Instructions to use Pablopigue/sam2-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sam2
How to use Pablopigue/sam2-lite with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("Pablopigue/sam2-lite") 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("Pablopigue/sam2-lite") 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
Upload sam2-lite
Browse files
README.md
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## License
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Released for **non-commercial research use only** (CC BY-NC 4.0):
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- It contains SAM 2.1 weights (memory encoder and mask decoder, plus a **modified**, fine-tuned memory attention) by Meta Platforms, Inc., licensed under the Apache License 2.0 (see `LICENSE` and `NOTICE`).
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## License
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sam2-lite is an independent project; it is not affiliated with, endorsed by or sponsored by Meta. "SAM 2" refers to the original model by Meta FAIR, on which this work is based.
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Released for **non-commercial research use only** (CC BY-NC 4.0):
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- It contains SAM 2.1 weights (memory encoder and mask decoder, plus a **modified**, fine-tuned memory attention) by Meta Platforms, Inc., licensed under the Apache License 2.0 (see `LICENSE` and `NOTICE`).
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