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README.md

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----
-license: cc-by-sa-4.0
----
+---
+license: apache-2.0
+pipeline_tag: image-segmentation
+language: []
+base_model: isnet-general-use.pth
+model_type: ty_fashion_bg_remover
+tags:
+  - computer-vision
+  - image-background-removal
+  - image-matting
+  - e-commerce
+  - is-net
+---
+
+# TY Fashion Background Remover
+
+_TY Fashion Background Remover is an IS-Net–based human segmentation and background-removal model designed to automatically detect and isolate people in images. It produces high-quality binary/alpha masks and trimmed RGBA composites intended for downstream editing, compositing, and automated image pipelines. Although optimized for fashion photography, it is suitable for any application where the image contains human and the goal is to separate them cleanly from the background._
+
+## Model Details
+
+- **Architecture**: IS-Net
+- **Objective**: Fine-tuning isnet-general-use model with TY fashion images to better performance of fashion images
+- **Training Data**: Large-scale Trendyol fashion product image dataset containing human models
+- **Hardware**: Multi-GPU training with PyTorch
+- **Framework**: PyTorch
+
+## Intended Use
+
+- Automatically remove backgrounds from images containing human, isolating the subject for further editing, compositing, or analysis.
+
+- Designed for use in applications such as e-commerce product photography, fashion catalogs, profile pictures, and creative media projects where the human subject needs to be cleanly separated from the background.
+
+- Optimized for images with clear human presence; not intended for objects, animals, or scenes without people.
+
+- Can be used as a preprocessing step for downstream tasks like virtual try-on, background replacement, and image-based content generation.
+
+## Usage
+
+Complete example to load the model, remove background of an image, and save the results:
+
+```python
+"""
+ONNX inference script for image segmentation model.
+
+This script loads an ONNX model and performs inference on an input image to generate
+an alpha mask. The mask is combined with the RGB image and saved as output.
+"""
+
+import onnxruntime as ort
+from utils import process_image
+
+if __name__ == "__main__":
+  MODEL_PATH = "model.onnx"
+  SRC = "https://cdn.dsmcdn.com/ty184/product/media/images/20210924/23/136268224/224296134/1/1_org_zoom.jpg"
+  OUTPUT_FILE = "out.png"
+
+  # Initialize ONNX runtime session with CUDA and CPU providers
+  ort_session = ort.InferenceSession(
+      MODEL_PATH,
+      providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
+  )
+  
+  process_image(SRC, ort_session, MODEL_PATH, OUTPUT_FILE)
+```
+
+## Model Performance
+
+- **Achieve high-accuracy image matting**: Especially for intricate details on human models, such as hair and clothing textures.
+
+### Training Configuration
+
+- **Backbone**: IS-Net general use model trained on DIS dataset V1.0: DIS5K
+- **Model Input Size**: 1800x1200
+- **Training Framework**: Torch 1.13.1
+
+## Limitations
+
+- **Domain Specificity**: Optimized for e-commerce fashion product images with human models included; may not generalize well to other image domains
+- **Image Quality**: Performance may degrade on low-quality, heavily compressed, or significantly distorted images
+- **Category Bias**: Performance may vary across different product categories based on training data distribution
+
+## Ethical Considerations
+
+- **Commercial Use**: Designed for e-commerce applications; consider potential impacts on market competition
+- **Privacy**: Ensure compliance with data protection regulations when processing product images
+- **Fairness**: Monitor for biased similarity judgments across different product categories or brands
+
+## Citation
+
+```bibtex
+@misc{trendyol2025fashionbgremover,
+  title={TY Fashion Background Remover},
+  author={Trendyol Data Science Team},
+  year={2025},
+  howpublished={\url{https://huggingface.co/trendyol/ty-fashion-bg-remover}}
+}
+```
+
+## Model Card Authors
+
+- Trendyol Data Science Team
+
+## License
+
+This model is released by Trendyol as a source-available, non-open-source model.
+
+### You are allowed to:
+
+- View, download, and evaluate the model weights.
+- Use the model for non-commercial research and internal testing.
+- Use the model or its derivatives for commercial purposes, provided that:
+  - You cite Trendyol as the original model creator.
+  - You notify Trendyol in advance via [[email protected]] or other designated contact.
+
+### You are not allowed to:
+
+- Redistribute or host the model or its derivatives on third-party platforms without prior written consent from Trendyol.
+- Use the model in applications violating ethical standards, including but not limited to surveillance, misinformation, or harm to individuals or groups.
+
+By downloading or using this model, you agree to the terms above.
+
+© 2025 Trendyol Teknoloji A.Ş. All rights reserved.
+
+See the [LICENSE](LICENSE) file for more details.
+
+---
+
+_For technical support or questions about this model, please contact the Trendyol Data Science team._

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b9ef43bab5c1a5538916d31066d5ae843a2200d1fd83ed5bac320643076332d0
+size 176215273

requirements.txt

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+torch==2.7.1
+torchvision==0.22.1
+opencv-python==4.12.0.88
+numpy
+requests
+Pillow
+onnxruntime==1.22.1

utils.py

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+import cv2
+import numpy as np
+import requests
+from PIL import Image
+from io import BytesIO
+import torch
+from pathlib import Path
+import torch.nn.functional as F
+from typing import Dict, Any, List, Union, Tuple
+from torchvision.transforms.functional import normalize
+
+INPUT_SIZE = [1200, 1800]
+
+def keep_large_components(a: np.ndarray) -> np.ndarray:
+    """Remove small connected components from a binary mask, keeping only large regions.
+    
+    Args:
+        a: Input binary mask as numpy array of shape (H,W) or (H,W,1)
+        
+    Returns:
+        Processed mask with only large connected components remaining, shape (H,W,1)
+    """
+    dilate_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(9, 9))
+    a_mask = (a > 25).astype(np.uint8) * 255
+
+    # Apply the Component analysis function
+    analysis = cv2.connectedComponentsWithStats(a_mask, 4, cv2.CV_32S)
+    (totalLabels, label_ids, values, centroid) = analysis
+
+    # Find the components to be kept
+    h, w = a.shape[:2]
+    area_limit = 50000 * (h * w) / (INPUT_SIZE[1] * INPUT_SIZE[0])
+    i_to_keep = []
+    for i in range(1, totalLabels):
+        area = values[i, cv2.CC_STAT_AREA]
+        if area > area_limit:
+            i_to_keep.append(i)
+
+    if len(i_to_keep) > 0:
+        # Or masks to be kept
+        final_mask = np.zeros_like(a, dtype=np.uint8)
+        for i in i_to_keep:
+            componentMask = (label_ids == i).astype("uint8") * 255
+            final_mask = cv2.bitwise_or(final_mask, componentMask)
+
+        # Remove other components
+        # Keep edges
+        final_mask = cv2.dilate(final_mask, dilate_kernel, iterations = 2)
+        a = cv2.bitwise_and(a, final_mask)
+        a = a.reshape((a.shape[0], a.shape[1], 1))
+        
+    return a
+
+def read_img(img: Union[str, Path]) -> np.ndarray:
+    """Read an image from a URL or local path.
+    
+    Args:
+        img: URL or file path to image
+        
+    Returns:
+        Image as numpy array in RGB format with shape (H,W,3)
+    """
+    if img[0: 4] == 'http':
+        response = requests.get(img)
+        im = np.asarray(Image.open(BytesIO(response.content)))
+        
+    else:
+        im = cv2.imread(str(img))
+        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+
+    return im
+
+def preprocess_input(im: np.ndarray) -> torch.Tensor:
+    """Preprocess image for model input.
+    
+    Args:
+        im: Input image as numpy array of shape (H,W,C)
+        
+    Returns:
+        Preprocessed image as normalized torch tensor of shape (1,3,H,W)
+    """
+    if len(im.shape) < 3:
+        im = im[:, :, np.newaxis]
+        
+    if im.shape[2] == 4:  # if image has alpha channel, remove it
+        im = im[:,:,:3]
+
+    im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
+    im_tensor = F.upsample(torch.unsqueeze(im_tensor,0), INPUT_SIZE, mode="bilinear").type(torch.uint8)
+    image = torch.divide(im_tensor,255.0)
+    image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
+
+    if torch.cuda.is_available():
+        image=image.cuda()
+    
+    return image
+
+def postprocess_output(result: np.ndarray, orig_im_shape: Tuple[int, int]) -> np.ndarray:
+    """Postprocess ONNX model output.
+    
+    Args:
+        result: Model output as numpy array of shape (1,1,H,W)
+        orig_im_shape: Original image dimensions (height, width)
+        
+    Returns:
+        Processed binary mask as numpy array of shape (H,W,1)
+    """
+    result = torch.squeeze(F.upsample(
+        torch.from_numpy(result).unsqueeze(0), (orig_im_shape), mode='bilinear'), 0)
+    ma = torch.max(result)
+    mi = torch.min(result)
+    result = (result-mi)/(ma-mi)
+
+    # a is alpha channel. 255 means foreground, 0 means background.
+    a = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
+    
+    # postprocessing
+    a = keep_large_components(a)
+
+    return a
+
+def process_image(src: Union[str, Path], ort_session: Any, model_path: Union[str, Path], outname: str) -> None:
+    """Process an image through ONNX model to generate alpha mask and save result.
+    
+    Args:
+        src: Source image URL or path
+        ort_session: ONNX runtime inference session
+        model_path: Path to ONNX model file 
+        outname: Output filename for saving result
+        
+    Returns:
+        None
+    """
+    # Load and preprocess image
+    image_orig = read_img(src)
+    image = preprocess_input(image_orig)
+    
+    # Prepare ONNX input
+    inputs: Dict[str, Any] = {ort_session.get_inputs()[0].name: image.numpy()}
+    
+    # Get ONNX output and post-process
+    result = ort_session.run(None, inputs)[0][0]
+    alpha = postprocess_output(result, (image_orig.shape[0], image_orig.shape[1]))
+    
+    # Combine RGB image with alpha mask and save
+    img_w_alpha = np.dstack((cv2.cvtColor(image_orig, cv2.COLOR_BGR2RGB), alpha))
+    cv2.imwrite(outname, img_w_alpha)
+    print(f"Saved: {outname}")