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--- |
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license: other |
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tags: |
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- background-removal |
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- Pytorch |
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- vision |
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--- |
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# BRIA Background Removal v1.4 Model Card |
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100% automatically Background removal capability across all categories and image types that capture the variety of the world. |
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Built and validated on a comprehensive dataset containing an equal distribution of general stock images, eComm, gaming and ads. |
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### Model Description |
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- **Developed by:** BRIA AI |
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- **Model type:** Background removal image-to-image model |
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- **License:** [bria-rmbg-1.4](https://bria.ai/bria-2-0-huggingface-model-license-agreement/) |
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- **Model Description:** BRIA RMBG 1.4 is an image-to-image model trained exclusively on a professional-grade dataset. It is designed and built for commercial use, subject to a commercial agreement with BRIA. |
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- **Resources for more information:** [BRIA AI](https://bria.ai/) |
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### Get Access |
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BRIA RMBG 1.4 is available under the BRIA RMBG 1.4 License Agreement. To access the model, please contact us. |
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By submitting this form, you agree to BRIA’s [Privacy policy](https://bria.ai/privacy-policy/) and [Terms & conditions](https://bria.ai/terms-and-conditions/). |
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## Training data |
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Bria-RMBG model was trained over 12000 high quality, high resolution, fully licensed images. |
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The training set as well as the validation benchmark if a holistic representation of the commercial world containing a distribution of general stock images, eComm, gaming and ads. |
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Distribution of images: |
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only people - 17.35% |
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people with objects/animals - 25.24% |
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only objects - 45.11% |
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only animals - 1.89% |
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only text - 2.52% |
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people/objects/animals with text - 8.52% |
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photorealistic - 87.70% |
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non photorealistic - 12.30% |
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solid bg - 47.95% |
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non solid bg - 52.05% |
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single main object in the foreground - 51.42% |
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multiple objects in the foreground- 48.58% |
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All images were manualy labeled pixel-wise accuratly. |
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## Qualitative Evaluation |
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## Usage |
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```python |
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import os |
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import numpy as np |
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from skimage import io |
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from glob import glob |
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from tqdm import tqdm |
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import cv2 |
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import torch.nn.functional as F |
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from torchvision.transforms.functional import normalize |
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from models import BriaRMBG |
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input_size=[1024,1024] |
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net=BriaRMBG() |
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model_path = "./model.pth" |
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im_path = "./example_image.jpg" |
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result_path = "." |
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if torch.cuda.is_available(): |
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net.load_state_dict(torch.load(model_path)) |
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net=net.cuda() |
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else: |
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net.load_state_dict(torch.load(model_path,map_location="cpu")) |
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net.eval() |
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# prepare input |
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im = io.imread(im_path) |
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if len(im.shape) < 3: |
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im = im[:, :, np.newaxis] |
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im_size=im.shape[0:2] |
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1) |
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im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=input_size, mode='bilinear').type(torch.uint8) |
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image = torch.divide(im_tensor,255.0) |
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image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0]) |
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if torch.cuda.is_available(): |
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image=image.cuda() |
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# inference |
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result=net(image) |
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# post process |
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result = torch.squeeze(F.interpolate(result[0][0], size=im_size, mode='bilinear') ,0) |
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ma = torch.max(result) |
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mi = torch.min(result) |
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result = (result-mi)/(ma-mi) |
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# save result |
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im_name=im_path.split('/')[-1].split('.')[0] |
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im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8) |
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cv2.imwrite(os.path.join(result_path, im_name+".png"), im_array) |
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``` |
