Welcome to the official repository for our paper Usup²-Net (U Square Net), published in Pattern Recognition 2020. This innovative framework presents a deeper nested U-structure for salient object detection, paving the way for advancements in image processing. Interested in knowing more? Check out the full paper: Usup²-Net: Going Deeper with Nested U-Structure for Salient Object Detection.

Getting Started

To embark on your journey with our model, follow these user-friendly steps:

1. Clone the repository: In your terminal, run:

git clone https://github.com/NathanUA/U-2-Net.git

2. Download the Pre-trained Models: Retrieve the u2net_portrait.pth model weights from Google Drive or Baidu Pan, then place it in the directory ./saved_models/u2net_portrait. You may also want the weights for u2net_human_seg.pth for human segmentation.
3. Run the Inference: Execute the following command to see the model in action:

python u2net_portrait_test.py

Understanding the Code: A Simple Analogy

Imagine you’re a chef trying to prepare a gourmet meal. The dish requires several key ingredients, each playing a significant role in enhancing flavors. In our code, each function serves as an ingredient, mixing together to create the final output of salient object detection. The sections where you clone the repository and download models are like gathering your ingredients. Running the inference stands as the moment you combine everything in the kitchen. Only when all the operations blend harmoniously do you produce the beautiful dish—your output image showcasing the salient object effectively detected by Usup²-Net.

Troubleshooting

Facing issues? Here are some troubleshooting tips:

• Ensure that Python and required libraries (numpy, scikit-image, etc.) are properly installed. You can create a virtual environment to manage dependencies better.
• Check if you have placed the downloaded model weights in the correct directory with appropriate read permissions.
• If you’re experiencing unexpectedly poor segmentation, remember that the image quality and the conditions under which the image was captured play a critical role in the output. Clear backgrounds yield better results.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

In Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Future Updates

Stay tuned for more updates! We’ve been actively improving Usup²-Net with new functionalities and features. Make sure to watch for our official announcements.

Explore More

For any further inquiries, feel free to reach out to us via our contact information provided in the repository.