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
Download checkpoints/MetalDAM/semantic_sam2_best.pth from WUT-AI-AI4Mat/microstructure-sam2: direct link, hf CLI and curl.
- Browser
- Download file 19.1 MB
-
https://huggingface.co/WUT-AI-AI4Mat/microstructure-sam2/resolve/main/checkpoints/MetalDAM/semantic_sam2_best.pth
- Command line
-
hf download hf://WUT-AI-AI4Mat/microstructure-sam2/checkpoints/MetalDAM/semantic_sam2_best.pth
-
curl -L -o semantic_sam2_best.pth https://huggingface.co/WUT-AI-AI4Mat/microstructure-sam2/resolve/main/checkpoints/MetalDAM/semantic_sam2_best.pth
19.1 MB
- Xet hash:
- cf6a03ba648f5a901a62b75c0b443e260434fe56efcba0196a459d8250e2393a
- Size of remote file:
- 19.1 MB
- SHA256:
- 5031b5040d1f05d65edd4b53ef8cddd135d7848ff57bab7db3ea2b6a90de7f0b
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