How to Use Context Encoders for Feature Learning by Inpainting

In this guide, we will explore how to train Context Encoders, a powerful tool for unsupervised feature learning through the technique of image inpainting. Originating from the innovative research presented in the CVPR 2016 paper titled “Context Encoders: Feature Learning by Inpainting,” this approach allows us to creatively fill in gaps in images, demonstrating remarkable applications in various fields such as art restoration, image editing, and more.

Contents

• 1. Semantic Inpainting Demo
• 2. Train Context Encoders
• 3. Download Features Caffemodel
• 4. TensorFlow Implementation
• 5. Project Website
• 6. Paris Street-View Dataset

1. Semantic Inpainting Demo

Before diving into training your own models, you can try out a demo to see Context Encoders in action. Just follow these straightforward steps:

1. Install Torch: Torch Installation Guide
2. Clone the repository:

git clone https://github.com/pathak22/context-encoder.git

3. Navigate into the cloned directory:

cd context-encoder

4. Download the pre-trained models by running the following command in the terminal:

bash .models/scripts/download_inpaintCenter_models.sh

5. Execute the demo:

net=models/inpaintCenter/paris_inpaintCenter.t7 name=paris_result imDir=images/paris overlapPred=4 manualSeed=222 batchSize=21 gpu=1 th demo.lua

2. Train Context Encoders

If the demo worked and you’re inspired to build your own Context Encoder for image inpainting, here are the steps:

1. Make the dataset folders:

mkdir -p path_to_wherever_you_want/mydataset/train/images

2. Place all your training images in the new folder created.

mkdir -p path_to_wherever_you_want/mydataset/val/images

3. Training the model is as simple as running:

DATA_ROOT=dataset/train display_id=11 name=inpaintCenter overlapPred=4 wtl2=0.999 nBottleneck=4000 niter=500 loadSize=350 fineSize=128 gpu=1 th train.lua

3. Download Features Caffemodel

To access the features for Context Encoders trained with reconstruction loss, simply download the following links:

• Prototxt
• Caffemodel

4. TensorFlow Implementation

If you’re looking for a version that utilizes TensorFlow, check out the implementation provided by Taeksoo here. Note that this version may lack some functionalities presented in the original paper.

5. Project Website

For further information, you can visit the project website.

6. Paris Street-View Dataset

If you require access to the Paris Street-View Dataset, please reach out via email, and I will provide you with a private link.

Troubleshooting

If you encounter any issues during installation or execution, consider the following troubleshooting tips:

• Ensure that all dependencies are correctly installed, especially Torch.
• Check that your dataset paths are accurate and that training images are properly organized in the specified directories.
• For GPU-related problems, ensure your environment is set up to use CUDA.
• If you run into memory issues, try reducing the batch size.

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Understanding the Code with a Fun Analogy

Think of the Context Encoder as a chef who is unique in the way he learns to cook delicious meals without any guidance. Picture a scenario in which the chef is given a set of unfinished recipes (just like the images with missing parts). Each time he receives a new recipe, he studies the available ingredients and tries to fill in the missing ones by imagining how the complete dish would look (this is the inpainting part!).

The chef has a special way of improving his skills; he competes with other chefs (adversarial loss) who are also trying to complete their dishes (images). As he practices with more and more recipes, he learns to fill in the gaps better and better until he can recreate a full meal that looks as delicious as it would have if he had all the ingredients from the start! Through this process, he ends up with a whole library of cooking skills, or in the case of the Context Encoder, a library of image features that he can draw upon for future dishes (or images).