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BiRefNet_config.py

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+from transformers import PretrainedConfig
+
+class BiRefNetConfig(PretrainedConfig):
+    model_type = "SegformerForSemanticSegmentation"
+    def __init__(
+        self,
+        bb_pretrained=False,
+        **kwargs
+    ):
+        self.bb_pretrained = bb_pretrained
+        super().__init__(**kwargs)

README.md

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 ---
-license: Apache License 2.0
-
-#model-type:
-##如 gpt、phi、llama、chatglm、baichuan 等
-#- gpt
-
-#domain:
-##如 nlp、cv、audio、multi-modal
-#- nlp
-
-#language:
-##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
-#- cn 
-
-#metrics:
-##如 CIDEr、Blue、ROUGE 等
-#- CIDEr
-
-#tags:
-##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
-#- pretrained
-
-#tools:
-##如 vllm、fastchat、llamacpp、AdaSeq 等
-#- vllm
+license: other
+license_name: bria-rmbg-2.0
+license_link: https://bria.ai/bria-huggingface-model-license-agreement/
+pipeline_tag: image-segmentation
+tags:
+- remove background
+- background
+- background-removal
+- Pytorch
+- vision
+- legal liability
+- transformers
 ---
-### 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。
-#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
 
-SDK下载
+# BRIA Background Removal v2.0 Model Card
+
+RMBG v2.0 is our new state-of-the-art background removal model, designed to effectively separate foreground from background in a range of
+categories and image types. This model has been trained on a carefully selected dataset, which includes:
+general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale. 
+The accuracy, efficiency, and versatility currently rival leading source-available models. 
+It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount. 
+
+Developed by BRIA AI, RMBG v2.0 is available as a source-available model for non-commercial use. 
+
+[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-2.0)
+![examples](t4.png)
+
+## Model Details
+#####
+### Model Description
+
+- **Developed by:** [BRIA AI](https://bria.ai/)
+- **Model type:** Background Removal 
+- **License:** [bria-rmbg-2.0](https://bria.ai/bria-huggingface-model-license-agreement/)
+  - The model is released under a Creative Commons license for non-commercial use.
+  - Commercial use is subject to a commercial agreement with BRIA. [Contact Us](https://bria.ai/contact-us) for more information. 
+
+- **Model Description:** BRIA RMBG-2.0 is a dichotomous image segmentation model trained exclusively on a professional-grade dataset.
+- **BRIA:** Resources for more information: [BRIA AI](https://bria.ai/)
+
+
+
+## Training data
+Bria-RMBG model was trained with over 15,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.
+Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.
+For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.
+
+### Distribution of images:
+
+| Category | Distribution |
+| -----------------------------------| -----------------------------------:|
+| Objects only | 45.11% |
+| People with objects/animals | 25.24% |
+| People only | 17.35% |
+| people/objects/animals with text | 8.52% |
+| Text only | 2.52% |
+| Animals only | 1.89% |
+
+| Category | Distribution |
+| -----------------------------------| -----------------------------------------:|
+| Photorealistic | 87.70% |
+| Non-Photorealistic | 12.30% |
+
+
+| Category | Distribution |
+| -----------------------------------| -----------------------------------:|
+| Non Solid Background | 52.05% |
+| Solid Background | 47.95% 
+
+
+| Category | Distribution |
+| -----------------------------------| -----------------------------------:|
+| Single main foreground object | 51.42% |
+| Multiple objects in the foreground | 48.58% |
+
+
+## Qualitative Evaluation
+Open source models comparison
+![diagram](diagram1.png)
+![examples](collage5.png)
+
+### Architecture
+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>
+If you use this model in your research, please cite:
+
+```
+@article{BiRefNet,
+  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},
+  year={2024}
+}
+```
+
+#### Requirements
 ```bash
-#安装ModelScope
-pip install modelscope
-```
-```python
-#SDK模型下载
-from modelscope import snapshot_download
-model_dir = snapshot_download('AI-ModelScope/RMBG-2.0')
-```
-Git下载
-```
-#Git模型下载
-git clone https://www.modelscope.cn/AI-ModelScope/RMBG-2.0.git
+torch
+torchvision
+pillow
+kornia
+transformers
+```
+
+### Usage
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+
+```python
+from PIL import Image
+import matplotlib.pyplot as plt
+import torch
+from torchvision import transforms
+from transformers import AutoModelForImageSegmentation
+
+model = AutoModelForImageSegmentation.from_pretrained('briaai/RMBG-2.0', trust_remote_code=True)
+torch.set_float32_matmul_precision(['high', 'highest'][0])
+model.to('cuda')
+model.eval()
+
+# Data settings
+image_size = (1024, 1024)
+transform_image = transforms.Compose([
+    transforms.Resize(image_size),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+
+image = Image.open(input_image_path)
+input_images = transform_image(image).unsqueeze(0).to('cuda')
+
+# Prediction
+with torch.no_grad():
+    preds = model(input_images)[-1].sigmoid().cpu()
+pred = preds[0].squeeze()
+pred_pil = transforms.ToPILImage()(pred)
+mask = pred_pil.resize(image.size)
+image.putalpha(mask)
+
+image.save("no_bg_image.png")
 ```
 
-<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>

config.json

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+{
+  "_name_or_path": "ZhengPeng7/BiRefNet",
+  "architectures": [
+    "BiRefNet"
+  ],
+  "auto_map": {
+    "AutoConfig": "BiRefNet_config.BiRefNetConfig",
+    "AutoModelForImageSegmentation": "birefnet.BiRefNet"
+  },
+  "custom_pipelines": {
+    "image-segmentation": {
+      "pt": [
+        "AutoModelForImageSegmentation"
+      ],
+      "tf": [],
+      "type": "image"
+    }
+  },
+  "bb_pretrained": false
+}

configuration.json

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+{"framework": "pytorch", "task": "image-segmentation", "allow_remote": true}