How to Zoom in on RAW Data: A User-Friendly Guide to the Zoom to Learn Project

Welcome to the exciting world of digital photography and machine learning! Today, we’ll dive into the intricacies of the “Zoom to Learn, Learn to Zoom” project. This unique work demonstrates how employing real, RAW sensor data can enhance machine learning applications in digital zoom photography. Let’s explore how to set up and run the code based on TensorFlow while providing troubleshooting tips along the way.

Getting Started with the SR-RAW Dataset

The first step in your journey is to download the SR-RAW dataset necessary for training and testing your models.

• Testing Dataset: If you only need the testing dataset (7 GB), you can easily download it using the following commands:

bash .scripts/download.sh 19zlN1fqRRm7E_6i5J3B1OskJocVeuvzG test.zip

unzip test.zip

rm test.zip

• Training Dataset: For those who want to dive deeper, the training dataset (58 GB) can be downloaded similarly:

bash .scripts/download.sh 1qp6z3F4Ru9srwq1lNZr3pQ4kcVN-AOlM train.zip

unzip train.zip

rm train.zip

For downloading the training dataset on Baidu Drive, you might need the key “wi02”.

Working with Your Own Data

If you’re using a different RAW format, like DNG files from an iPhone, you will need to fine-tune the model accordingly. The usage of raw data in this setup is akin to having a perfect canvas for a painter— using the correct mediums makes a world of difference!

Quick Inference: Getting Started

If you’re ready to make predictions using a pre-trained model, follow these simple commands:

bash git clone https://github.com/cecilia-vision/zoom-learn-zoom.git

cd zoom-learn-zoom

bash .scripts/download.sh 1iForbFhhWqrq22FA1xIusfUpdi8td4Kq model.zip

unzip model.zip

bash .scripts/download.sh 1WVSGaKIJVHwphTKhcr9ajolEnBh3aUkR quick_inference.zip

unzip quick_inference.zip

rm *.zip

python3 inference.py

Don’t forget to edit the inference.yaml configuration file to set the appropriate mode and paths for your data!

Training Your Model

To train your model effectively, you need to implement the CoBi loss as introduced in the paper. Think of CoBi loss as a skilled mentor guiding the model to learn from its mistakes and improve over time.

To preprocess the data and run the various scripts, use:

• For aligning the training data:

  bash .scripts/run_align.sh [TRAIN_PATH]

• For computing white balance:

  bash .scripts/run_wb.sh [TRAIN_PATH]

If you run into issues, feel free to reach out for further assistance!

Troubleshooting

• If your model isn’t performing as expected, double-check the formats of your RAW data. Just as a musician needs the correct instrument to play a song, your model needs the right data format!
• It’s recommended to verify your installation of TensorFlow to ensure you’re using version 1.13.1 as the project was tested on this version.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

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. Dive into the world of machine learning in photography and unleash endless possibilities!