WebAPI for image recognition with a custom Residual CNN using PyTorch and Flask

Network

The architecture consists of seven basic residual blocks with additional dropout and pooling layers. The model was trained on the CIFAR-100 (link) dataset (62.4% top-1 and 87.9% top-5 accuracy on the testing subset).

Interface

Upload an image to obtain a classification with a detailed visualization of the results:

To run the application, install dependencies with pip install -r requirements.txt and run Flask server:

flask --app ./server.py run

Training and evaluation

Training script, detailed evaluation and examples of inference are in notebooks directory.

References

Dataset: Learning Multiple Layers of Features from Tiny Images, Alex Krizhevsky, 2009