Inception-ResNet-V2 : Face Recognition

Design

Written in PyTorch, this model has a total of 26 high-level blocks and can classify upto 1001 different classes of images.
It has a complete depth of 164 layers
Input-size = 3 x 299 x 299

The individual blocks have been defined seperately with explicit mention of in_channels and out_channels for each layer to maintain a visual flow of how images are moving around in the network
A custom LambdaScale layer has been introduced to scale the residuals, as discussed in the original paper to tackle the problem of the later layers dying early in training
Batch Normalization has been done to ensure regularization

Layer design - Overview

Loss function : torch.nn.CrossEntropyLoss()
Optimizer : torch.optim.Adam(amsgrad=True)
Scheduler : torch.optim.lr_scheduler.ReduceLROnPlateau(mode='min', factor=0.2, threshold=0.01, patience=5)

prefetch_generator.BackgroundGenerator has been used to bring about computational efficiency by pre-loading the next mini-batch during training
The state_dict of each epoch is stored in the resnet-v2-epochs directory (created if does not exist)
By default, it will try to run training using a CUDA GPU, but it will back up to a CPU on not being able to detect the presence of one
Parallelization has not been implemented as a design choice in order to keep the training function readable and easy to implement
The results of the training session can be viewed interactively using TensorBoard with logs being stored in /runs directory
A benchmark of 00:30:03 hours was seen on a NVIDIA GTX 1650Ti 4GB, Intel i7-10750H, 16GB RAM, SSD-enabled computer to train 1 epoch

Using the Face-Expression-Recognition-Dataset from jonathanoheix on Kaggle, we train on a total of 28,821 images of 7 different classes including 'Anger', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad' and 'Surprise'
We perform some simple preprocessing using torchvision.transforms.Resize(), torchvision.transforms.ToTensor() and torchvision.transforms.Normalize() to get a good representation for the images in tensor format.
We make use of torch.utils.data.DataLoader() to improve load times and process images in random mini-batches in an efficient and optimized manner

You can choose to run either the Jupyter notebook, or the scripts present within the Scripts folder of the repository

Run the cells in order. Adjust parameters as you may see fit. Preferable number of epochs could be easily increased with availability of hardware
There are helper functions present within the cells that you can use to generate predictions for images using the models. Feel free to use them

Make sure you have the dependencies set up. For being on the safe side, you can run pip install -r requirements.txt --no-index
Make changes as needed to the parameters in train.py as it contains the required code for training the model present in resnet_model.py.
If using VS Code, you can deploy a Tensorboard session directly by clicking on Launch TensorBoard session above the Tensorboard import present in the file.
Else, you can deploy by following the steps here. Using TensorBoard with PyTorch

Paper: Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Authors: Christian Szegedy, Sergey Ioffe and Vincent Vanhoucke
Images dataset - Source : Face Expression Recognition Dataset