M2cai16-tool-locations [LATEST]

yolo detect train data=m2cai16.yaml model=yolov8n.pt epochs=100 imgsz=640 Example m2cai16.yaml :

import json import os from PIL import Image import torch from torch.utils.data import Dataset from torchvision.ops import box_convert class M2CAI16ToolLocations(Dataset): """Dataset for m2cai16-tool-locations bounding box annotations.""" m2cai16-tool-locations

m2cai16-tool-locations/ annotations/ video01.json # or .xml / .txt video02.json frames/ video01/ frame_000001.jpg ... Here’s a robust parser using and torchvision : yolo detect train data=m2cai16

This dataset is designed for (bounding boxes) in laparoscopic cholecystectomy videos. It contains annotations for 16 tools, including their positions in video frames. 1. Dataset Overview & Utility Purpose : Train object detection models (e.g., YOLO, Faster R-CNN, DETR) to locate surgical instruments in real-time. m2cai16-tool-locations

def __len__(self): return len(self.samples)