Image Classification
PyTorch
sam2
medical-imaging
wound-care
wound-segmentation
vision-transformer
dinov2
convnext
multi-task
mlhc-2026
Instructions to use QianGroup/willie-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use QianGroup/willie-weights with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(QianGroup/willie-weights) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): predictor.set_image(<your_image>) masks, _, _ = predictor.predict(<input_prompts>)# Use SAM2 with videos import torch from sam2.sam2_video_predictor import SAM2VideoPredictor predictor = SAM2VideoPredictor.from_pretrained(QianGroup/willie-weights) with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16): state = predictor.init_state(<your_video>) # add new prompts and instantly get the output on the same frame frame_idx, object_ids, masks = predictor.add_new_points(state, <your_prompts>): # propagate the prompts to get masklets throughout the video for frame_idx, object_ids, masks in predictor.propagate_in_video(state): ... - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ff7afc0bd72a8e64f0d576ae6ebb2f3589ac8b124e8030f2fb73ea30e0623f0
- Size of remote file:
- 16.9 MB
- SHA256:
- 0c973c54d79e6fd4ddc8603455eb04e76da24c134508fe1325f88e45ba499e8e
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