Welcome to our comprehensive guide on implementing SCOPS (Self-Supervised Co-Part Segmentation), a remarkable project that harnesses the power of self-supervision for segmentation tasks. In this blog, you’ll find a user-friendly walkthrough on how to set it up and troubleshoot any issues you may encounter along the way.

What is SCOPS?

SCOPS is a self-supervised learning framework developed for co-part segmentation, a task aimed at segmenting parts of objects in images accurately. The original paper was presented at CVPR 2019 by talented researchers from NVIDIA. This implementation uses PyTorch and integrates with TensorboardX for visualization.

Installation Steps

Before diving into the code, let’s ensure you have everything ready. The following steps will help you set up your environment:

• First, create a virtual environment to avoid conflicts:

$ virtualenv -p python3 scops_env

• Activate the environment:

$ source scops_env/bin/activate

• Next, install the required packages:

$ pip install -r requirements.txt

• To deactivate the virtual environment after you finish, simply run:

$ deactivate

• Whenever you wish to reactivate it, use:

$ source scops_env/bin/activate

SCOPS on Unaligned CelebA

To implement SCOPS with the CelebA dataset, follow these steps:

• Download the required data, saliency maps, and pretrained model:

$ .download_CelebA.sh

• Download the CelebA unaligned dataset from here.
• To evaluate the pretrained model, run:

$ .evaluate_celebA_Wild.sh

• Results will be stored on a webpage at results_CelebA_SCOPS_K8_ITER_100000/web_html/index.html.

Training the Model

Ready to take your implementation a step further? Start training the model using the following command:

$ CUDA_VISIBLE_DEVICES=GPU python train.py -f exps/SCOPS_K8_retrain.json

Here, replace GPU with the specific GPU device number you wish to utilize.

SCOPS on Caltech-UCSD Birds

Implement SCOPS with the Caltech-UCSD Birds dataset by testing the pretrained model:

• First, set the image and annotation paths in line 35 and line 37 of dataset_cub.py.
• Then, run the following script for evaluation:

sh eval_cub.sh

• Your results and visualizations will be located in the results/cub_ITER_60000/train folder.

Understanding the Code: An Analogy

Think of implementing SCOPS like assembling a puzzle. Each piece represents a line of code or a command. Just as you need to ensure each piece connects perfectly to reveal the complete picture, each command must be executed correctly in order for the segmentation task to function seamlessly. If the pieces don’t fit, or a command fails, the image (or results) may not turn out as expected!

Troubleshooting

If you encounter issues during installation or while running the code, consider the following troubleshooting ideas:

• Ensure that your Python version is compatible with PyTorch. Sometimes using a different version can lead to unexpected errors.
• Check if you have all the required dependencies installed, as missing packages may halt the execution.
• If an error occurs during the evaluation phase, confirm that paths specified for datasets and annotations are correct.

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Conclusion

SCOPS provides a fascinating foray into self-supervised learning for segmentation. By adhering to the specified steps and understanding the underlying concepts, you’ll be well on your way to mastering this innovative approach. At **[fxis.ai](https://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.

References

• For more information, refer to the original paper.
• You can find supplementary materials here.

Licenses

This project is licensed under the CC BY-NC-SA 4.0 license, details can be found here.