Experiment with development, training and hyperparameter tuning of multitask, multiclass classification model to classify attributes including sex, race, and age group.
The training Dataset used is UTK face dataset.
Source: https://susanqq.github.io/UTKFace/
Dataset summary: This dataset has wild images for Race, Gender, and age annotated: Sex : Male, Female Age : 0 to 116 Race : White, Black, Asian, Indian, and Others (like Hispanic, Latino, Middle Eastern)
Some look into the dataset further pre-processing and examples can be found in the notebook UTKFace_and_wild_dataset_prep.ipynb
Information about model architecture used can be found in model.py which uses ResNet architecture as backbone with different linear layers acting as head for the respective tasks.
Fine-tuning and Hyperparameter tuning: Pre-trained ResNet model architectures trained on ImageNet are fine-tuned.
Wandb sweep is utilized to perform hyperparameter tuning. The configuration of tuning is found in sweep_configuration.yaml and logic to run each sweep is present in train.py
To run the sweep in the terminal execute the following commands:
wandb sweep --project <wandb-project-name> sweep_configuration.yamlwandb agent <the generated id by above command>
Extensive hyperparameter tuning is not done and there is scope of improvement. Some results can be found here.
A demo link build using streamlit and deployed on huggingface is here. You might have to restart the huggingface space and wait a short while to try the demo.
You can run your own streamlit app using streamlit run app.py after necessary packages installation.
The required packages can be viewed in reqs.txt. This file could include extra packages which might not be necessary. A new conda environment is recommended if you want to test out on your own.