Instructions to use litert-community/SAM2.1-Hiera-Tiny-Image-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use litert-community/SAM2.1-Hiera-Tiny-Image-Encoder with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sam2
How to use litert-community/SAM2.1-Hiera-Tiny-Image-Encoder with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(litert-community/SAM2.1-Hiera-Tiny-Image-Encoder) 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(litert-community/SAM2.1-Hiera-Tiny-Image-Encoder) 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:
- d822b8cc3d1d09f61597ca4fdb35d34e63b0168515f42c7bb2f4c6b46ec538f7
- Size of remote file:
- 80.3 MB
- SHA256:
- 9f87693052c7c9bee2271f86d947420d7409ef0c2bec36c1885335afab90f6be
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