Implementation of various deep learning models to identify a person's emotional state through an image of his/her facial features.
This was done as an assignment in the course on Machine Learning (COL774). The predictions obtained from the model were used in the kaggle competition https://www.kaggle.com/c/col-774-autumn-2020/overview in which our team Cypher placed 4th on the leaderboard.
The following models are implemented for the problem:
- A vanilla neural network with a single hidden layer
- A CNN with the following layers: CONV1, POOL1, CONV2, POOL2, FC1, FC2, Softmax
- Various Resnet and Resnext pretrained models in Pytorch
The following are also tested:
- Feature engineering: Gabor filters and Histogram of oriented gradients from skimage library
- Data augmentation: Augmented training data using horizontally-flipped and rotated versions of the images
More details can be found in the problem statement (assignment4.pdf) and report (final_submission/COL774_Assign4.pdf)
- Set up a python environment
conda create -n env python=3.6
conda activate env
-
Install prerequisites
- numpy, matplotlib, scikit-learn, scikit-image
conda install numpy matplotlib conda install -c conda-forge scikit-learn scikit-image- Pytorch
Follow installation instructions at https://pytorch.org/
e.g. for CUDA 10.2
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch -
Download files train.csv, public_test.csv and private.csv from https://www.kaggle.com/c/col-774-autumn-2020/data. Place them in the datasets directory.
neural-net.py contains implementation of vanilla neural network, CNN and feature engineering techniques. compe_part*.py contains implementation of resnet and resnext models, alongwith data augmentation techniques.
To run any file, set appropriate dataset file paths and parameters, and simply run as python3 <file-name>