Instructions to use avbiswas/sam2.1-hiera-tiny-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use avbiswas/sam2.1-hiera-tiny-mlx-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir sam2.1-hiera-tiny-mlx-4bit avbiswas/sam2.1-hiera-tiny-mlx-4bit
- sam2
How to use avbiswas/sam2.1-hiera-tiny-mlx-4bit with sam2:
# Use SAM2 with images import torch from sam2.sam2_image_predictor import SAM2ImagePredictor predictor = SAM2ImagePredictor.from_pretrained(avbiswas/sam2.1-hiera-tiny-mlx-4bit) 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(avbiswas/sam2.1-hiera-tiny-mlx-4bit) 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
- Local Apps
- LM Studio
Create README
Browse files
README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- facebook/sam2.1-hiera-tiny
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- avbiswas/sam2.1-hiera-tiny-mlx
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pipeline_tag: image-segmentation
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tags:
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- sam
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- sam2
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- segment
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- segment-anything
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---
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To be used with the [mlx-sam](https://github.com/avbiswas/sam2-mlx) repo
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