/image-classification

Image classification using tf.data and tf.keras API

Primary LanguagePython

Image Classification using tf.data and tf.keras

This project helps understand the usage of tf.data.Dataset and tf.keras API in image classifcation tasks.

Requirements

  • python3
  • tensorflow 1.12.0
  • sklearn

Command-line arguments

  • Input Parameters
    • --data_dir : Directory contains train images. Expecting a directory structure like below. Sub directory names are the class labels.
    ├── train
│   ├── Class_Label_1
│   │   ├── Class_Label_1_1139.jpg
│   │   ├── Class_Label_1_1140.jpg
│   ├── Class_Label_2
│   │   ├── Class_Label_2_1139.jpg
│   │   ├── Class_Label_2_1140.jpg
│   ├── Class_Label_3
│   │   ├── Class_Label_3_1139.jpg
│   │   ├── Class_Label_3_1140.jpg
    • --seed : Random seed to re-produce train-test split
    • --batch_size : Train batch size to fetch from tf.data.Dataset
    • --no_threads : Number of threads to run pre-processing and augmentation process with in tf.data.Dataset.map
    • --mode : Mode to execute. train or test
  • Train Parameters
    • --no_epochs : Number of epochs
    • --no_class : Total number of class labels in the training data
    • --epochs_steps : Total number of epoch steps per epoch
    • --val_split_ratio : Validation split ratio
  • Pre-process/Augmentation Parameters
    • --center_crop : Whether to perform center crop or not.
    • --crop_size : If --center_crop enabled , provide --crop_size
    • --resize : Whether to resize images or not.
    • --image_size : If --resize enabled , then provide --image_size
    • --flip : If enabled , images will be flipped horizontally left to right
    • --transpose : If enabled , images will be transposed
    • --random_augment : If enabled , random hue, saturation, brightness and contrast will be applied to images.
  • Output Parameters
    • --model_dir : Directory to checkpoint model at each epoch, write model json and model.h5
    • --log_dir : Direcory to write logs
  • Test Parameters
    • --test_dir : Test directory containing test images to evaluvate and predict. Structure should be same as above.
    • --model_file' : File path of stored model.h5 file

Training and Validation

python train.py --resize --image_size 300 --center_crop --crop_size 1000 --epochs_steps 50 --no_epochs 100 --data_dir ../data/train_val --no_class 20 --batch_size 128 --flip --transpose --random_augment --mode train

Evaluvate and Predict

python train.py --resize --image_size 300 --center_crop --crop_size 1000 --test_dir ../data/test/ --model_file ../model/model.h5 --mode test

--center_crop and --resize applied at the training phase has to be replicated for test mode with excat --image_size and --crop_size