparrow
+  480: Spatula
+  481: Spice rack
+  482: Spider
+  483: Spoon
+  484: Sports equipment
+  485: Sports uniform
+  486: Squash (Plant)
+  487: Squid
+  488: Squirrel
+  489: Stairs
+  490: Stapler
+  491: Starfish
+  492: Stationary bicycle
+  493: Stethoscope
+  494: Stool
+  495: Stop sign
+  496: Strawberry
+  497: Street light
+  498: Stretcher
+  499: Studio couch
+  500: Submarine
+  501: Submarine sandwich
+  502: Suit
+  503: Suitcase
+  504: Sun hat
+  505: Sunglasses
+  506: Surfboard
+  507: Sushi
+  508: Swan
+  509: Swim cap
+  510: Swimming pool
+  511: Swimwear
+  512: Sword
+  513: Syringe
+  514: Table
+  515: Table tennis racket
+  516: Tablet computer
+  517: Tableware
+  518: Taco
+  519: Tank
+  520: Tap
+  521: Tart
+  522: Taxi
+  523: Tea
+  524: Teapot
+  525: Teddy bear
+  526: Telephone
+  527: Television
+  528: Tennis ball
+  529: Tennis racket
+  530: Tent
+  531: Tiara
+  532: Tick
+  533: Tie
+  534: Tiger
+  535: Tin can
+  536: Tire
+  537: Toaster
+  538: Toilet
+  539: Toilet paper
+  540: Tomato
+  541: Tool
+  542: Toothbrush
+  543: Torch
+  544: Tortoise
+  545: Towel
+  546: Tower
+  547: Toy
+  548: Traffic light
+  549: Traffic sign
+  550: Train
+  551: Training bench
+  552: Treadmill
+  553: Tree
+  554: Tree house
+  555: Tripod
+  556: Trombone
+  557: Trousers
+  558: Truck
+  559: Trumpet
+  560: Turkey
+  561: Turtle
+  562: Umbrella
+  563: Unicycle
+  564: Van
+  565: Vase
+  566: Vegetable
+  567: Vehicle
+  568: Vehicle registration plate
+  569: Violin
+  570: Volleyball (Ball)
+  571: Waffle
+  572: Waffle iron
+  573: Wall clock
+  574: Wardrobe
+  575: Washing machine
+  576: Waste container
+  577: Watch
+  578: Watercraft
+  579: Watermelon
+  580: Weapon
+  581: Whale
+  582: Wheel
+  583: Wheelchair
+  584: Whisk
+  585: Whiteboard
+  586: Willow
+  587: Window
+  588: Window blind
+  589: Wine
+  590: Wine glass
+  591: Wine rack
+  592: Winter melon
+  593: Wok
+  594: Woman
+  595: Wood-burning stove
+  596: Woodpecker
+  597: Worm
+  598: Wrench
+  599: Zebra
+  600: Zucchini
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import warnings
+
+  from ultralytics.utils import LOGGER, SETTINGS, Path
+  from ultralytics.utils.checks import check_requirements
+
+  check_requirements("fiftyone")
+
+  import fiftyone as fo
+  import fiftyone.zoo as foz
+
+  name = "open-images-v7"
+  fo.config.dataset_zoo_dir = Path(SETTINGS["datasets_dir"]) / "fiftyone" / name
+  fraction = 1.0  # fraction of full dataset to use
+  LOGGER.warning("Open Images V7 dataset requires at least **561 GB of free space. Starting download...")
+  for split in "train", "validation":  # 1743042 train, 41620 val images
+      train = split == "train"
+
+      # Load Open Images dataset
+      dataset = foz.load_zoo_dataset(
+          name,
+          split=split,
+          label_types=["detections"],
+          max_samples=round((1743042 if train else 41620) * fraction),
+      )
+
+      # Define classes
+      if train:
+          classes = dataset.default_classes  # all classes
+          # classes = dataset.distinct('ground_truth.detections.label')  # only observed classes
+
+      # Export to YOLO format
+      with warnings.catch_warnings():
+          warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
+          dataset.export(
+              export_dir=str(Path(SETTINGS["datasets_dir"]) / name),
+              dataset_type=fo.types.YOLOv5Dataset,
+              label_field="ground_truth",
+              split="val" if split == "validation" else split,
+              classes=classes,
+              overwrite=train,
+          )

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/package-seg.yaml

@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Package-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
+# Example usage: yolo train data=package-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── package-seg ← downloads here (103 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: package-seg # dataset root dir
+train: images/train # train images (relative to 'path') 1920 images
+val: images/val # val images (relative to 'path') 89 images
+test: images/test # test images (relative to 'path') 188 images
+
+# Classes
+names:
+  0: package
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/package-seg.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/signature.yaml

@@ -0,0 +1,21 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Signature dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/signature/
+# Example usage: yolo train data=signature.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── signature ← downloads here (11.3 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: signature # dataset root dir
+train: images/train # train images (relative to 'path') 143 images
+val: images/val # val images (relative to 'path') 35 images
+
+# Classes
+names:
+  0: signature
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/signature.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/tiger-pose.yaml

@@ -0,0 +1,25 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Tiger Pose dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/tiger-pose/
+# Example usage: yolo train data=tiger-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── tiger-pose ← downloads here (49.8 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: tiger-pose # dataset root dir
+train: images/train # train images (relative to 'path') 210 images
+val: images/val # val images (relative to 'path') 53 images
+
+# Keypoints
+kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+
+# Classes
+names:
+  0: tiger
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/tiger-pose.zip

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/datasets/xView.yaml

@@ -0,0 +1,155 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
+# --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
+# Documentation: https://docs.ultralytics.com/datasets/detect/xview/
+# Example usage: yolo train data=xView.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── xView ← downloads here (20.7 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: xView # dataset root dir
+train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
+val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
+
+# Classes
+names:
+  0: Fixed-wing Aircraft
+  1: Small Aircraft
+  2: Cargo Plane
+  3: Helicopter
+  4: Passenger Vehicle
+  5: Small Car
+  6: Bus
+  7: Pickup Truck
+  8: Utility Truck
+  9: Truck
+  10: Cargo Truck
+  11: Truck w/Box
+  12: Truck Tractor
+  13: Trailer
+  14: Truck w/Flatbed
+  15: Truck w/Liquid
+  16: Crane Truck
+  17: Railway Vehicle
+  18: Passenger Car
+  19: Cargo Car
+  20: Flat Car
+  21: Tank car
+  22: Locomotive
+  23: Maritime Vessel
+  24: Motorboat
+  25: Sailboat
+  26: Tugboat
+  27: Barge
+  28: Fishing Vessel
+  29: Ferry
+  30: Yacht
+  31: Container Ship
+  32: Oil Tanker
+  33: Engineering Vehicle
+  34: Tower crane
+  35: Container Crane
+  36: Reach Stacker
+  37: Straddle Carrier
+  38: Mobile Crane
+  39: Dump Truck
+  40: Haul Truck
+  41: Scraper/Tractor
+  42: Front loader/Bulldozer
+  43: Excavator
+  44: Cement Mixer
+  45: Ground Grader
+  46: Hut/Tent
+  47: Shed
+  48: Building
+  49: Aircraft Hangar
+  50: Damaged Building
+  51: Facility
+  52: Construction Site
+  53: Vehicle Lot
+  54: Helipad
+  55: Storage Tank
+  56: Shipping container lot
+  57: Shipping Container
+  58: Pylon
+  59: Tower
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+  import json
+  import os
+  from pathlib import Path
+
+  import numpy as np
+  from PIL import Image
+  from tqdm import tqdm
+
+  from ultralytics.data.utils import autosplit
+  from ultralytics.utils.ops import xyxy2xywhn
+
+
+  def convert_labels(fname=Path("xView/xView_train.geojson")):
+      """Converts xView geoJSON labels to YOLO format, mapping classes to indices 0-59 and saving as text files."""
+      path = fname.parent
+      with open(fname, encoding="utf-8") as f:
+          print(f"Loading {fname}...")
+          data = json.load(f)
+
+      # Make dirs
+      labels = Path(path / "labels" / "train")
+      os.system(f"rm -rf {labels}")
+      labels.mkdir(parents=True, exist_ok=True)
+
+      # xView classes 11-94 to 0-59
+      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
+                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
+                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
+                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
+
+      shapes = {}
+      for feature in tqdm(data["features"], desc=f"Converting {fname}"):
+          p = feature["properties"]
+          if p["bounds_imcoords"]:
+              id = p["image_id"]
+              file = path / "train_images" / id
+              if file.exists():  # 1395.tif missing
+                  try:
+                      box = np.array([int(num) for num in p["bounds_imcoords"].split(",")])
+                      assert box.shape[0] == 4, f"incorrect box shape {box.shape[0]}"
+                      cls = p["type_id"]
+                      cls = xview_class2index[int(cls)]  # xView class to 0-60
+                      assert 59 >= cls >= 0, f"incorrect class index {cls}"
+
+                      # Write YOLO label
+                      if id not in shapes:
+                          shapes[id] = Image.open(file).size
+                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
+                      with open((labels / id).with_suffix(".txt"), "a", encoding="utf-8") as f:
+                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
+                  except Exception as e:
+                      print(f"WARNING: skipping one label for {file}: {e}")
+
+
+  # Download manually from https://challenge.xviewdataset.org
+  dir = Path(yaml["path"])  # dataset root dir
+  # urls = [
+  #     "https://d307kc0mrhucc3.cloudfront.net/train_labels.zip",  # train labels
+  #     "https://d307kc0mrhucc3.cloudfront.net/train_images.zip",  # 15G, 847 train images
+  #     "https://d307kc0mrhucc3.cloudfront.net/val_images.zip",  # 5G, 282 val images (no labels)
+  # ]
+  # download(urls, dir=dir)
+
+  # Convert labels
+  convert_labels(dir / "xView_train.geojson")
+
+  # Move images
+  images = Path(dir / "images")
+  images.mkdir(parents=True, exist_ok=True)
+  Path(dir / "train_images").rename(dir / "images" / "train")
+  Path(dir / "val_images").rename(dir / "images" / "val")
+
+  # Split
+  autosplit(dir / "images" / "train")

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/default.yaml

@@ -0,0 +1,127 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global configuration YAML with settings and hyperparameters for YOLO training, validation, prediction and export
+# For documentation see https://docs.ultralytics.com/usage/cfg/
+
+task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
+mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
+
+# Train settings -------------------------------------------------------------------------------------------------------
+model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
+data: # (str, optional) path to data file, i.e. coco8.yaml
+epochs: 100 # (int) number of epochs to train for
+time: # (float, optional) number of hours to train for, overrides epochs if supplied
+patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
+batch: 16 # (int) number of images per batch (-1 for AutoBatch)
+imgsz: 640 # (int | list) input images size as int for train and val modes, or list[h,w] for predict and export modes
+save: True # (bool) save train checkpoints and predict results
+save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
+cache: False # (bool) True/ram, disk or False. Use cache for data loading
+device: # (int | str | list) device: CUDA device=0 or [0,1,2,3] or "cpu/mps" or -1 or [-1,-1] to auto-select idle GPUs
+workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
+project: # (str, optional) project name
+name: # (str, optional) experiment name, results saved to 'project/name' directory
+exist_ok: False # (bool) whether to overwrite existing experiment
+pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
+optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
+verbose: True # (bool) whether to print verbose output
+seed: 0 # (int) random seed for reproducibility
+deterministic: True # (bool) whether to enable deterministic mode
+single_cls: False # (bool) train multi-class data as single-class
+rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
+cos_lr: False # (bool) use cosine learning rate scheduler
+close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
+resume: False # (bool) resume training from last checkpoint
+amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
+fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
+profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
+freeze: # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
+multi_scale: False # (bool) Whether to use multiscale during training
+# Segmentation
+overlap_mask: True # (bool) merge object masks into a single image mask during training (segment train only)
+mask_ratio: 4 # (int) mask downsample ratio (segment train only)
+# Classification
+dropout: 0.0 # (float) use dropout regularization (classify train only)
+
+# Val/Test settings ----------------------------------------------------------------------------------------------------
+val: True # (bool) validate/test during training
+split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
+save_json: False # (bool) save results to JSON file
+conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
+iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
+max_det: 300 # (int) maximum number of detections per image
+half: False # (bool) use half precision (FP16)
+dnn: False # (bool) use OpenCV DNN for ONNX inference
+plots: True # (bool) save plots and images during train/val
+
+# Predict settings -----------------------------------------------------------------------------------------------------
+source: # (str, optional) source directory for images or videos
+vid_stride: 1 # (int) video frame-rate stride
+stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
+visualize: False # (bool) visualize model features
+augment: False # (bool) apply image augmentation to prediction sources
+agnostic_nms: False # (bool) class-agnostic NMS
+classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
+retina_masks: False # (bool) use high-resolution segmentation masks
+embed: # (list[int], optional) return feature vectors/embeddings from given layers
+
+# Visualize settings ---------------------------------------------------------------------------------------------------
+show: False # (bool) show predicted images and videos if environment allows
+save_frames: False # (bool) save predicted individual video frames
+save_txt: False # (bool) save results as .txt file
+save_conf: False # (bool) save results with confidence scores
+save_crop: False # (bool) save cropped images with results
+show_labels: True # (bool) show prediction labels, i.e. 'person'
+show_conf: True # (bool) show prediction confidence, i.e. '0.99'
+show_boxes: True # (bool) show prediction boxes
+line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
+
+# Export settings ------------------------------------------------------------------------------------------------------
+format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
+keras: False # (bool) use Kera=s
+optimize: False # (bool) TorchScript: optimize for mobile
+int8: False # (bool) CoreML/TF INT8 quantization
+dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
+simplify: True # (bool) ONNX: simplify model using `onnxslim`
+opset: # (int, optional) ONNX: opset version
+workspace: # (float, optional) TensorRT: workspace size (GiB), `None` will let TensorRT auto-allocate memory
+nms: False # (bool) CoreML: add NMS
+
+# Hyperparameters ------------------------------------------------------------------------------------------------------
+lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
+lrf: 0.01 # (float) final learning rate (lr0 * lrf)
+momentum: 0.937 # (float) SGD momentum/Adam beta1
+weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
+warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
+warmup_momentum: 0.8 # (float) warmup initial momentum
+warmup_bias_lr: 0.1 # (float) warmup initial bias lr
+box: 7.5 # (float) box loss gain
+cls: 0.5 # (float) cls loss gain (scale with pixels)
+dfl: 1.5 # (float) dfl loss gain
+pose: 12.0 # (float) pose loss gain
+kobj: 1.0 # (float) keypoint obj loss gain
+nbs: 64 # (int) nominal batch size
+hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
+degrees: 0.0 # (float) image rotation (+/- deg)
+translate: 0.1 # (float) image translation (+/- fraction)
+scale: 0.5 # (float) image scale (+/- gain)
+shear: 0.0 # (float) image shear (+/- deg)
+perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # (float) image flip up-down (probability)
+fliplr: 0.5 # (float) image flip left-right (probability)
+bgr: 0.0 # (float) image channel BGR (probability)
+mosaic: 1.0 # (float) image mosaic (probability)
+mixup: 0.0 # (float) image mixup (probability)
+cutmix: 0.0 # (float) image cutmix (probability)
+copy_paste: 0.0 # (float) segment copy-paste (probability)
+copy_paste_mode: "flip" # (str) the method to do copy_paste augmentation (flip, mixup)
+auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
+erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
+
+# Custom config.yaml ---------------------------------------------------------------------------------------------------
+cfg: # (str, optional) for overriding defaults.yaml
+
+# Tracker settings ------------------------------------------------------------------------------------------------------
+tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yolo11-cls-resnet18.yaml

@@ -0,0 +1,17 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-cls image classification model with ResNet18 backbone
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+
+# ResNet18 backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, TorchVision, [512, resnet18, DEFAULT, True, 2]] # truncate two layers from the end
+
+# YOLO11n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yolo11-cls.yaml

@@ -0,0 +1,33 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-cls image classification model
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-cls.yaml' will call yolo11-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 86 layers, 1633584 parameters, 1633584 gradients, 0.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 86 layers, 5545488 parameters, 5545488 gradients, 1.6 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 106 layers, 10455696 parameters, 10455696 gradients, 5.0 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 176 layers, 12937104 parameters, 12937104 gradients, 6.2 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 176 layers, 28458544 parameters, 28458544 gradients, 13.7 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 2, C2PSA, [1024]] # 9
+
+# YOLO11n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yolo11-obb.yaml

@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/obb
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-obb.yaml' will call yolo11-obb.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 196 layers, 2695747 parameters, 2695731 gradients, 6.9 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 196 layers, 9744931 parameters, 9744915 gradients, 22.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 246 layers, 20963523 parameters, 20963507 gradients, 72.2 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 372 layers, 26220995 parameters, 26220979 gradients, 91.3 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 372 layers, 58875331 parameters, 58875315 gradients, 204.3 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yolo11-pose.yaml

@@ -0,0 +1,51 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 80 # number of classes
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolo11n-pose.yaml' will call yolo11.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 196 layers, 2908507 parameters, 2908491 gradients, 7.7 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 196 layers, 9948811 parameters, 9948795 gradients, 23.5 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 246 layers, 20973273 parameters, 20973257 gradients, 72.3 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 372 layers, 26230745 parameters, 26230729 gradients, 91.4 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 372 layers, 58889881 parameters, 58889865 gradients, 204.3 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yolo11-seg.yaml

@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 203 layers, 2876848 parameters, 2876832 gradients, 10.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 203 layers, 10113248 parameters, 10113232 gradients, 35.8 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 253 layers, 22420896 parameters, 22420880 gradients, 123.9 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 379 layers, 27678368 parameters, 27678352 gradients, 143.0 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 379 layers, 62142656 parameters, 62142640 gradients, 320.2 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Segment, [nc, 32, 256]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yolo11.yaml

@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yoloe-11-seg.yaml

@@ -0,0 +1,48 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO11-seg instance segmentation model. For Usage examples see https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 355 layers, 2876848 parameters, 2876832 gradients, 10.5 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 355 layers, 10113248 parameters, 10113232 gradients, 35.8 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 445 layers, 22420896 parameters, 22420880 gradients, 123.9 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 667 layers, 27678368 parameters, 27678352 gradients, 143.0 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 667 layers, 62142656 parameters, 62142640 gradients, 320.2 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, YOLOESegment, [nc, 32, 256, 512, True]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/11/yoloe-11.yaml

@@ -0,0 +1,48 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, YOLOEDetect, [nc, 512, True]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/12/yolo12-cls.yaml

@@ -0,0 +1,32 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO12-cls image classification model
+# Model docs: https://docs.ultralytics.com/models/yolo12
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo12n-cls.yaml' will call yolo12-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 152 layers, 1,820,976 parameters, 1,820,976 gradients, 3.7 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 152 layers, 6,206,992 parameters, 6,206,992 gradients, 13.6 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 172 layers, 12,083,088 parameters, 12,083,088 gradients, 44.2 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 312 layers, 15,558,640 parameters, 15,558,640 gradients, 56.9 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 312 layers, 34,172,592 parameters, 34,172,592 gradients, 126.5 GFLOPs
+
+# YOLO12n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 4, A2C2f, [512, True, 4]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+# YOLO12n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/12/yolo12-obb.yaml

@@ -0,0 +1,48 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO12-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo12
+# Task docs: https://docs.ultralytics.com/tasks/obb
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo12n-obb.yaml' will call yolo12-obb.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 287 layers, 2,673,955 parameters, 2,673,939 gradients, 6.9 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 287 layers, 9,570,275 parameters, 9,570,259 gradients, 22.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 307 layers, 21,048,003 parameters, 21,047,987 gradients, 71.8 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 503 layers, 27,299,619 parameters, 27,299,603 gradients, 93.4 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 503 layers, 61,119,939 parameters, 61,119,923 gradients, 208.6 GFLOPs
+
+# YOLO12n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 4, A2C2f, [512, True, 4]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+# YOLO12n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 11
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, A2C2f, [256, False, -1]] # 14
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 17
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 8], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
+
+  - [[14, 17, 20], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/12/yolo12-pose.yaml

@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO12-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo12
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 80 # number of classes
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolo12n-pose.yaml' will call yolo12-pose.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 287 layers, 2,886,715 parameters, 2,886,699 gradients, 7.8 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 287 layers, 9,774,155 parameters, 9,774,139 gradients, 23.5 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 307 layers, 21,057,753 parameters, 21,057,737 gradients, 71.8 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 503 layers, 27,309,369 parameters, 27,309,353 gradients, 93.5 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 503 layers, 61,134,489 parameters, 61,134,473 gradients, 208.7 GFLOPs
+
+# YOLO12n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 4, A2C2f, [512, True, 4]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+# YOLO12n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 11
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, A2C2f, [256, False, -1]] # 14
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 17
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 8], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
+
+  - [[14, 17, 20], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/12/yolo12-seg.yaml

@@ -0,0 +1,48 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO12-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo12
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo12n-seg.yaml' will call yolo12-seg.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 294 layers, 2,855,056 parameters, 2,855,040 gradients, 10.6 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 294 layers, 9,938,592 parameters, 9,938,576 gradients, 35.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 314 layers, 22,505,376 parameters, 22,505,360 gradients, 123.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 510 layers, 28,756,992 parameters, 28,756,976 gradients, 145.1 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 510 layers, 64,387,264 parameters, 64,387,248 gradients, 324.6 GFLOPs
+
+# YOLO12n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 4, A2C2f, [512, True, 4]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+# YOLO12n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 11
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, A2C2f, [256, False, -1]] # 14
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 17
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 8], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
+
+  - [[14, 17, 20], 1, Segment, [nc, 32, 256]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/12/yolo12.yaml

@@ -0,0 +1,48 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLO12 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo12
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo12n.yaml' will call yolo12.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 272 layers, 2,602,288 parameters, 2,602,272 gradients, 6.7 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 272 layers, 9,284,096 parameters, 9,284,080 gradients, 21.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 292 layers, 20,199,168 parameters, 20,199,152 gradients, 68.1 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 488 layers, 26,450,784 parameters, 26,450,768 gradients, 89.7 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 488 layers, 59,210,784 parameters, 59,210,768 gradients, 200.3 GFLOPs
+
+# YOLO12n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 4, A2C2f, [512, True, 4]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+# YOLO12n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 11
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, A2C2f, [256, False, -1]] # 14
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P4
+  - [-1, 2, A2C2f, [512, False, -1]] # 17
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 8], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
+
+  - [[14, 17, 20], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/README.md

@@ -0,0 +1,65 @@
+<a href="https://www.ultralytics.com/" target="_blank"><img src="https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" width="320" alt="Ultralytics logo"></a>
+
+# Ultralytics Model Configurations
+
+Welcome to the [Ultralytics](https://www.ultralytics.com/) Models configuration directory! This directory contains a comprehensive collection of pre-configured model configuration files (`*.yaml`). These files serve as blueprints for creating custom [Ultralytics YOLO](https://docs.ultralytics.com/models/yolo11/) models, meticulously crafted and fine-tuned by the Ultralytics team. Our goal is to provide optimal performance across a diverse range of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks, including [object detection](https://docs.ultralytics.com/tasks/detect/), [image segmentation](https://docs.ultralytics.com/tasks/segment/), pose estimation, and [object tracking](https://docs.ultralytics.com/modes/track/).
+
+These configurations cater to various scenarios and are engineered for efficiency, running smoothly on different hardware platforms, from standard [CPUs](https://en.wikipedia.org/wiki/Central_processing_unit) to powerful [GPUs](https://www.ultralytics.com/glossary/gpu-graphics-processing-unit). Whether you're an experienced [machine learning](https://en.wikipedia.org/wiki/Machine_learning) practitioner or new to the YOLO ecosystem, this directory offers an excellent starting point for your custom model development journey.
+
+To begin, explore the models within this directory and select one that aligns with your project requirements. You can then use the corresponding `*.yaml` file (learn more about the [YAML format](https://www.ultralytics.com/glossary/yaml)) to [train](https://docs.ultralytics.com/modes/train/) and deploy your custom YOLO model effortlessly. For detailed guidance, refer to the Ultralytics [Documentation](https://docs.ultralytics.com/), and don't hesitate to reach out to the community via [GitHub Issues](https://github.com/ultralytics/ultralytics/issues) if you need support. Start building your custom YOLO model today!
+
+## 🚀 Usage
+
+Model `*.yaml` configuration files can be directly utilized in the [Command Line Interface (CLI)](https://docs.ultralytics.com/usage/cli/) using the `yolo` command:
+
+```bash
+# Train a YOLO11n detection model using the coco8 dataset for 100 epochs
+yolo task=detect mode=train model=yolo11n.yaml data=coco8.yaml epochs=100 imgsz=640
+```
+
+These files are [Python](https://www.python.org/)-compatible, accepting the same [configuration arguments](https://docs.ultralytics.com/usage/cfg/) as shown in the CLI example:
+
+```python
+from ultralytics import YOLO
+
+# Initialize a YOLO11n model from a YAML configuration file
+# This creates a model architecture without loading pre-trained weights
+model = YOLO("yolo11n.yaml")
+
+# Alternatively, load a pre-trained YOLO11n model directly
+# This loads both the architecture and the weights trained on COCO
+# model = YOLO("yolo11n.pt")
+
+# Display model information (architecture, layers, parameters, etc.)
+model.info()
+
+# Train the model using the COCO8 dataset (a small subset of COCO) for 100 epochs
+results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
+
+# Run inference with the trained model on an image
+results = model("path/to/image.jpg")
+```
+
+## 🏗️ Pre-trained Model Architectures
+
+Ultralytics supports a variety of cutting-edge model architectures. Visit the [Ultralytics Models](https://docs.ultralytics.com/models/) documentation page for in-depth information and usage examples for each model, including:
+
+- [YOLO12](https://docs.ultralytics.com/models/yolo12/)
+- [YOLO11](https://docs.ultralytics.com/models/yolo11/)
+- [YOLOv10](https://docs.ultralytics.com/models/yolov10/)
+- [YOLOv9](https://docs.ultralytics.com/models/yolov9/)
+- [YOLOv8](https://docs.ultralytics.com/models/yolov8/)
+- [YOLOv5](https://docs.ultralytics.com/models/yolov5/)
+- [And more...](https://docs.ultralytics.com/models/)
+
+You can easily use any of these models by loading their configuration files (`.yaml`) or their [pre-trained](https://docs.pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) checkpoints (`.pt`).
+
+## 🤝 Contribute New Models
+
+Have you developed a novel YOLO variant, experimented with a unique architecture, or achieved state-of-the-art results through specific tuning? We encourage you to share your innovations with the community by contributing to our Models section! Contributions like new model configurations, architectural improvements, or performance optimizations are highly valuable and help enrich the Ultralytics ecosystem.
+
+Sharing your work here allows others to benefit from your insights and expands the range of available model choices. It's an excellent way to showcase your expertise and make the Ultralytics YOLO platform even more versatile and powerful.
+
+To contribute, please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for detailed instructions on submitting a [Pull Request (PR)](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests) 🛠️. We eagerly await your contributions!
+
+Let's collaborate to enhance the capabilities and diversity of the Ultralytics YOLO models 🙏!

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/rt-detr/rtdetr-l.yaml

@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-l hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  l: [1.00, 1.00, 1024]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, HGStem, [32, 48]] # 0-P2/4
+  - [-1, 6, HGBlock, [48, 128, 3]] # stage 1
+
+  - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
+  - [-1, 6, HGBlock, [96, 512, 3]] # stage 2
+
+  - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
+  - [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
+  - [-1, 6, HGBlock, [192, 1024, 5, True, True]]
+  - [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
+
+  - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
+  - [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
+
+head:
+  - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
+  - [-1, 1, AIFI, [1024, 8]]
+  - [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
+  - [[-2, -1], 1, Concat, [1]]
+  - [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
+  - [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
+  - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
+
+  - [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
+  - [[-1, 17], 1, Concat, [1]] # cat Y4
+  - [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
+
+  - [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
+  - [[-1, 12], 1, Concat, [1]] # cat Y5
+  - [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
+
+  - [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-ResNet101 hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  l: [1.00, 1.00, 1024]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
+  - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
+  - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
+  - [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
+  - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
+
+head:
+  - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
+  - [-1, 1, AIFI, [1024, 8]]
+  - [-1, 1, Conv, [256, 1, 1]] # 7
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
+  - [[-2, -1], 1, Concat, [1]]
+  - [-1, 3, RepC3, [256]] # 11
+  - [-1, 1, Conv, [256, 1, 1]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
+  - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
+
+  - [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
+  - [[-1, 12], 1, Concat, [1]] # cat Y4
+  - [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
+
+  - [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
+  - [[-1, 7], 1, Concat, [1]] # cat Y5
+  - [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
+
+  - [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-ResNet50 hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  l: [1.00, 1.00, 1024]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
+  - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
+  - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
+  - [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
+  - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
+
+head:
+  - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
+  - [-1, 1, AIFI, [1024, 8]]
+  - [-1, 1, Conv, [256, 1, 1]] # 7
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
+  - [[-2, -1], 1, Concat, [1]]
+  - [-1, 3, RepC3, [256]] # 11
+  - [-1, 1, Conv, [256, 1, 1]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
+  - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
+
+  - [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
+  - [[-1, 12], 1, Concat, [1]] # cat Y4
+  - [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
+
+  - [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
+  - [[-1, 7], 1, Concat, [1]] # cat Y5
+  - [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
+
+  - [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/rt-detr/rtdetr-x.yaml

@@ -0,0 +1,57 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-x hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  x: [1.00, 1.00, 2048]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, HGStem, [32, 64]] # 0-P2/4
+  - [-1, 6, HGBlock, [64, 128, 3]] # stage 1
+
+  - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
+  - [-1, 6, HGBlock, [128, 512, 3]]
+  - [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
+
+  - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
+  - [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
+  - [-1, 6, HGBlock, [256, 1024, 5, True, True]]
+  - [-1, 6, HGBlock, [256, 1024, 5, True, True]]
+  - [-1, 6, HGBlock, [256, 1024, 5, True, True]]
+  - [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
+
+  - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
+  - [-1, 6, HGBlock, [512, 2048, 5, True, False]]
+  - [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
+
+head:
+  - [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
+  - [-1, 1, AIFI, [2048, 8]]
+  - [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
+  - [[-2, -1], 1, Concat, [1]]
+  - [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
+  - [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
+  - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
+
+  - [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
+  - [[-1, 21], 1, Concat, [1]] # cat Y4
+  - [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
+
+  - [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
+  - [[-1, 16], 1, Concat, [1]] # cat Y5
+  - [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
+
+  - [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v10/yolov10b.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10b object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+  # [depth, width, max_channels]
+  b: [0.67, 1.00, 512]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v10/yolov10l.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10l object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+  # [depth, width, max_channels]
+  l: [1.00, 1.00, 512]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v10/yolov10m.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10m object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+  # [depth, width, max_channels]
+  m: [0.67, 0.75, 768]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v10/yolov10n.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10n object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v10/yolov10s.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10s object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+  # [depth, width, max_channels]
+  s: [0.33, 0.50, 1024]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v10/yolov10x.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10x object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+  # [depth, width, max_channels]
+  x: [1.00, 1.25, 512]
+
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2fCIB, [512, True]]
+  - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2fCIB, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2fCIB, [512, True]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+  - [-1, 1, SCDown, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v3/yolov3-spp.yaml

@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv3-SPP object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov3
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  - [-1, 1, Conv, [32, 3, 1]] # 0
+  - [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
+  - [-1, 1, Bottleneck, [64]]
+  - [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
+  - [-1, 2, Bottleneck, [128]]
+  - [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
+  - [-1, 8, Bottleneck, [256]]
+  - [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
+  - [-1, 8, Bottleneck, [512]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
+  - [-1, 4, Bottleneck, [1024]] # 10
+
+# YOLOv3-SPP head
+head:
+  - [-1, 1, Bottleneck, [1024, False]]
+  - [-1, 1, SPP, [512, [5, 9, 13]]]
+  - [-1, 1, Conv, [1024, 3, 1]]
+  - [-1, 1, Conv, [512, 1, 1]]
+  - [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
+
+  - [-2, 1, Conv, [256, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P4
+  - [-1, 1, Bottleneck, [512, False]]
+  - [-1, 1, Bottleneck, [512, False]]
+  - [-1, 1, Conv, [256, 1, 1]]
+  - [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
+
+  - [-2, 1, Conv, [128, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P3
+  - [-1, 1, Bottleneck, [256, False]]
+  - [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
+
+  - [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v3/yolov3-tiny.yaml

@@ -0,0 +1,40 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv3-tiiny object detection model with P4/16 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov3
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# YOLOv3-tiny backbone
+backbone:
+  # [from, number, module, args]
+  - [-1, 1, Conv, [16, 3, 1]] # 0
+  - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
+  - [-1, 1, Conv, [32, 3, 1]]
+  - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
+  - [-1, 1, Conv, [64, 3, 1]]
+  - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
+  - [-1, 1, Conv, [128, 3, 1]]
+  - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
+  - [-1, 1, Conv, [256, 3, 1]]
+  - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
+  - [-1, 1, Conv, [512, 3, 1]]
+  - [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
+  - [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
+
+# YOLOv3-tiny head
+head:
+  - [-1, 1, Conv, [1024, 3, 1]]
+  - [-1, 1, Conv, [256, 1, 1]]
+  - [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
+
+  - [-2, 1, Conv, [128, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P4
+  - [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
+
+  - [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v3/yolov3.yaml

@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv3 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov3
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# darknet53 backbone
+backbone:
+  # [from, number, module, args]
+  - [-1, 1, Conv, [32, 3, 1]] # 0
+  - [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
+  - [-1, 1, Bottleneck, [64]]
+  - [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
+  - [-1, 2, Bottleneck, [128]]
+  - [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
+  - [-1, 8, Bottleneck, [256]]
+  - [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
+  - [-1, 8, Bottleneck, [512]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
+  - [-1, 4, Bottleneck, [1024]] # 10
+
+# YOLOv3 head
+head:
+  - [-1, 1, Bottleneck, [1024, False]]
+  - [-1, 1, Conv, [512, 1, 1]]
+  - [-1, 1, Conv, [1024, 3, 1]]
+  - [-1, 1, Conv, [512, 1, 1]]
+  - [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
+
+  - [-2, 1, Conv, [256, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P4
+  - [-1, 1, Bottleneck, [512, False]]
+  - [-1, 1, Bottleneck, [512, False]]
+  - [-1, 1, Conv, [256, 1, 1]]
+  - [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
+
+  - [-2, 1, Conv, [128, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P3
+  - [-1, 1, Bottleneck, [256, False]]
+  - [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
+
+  - [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v5/yolov5-p6.yaml

@@ -0,0 +1,62 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv5 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov5
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 1024]
+  l: [1.00, 1.00, 1024]
+  x: [1.33, 1.25, 1024]
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  - [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C3, [128]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C3, [256]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 9, C3, [512]]
+  - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+  - [-1, 3, C3, [768]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+  - [-1, 3, C3, [1024]]
+  - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv5 v6.0 head
+head:
+  - [-1, 1, Conv, [768, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+  - [-1, 3, C3, [768, False]] # 15
+
+  - [-1, 1, Conv, [512, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C3, [512, False]] # 19
+
+  - [-1, 1, Conv, [256, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C3, [256, False]] # 23 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 20], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 16], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C3, [768, False]] # 29 (P5/32-large)
+
+  - [-1, 1, Conv, [768, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P6
+  - [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
+
+  - [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v5/yolov5.yaml

@@ -0,0 +1,51 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv5 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov5
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 1024]
+  l: [1.00, 1.00, 1024]
+  x: [1.33, 1.25, 1024]
+
+# YOLOv5 v6.0 backbone
+backbone:
+  # [from, number, module, args]
+  - [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C3, [128]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C3, [256]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 9, C3, [512]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C3, [1024]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv5 v6.0 head
+head:
+  - [-1, 1, Conv, [512, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C3, [512, False]] # 13
+
+  - [-1, 1, Conv, [256, 1, 1]]
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C3, [256, False]] # 17 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 14], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C3, [512, False]] # 20 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C3, [1024, False]] # 23 (P5/32-large)
+
+  - [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v6/yolov6.yaml

@@ -0,0 +1,56 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Meituan YOLOv6 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov6
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+activation: torch.nn.ReLU() # (optional) model default activation function
+scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 768]
+  l: [1.00, 1.00, 512]
+  x: [1.00, 1.25, 512]
+
+# YOLOv6-3.0s backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 6, Conv, [128, 3, 1]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 12, Conv, [256, 3, 1]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 18, Conv, [512, 3, 1]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 6, Conv, [1024, 3, 1]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv6-3.0s head
+head:
+  - [-1, 1, Conv, [256, 1, 1]]
+  - [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 1, Conv, [256, 3, 1]]
+  - [-1, 9, Conv, [256, 3, 1]] # 14
+
+  - [-1, 1, Conv, [128, 1, 1]]
+  - [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 1, Conv, [128, 3, 1]]
+  - [-1, 9, Conv, [128, 3, 1]] # 19
+
+  - [-1, 1, Conv, [128, 3, 2]]
+  - [[-1, 15], 1, Concat, [1]] # cat head P4
+  - [-1, 1, Conv, [256, 3, 1]]
+  - [-1, 9, Conv, [256, 3, 1]] # 23
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 1, Conv, [512, 3, 1]]
+  - [-1, 9, Conv, [512, 3, 1]] # 27
+
+  - [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yoloe-v8-seg.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-world summary: 161 layers, 4204111 parameters, 4204095 gradients, 39.6 GFLOPs
+  s: [0.33, 0.50, 1024] # YOLOv8s-world summary: 161 layers, 13383496 parameters, 13383480 gradients, 71.5 GFLOPs
+  m: [0.67, 0.75, 768] # YOLOv8m-world summary: 201 layers, 29065310 parameters, 29065294 gradients, 131.4 GFLOPs
+  l: [1.00, 1.00, 512] # YOLOv8l-world summary: 241 layers, 47553970 parameters, 47553954 gradients, 225.6 GFLOPs
+  x: [1.00, 1.25, 512] # YOLOv8x-world summary: 241 layers, 73690217 parameters, 73690201 gradients, 330.8 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+  - [15, 1, Conv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+  - [[15, 18, 21], 1, YOLOESegment, [nc, 32, 256, 512, True]] # Segment(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yoloe-v8.yaml

@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-worldv2 summary: 148 layers, 3695183 parameters, 3695167 gradients, 19.5 GFLOPS
+  s: [0.33, 0.50, 1024] # YOLOv8s-worldv2 summary: 148 layers, 12759880 parameters, 12759864 gradients, 51.0 GFLOPS
+  m: [0.67, 0.75, 768] # YOLOv8m-worldv2 summary: 188 layers, 28376158 parameters, 28376142 gradients, 110.5 GFLOPS
+  l: [1.00, 1.00, 512] # YOLOv8l-worldv2 summary: 228 layers, 46832050 parameters, 46832034 gradients, 204.5 GFLOPS
+  x: [1.00, 1.25, 512] # YOLOv8x-worldv2 summary: 228 layers, 72886377 parameters, 72886361 gradients, 309.3 GFLOPS
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+  - [15, 1, Conv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+  - [[15, 18, 21], 1, YOLOEDetect, [nc, 512, True]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml

@@ -0,0 +1,28 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-cls image classification model with ResNet101 backbone
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 1024]
+  l: [1.00, 1.00, 1024]
+  x: [1.00, 1.25, 1024]
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
+  - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
+  - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
+  - [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3-P4/16
+  - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml

@@ -0,0 +1,28 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-cls image classification model with ResNet50 backbone
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 1024]
+  l: [1.00, 1.00, 1024]
+  x: [1.00, 1.25, 1024]
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
+  - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
+  - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
+  - [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3-P4/16
+  - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-cls.yaml

@@ -0,0 +1,32 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-cls image classification model with YOLO backbone
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 1024]
+  l: [1.00, 1.00, 1024]
+  x: [1.00, 1.25, 1024]
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml

@@ -0,0 +1,58 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P2/4 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p2 summary: 290 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
+  s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 290 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
+  m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 434 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
+  l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 578 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
+  x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 578 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
+
+# YOLOv8.0-ghost backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C3Ghost, [128, True]]
+  - [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C3Ghost, [256, True]]
+  - [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C3Ghost, [512, True]]
+  - [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C3Ghost, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0-ghost-p2 head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C3Ghost, [512]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 2], 1, Concat, [1]] # cat backbone P2
+  - [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
+
+  - [-1, 1, GhostConv, [128, 3, 2]]
+  - [[-1, 15], 1, Concat, [1]] # cat head P3
+  - [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
+
+  - [-1, 1, GhostConv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
+
+  - [-1, 1, GhostConv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
+
+  - [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml

@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 312 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
+  s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 312 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
+  m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 468 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
+  l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 624 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
+  x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 624 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
+
+# YOLOv8.0-ghost backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C3Ghost, [128, True]]
+  - [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C3Ghost, [256, True]]
+  - [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C3Ghost, [512, True]]
+  - [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
+  - [-1, 3, C3Ghost, [768, True]]
+  - [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
+  - [-1, 3, C3Ghost, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0-ghost-p6 head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+  - [-1, 3, C3Ghost, [768]] # 14
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C3Ghost, [512]] # 17
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
+
+  - [-1, 1, GhostConv, [256, 3, 2]]
+  - [[-1, 17], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
+
+  - [-1, 1, GhostConv, [512, 3, 2]]
+  - [[-1, 14], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
+
+  - [-1, 1, GhostConv, [768, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P6
+  - [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
+
+  - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-ghost.yaml

@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-ghost summary: 237 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
+  s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 237 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
+  m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 357 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
+  l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 477 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
+  x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 477 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
+
+# YOLOv8.0n-ghost backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C3Ghost, [128, True]]
+  - [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C3Ghost, [256, True]]
+  - [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C3Ghost, [512, True]]
+  - [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C3Ghost, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C3Ghost, [512]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
+
+  - [-1, 1, GhostConv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
+
+  - [-1, 1, GhostConv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
+
+  - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-obb.yaml

@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/obb
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-obb summary: 144 layers, 3228867 parameters, 3228851 gradients, 9.1 GFLOPs
+  s: [0.33, 0.50, 1024] # YOLOv8s-obb summary: 144 layers, 11452739 parameters, 11452723 gradients, 29.8 GFLOPs
+  m: [0.67, 0.75, 768] # YOLOv8m-obb summary: 184 layers, 26463235 parameters, 26463219 gradients, 81.5 GFLOPs
+  l: [1.00, 1.00, 512] # YOLOv8l-obb summary: 224 layers, 44540355 parameters, 44540339 gradients, 169.4 GFLOPs
+  x: [1.00, 1.25, 512] # YOLOv8x-obb summary: 224 layers, 69555651 parameters, 69555635 gradients, 264.3 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+  - [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-p2.yaml

@@ -0,0 +1,57 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P2/4 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 768]
+  l: [1.00, 1.00, 512]
+  x: [1.00, 1.25, 512]
+
+# YOLOv8.0 backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0-p2 head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2f, [512]] # 12
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 2], 1, Concat, [1]] # cat backbone P2
+  - [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
+
+  - [-1, 1, Conv, [128, 3, 2]]
+  - [[-1, 15], 1, Concat, [1]] # cat head P3
+  - [-1, 3, C2f, [256]] # 21 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 12], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2f, [512]] # 24 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 9], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2f, [1024]] # 27 (P5/32-large)
+
+  - [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-p6.yaml

@@ -0,0 +1,59 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024] # YOLOv8n-p6 summary: 170 layers, 4984352 parameters, 4984336 gradients, 8.8 GFLOPs
+  s: [0.33, 0.50, 1024] # YOLOv8s-p6 summary: 170 layers, 17911792 parameters, 17911776 gradients, 28.7 GFLOPs
+  m: [0.67, 0.75, 768] # YOLOv8m-p6 summary: 222 layers, 44887488 parameters, 44887472 gradients, 83.5 GFLOPs
+  l: [1.00, 1.00, 512] # YOLOv8l-p6 summary: 274 layers, 62384016 parameters, 62384000 gradients, 167.9 GFLOPs
+  x: [1.00, 1.25, 512] # YOLOv8x-p6 summary: 274 layers, 97423072 parameters, 97423056 gradients, 261.8 GFLOPs
+
+# YOLOv8.0x6 backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [768, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0x6 head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+  - [-1, 3, C2, [768, False]] # 14
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2, [512, False]] # 17
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2, [256, False]] # 20 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 17], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 14], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2, [768, False]] # 26 (P5/32-large)
+
+  - [-1, 1, Conv, [768, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P6
+  - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
+
+  - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)

HiveCoreR0/src/cv_part/src/detect_part/detect_part/ultralytics/cfg/models/v8/yolov8-pose-p6.yaml

@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-pose keypoints/pose estimation model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 1 # number of classes
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.33, 0.25, 1024]
+  s: [0.33, 0.50, 1024]
+  m: [0.67, 0.75, 768]
+  l: [1.00, 1.00, 512]
+  x: [1.00, 1.25, 512]
+
+# YOLOv8.0x6 backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 3, C2f, [128, True]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 6, C2f, [256, True]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 6, C2f, [512, True]]
+  - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+  - [-1, 3, C2f, [768, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+  - [-1, 3, C2f, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0x6 head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+  - [-1, 3, C2, [768, False]] # 14
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 3, C2, [512, False]] # 17
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 3, C2, [256, False]] # 20 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 17], 1, Concat, [1]] # cat head P4
+  - [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 14], 1, Concat, [1]] # cat head P5
+  - [-1, 3, C2, [768, False]] # 26 (P5/32-large)
+
+  - [-1, 1, Conv, [768, 3, 2]]
+  - [[-1, 11], 1, Concat, [1]] # cat head P6
+  - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
+
+  - [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)