Instructions to use SharpAI/sam2-hiera-tiny-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use SharpAI/sam2-hiera-tiny-onnx with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("SharpAI/sam2-hiera-tiny-onnx") 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("SharpAI/sam2-hiera-tiny-onnx") 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 .gitattributes from SharpAI/sam2-hiera-tiny-onnx: direct link, hf CLI and curl.
- Browser
- Download file 85 Bytes
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https://huggingface.co/SharpAI/sam2-hiera-tiny-onnx/resolve/main/.gitattributes
- Command line
-
hf download hf://SharpAI/sam2-hiera-tiny-onnx/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/SharpAI/sam2-hiera-tiny-onnx/resolve/main/.gitattributes
85 Bytes
| *.onnx filter=lfs diff=lfs merge=lfs -text | |
| *.ort filter=lfs diff=lfs merge=lfs -text | |