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--- |
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license: other |
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licence_name: bria-rmbg-1.4 |
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license_link: https://bria.ai/bria-huggingface-model-license-agreement/ |
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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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extra_gated_prompt: This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you. |
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extra_gated_fields: |
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Name: text |
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Company/Org name: text |
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Org Type (Early/Growth Startup, Enterprise, Academy): text |
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Role: text |
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Country: text |
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Email: text |
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By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox |
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--- |
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# BRIA Background Removal v1.4 Model Card |
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RMBG v1.4 is our 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 various use cases. |
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Developed by BRIA AI, RMBG v1.4 is available as an open-source tool for non-commercial use. |
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[CLICK HERE FOR A DEMO](https://huggingface.co./spaces/briaai/BRIA-RMBG-1.4) |
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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-1.4](https://bria.ai/bria-huggingface-model-license-agreement/) |
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- The model is open 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 1.4 is an saliency 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 over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images. |
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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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- **Inference Time :** 1 sec on Nvidia A10 GPU |
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## Architecture |
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RMBG v1.4 is developed on the [IS-Net](https://github.com/xuebinqin/DIS) enhanced with our unique training scheme and proprietary dataset. These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios. |
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## installation |
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```bash |
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git clone https://huggingface.co./briaai/RMBG-1.4 |
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cd RMBG-1.4/ |
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pip install -r requirements.txt |
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``` |
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## Usage |
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```python |
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from skimage import io |
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import torch, os |
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from PIL import Image |
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from briarmbg import BriaRMBG |
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from utilities import preprocess_image, postprocess_image |
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model_path = f"{os.path.dirname(os.path.abspath(__file__))}/model.pth" |
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im_path = f"{os.path.dirname(os.path.abspath(__file__))}/example_input.jpg" |
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net = BriaRMBG() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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net.load_state_dict(torch.load(model_path, map_location=device)) |
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net.eval() |
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# prepare input |
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model_input_size = [1024,1024] |
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orig_im = io.imread(im_path) |
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orig_im_size = orig_im.shape[0:2] |
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image = preprocess_image(orig_im, model_input_size).to(device) |
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# inference |
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result=net(image) |
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# post process |
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result_image = postprocess_image(result[0][0], orig_im_size) |
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# save result |
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pil_im = Image.fromarray(result_image) |
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no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0)) |
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orig_image = Image.open(im_path) |
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no_bg_image.paste(orig_image, mask=pil_im) |
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no_bg_image.save("example_image_no_bg.png") |
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``` |
