---
license: other
license_name: apache-2.0-mit-and-cc-by-4.0
license_link: https://huggingface.co/pch/rawmakase-models/blob/main/NOTICE
library_name: onnx
pipeline_tag: image-segmentation
tags:
  - onnx
  - sam2
  - detr
  - upernet
  - rawnind
  - denoising
  - image-segmentation
  - rawmakase
---

# RAWmakase model files

The models [RAWmakase](https://github.com/pch/rawmakase), an open source RAW photo
editor, downloads when you first use **Select Subject**, **Select Sky** or **Select
Background** in its Masking tool, or **AI Denoise** in its Detail panel. They run on your computer with ONNX Runtime; no
photo is sent anywhere. The app fetches these files at a pinned commit of this
repository and checks each against the SHA-256 below before using it.

Nothing here was trained by RAWmakase. The SAM 2 and DETR files are copies of public ONNX
exports of Meta's Apache-2.0 models, kept here so the app's downloads do not depend on
third-party repositories. The UperNet file is RAWmakase's own ONNX export of an MIT-licensed
checkpoint. The RawNIND file is RAWmakase's ONNX conversion of Benoit Brummer's checkpoint,
weights unchanged, under CC BY 4.0. See [NOTICE](NOTICE) for exact provenance, [LICENSE](LICENSE)
for the Apache license, [upernet-convnext-base/LICENSE](upernet-convnext-base/LICENSE) for the
MIT one and [rawnind-utnet2-bayer/LICENSE](rawnind-utnet2-bayer/LICENSE) for CC BY 4.0.

| File | Bytes | SHA-256 |
| --- | --- | --- |
| `sam2.1-hiera-small/vision_encoder.onnx` | 467440 | `aacf1f7137bb6fffcf6bf166abcfabe28f57a76059254f3fb611c4a64a208119` |
| `sam2.1-hiera-small/vision_encoder.onnx_data` | 162476288 | `260fd1f0a34e72a3dc79a739e563b4facc0ba75504818b433a1f808e66637456` |
| `sam2.1-hiera-small/prompt_encoder_mask_decoder.onnx` | 213114 | `079c59b261f723ff5c6a125e69b0170a957b21c58738c28d2b0394ecd0587d7f` |
| `sam2.1-hiera-small/prompt_encoder_mask_decoder.onnx_data` | 20958208 | `f9e59a584ab8ced21fa812c211bc01084204db1c9e92a5ef4fb3a49972b4e864` |
| `detr-resnet-50-panoptic/detr-panoptic-fp16.onnx` | 86559030 | `afd9f02d864302d690356fd4bfcb2feed2397a1190bf46a7306cb430464d734a` |
| `upernet-convnext-base/upernet-convnext-base-sky-fp16.onnx` | 242162020 | `ed75b2ad81ad209949c9580071ed486a72a423037822a49faa4e98991a43b891` |
| `rawnind-utnet2-bayer/model_bayer.onnx` | 31057086 | `5111f19a66a9377002e2ff4ca87de8e3303085b33e6dc11746e42ce811bf6cc1` |
| `rawnind-utnet2-bayer/LICENSE` | 18657 | `9ba9550ad48438d0836ddab3da480b3b69ffa0aac7b7878b5a0039e7ab429411` |
| `rawnind-utnet2-bayer/NOTICE` | 777 | `809b7f8aac9b4bf7134e7543de867b031f194895b280efebe0206e1ddca868e6` |
| `rawnind-utnet2-bayer/MODEL_CARD.md` | 3670 | `3c2d3d55e8bb0d6b4d0b1e5e357ce2cb4aa0e5f0dd6383cd6bf46c44f755ddf6` |

## Segment Anything Model 2.1, Hiera small

- **What it does in RAWmakase:** draws the outline of each person and animal (and of the
  sky in earlier releases), and answers clicks when you refine a selection.
- **Made by:** Meta AI (Ravi et al., *SAM 2: Segment Anything in Images and Videos*,
  2024). Weights and code are Apache-2.0:
  [facebookresearch/sam2](https://github.com/facebookresearch/sam2),
  [facebook/sam2.1-hiera-small](https://huggingface.co/facebook/sam2.1-hiera-small).
  Trained on Meta's SA-1B and SA-V datasets.
- **This export:** [onnx-community/sam2.1-hiera-small-ONNX](https://huggingface.co/onnx-community/sam2.1-hiera-small-ONNX)
  at commit `a7df49d8de14b9d2e4504d1687b0d568f905fd8d` (float32). An image encoder
  (`pixel_values` [1,3,1024,1024], ImageNet mean/std, stretched) and a prompt encoder
  with mask decoder (points, labels, boxes and the three image embeddings in; three
  candidate 256×256 mask logits, IoU scores and an object score out). Each graph's
  weights are in the `.onnx_data` file beside it.

## DETR ResNet-50 panoptic

- **What it does in RAWmakase:** finds the people and animals in the photo (and the
  sky in earlier releases), so the right outlines are chosen.
- **Made by:** Facebook AI Research (Carion et al., *End-to-End Object Detection with
  Transformers*, 2020). Weights and code are Apache-2.0:
  [facebookresearch/detr](https://github.com/facebookresearch/detr),
  [facebook/detr-resnet-50-panoptic](https://huggingface.co/facebook/detr-resnet-50-panoptic).
  Trained on COCO 2017 panoptic.
- **This export:** [Xenova/detr-resnet-50-panoptic](https://huggingface.co/Xenova/detr-resnet-50-panoptic)
  at commit `ea24b2d4e0bfae31f0a1299ba3fb892a2df064de`, `onnx/model_fp16.onnx` (half
  precision weights, float32 inputs). `pixel_values` [1,3,H,W] (ImageNet mean/std;
  RAWmakase uses a long edge of 800) and `pixel_mask` [1,64,64]; `logits` [1,100,251]
  (COCO category ids, last class is "no object"; person 1, animals 16–25, sky 187) and
  `pred_masks` [1,100,H/4,W/4]. ONNX Runtime's full graph optimisation fails on this
  file; RAWmakase loads it with basic optimisation.

## UperNet ConvNeXt base

- **What it does in RAWmakase:** finds the sky, for **Select Sky**.
- **Made by:** OpenMMLab's training of UperNet (Xiao et al., 2018) with ConvNeXt (Liu et al.,
  Meta AI, 2022), [openmmlab/upernet-convnext-base](https://huggingface.co/openmmlab/upernet-convnext-base),
  whose card says MIT. Trained on [ADE20K](https://groups.csail.mit.edu/vision/datasets/ADE20K/)
  (MIT CSAIL), whose images are offered for non-commercial research and education.
- **This export:** made by [export_upernet_sky.py](upernet-convnext-base/export_upernet_sky.py)
  from that repository at commit `13a9c410ff779f25236a8d24802d2742e4694e9c`, which reproduces
  it byte for byte with the same package versions. Half precision weights, cast to float32 as it loads; the
  auxiliary head dropped; adaptive pooling written as the same averages so any size works.
  `pixel_values` [1,3,H,W] (ImageNet mean/std; RAWmakase uses a long edge of 1024); `sky`
  [1,H,W], the probability of ADE20K's sky at each pixel.

## RawNIND UtNet2 (Bayer)

- **What it does in RAWmakase:** **AI Denoise** denoises and demosaics a RAW photo's mosaic
  in one pass, on your computer.
- **Made by:** Benoit Brummer (UCLouvain), [rawnind_jddc](https://github.com/trougnouf/rawnind_jddc)
  at commit `e80fb7b14dc440bba38c01f312fd3cc02deb566c`; Brummer and De Vleeschouwer,
  [arXiv:2501.08924](https://arxiv.org/abs/2501.08924). Trained on the RawNIND dataset
  ([doi:10.14428/DVN/DEQCIM](https://doi.org/10.14428/DVN/DEQCIM)). The author licenses the
  weights under GPL-3.0 or CC BY 4.0; RAWmakase takes and redistributes them under
  **CC BY 4.0** ([LICENSE](rawnind-utnet2-bayer/LICENSE), credit in
  [NOTICE](rawnind-utnet2-bayer/NOTICE)). RAWmakase is not endorsed by the authors.
- **This export:** RAWmakase's conversion of checkpoint
  `DenoiserTrainingBayerToProfiledRGB_4ch_2024-02-21-bayer_ms-ssim_mgout_notrans_valeither_-4`
  (SHA-256 `c1f7de3de24cb12f4803ee65e11cfb467e9a5b80c3cb7126746ef026106c15e0`) to ONNX
  with darktable-ai `76513a1e379b4265007bae768825b9a581944ff8`; the weights are unchanged.
  `input` [1,4,512,512] (packed RGGB planes, 0–1, not white balanced) and `output`
  [1,3,1024,1024] (camera RGB at an arbitrary scale). Details and measurements in its
  [model card](rawnind-utnet2-bayer/MODEL_CARD.md).

## Citation

```bibtex
@article{ravi2024sam2,
  title={SAM 2: Segment Anything in Images and Videos},
  author={Ravi, Nikhila and Gabeur, Valentin and Hu, Yuan-Ting and Hu, Ronghang and Ryali, Chaitanya and Ma, Tengyu and Khedr, Haitham and R{\"a}dle, Roman and Rolland, Chloe and Gustafson, Laura and Mintun, Eric and Pan, Junting and Alwala, Kalyan Vasudev and Carion, Nicolas and Wu, Chao-Yuan and Girshick, Ross and Doll{\'a}r, Piotr and Feichtenhofer, Christoph},
  journal={arXiv preprint arXiv:2408.00714},
  year={2024}
}
@inproceedings{xiao2018upernet,
  title={Unified Perceptual Parsing for Scene Understanding},
  author={Xiao, Tete and Liu, Yingcheng and Zhou, Bolei and Jiang, Yuning and Sun, Jian},
  booktitle={ECCV},
  year={2018}
}
@inproceedings{liu2022convnext,
  title={A ConvNet for the 2020s},
  author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
  booktitle={CVPR},
  year={2022}
}
@article{brummer2025rawnind,
  title={Learning Joint Denoising, Demosaicing, and Compression from the Raw Natural Image Noise Dataset},
  author={Brummer, Benoit and De Vleeschouwer, Christophe},
  journal={arXiv preprint arXiv:2501.08924},
  year={2025}
}
@inproceedings{carion2020detr,
  title={End-to-End Object Detection with Transformers},
  author={Carion, Nicolas and Massa, Francisco and Synnaeve, Gabriel and Usunier, Nicolas and Kirillov, Alexander and Zagoruyko, Sergey},
  booktitle={ECCV},
  year={2020}
}
```