TensorFlow ResNet50

Custom implementation of ResNet50 Image Classification model using pure TensorFlow

Requirements

• Python 3.7
• Tensorflow 1.x

Dataset Requirements

Dataset Folder should only have folders of each class. Dataloader will automatically split the dataset into training and validation data in 80:20 ratio.
Example:

.
└── DatasetFolder
    ├── ClassOne                 
    │   ├── FirstImage.jpg                       
    │   ├── SecondImage.jpg                 
    │   └── ...    
    ├── ClassTwo  
    │   └── ...    
    ├── ClassThree               
    │   └── ...    
    └── ...

Usage

Training

python train.py -e=[number of epochs] -f=[dataset folder path] -d=[optional: if use TF Debugger]

TensorBoard

To see metrics while training, run tensorboard.
Plotted metrics are:

• Each batch accuracy, both train and val
• Each batch loss, both train and val
• Epoch accuracy, both train and val
• Epoch loss, both train and val

tensorboard --logdir=logs

Prediction

python predict.py -img=[path to fodler with images awaiting prediction] -f=[path to dataset folder] 
-mod=[path to saved model folder] -d=[optional: if use TFDebugger]

Project Structure

.
├── data                       
│   ├── data.py                 # Dataloader  
│   └── utils.py                # Image Parser
├── model                       
│   ├── resnet.py               # Resnet50 Model
│   └── layers.py               # Model's Layers 
├── logs                        # TensorBoard Logs         
├── training                    # Model's Weights
├── config.json                 # Configuration File
├── train.py                    # Training Script
└── predict.py                  # Preidction Script