Image Segmentation
PyTorch
sam2
semantic-segmentation
microstructure
materials-science
segment-anything-2
lora
Instructions to use WUT-AI-AI4Mat/microstructure-sam2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use WUT-AI-AI4Mat/microstructure-sam2 with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(WUT-AI-AI4Mat/microstructure-sam2) 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(WUT-AI-AI4Mat/microstructure-sam2) 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:
- ce2703de32e4a1d2fba6a3d346b46feef88c259106ecf36b26b2ddb31ba3e191
- Size of remote file:
- 19.1 MB
- SHA256:
- 7bb0e3036b685eddb1c9b6d4ce3f62c3ce33d4cad971f9d6ae4913601566d37e
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