Instructions to use mlboydaisuke/SAM2.1-Hiera-Tiny-Image-Encoder-LiteRT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use mlboydaisuke/SAM2.1-Hiera-Tiny-Image-Encoder-LiteRT 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 mlboydaisuke/SAM2.1-Hiera-Tiny-Image-Encoder-LiteRT with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained("mlboydaisuke/SAM2.1-Hiera-Tiny-Image-Encoder-LiteRT") 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("mlboydaisuke/SAM2.1-Hiera-Tiny-Image-Encoder-LiteRT") 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
Card: base_model_relation: quantized (so the conversion lists under the base model's Quantizations, not Finetunes)
Browse files
README.md
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- on-device
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- gpu
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base_model: facebook/sam2.1-hiera-tiny
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# SAM 2.1 (Hiera-Tiny) image encoder — LiteRT GPU
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- on-device
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- gpu
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base_model: facebook/sam2.1-hiera-tiny
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base_model_relation: quantized
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---
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# SAM 2.1 (Hiera-Tiny) image encoder — LiteRT GPU
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