Master Mask — model files
ONNX model files downloaded by the Master Mask desktop app (macOS / Windows) on first launch. The app runs fully offline after this download. These files are not meant to be used on their own.
manifest.json lists every file with its size and SHA-256; the app checks each download against it.
Models and licenses
| Folder | Model | License | Original source |
|---|---|---|---|
birefnet_lite, birefnet, birefnet_lite_2k, birefnet_lite_win, birefnet_win |
BiRefNet lite / full / lite-2K | MIT (LICENSE-BiRefNet-MIT.txt) | Peng Zheng et al., "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" — https://github.com/ZhengPeng7/BiRefNet (weights: huggingface.co/ZhengPeng7/BiRefNet, BiRefNet_lite, BiRefNet_lite-2K) |
sam2.1_tiny, sam2.1_small, sam2.1_base_plus, sam2.1_tiny_512, sam2.1_small_768 |
SAM 2.1 hiera-tiny / small / base-plus | Apache-2.0 (LICENSE-SAM2-Apache-2.0.txt) | Meta AI, "SAM 2: Segment Anything in Images and Videos" — https://github.com/facebookresearch/sam2 |
Changes from the originals
- Converted from the official PyTorch weights to ONNX by Master Mask's export tool.
- BiRefNet: two tensor reshapes are written differently (same maths) so the model runs on Apple's CoreML;
birefnet_lite_2kis exported at two fixed input sizes (landscape and portrait). - BiRefNet for Windows (
birefnet_lite_win,birefnet_win): the same models with each DeformConv (deformable convolution) written as standard operations (GridSample, MatMul), because the DirectML build of ONNX Runtime has no DeformConv. Same maths: largest output difference 1.7e-5. - SAM 2.1: split into image encoder, mask decoder, memory encoder and memory attention (for video tracking);
*_512/*_768are the same weights exported at a lower working resolution. Small constant tensors are stored inconstants.npz/video_constants.npz.
The weights themselves are unchanged.