Muhammad8815
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inno-rmbg-removal-8815

@@ -1,175 +1,40 @@
----
-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: custom-non-commercial
+license_link: https://huggingface.co/Muhammad8815/inno-rmbg-removal-8815/blob/main/LICENSE
+pipeline_tag: image-segmentation
+tags:
+- remove-background
+- background-removal
+- pytorch
+- onnx
+- image-segmentation
+---
+# Inno RMBG Removal v1.0
+This is a custom-trained image background removal model using PyTorch and ONNX. It performs pixel-wise background removal on input images with high accuracy.
+## Model Highlights
+- Supports PyTorch `.pth` and ONNX inference.
+- Trained on diverse images of objects, people, and scenes.
+- Provides a binary mask of the foreground, suitable for downstream tasks (e.g. background replacement, transparency effects).
+## Example Input/Output
+**Input:**
+![example input](example_input.jpg)
+**Output (no background):**
+![result](results.png)
+## Inference Code (PyTorch)
+```python
+from PIL import Image
+from utilities import preprocess_image, postprocess_image, load_model
+model = load_model("model.pth")
+image = Image.open("example_input.jpg")
+mask = model.predict(image)
+image.putalpha(mask)
+image.save("output.png")