base on [CAAI AIR'24] Bilateral Reference for High-Resolution Dichotomous Image Segmentation <h1 align="center">Bilateral Reference for High-Resolution Dichotomous Image Segmentation</h1> <div align='center'> <a href='https://scholar.google.com/citations?user=TZRzWOsAAAAJ' target='_blank'><strong>Peng Zheng</strong></a>^1,4,5,6,&thinsp; <a href='https://scholar.google.com/citations?user=0uPb8MMAAAAJ' target='_blank'><strong>Dehong Gao</strong></a>²,&thinsp; <a href='https://scholar.google.com/citations?user=kakwJ5QAAAAJ' target='_blank'><strong>Deng-Ping Fan</strong></a>^1*,&thinsp; <a href='https://scholar.google.com/citations?user=9cMQrVsAAAAJ' target='_blank'><strong>Li Liu</strong></a>³,&thinsp; <a href='https://scholar.google.com/citations?user=qQP6WXIAAAAJ' target='_blank'><strong>Jorma Laaksonen</strong></a>⁴,&thinsp; <a href='https://scholar.google.com/citations?user=pw_0Z_UAAAAJ' target='_blank'><strong>Wanli Ouyang</strong></a>⁵,&thinsp; <a href='https://scholar.google.com/citations?user=stFCYOAAAAAJ' target='_blank'><strong>Nicu Sebe</strong></a>⁶ </div> <div align='center'> ¹Nankai University&ensp; ²Northwestern Polytechnical University&ensp; ³National University of Defense Technology&ensp; <br /> ⁴Aalto University&ensp; ⁵Shanghai AI Laboratory&ensp; ⁶University of Trento&ensp; </div> <div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;"> <a href='https://www.sciopen.com/article/pdf/10.26599/AIR.2024.9150038.pdf'><img src='https://img.shields.io/badge/Journal-Paper-red'></a>&ensp; <a href='https://arxiv.org/pdf/2401.03407'><img src='https://img.shields.io/badge/arXiv-Paper-red'></a>&ensp; <a href='https://drive.google.com/file/d/1FWvKDWTnK9RsiywfCsIxsnQzqv-dlO5u/view'><img src='https://img.shields.io/badge/中文版-Paper-red'></a>&ensp; <a href='https://www.birefnet.top'><img src='https://img.shields.io/badge/Page-Project-red'></a>&ensp; <a href='https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM'><img src='https://img.shields.io/badge/GDrive-Stuff-green'></a>&ensp; <a href='LICENSE'><img src='https://img.shields.io/badge/License-MIT-yellow'></a>&ensp; <a href='https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF-Space-blue'></a>&ensp; <a href='https://huggingface.co/ZhengPeng7/BiRefNet'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HF-Model-blue'></a>&ensp; </div> <div align="center" style="display: flex; justify-content: center; flex-wrap: wrap;"> <a href='https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba'><img src='https://img.shields.io/badge/Multiple_Images_Inference-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; <a href='https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl'><img src='https://img.shields.io/badge/Inference_&_Evaluation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; <a href='https://colab.research.google.com/drive/1B6aKZ3ekcvKMkSBn0N5mCASLUYMp0whK'><img src='https://img.shields.io/badge/Box_Guided_Segmentation-F9AB00?style=for-the-badge&logo=googlecolab&color=525252'></a>&ensp; </div> | *DIS-Sample_1* | *DIS-Sample_2* | | :------------------------------: | :-------------------------------: | | <img src="https://drive.google.com/thumbnail?id=1ItXaA26iYnE8XQ_GgNLy71MOWePoS2-g&sz=w400" /> | <img src="https://drive.google.com/thumbnail?id=1Z-esCujQF_uEa_YJjkibc3NUrW4aR_d4&sz=w400" /> | This repo is the official implementation of "[**Bilateral Reference for High-Resolution Dichotomous Image Segmentation**](https://arxiv.org/pdf/2401.03407)" (___CAAI AIR 2024___). > [!note] > **We need more GPU resources** to push forward the performance of BiRefNet, especially on *video* tasks and more *efficient* model designs on higher-resolution images. If you are happy to cooperate, please contact me at [email protected]. ## News :newspaper: * **`Dec 5, 2024`:** Fix the bug of using `torch.compile` in latest PyTorch versions (2.5.1) and the slow iteration in FP16 training with accelerate (set as default). * **`Nov 28, 2024`:** Congrats to students @Nankai University employed BiRefNet to build their project and won the [provincial gold medal](https://drive.google.com/file/d/1WDgcHzzmbPtj3O4tlZyT3HLfNKLBPkje/view?usp=drive_link) and national bronze medal on the [China International College Students’ Innovation Competition 2024](https://cy.ncss.cn/en). * **`Oct 26, 2024`:** We added the [guideline of conducting fine-tuning on custom data](https://github.com/ZhengPeng7/BiRefNet?tab=readme-ov-file#pen-fine-tuning-on-custom-data) with existing weights. * **`Oct 6, 2024`:** We uploaded the [BiRefNet-matting](https://huggingface.co/ZhengPeng7/BiRefNet-matting) model for general trimap-free matting use. * **`Sep 24, 2024`:** We uploaded the [BiRefNet_lite-2K](https://huggingface.co/ZhengPeng7/BiRefNet_lite-2K) model, which takes inputs in a much higher resolution (2560x1440). We also added the [notebook](https://github.com/ZhengPeng7/BiRefNet/blob/main/tutorials/BiRefNet_inference_video.ipynb) for inference on videos. * **`Sep 7, 2024`:** Thanks to [Freepik](https://www.freepik.com) for supporting me with GPUs for more extensive experiments, especially on BiRefNet for 2K inference! * **`Aug 30, 2024`:** We uploaded notebooks in `tutorials` to run the inference and ONNX conversion locally. * **`Aug 23, 2024`:** Our BiRefNet is now officially released [online](https://www.sciopen.com/article/10.26599/AIR.2024.9150038) on CAAI AIR journal. And thanks to the [press release](https://www.eurekalert.org/news-releases/1055380). * **`Aug 19, 2024`:** We uploaded the ONNX model files of all weights in the [GitHub release](https://github.com/ZhengPeng7/BiRefNet/releases/tag/v1) and [GDrive folder](https://drive.google.com/drive/u/0/folders/1kZM55bwsRdS__bdnsXpkmH6QPyza-9-N). Check out the **ONNX conversion** part in [model zoo](https://github.com/ZhengPeng7/BiRefNet?tab=readme-ov-file#model-zoo) for more details. * **`Jul 30, 2024`:** Thanks to @not-lain for his kind efforts in adding BiRefNet to the official huggingface.js [repo](https://github.com/huggingface/huggingface.js/blob/3a8651fbc6508920475564a692bf0e5b601d9343/packages/tasks/src/model-libraries-snippets.ts#L763). * **`Jul 28, 2024`:** We released the [Colab demo for box-guided segmentation](https://colab.research.google.com/drive/1B6aKZ3ekcvKMkSBn0N5mCASLUYMp0whK). * **`Jul 15, 2024`:** We deployed our BiRefNet on [Hugging Face Models](https://huggingface.co/ZhengPeng7/BiRefNet) for users to easily load it in one line code. * **`Jun 21, 2024`:** We released and uploaded the Chinese version of our original paper to my [GDrive](https://drive.google.com/file/d/1aBnJ_R9lbnC2dm8dqD0-pzP2Cu-U1Xpt/view). * **`May 28, 2024`:** We hold a [model zoo](https://github.com/ZhengPeng7/BiRefNet?tab=readme-ov-file#model-zoo) with well-trained weights of our BiRefNet in different sizes and for different tasks, including general use, matting segmentation, DIS, HRSOD, COD, etc. * **`May 7, 2024`:** We also released the [Colab demo for multiple images inference](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba). Many thanks to @rishabh063 for his support on it. * **`Apr 9, 2024`:** Thanks to [Features and Labels Inc.](https://fal.ai/) for deploying a cool online BiRefNet [inference API](https://fal.ai/models/fal-ai/birefnet/playground) and providing me with strong GPU resources for 4 months on more extensive experiments! * **`Mar 7, 2024`:** We released BiRefNet codes, the well-trained weights for all tasks in the original papers, and all related stuff in my [GDrive folder](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM). Meanwhile, we also deployed our BiRefNet on [Hugging Face Spaces](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo) for easier online use and released the [Colab demo for inference and evaluation](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl). * **`Jan 7, 2024`:** We released our paper on [arXiv](https://arxiv.org/pdf/2401.03407). ## :rocket: Load BiRefNet in _ONE LINE_ by HuggingFace, check more: [](https://huggingface.co/ZhengPeng7/birefnet) ```python from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained('zhengpeng7/BiRefNet', trust_remote_code=True) ``` ## :flight_arrival: Inference Partner: We are really happy to collaborate with [FAL](https://fal.ai) to deploy the **inference API** of BiRefNet. You can access this service via the link below: + https://fal.ai/models/fal-ai/birefnet Our BiRefNet has achieved SOTA on many similar HR tasks: **DIS**: [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te1?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te2?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te3?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-te4?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/dichotomous-image-segmentation-on-dis-vd?p=bilateral-reference-for-high-resolution) <details><summary>Figure of Comparison on DIS Papers with Codes (by the time of this work):</summary> <img src="https://drive.google.com/thumbnail?id=1DLt6CFXdT1QSWDj_6jRkyZINXZ4vmyRp&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=1gn5GyKFlJbMIkre1JyEdHDSYcrFmcLD0&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=16CVYYOtafEeZhHqv0am2Daku1n_exMP6&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=10K45xwPXmaTG4Ex-29ss9payA9yBnyLn&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=16EuyqKFJOqwMmagvfnbC9hUurL9pYLLB&sz=w1620" /> </details> <br /> **COD**:[](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-cod?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-nc4k?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-camo?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/camouflaged-object-segmentation-on-chameleon?p=bilateral-reference-for-high-resolution) <details><summary>Figure of Comparison on COD Papers with Codes (by the time of this work):</summary> <img src="https://drive.google.com/thumbnail?id=1DLt6CFXdT1QSWDj_6jRkyZINXZ4vmyRp&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=1gn5GyKFlJbMIkre1JyEdHDSYcrFmcLD0&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=16CVYYOtafEeZhHqv0am2Daku1n_exMP6&sz=w1620" /> </details> <br /> **HRSOD**: [](https://paperswithcode.com/sota/rgb-salient-object-detection-on-davis-s?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/rgb-salient-object-detection-on-hrsod?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/rgb-salient-object-detection-on-uhrsd?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/salient-object-detection-on-duts-te?p=bilateral-reference-for-high-resolution) [](https://paperswithcode.com/sota/salient-object-detection-on-dut-omron?p=bilateral-reference-for-high-resolution) <details><summary>Figure of Comparison on HRSOD Papers with Codes (by the time of this work):</summary> <img src="https://drive.google.com/thumbnail?id=1hNfQtlTAHT4-AVbk_47852zyRp1NOFLs&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=1bcVldUAxYkMI3OMTyaP_jNuOugDfYj-d&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=1p1zgyVz27cGEqQMtOKzm_6zoYK3Sw_Zk&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=1TubAvcoEbH_mHu3I-AxflnB71nkf35jJ&sz=w1620" /> <img src="https://drive.google.com/thumbnail?id=1A3V9HjVtcMQdnGPwuy-DBVhwKuo0q2lT&sz=w1620" /> </details> <br /> #### Try our online demos for inference: + **Inference and evaluation** of your given weights: [](https://colab.research.google.com/drive/1MaEiBfJ4xIaZZn0DqKrhydHB8X97hNXl) + **Online Inference with GUI** with adjustable resolutions: [](https://huggingface.co/spaces/ZhengPeng7/BiRefNet_demo) + Online **Multiple Images Inference** on Colab: [](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba) <img src="https://drive.google.com/thumbnail?id=12XmDhKtO1o2fEvBu4OE4ULVB2BK0ecWi&sz=w1620" /> ## Model Zoo > For more general use of our BiRefNet, I extended the original academic one to more general ones for better real-life application. > > Datasets and datasets are suggested to be downloaded from official pages. But you can also download the packaged ones: [DIS](https://drive.google.com/drive/folders/1hZW6tAGPJwo9mPS7qGGGdpxuvuXiyoMJ), [HRSOD](https://drive.google.com/drive/folders/18_hAE3QM4cwAzEAKXuSNtKjmgFXTQXZN), [COD](https://drive.google.com/drive/folders/1EyHmKWsXfaCR9O0BiZEc3roZbRcs4ECO), [Backbones](https://drive.google.com/drive/folders/1cmce_emsS8A5ha5XT2c_CZiJzlLM81ms). > > Find performances (almost all metrics) of all models in the `exp-TASK_SETTINGS` folders in [[**stuff**](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM)]. <details><summary>Models in the original paper, for <b>comparison on benchmarks</b>:</summary> | Task | Training Sets | Backbone | Download | | :---: | :-------------------------: | :-----------: | :----------------------------------------------------------: | | DIS | DIS5K-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1J90LucvDQaS3R_-9E7QUh1mgJ8eQvccb/view) | | COD | COD10K-TR, CAMO-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1tM5M72k7a8aKF-dYy-QXaqvfEhbFaWkC/view) | | HRSOD | DUTS-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1f7L0Pb1Y3RkOMbqLCW_zO31dik9AiUFa/view) | | HRSOD | DUTS-TR, HRSOD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1WJooyTkhoDLllaqwbpur_9Hle0XTHEs_/view) | | HRSOD | DUTS-TR, UHRSD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1Pu1mv3ORobJatIuUoEuZaWDl2ylP3Gw7/view) | | HRSOD | HRSOD-TR, UHRSD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/1xEh7fsgWGaS5c3IffMswasv0_u-aVM9E/view) | | HRSOD | DUTS-TR, HRSOD-TR, UHRSD-TR | swin_v1_large | [google-drive](https://drive.google.com/file/d/13FaxyyOwyCddfZn2vZo1xG1KNZ3cZ-6B/view) | </details> <details><summary>Models trained with customed data (general, matting), for <b>general use in practical application</b>:</summary> | Task | Training Sets | Backbone | Test Set | Metric (S, wF[, HCE]) | Download | | :-----------------------: | :----------------------------------------------------------: | :-----------: | :-------: | :-------------------: | :----------------------------------------------------------: | | **general use** | DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE, TR-P3M-10k, TE-P3M-500-NP, TE-P3M-500-P, [TR-humans](https://huggingface.co/datasets/schirrmacher/humans) | swin_v1_large | DIS-VD | 0.911, 0.875, 1069 | [google-drive](https://drive.google.com/file/d/1_IfUnu8Fpfn-nerB89FzdNXQ7zk6FKxc/view) | | **general use** | DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE, TR-P3M-10k, TE-P3M-500-NP, TE-P3M-500-P, [TR-humans](https://huggingface.co/datasets/schirrmacher/humans) | swin_v1_tiny | DIS-VD | 0.882, 0.830, 1175 | [google-drive](https://drive.google.com/file/d/1fzInDWiE2n65tmjaHDSZpqhL0VME6-Yl/view) | | **general use** | DIS5K-TR, DIS-TEs | swin_v1_large | DIS-VD | 0.907, 0.865, 1059 | [google-drive](https://drive.google.com/file/d/1P6NJzG3Jf1sl7js2q1CPC3yqvBn_O8UJ/view) | | **general matting** | P3M-10k (except TE-P3M-500-NP), [TR-humans](https://huggingface.co/datasets/schirrmacher/humans), AM-2k, AIM-500, Human-2k (synthesized with BG-20k), Distinctions-646 (synthesized with BG-20k), HIM2K, PPM-100 | swin_v1_large | TE-P3M-500-NP | 0.979, 0.988 | [google-drive](https://drive.google.com/file/d/1Nlcg58d5bvE-Tbbm8su_eMQba10hdcwQ/view) | | **portrait matting** | [P3M-10k](https://github.com/JizhiziLi/P3M), [humans](https://huggingface.co/datasets/schirrmacher/humans) | swin_v1_large | P3M-500-P | 0.983, 0.989 | [google-drive](https://drive.google.com/file/d/1uUeXjEUoD2XF_6YjD_fsct-TJp7TFiqh) | </details> <details><summary>Segmentation with box <b>guidance</b>:</summary> + Given box guidance: [](https://colab.research.google.com/drive/1B6aKZ3ekcvKMkSBn0N5mCASLUYMp0whK) </details> <details><summary>Model <b>efficiency</b>:</summary> > Screenshot from the original paper. All tests are conducted on a single A100 GPU. <img src="https://drive.google.com/thumbnail?id=1mTfSD_qt-rFO1t8DRQcyIa5cgWLf1w2-&sz=h300" /> <img src="https://drive.google.com/thumbnail?id=1F_OURIWILVe4u1rSz-aqt6ur__bAef25&sz=h300" /> </details> <details><summary><b>ONNX</b> conversion:</summary> > We converted from `.pth` weights files to `.onnx` files. > We referred a lot to the [Kazuhito00/BiRefNet-ONNX-Sample](https://github.com/Kazuhito00/BiRefNet-ONNX-Sample), many thanks to @Kazuhito00. + Check our [Colab demo for ONNX conversion](https://colab.research.google.com/drive/1z6OruR52LOvDDpnp516F-N4EyPGrp5om) or the [notebook file for local running](https://drive.google.com/file/d/1cgL2qyvOO5q3ySfhytypX46swdQwZLrJ), where you can do the conversion/inference by yourself and find all relevant info. + As tested, BiRefNets with SwinL (default backbone) cost `~90%` more time (the inference costs `~165ms` on an A100 GPU) using ONNX files. Meanwhile, BiRefNets with SwinT (lightweight) cost `~75%` more time (the inference costs `~93.8ms` on an A100 GPU) using ONNX files. Input resolution is `1024x1024` as default. + The results of the original pth files and the converted onnx files are slightly different, which is acceptable. + Pay attention to the compatibility among `onnxruntime-gpu, CUDA, and CUDNN` (we use `torch==2.0.1, cuda=11.8` here). </details> ## Third-Party Creations >We found there've been some 3rd party applications based on our BiRefNet. Many thanks for their contribution to the community! Choose the one you like to try with clicks instead of codes: 1. **Applications**: + Thanks [**tin2tin/2D_Asset_Generator**](https://github.com/tin2tin/2D_Asset_Generator): this project combined BiRefNet and FLUX as a **Blender add-on** for "AI generating 2D cutout assets for ex. previz". https://github.com/user-attachments/assets/6cce7ca7-7817-4406-b6c4-6d4e8c414ed4 + Thanks [**camenduru/text-behind-tost**](https://github.com/camenduru/text-behind-tost): this project employed BiRefNet to extract foreground subjects and **add texts between the subjects and background**, which looks amazing especially for videos. Check their [tweets](https://x.com/camenduru/status/1856290408294220010) for more examples. <p align="center"><img src="https://github.com/user-attachments/assets/9969dd10-38a8-4cf2-a6c7-5b11f074b9b4" height="300"/></p> + Thanks [**briaai/RMBG-2.0**](https://huggingface.co/briaai/RMBG-2.0): this project trained BiRefNet with their **high-quality private data**, which brings improvement on the DIS task. Note that their weights are for only **non-commercial use** and are **not aware of transparency** due to training in the DIS task setting, which focuses only on predicting binary masks. <p align="center"><img src="https://huggingface.co/briaai/RMBG-2.0/resolve/main/t4.png" height="300"/></p> + Thanks [**lldacing/ComfyUI_BiRefNet_ll**](https://github.com/lldacing/ComfyUI_BiRefNet_ll): this project further upgrade the **ComfyUI node** for BiRefNet with both our **latest weights** and **the legacy ones**. <p align="center"><img src="https://github.com/lldacing/ComfyUI_BiRefNet_ll/raw/main/doc/video.gif" height="300"/></p> + Thanks [**MoonHugo/ComfyUI-BiRefNet-Hugo**](https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo): this project further upgrade the **ComfyUI node** for BiRefNet with our **latest weights**. <p align="center"><img src="https://github.com/MoonHugo/ComfyUI-BiRefNet-Hugo/raw/main/assets/demo4.gif" height="300"/></p> + Thanks [**lbq779660843/BiRefNet-Tensorrt**](https://github.com/lbq779660843/BiRefNet-Tensorrt) and [**yuanyang1991/birefnet_tensorrt**](https://github.com/yuanyang1991/birefnet_tensorrt): they both provided the project to convert BiRefNet to **TensorRT**, which is faster and better for deployment. Their repos offer solid local establishment (Win and Linux) and [colab demo](https://colab.research.google.com/drive/1r8GkFPyMMO0OkMX6ih5FjZnUCQrl2SHV?usp=sharing), respectively. And @yuanyang1991 kindly offered the comparison among the inference efficiency of naive PyTorch, ONNX, and TensorRT on an RTX 4080S: | Methods | [Pytorch](https://drive.google.com/file/d/1_IfUnu8Fpfn-nerB89FzdNXQ7zk6FKxc/view) | [ONNX](https://drive.google.com/drive/u/0/folders/1kZM55bwsRdS__bdnsXpkmH6QPyza-9-N) | TensorRT | |:------------------------------------------------------------------------------------:|:--------------:|:--------------:|:--------------:| | &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;First Inference Time&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; | 0.71s | 5.32s | **0.17s** | | Methods | [Pytorch](https://drive.google.com/file/d/1_IfUnu8Fpfn-nerB89FzdNXQ7zk6FKxc/view) | [ONNX](https://drive.google.com/drive/u/0/folders/1kZM55bwsRdS__bdnsXpkmH6QPyza-9-N) | TensorRT | |:------------------------------------------------------------------------------------:|:--------------:|:--------------:|:--------------:| | Avg Inf Time (excluding 1st) | 0.15s | 4.43s | **0.11s** | + Thanks [**dimitribarbot/sd-webui-birefnet**](https://github.com/dimitribarbot/sd-webui-birefnet): this project allows to add a BiRefNet section to the original **Stable Diffusion WebUI**'s Extras tab. <p align="center"><img src="https://drive.google.com/thumbnail?id=159bLXI71FWh4ZsHTvc-wApSN9ytVRmua&sz=w1620" /></p> + Thanks [**fal.ai/birefnet**](https://fal.ai/models/birefnet): this project on `fal.ai` encapsulates BiRefNet **online** with more useful options in **UI** and **API** to call the model. <p align="center"><img src="https://drive.google.com/thumbnail?id=1rNk81YV_Pzb2GykrzfGvX6T7KBXR0wrA&sz=w1620" /></p> + Thanks [**ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO**](https://github.com/ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO): this project further improves the **UI** for BiRefNet in ComfyUI, especially for **video data**. <p align="center"><img src="https://drive.google.com/thumbnail?id=1GOqEreyS7ENzTPN0RqxEjaA76RpMlkYM&sz=w1620" /></p> <https://github.com/ZhengPeng7/BiRefNet/assets/25921713/3a1c7ab2-9847-4dac-8935-43a2d3cd2671> + Thanks [**viperyl/ComfyUI-BiRefNet**](https://github.com/viperyl/ComfyUI-BiRefNet): this project packs BiRefNet as **ComfyUI nodes**, and makes this SOTA model easier use for everyone. <p align="center"><img src="https://drive.google.com/thumbnail?id=1KfxCQUUa2y9T-aysEaeVVjCUt3Z0zSkL&sz=w1620" /></p> + Thanks [**Rishabh**](https://github.com/rishabh063) for offering a demo for the [easier multiple images inference on colab](https://colab.research.google.com/drive/14Dqg7oeBkFEtchaHLNpig2BcdkZEogba). 2. **More Visual Comparisons** + Thanks [**twitter.com/ZHOZHO672070**](https://twitter.com/ZHOZHO672070) for the comparison with more background-removal methods in images: <img src="https://drive.google.com/thumbnail?id=1nvVIFt_Ezs-crPSQxUDqkUBz598fTe63&sz=w1620" /> + Thanks [**twitter.com/toyxyz3**](https://twitter.com/toyxyz3) for the comparison with more background-removal methods in videos: <https://github.com/ZhengPeng7/BiRefNet/assets/25921713/40136198-01cc-4106-81f9-81c985f02e31> <https://github.com/ZhengPeng7/BiRefNet/assets/25921713/1a32860c-0893-49dd-b557-c2e35a83c160> ## Usage #### Environment Setup ```shell # PyTorch==2.0.1+CUDA11.8 (or 2.5.1+CUDA12.4) is used for faster training (~40%) with compilation. conda create -n birefnet python=3.11 -y && conda activate birefnet pip install -r requirements.txt ``` #### Dataset Preparation Download combined training / test sets I have organized well from: [DIS](https://drive.google.com/drive/folders/1hZW6tAGPJwo9mPS7qGGGdpxuvuXiyoMJ)--[COD](https://drive.google.com/drive/folders/1EyHmKWsXfaCR9O0BiZEc3roZbRcs4ECO)--[HRSOD](https://drive.google.com/drive/folders/18_hAE3QM4cwAzEAKXuSNtKjmgFXTQXZN) or the single official ones in the `single_ones` folder, or their official pages. You can also find the same ones on my **BaiduDisk**: [DIS](https://pan.baidu.com/s/1O_pQIGAE4DKqL93xOxHpxw?pwd=PSWD)--[COD](https://pan.baidu.com/s/1RnxAzaHSTGBC1N6r_RfeqQ?pwd=PSWD)--[HRSOD](https://pan.baidu.com/s/1_Del53_0lBuG0DKJJAk4UA?pwd=PSWD). #### Weights Preparation Download backbone weights from [my google-drive folder](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM) or their official pages. ## Run ```shell # Train & Test & Evaluation ./train_test.sh RUN_NAME GPU_NUMBERS_FOR_TRAINING GPU_NUMBERS_FOR_TEST # Example: ./train_test.sh tmp-proj 0,1,2,3,4,5,6,7 0 # See train.sh / test.sh for only training / test-evaluation. # After the evaluation, run `gen_best_ep.py` to select the best ckpt from a specific metric (you choose it from Sm, wFm, HCE (DIS only)). ``` ### :pen: Fine-tuning on Custom Data <details><summary><b>Guideline</b>:</summary> > Suppose you have some custom data, fine-tuning on it tends to bring improvement. 1. **Pre-requisites**: you have put your datasets in the path `${data_root_dir}/TASK_NAME/DATASET_NAME`. For example, `${data_root_dir}/DIS5K/DIS-TR` and `${data_root_dir}/General/TR-HRSOD`, where `im` and `gt` are both in each dataset folder. 2. **Change an existing task to your custom one**: replace all `'General'` (with single quotes) in the whole project with `your custom task name` as the screenshot of vscode given below shows:<img src="https://drive.google.com/thumbnail?id=1J6gzTmrVnQsmtt3hi6ch3ZrH7Op9PKSB&sz=w400" /> 3. **Adapt settings**: + `sys_home_dir`: path to the root folder, which contains codes / datasets / weights / ... -- project folder / data folder / backbone weights folder are `${sys_home_dir}/codes/dis/BiRefNet / ${sys_home_dir}/datasets/dis/General / ${sys_home_dir}/weights/cv/swin_xxx`, respectively. + `testsets`: your validation set. + `training_set`: your training set. + `lambdas_pix_last`: adapt the weights of different losses if you want, especially for the difference between segmentation (classification task) and matting (regression task). 4. **Use existing weights**: if you want to use some existing weights to fine-tune that model, please refer to the `resume` argument in `train.py`. Attention: the epoch of training continues from the epochs the weights file name indicates (e.g., `244` in `BiRefNet-general-epoch_244.pth`), instead of `1`. So, if you want to fine-tune `50` more epochs, please specify the epochs as `294`. `\#Epochs, \#last epochs for validation, and validation step` are set in `train.sh`. 5. Good luck to your training :) If you still have questions, feel free to leave issues (recommended way) or contact me. </details> ## Well-trained weights: Download the `BiRefNet-{TASK}-{EPOCH}.pth` from [[**stuff**](https://drive.google.com/drive/folders/1s2Xe0cjq-2ctnJBR24563yMSCOu4CcxM)] and [the release page](https://github.com/ZhengPeng7/BiRefNet/releases) of this repo. Info of the corresponding (predicted\_maps/performance/training\_log) weights can be also found in folders like `exp-BiRefNet-{TASK_SETTINGS}` in the same directory. You can also download the weights from the release of this repo. The results might be a bit different from those in the original paper, you can see them in the `eval_results-BiRefNet-{TASK_SETTINGS}` folder in each `exp-xx`, we will update them in the following days. Due to the very high cost I used (A100-80G x 8), which many people cannot afford (including myself....), I re-trained BiRefNet on a single A100-40G only and achieved the performance on the same level (even better). It means you can directly train the model on a single GPU with 36.5G+ memory. BTW, 5.5G GPU memory is needed for inference in 1024x1024. (I personally paid a lot for renting an A100-40G to re-train BiRefNet on the three tasks... T_T. Hope it can help you.) But if you have more and more powerful GPUs, you can set GPU IDs and increase the batch size in `config.py` to accelerate the training. We have made all these kinds of things adaptive in scripts to seamlessly switch between single-card training and multi-card training. Enjoy it :) ## Some of my messages: This project was originally built for DIS only. But after the updates one by one, I made it larger and larger with many functions embedded together. Finally, you can **use it for any binary image segmentation tasks**, such as DIS/COD/SOD, medical image segmentation, anomaly segmentation, etc. You can eaily open/close below things (usually in `config.py`): + Multi-GPU training: open/close with one variable. + Backbone choices: Swin_v1, PVT_v2, ConvNets, ... + Weighted losses: BCE, IoU, SSIM, MAE, Reg, ... + Training tricks: multi-scale supervision, freezing backbone, multi-scale input... + Data collator: loading all in memory, smooth combination of different datasets for combined training and test. + ... I really hope you enjoy this project and use it in more works to achieve new SOTAs. ### Quantitative Results <p align="center"><img src="https://drive.google.com/thumbnail?id=1Ymkh8WN16XMTBOS8dmPTg5eAf-NIl2m5&sz=w1620" /></p> <p align="center"><img src="https://drive.google.com/thumbnail?id=1W0mi0ZiYbqsaGuohNXU8Gh7Zj4M3neFg&sz=w1620" /></p> ### Qualitative Results <p align="center"><img src="https://drive.google.com/thumbnail?id=1TYZF8pVZc2V0V6g3ik4iAr9iKvJ8BNrf&sz=w1620" /></p> <p align="center"><img src="https://drive.google.com/thumbnail?id=1ZGHC32CAdT9cwRloPzOCKWCrVQZvUAlJ&sz=w1620" /></p> ## Acknowledgement: Many of my thanks to the companies / institutes below. + [FAL](https://fal.ai). + [Freepik](https://www.freepik.com). + [Redmond.ai](https://redmond.ai). + [Alibaba-ICBU](https://www.alibaba.com). ### Citation ``` @article{zheng2024birefnet, title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation}, author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu}, journal={CAAI Artificial Intelligence Research}, volume = {3}, pages = {9150038}, year={2024} } ``` ## Contact Any questions, discussions, or even complaints, feel free to leave issues here (recommended) or send me e-mails ([email protected]) or book a meeting with me: [calendly.com/zhengpeng0108/30min](https://calendly.com/zhengpeng0108/30min). You can also join the Discord Group (https://discord.gg/d9NN5sgFrq) or QQ Group (https://qm.qq.com/q/y6WPy7WOIK) if you want to talk a lot publicly. ", Assign "at most 3 tags" to the expected json: {"id":"11502","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"