Reference Paper: Inception-v4, Inception-ResNet, and the Impact of Residual Connections on Learning

This PyTorch model is based on the Inception-ResNet-V2 architecture and is designed for facial emotion recognition.

• 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.
• The model has a depth of 164 layers with input-size = 3 x 299 x 299.

• The model's blocks are explicitly defined, specifying in_channels and out_channels for each layer, enhancing the visual flow of image processing.

• A custom LambdaScale layer is introduced to scale the residuals, addressing the issue of early training convergence in later layers.

• Batch normalization is applied to ensure regularization.

  

  Layer design - Overview

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

• prefetch_generator.BackgroundGenerator is utilized to improve 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 it doesn't exist.
• The model is designed to run on a CUDA GPU, falling back to a CPU if no GPU is detected.
• Parallelization is not implemented to maintain code readability and ease of implementation.
• Training results can be interactively monitored using TensorBoard, with logs stored in the /runs directory.
• A benchmark of 00:30:03 hours was achieved on a system with an NVIDIA GTX 1650Ti 4GB, Intel i7-10750H, 16GB RAM, and an SSD during one epoch of training.

• The model is trained on the Face-Expression-Recognition-Dataset from jonathanoheix on Kaggle, containing 28,821 images of 7 emotion classes: 'Anger', 'Disgust', 'Fear', 'Happy', 'Neutral', 'Sad', and 'Surprise'.
• Preprocessing includes resizing using torchvision.transforms.Resize(), converting to tensors with torchvision.transforms.ToTensor(), and normalization with torchvision.transforms.Normalize().
• torch.utils.data.DataLoader() is used for efficient loading and processing of random mini-batches.

You can choose to run the project using either the Jupyter notebook or the provided scripts in the Scripts folder of this repository.

1. Run the cells in order, adjusting parameters as needed. You can increase the number of epochs with available hardware.
2. Helper functions within the notebook cells allow generating predictions for images using the trained models.

1. Ensure you have the required dependencies installed by running pip install -r requirements.txt --no-index.
2. Modify parameters in train.py, which contains the code for training the model defined in resnet_model.py.
3. If using VS Code, deploy a TensorBoard session by clicking on Launch TensorBoard session above the Tensorboard import in the file. Otherwise, follow the steps provided in 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
• Image Dataset Source: Face Expression Recognition Dataset