Mask Generation
sam2
segment-anything
instance-segmentation
robotics
IB-Robot
ascend
edge-deployment
Instructions to use openEuler/sam2.1_hiera_tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use openEuler/sam2.1_hiera_tiny with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("openEuler/sam2.1_hiera_tiny") 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("openEuler/sam2.1_hiera_tiny") 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 assets/sam2.1_hiera_tiny.pt from openEuler/sam2.1_hiera_tiny: direct link, hf CLI and curl.
- Browser
- Download file 156 MB
-
https://huggingface.co/openEuler/sam2.1_hiera_tiny/resolve/main/assets/sam2.1_hiera_tiny.pt
- Command line
-
hf download hf://openEuler/sam2.1_hiera_tiny/assets/sam2.1_hiera_tiny.pt
-
curl -L -o sam2.1_hiera_tiny.pt https://huggingface.co/openEuler/sam2.1_hiera_tiny/resolve/main/assets/sam2.1_hiera_tiny.pt
156 MB
- Xet hash:
- ef640f67c445bc6975c90580b765a366bc8c8651dd2200ece7dbb9e4f5fedebd
- Size of remote file:
- 156 MB
- SHA256:
- 7402e0d864fa82708a20fbd15bc84245c2f26dff0eb43a4b5b93452deb34be69
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