rishusiva/emotion-classification

Advanced ML models for emotion classification in images and text.

Emotion Classification in Relation to Mental Health

Welcome to our project repository for CSCI-567, the Machine Learning graduate course at USC. This repository features advanced models for facial emotion classification in images and emotion classification in text. Dive into our final report for a detailed exploration of our project, including experiments and results.

Note: For tracking experiments, we use Comet ML. Follow the instructions here to get an API key. If not using Comet ML, ignore related code sections.

Repository Overview:

Image Emotion Classifier

• Folders:
  • image-emotion-classifier: CNN-based models for image emotion classification.
    • cnn-architecture: Custom CNN models trained on the fer2013 dataset.
    • resnet50: Variations of ResNet50 with custom layers, also trained on fer2013.
    • vgg: VGG16 and VGG19 models with fine-tuning and custom layers. Specific files include vgg16_v1.ipynb, vgg16_v1_mtcnn.ipynb, and vgg19_fer.ipynb.

Text Emotion Classifier

• Folders:
  • text-emotion-classifier: Transformer-based models for text emotion classification.
    • cleaning_augmentation: Data cleaning and augmentation code for text dataset.
    • roberta-base: Our high-performing RoBERTa model with complete training and testing code.
    • hybrid_models: Combining Bi-LSTM with attention layers and transformer models.
      • bi-lstm_attention_roberta and bi-lstm_attention_distilbert.
    • alternate_dataset_checkpoint_model: Finetuning models with the Self-Reported Emotion dataset.

Note: All models besides alternate_dataset_checkpoint_model use the GoEmotions dataset.

Getting Started with the Classifiers:

Image Classifier

• Setup:
  1. Install Anaconda or create a virtual environment.
  2. Install dependencies: pip install -r requirements.txt (upgrade pip beforehand).
  3. Download the required dataset and adjust file paths in the code.
  4. Set up a .env file with Comet ML credentials (optional).

  Note: To reproduce vgg16_v1_mtcnn.ipynb, first download the mtcnn fer 2013 dataset from the link above. Then, execute all cells in the notebook, except those containing the preprocess_images function.

Text Classifier

• Google Colab Usage:
  • Copy the code from .ipynb files in the text-emotion-classifier folder to a new Google Colab notebook to run code directly.
• Local Setup:
  1. Create a virtual environment.
  2. Install dependencies: pip install -r requirements.txt (upgrade pip first).
  3. Run main.py.

  Note: cleaning_augmentation is the only code that needs to be run locally. The rest of the code can be run on Google Colab.

Workflow and Contribution Guidelines:

• Before Starting:

  • Discuss work items in team meetings.
  • Ensure there's a GitHub issue for the task you're taking on.

• Starting Work:

  • Assign yourself to the issue on GitHub.
  • Sync the latest changes from the main branch.
  • Create and switch to a new branch for your work.

• Development Process:

  • Commit and push changes frequently, adhering to Conventional Commits.
  • Start a pull request early, marking it as a draft.
  • Write and run unit tests (if applicable).
  • Resolve merge conflicts promptly.

• Finishing Up:

  • Clean and review your code.
  • Ensure all tests pass.
  • Finalize the pull request and request code reviews.
  • Implement review feedbacks and merge upon approval.