Network UPS Tools
Power Devices support
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The "grasper" appears in 92% of frames, while "specimen bag" in <1%. : Apply class-weighted loss or oversampling.
The m2cai16-tool-locations dataset is more than a static collection of bounding boxes – it is a litmus test for how well computer vision handles the messy, reflective, and rapid reality of surgery. While larger and more complex datasets have emerged, the core challenges encoded in those 15,000 frames remain unsolved: robust occlusion handling, real-time inference under smoke, and generalization across patients. m2cai16-tool-locations
Stick to COCO-style metrics:
"class": "grasper", "bbox": [342, 511, 128, 94], "occluded": false, "confidence": 1.0 , : The "grasper" appears in 92% of frames,
With the rise of datasets like (which includes tool, anatomy, and action) and SurgVisDom (domain adaptation), some argue that m2cai16-tool-locations is outdated. However, three factors secure its legacy:
One notable extension is , where the original authors released an updated version with interpolated annotations every 5 frames to support video object segmentation. While larger and more complex datasets have emerged,
def __len__(self): return len(self.samples)
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