| DIS-Sample_1 | DIS-Sample_2 |
|---|---|
This repo is the official implementation of "Bilateral Reference for High-Resolution Dichotomous Image Segmentation" (arXiv 2024).
Authors: Peng Zheng, Dehong Gao, Deng-Ping Fan, Li Liu, Jorma Laaksonen, Wanli Ouyang, & Nicu Sebe.
Our BiRefNet has achieved SOTA on many similar HR tasks:
DIS: 
Figure of Comparison on Papers with Codes (by the time of this work):
COD:
Figure of Comparison on Papers with Codes (by the time of this work):
HRSOD: 
Figure of Comparison on Papers with Codes (by the time of this work):
- Inference and evaluation of your given weights:
- Online Inference with GUI with adjustable resolutions:
- Online Single Image Inference on Colab:
For more general use of our BiRefNet, I managed to extend the original adademic one to more general ones for better application in real life.
Datasets and datasets are suggested to download from official pages. But you can also download the packaged ones: DIS, HRSOD, COD, Backbones.
Find performances (almost all metrics) of all models in the
exp-TASK_SETTINGSfolders in [stuff].
Models in the original paper, for comparison on benchmarks:
| Task | Training Sets | Backbone | Download |
|---|---|---|---|
| DIS | DIS5K-TR | swin_v1_large | google-drive |
| COD | COD10K-TR, CAMO-TR | swin_v1_large | google-drive |
| HRSOD | DUTS-TR | swin_v1_large | google-drive |
| HRSOD | HRSOD-TR | swin_v1_large | google-drive |
| HRSOD | UHRSD-TR | swin_v1_large | google-drive |
| HRSOD | DUTS-TR, HRSOD-TR | swin_v1_large | google-drive |
| HRSOD | DUTS-TR, UHRSD-TR | swin_v1_large | google-drive |
| HRSOD | HRSOD-TR, UHRSD-TR | swin_v1_large | google-drive |
| HRSOD | DUTS-TR, HRSOD-TR, UHRSD-TR | swin_v1_large | google-drive |
Models trained with customed data (massive, portrait), for general use in practical application:
| 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 | swin_v1_large | DIS-VD | 0.889, 0.840, 1152 | google-drive |
| general use | DIS5K-TR,DIS-TEs, DUTS-TR_TE,HRSOD-TR_TE,UHRSD-TR_TE, HRS10K-TR_TE | swin_v1_tiny | DIS-VD | 0.867, 0.809, 1182 | Google-drive |
| general use | DIS5K-TR, DIS-TEs | swin_v1_large | DIS-VD | 0.907, 0.865, 1059 | google-drive |
| portrait segmentation | P3M-10k | swin_v1_large | P3M-500-P | 0.982, 0.990 | google-drive |
Segmentation with box guidance:
In progress...
Model efficiency:
Screenshot from the original paper. All tests are conducted on a single A100 GPU.
Concerning edge devices with less computing power, we provide a lightweight version with
swin_v1_tinyas the backbone, which is x4+ faster and x5+ smaller. The details can be found in this issue and links there.
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:
-
Applications:
-
Thanks fal.ai/birefnet: this project on
fal.aiencapsulates BiRefNet online with more useful options in UI and API to call the model. -
Thanks ZHO-ZHO-ZHO/ComfyUI-BiRefNet-ZHO: this project further improves the UI for BiRefNet in ComfyUI, especially for video data.
app-comfyUI_ZHO.mp4
-
Thanks viperyl/ComfyUI-BiRefNet: this project packs BiRefNet as ComfyUI nodes, and makes this SOTA model easier use for everyone.
-
Thanks Rishabh for offerring a demo for the easier single image inference on colab.
-
-
More Visual Comparisons
-
Thanks twitter.com/ZHOZHO672070 for the comparison with more background-removal methods in images:
-
Thanks twitter.com/toyxyz3 for the comparison with more background-removal methods in videos:
video-from_twitter_toyxyz3_2.mp4
video-from_twitter_toyxyz3_1.mp4
-
# PyTorch==2.0.1 is used for faster training with compilation.
conda create -n dis python=3.9 -y && conda activate dis
pip install -r requirements.txtDownload combined training / test sets I have organized well from: DIS--COD--HRSOD 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--COD--HRSOD.
Download backbone weights from my google-drive folder or their official pages.
# Train & Test & Evaluation
./train_test.sh RUN_NAME GPU_NUMBERS_FOR_TRAINING GPU_NUMBERS_FOR_TEST
# See train.sh / test.sh for only training / test-evaluation.
# After the evluation, run `gen_best_ep.py` to select the best ckpt from a specific metric (you choose it from Sm, wFm, HCE (DIS only)).Download the BiRefNet-{TASK}-{EPOCH}.pth from [stuff]. 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 to (including myself....), I re-trained BiRefNet on a single A100-40G only and achieve 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 this kind of things adaptive in scripts to seamlessly switch between single-card training and multi-card training. Enjoy it :)
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, ...
- Adversarial loss for binary segmentation (proposed in my previous work MCCL).
- 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.
@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={arXiv},
year={2024}
}
Any question, discussion or even complaint, feel free to leave issues here or send me e-mails ([email protected]).
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent