The images were resized to 224 x 224 and image data augmentation through rotation, scaling, and shifting was applied. Below are examples of data augmentation.
Experiment Result
The evaluation criterion for this Kaggle competition is multi-class logarithmic loss.
As the validation set, 25% of the images were randomly assigned. However, in the case of the 5-fold CV ensemble, the dataset was divided into 5 equal parts.
20 samples were randomly selected from the test set and visualized using the Grad-CAM technique. Labels shown are predicted.
Model Serving
TF Serving
You need to download and run the Docker image via scripts/run.sh file. Then, you can test model inference through a locally hosted TF Serving.
SageMaker
SageMaker allows you to train TensorFlow models and deploy endpoints for serving. You can also use the SageMakerEstimator's Pipe mode to train a model without downloading a dataset directly.