In the realm of convolutional neural networks (CNNs), the decision to focus on texture or shape can significantly affect model performance. Enter Stylized-ImageNet, a novel dataset that tilts the balance toward shape awareness. This blog will guide you through the process of creating and utilizing Stylized-ImageNet, along with troubleshooting tips to smooth your journey.

Step-by-Step Guide to Create Stylized-ImageNet

• Step 1: Acquire Style Images

  First, you need to collect style images for painting effects. Download the train.zip from Kaggle’s painter-by-numbers dataset. Extract the contents (paintings) into a new directory named code/paintings_raw (approx. 38 GB).

• Step 2: Set Up ImageNet Paths

  If you have the ImageNet images, set the IMAGENET_PATH variable located in code/general.py. If not, obtain them from the ImageNet website and ensure they are split into train and val subdirectories. Also, specify the STYLIZED_IMAGENET_PATH for storing the final dataset (requires 134 GB).

• Step 3: Execute the Creation Script

  Navigate to the code directory and run create_stylized_imagenet.sh. The simplest way to handle this is by using the provided Docker image (see Section below).

• Step 4: Clean Up

  After successfully creating Stylized-ImageNet, you may delete the paintings_raw, paintings_excluded, and paintings_preprocessed directories as they are no longer needed.

Using Docker for Convenience

Utilizing Docker can simplify the installation of necessary libraries. You can get started with the pre-built image available at Docker Hub, specifically bethgelab/deeplearning:cuda9.0-cudnn7.

Pre-trained CNNs on Stylized-ImageNet

For those interested, pre-trained CNNs can be found at rgeirhos:texture-vs-shape. These models can save you time and effort while conducting your experiments.

Training with Stylized-ImageNet

Stylized-ImageNet serves as a drop-in replacement for the standard ImageNet dataset. It uses the same normalization parameters. Essentially, the training process remains unchanged—making it easy to integrate and test.

Stylizing Your Own Datasets

If you want to stylize a different dataset, you can use the code available at bethgelab:stylize-datasets, which allows you to apply stylization to any image collection.

Troubleshooting Tips

While creating your dataset, you might encounter some issues. Here are some troubleshooting ideas:

• Ensure sufficient disk space before starting the process. A requirement of 134 GB for Stylized-ImageNet is essential.
• If you run into errors while executing the script, double-check your paths in the code/general.py file for accuracy.
• Refer to the FAQ section if you’re seeking direct dataset downloads; consider stylizing datasets yourself or using proxy datasets offered as alternatives.

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Final Thoughts

By employing Stylized-ImageNet, you’re participating in a fascinating scientific pursuit—nudging CNNs toward a deeper understanding of shape over texture.

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.