metadata
language:
- en
pipeline_tag: image-segmentation
tags:
- sam2
- segment-anything
- mlx
- apple-silicon
- video-segmentation
- object-tracking
license: apache-2.0
SAM2.1 MLX Weights
MLX-format weights for SAM2.1 (Segment Anything Model 2.1) ported to Apple MLX.
Quick Start
1. Clone the code:
git clone https://github.com/eisneim/sam2.1_mlx.git
cd sam2.1_mlx
pip install mlx opencv-python safetensors numpy
2. Download weights from this repo:
# Base Plus (recommended, best quality/speed balance)
huggingface-cli download eisneim/sam2.1_mlx_weights sam2.1_hiera_base_plus.safetensors --local-dir weights/
# Small (faster, slightly lower quality)
huggingface-cli download eisneim/sam2.1_mlx_weights sam2.1_hiera_small.safetensors --local-dir weights/
Or manually download the .safetensors files and place them in weights/.
3. Run:
# Video tracking — click on an object in the first frame
python inference_video.py -i your_video.mp4
# Image segmentation — click on an object
python inference_image.py -i your_image.jpg
# Use the small model
python inference_video.py -i your_video.mp4 --model small
Available Models
| Model | File | Size | Quality | Speed |
|---|---|---|---|---|
| base_plus | sam2.1_hiera_base_plus.safetensors |
~300MB | Best | ~130 fps |
| small | sam2.1_hiera_small.safetensors |
~150MB | Good | ~200 fps |
Converting Weights Yourself
If you prefer to convert from the original PyTorch checkpoints:
# Download PyTorch weights from Meta
wget https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_base_plus.pt -P weights/
# Convert to MLX safetensors
python -m src.sam2.convert --src weights/sam2.1_hiera_base_plus.pt --dst weights/sam2.1_hiera_base_plus.safetensors
Links
- Code: https://github.com/eisneim/sam2.1_mlx
- Original SAM2: https://github.com/facebookresearch/sam2
License
Apache 2.0 (same as the original SAM2).