Update README.md

README.md

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----
-license: other
-license_name: bria-rmbg-1.4
-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
-- transformers.js
-
-extra_gated_description: RMBG v1.4 is available as a source-available model for non-commercial use
-extra_gated_heading: "Fill in this form to get instant access"
-extra_gated_fields:
-  Name: text
-  Company/Org name: text
-  Org Type (Early/Growth Startup, Enterprise, Academy): text
-  Role: text
-  Country: text
-  Email: text
-  By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox
----
-
-# BRIA Background Removal v1.4 Model Card
-
-RMBG v1.4 is our 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 v1.4 is available as a source-available model for non-commercial use. 
-
-
-To purchase a commercial license, simply click [Here](https://go.bria.ai/3D5EGp0).
-
-
-[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4)
-
-**NOTE** New RMBG version available! Check out [RMBG-2.0](https://huggingface.co/briaai/RMBG-2.0)
-
-Join our [Discord community](https://discord.gg/Nxe9YW9zHS) for more information, tutorials, tools, and to connect with other users!
-
-
-![examples](t4.png)
-
-
-### Model Description
-
-- **Developed by:** [BRIA AI](https://bria.ai/)
-- **Model type:** Background Removal 
-- **License:** [bria-rmbg-1.4](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. To purchase a commercial license simply click [Here](https://go.bria.ai/3B4Asxv).
-
-- **Model Description:** BRIA RMBG 1.4 is a saliency 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 12,000 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
-
-![examples](results.png)
-
-
-## Architecture
-
-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.
-
-## Installation
-```bash
-pip install -qr https://huggingface.co/briaai/RMBG-1.4/resolve/main/requirements.txt
-```
-
-## Usage
-
-Either load the pipeline
-```python
-from transformers import pipeline
-image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
-pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
-pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
-pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
-```
-
-Or load the model 
-```python
-from PIL import Image
-from skimage import io
-import torch
-import torch.nn.functional as F
-from transformers import AutoModelForImageSegmentation
-from torchvision.transforms.functional import normalize
-model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
-def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
-    if len(im.shape) < 3:
-        im = im[:, :, np.newaxis]
-    # orig_im_size=im.shape[0:2]
-    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
-    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
-    image = torch.divide(im_tensor,255.0)
-    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
-    return image
-
-def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
-    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
-    ma = torch.max(result)
-    mi = torch.min(result)
-    result = (result-mi)/(ma-mi)
-    im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
-    im_array = np.squeeze(im_array)
-    return im_array
-
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-model.to(device)
-
-# prepare input
-image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
-orig_im = io.imread(image_path)
-orig_im_size = orig_im.shape[0:2]
-model_input_size = [1024, 1024]
-image = preprocess_image(orig_im, model_input_size).to(device)
-
-# inference 
-result=model(image)
-
-# post process
-result_image = postprocess_image(result[0][0], orig_im_size)
-
-# save result
-pil_mask_im = Image.fromarray(result_image)
-orig_image = Image.open(image_path)
-no_bg_image = orig_image.copy()
-no_bg_image.putalpha(pil_mask_im)
-```
-
+---
+license: other
+license_name: bria-rmbg-1.4
+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
+- transformers.js
+extra_gated_description: RMBG v1.4 is available as a source-available model for non-commercial use
+extra_gated_heading: Fill in this form to get instant access
+extra_gated_fields:
+  Name: text
+  Company/Org name: text
+  Org Type (Early/Growth Startup, Enterprise, Academy): text
+  Role: text
+  Country: text
+  Email: text
+  By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox
+title: Background Remover Pro
+sdk: gradio
+emoji: ⚡
+colorFrom: blue
+colorTo: purple
+---
+
+# BRIA Background Removal v1.4 Model Card
+
+RMBG v1.4 is our 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 v1.4 is available as a source-available model for non-commercial use. 
+
+
+To purchase a commercial license, simply click [Here](https://go.bria.ai/3D5EGp0).
+
+
+[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4)
+
+**NOTE** New RMBG version available! Check out [RMBG-2.0](https://huggingface.co/briaai/RMBG-2.0)
+
+Join our [Discord community](https://discord.gg/Nxe9YW9zHS) for more information, tutorials, tools, and to connect with other users!
+
+
+![examples](t4.png)
+
+
+### Model Description
+
+- **Developed by:** [BRIA AI](https://bria.ai/)
+- **Model type:** Background Removal 
+- **License:** [bria-rmbg-1.4](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. To purchase a commercial license simply click [Here](https://go.bria.ai/3B4Asxv).
+
+- **Model Description:** BRIA RMBG 1.4 is a saliency 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 12,000 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
+
+![examples](results.png)
+
+
+## Architecture
+
+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.
+
+## Installation
+```bash
+pip install -qr https://huggingface.co/briaai/RMBG-1.4/resolve/main/requirements.txt
+```
+
+## Usage
+
+Either load the pipeline
+```python
+from transformers import pipeline
+image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
+pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
+pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
+pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
+```
+
+Or load the model 
+```python
+from PIL import Image
+from skimage import io
+import torch
+import torch.nn.functional as F
+from transformers import AutoModelForImageSegmentation
+from torchvision.transforms.functional import normalize
+model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
+def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
+    if len(im.shape) < 3:
+        im = im[:, :, np.newaxis]
+    # orig_im_size=im.shape[0:2]
+    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+    im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
+    image = torch.divide(im_tensor,255.0)
+    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
+    return image
+
+def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
+    result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
+    ma = torch.max(result)
+    mi = torch.min(result)
+    result = (result-mi)/(ma-mi)
+    im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
+    im_array = np.squeeze(im_array)
+    return im_array
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# prepare input
+image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
+orig_im = io.imread(image_path)
+orig_im_size = orig_im.shape[0:2]
+model_input_size = [1024, 1024]
+image = preprocess_image(orig_im, model_input_size).to(device)
+
+# inference 
+result=model(image)
+
+# post process
+result_image = postprocess_image(result[0][0], orig_im_size)
+
+# save result
+pil_mask_im = Image.fromarray(result_image)
+orig_image = Image.open(image_path)
+no_bg_image = orig_image.copy()
+no_bg_image.putalpha(pil_mask_im)
+```