Create README.md

README.md

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+---
+license: mit
+pipeline_tag: image-segmentation
+tags:
+- remove background
+- background
+- background-removal
+- Pytorch
+- vision
+- legal liability
+- transformers
+---
+
+# BRIA Background Removal v2.0 Model Card
+
+RMBG v2.0 is our new state-of-the-art background removal model significantly improves RMBG v1.4. The model is 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.
+
+## Model Details
+#####
+### Model Description
+
+- **Developed by:** [BRIA AI](https://bria.ai/)
+- **Model type:** Background Removal 
+
+- **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
+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")
+```