| --- |
| license: other |
| license_name: mixed |
| tags: |
| - onnx |
| - image-segmentation |
| - depth-estimation |
| --- |
| |
| # Gamut — mask models |
|
|
| The on-device models behind Gamut's subject, sky and depth masks, kept here so every |
| copy of Gamut downloads exactly the same, checksum-verified files. Gamut downloads a |
| model only when someone chooses to use masks, and never sends a photograph anywhere: |
| all of this runs on the person's own computer. |
|
|
| These are unmodified copies of the files named below. Each keeps its own licence and |
| its authors' credit; nothing here is relicensed. |
|
|
| | File | What it does in Gamut | Model and authors | Copied from | Licence | |
| |---|---|---|---|---| |
| | `birefnet_lite.onnx` | Finds the subject | BiRefNet (lite), Peng Zheng et al. | `onnx-community/BiRefNet_lite-ONNX` (`onnx/model.onnx`), an ONNX conversion of `ZhengPeng7/BiRefNet_lite` | MIT — `LICENSE-BiRefNet.txt` | |
| | `sam2.1_hiera_tiny_encoder.onnx`, `sam2.1_hiera_tiny_decoder.onnx` | Picks out the subject someone points at | Segment Anything 2.1 (Hiera tiny), Meta | `vietanhdev/segment-anything-2.1-onnx-models` (`sam2.1_hiera_tiny_20260221.zip`), exported with samexporter by Viet-Anh Nguyen | Apache-2.0 — `LICENSE-Apache-2.0.txt` | |
| | `depth_anything_v2_small.onnx` | Near and far | Depth Anything V2 **Small**, Lihe Yang et al. | `onnx-community/depth-anything-v2-small` (`onnx/model.onnx`) | Apache-2.0 — `LICENSE-Apache-2.0.txt`. Only the Small model is Apache-2.0; the Base, Large and Giant models are CC-BY-NC-4.0 and are not used. | |
| | `skyseg.onnx` | Finds the sky | Sky segmentation by xiongzhu666 (U²-Net architecture, Qin et al.) | `JianyuanWang/skyseg`, from `xiongzhu666/Sky-Segmentation-and-Post-processing` | MIT — `LICENSE-skyseg.txt` | |
|
|
| ## Papers |
|
|
| - Zheng et al., *Bilateral Reference for High-Resolution Dichotomous Image Segmentation*, CAAI AIR 2024. |
| - Ravi et al., *SAM 2: Segment Anything in Images and Videos*, ICLR 2025. |
| - Yang et al., *Depth Anything V2*, NeurIPS 2024. |
| - Qin et al., *U²-Net: Going Deeper with Nested U-Structure for Salient Object Detection*, Pattern Recognition 2020. |
|
|
