M2cai16-tool-locations [HD 2026]

Modern lightweight models like UK-YOLOv10 or MCPD-YOLOv3 have achieved high mean Average Precision (mAP) scores—ranging from 91.9% to over 96%—on this specific dataset. Accessing the Data

To understand its relevance, contrast it with similar benchmarks: m2cai16-tool-locations

with open(ann_path, 'r') as f: annotations = json.load(f) for frame_name, boxes_info in annotations.items(): frame_path = os.path.join(frame_dir, frame_name) if os.path.exists(frame_path): self.samples.append((frame_path, boxes_info)) boxes_info)) def __init__(self

def __init__(self, root_dir, transform=None): self.root_dir = root_dir self.transform = transform self.samples = [] m2cai16-tool-locations

The is a publicly available dataset used for localized surgical instrument detection in laparoscopic cholecystectomy videos. It was created by researchers at Stanford University by adding spatial bounding box annotations to the original M2CAI 2016 Tool Presence Detection Challenge dataset. Dataset Composition