train

Imagelytics - Training scripts

Prerequisites

• Training process relies on TensorFlow 2.9.3.
• To be able to use TensorFlow 2.9.3 on GPU you must first install CUDA 11.2 and cuDNN 11.2.
• After setting up CUDA and cuDNN, install project requirements listed in requirements.txt file.

Train model on a custom dataset

1. Create root folder for the dataset and place images into data/data_original subfolder. Images for each class should be placed in a separate folder. Name of these folders should correspond to class names.
2. In settings.py change value for root_folder variable to appropriate value.
3. Reset experiment_no to 1 and check other parameters available in settings.py.
4. Run prepare_data.py script to resize images and divide them into train, validation, and test subsets.
5. Run train.py to train the model, generate confusion matrices and Grad-CAM heatmaps. Results will be saved to tmp dataset subfolder.
6. In case training crushes due to lack of memory, reduce batch_size or choose smaller model and repeat training.
7. Once training is finished, you may find results in tmp folder, under the current experiment subfolder.