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README.md
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---
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license: Apache License 2.0
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#model-type:
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##如 gpt、phi、llama、chatglm、baichuan 等
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#- gpt
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#domain:
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##如 nlp、cv、audio、multi-modal
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#- nlp
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#language:
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##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
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#- cn
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#metrics:
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##如 CIDEr、Blue、ROUGE 等
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#- CIDEr
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#tags:
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##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
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#- pretrained
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#tools:
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##如 vllm、fastchat、llamacpp、AdaSeq 等
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#- vllm
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license: other
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license_name: bria-rmbg-2.0
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license_link: https://bria.ai/bria-huggingface-model-license-agreement/
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pipeline_tag: image-segmentation
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tags:
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- remove background
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- background
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- background-removal
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- Pytorch
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- vision
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- legal liability
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- transformers
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---
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### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
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#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
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SDK下载
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# BRIA Background Removal v2.0 Model Card
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RMBG v2.0 is our new state-of-the-art background removal model, designed to effectively separate foreground from background in a range of
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categories and image types. This model has been trained on a carefully selected dataset, which includes:
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general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale.
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The accuracy, efficiency, and versatility currently rival leading source-available models.
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It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.
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Developed by BRIA AI, RMBG v2.0 is available as a source-available model for non-commercial use.
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[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-2.0)
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## Model Details
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#####
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### Model Description
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- **Developed by:** [BRIA AI](https://bria.ai/)
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- **Model type:** Background Removal
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- **License:** [bria-rmbg-2.0](https://bria.ai/bria-huggingface-model-license-agreement/)
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- The model is released under a Creative Commons license for non-commercial use.
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- Commercial use is subject to a commercial agreement with BRIA. [Contact Us](https://bria.ai/contact-us) for more information.
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- **Model Description:** BRIA RMBG-2.0 is a dichotomous image segmentation model trained exclusively on a professional-grade dataset.
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- **BRIA:** Resources for more information: [BRIA AI](https://bria.ai/)
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## Training data
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Bria-RMBG model was trained with over 15,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.
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Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.
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For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.
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### Distribution of images:
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| Category | Distribution |
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| -----------------------------------| -----------------------------------:|
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| Objects only | 45.11% |
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| People with objects/animals | 25.24% |
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| People only | 17.35% |
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| people/objects/animals with text | 8.52% |
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| Text only | 2.52% |
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| Animals only | 1.89% |
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| Category | Distribution |
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| -----------------------------------| -----------------------------------------:|
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| Photorealistic | 87.70% |
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| Non-Photorealistic | 12.30% |
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| Category | Distribution |
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| -----------------------------------| -----------------------------------:|
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| Non Solid Background | 52.05% |
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| Solid Background | 47.95%
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| Category | Distribution |
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| -----------------------------------| -----------------------------------:|
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| Single main foreground object | 51.42% |
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| Multiple objects in the foreground | 48.58% |
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## Qualitative Evaluation
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Open source models comparison
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### Architecture
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RMBG-2.0 is developed on the [BiRefNet](https://github.com/ZhengPeng7/BiRefNet) architecture enhanced with our proprietary dataset and training scheme. This training data significantly improves the model’s accuracy and effectiveness for background-removal task.<br>
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If you use this model in your research, please cite:
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```
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@article{BiRefNet,
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title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation},
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author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu},
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journal={CAAI Artificial Intelligence Research},
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year={2024}
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}
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```
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#### Requirements
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```bash
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#安装ModelScope
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pip install modelscope
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```
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```python
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#SDK模型下载
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from modelscope import snapshot_download
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model_dir = snapshot_download('AI-ModelScope/RMBG-2.0')
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```
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Git下载
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```
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#Git模型下载
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git clone https://www.modelscope.cn/AI-ModelScope/RMBG-2.0.git
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torch
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torchvision
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pillow
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kornia
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transformers
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```
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### Usage
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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```python
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from PIL import Image
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import matplotlib.pyplot as plt
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import torch
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from torchvision import transforms
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from transformers import AutoModelForImageSegmentation
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model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True)
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torch.set_float32_matmul_precision(['high', 'highest'][0])
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model.to('cuda')
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model.eval()
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# Data settings
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image_size = (1024, 1024)
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transform_image = transforms.Compose([
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transforms.Resize(image_size),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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image = Image.open(input_image_path)
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input_images = transform_image(image).unsqueeze(0).to('cuda')
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# Prediction
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with torch.no_grad():
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preds = model(input_images)[-1].sigmoid().cpu()
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pred = preds[0].squeeze()
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pred_pil = transforms.ToPILImage()(pred)
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mask = pred_pil.resize(image.size)
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image.putalpha(mask)
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image.save("no_bg_image.png")
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```
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<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
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