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app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+from fire import Fire
+
+from s3od import BackgroundRemoval
+from s3od.visualizer import visualize_removal
+
+detector = BackgroundRemoval()
+
+VISUALIZATION_METHODS = {
+    'Transparent Background': 'transparent',
+    'White Background': 'white',
+    'Green Background': 'green',
+    'Mask Only': 'mask'
+}
+
+
+def process_image(image, method, threshold):
+    if image is None:
+        return None
+    
+    result = detector.remove_background(image, threshold=threshold)
+    
+    if method == 'transparent':
+        return result.rgba_image
+    elif method == 'white':
+        return visualize_removal(image, result, background_color=(255, 255, 255))
+    elif method == 'green':
+        return visualize_removal(image, result, background_color=(0, 255, 0))
+    elif method == 'mask':
+        mask_vis = (result.predicted_mask * 255).astype(np.uint8)
+        return Image.fromarray(mask_vis, mode='L')
+    
+    return result.rgba_image
+
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="numpy", label="Upload an Image"),
+        gr.Radio(
+            list(VISUALIZATION_METHODS.keys()),
+            label="Output Format",
+            value='Transparent Background'
+        ),
+        gr.Slider(
+            minimum=0.0,
+            maximum=1.0,
+            value=0.5,
+            step=0.05,
+            label="Mask Threshold"
+        )
+    ],
+    outputs=gr.Image(type="pil", label="Result"),
+    title="Demo: S3OD - Synthetic Salient Object Detection",
+    description="""
+    Upload an image to remove its background using **S3OD**! 
+    
+    S3OD is trained on a large-scale fully synthetic dataset (140K+ images) generated with diffusion models. 
+    Despite being trained only on synthetic data, it achieves state-of-the-art performance on real-world images.
+    
+    The model uses a DPT-based architecture with DINOv3 vision transformer backbone for robust salient object detection 
+    and can process images of any size. Choose from four visualization methods: transparent background (RGBA), 
+    white background, green background (chroma key), or mask only.
+    
+    **Key Features:**
+    - Single-step background removal
+    - Multi-mask prediction with IoU scoring
+    - Adjustable threshold for fine-tuning
+    - Works on any image resolution
+    
+    Ideal for applications in e-commerce, content creation, photo editing, and computer vision research.
+    
+    📄 [Paper](https://arxiv.org/abs/XXXX.XXXXX) | 💻 [GitHub](https://github.com/KupynOrest/s3od) | 🤗 [Model](https://huggingface.co/okupyn/s3od) | 🗂️ [Dataset](https://huggingface.co/datasets/okupyn/s3od_dataset)
+    """,
+    allow_flagging='never',
+    examples=[
+        # Add example images here when available
+    ]
+)
+
+
+def main(server_name="0.0.0.0", server_port=7860, share=False):
+    iface.launch(
+        server_name=server_name,
+        server_port=server_port,
+        share=share
+    )
+
+
+if __name__ == '__main__':
+    Fire(main)
+

requirements.txt

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+git+https://github.com/KupynOrest/s3od.git
+gradio>=4.0.0
+fire>=0.5.0
+numpy>=1.20.0
+Pillow>=9.0.0
+