ResNets (Deep Residual Learning for Image Recognition)

This is my replication of the ResNet Model using the ImageNet Dataset

Model Specification

Due to GPU Size constraints I chose to implement ResNet-34 in both the Resnet-34 A and ResNet-34 B options

Setup and Running

This project was created using uv and is highly recommended
After installing uv this project should run out of the box

Data Setup

First, you should download the ImageNet Dataset from Kaggle here
Once this is downloaded you will need to run the create_dataset_csv() script to create our datasets
You will need to set the data variable to your ImageNet location

uv run create_dataset_csv.py train
uv run create_dataset_csv.py val

Training

Before kicking off training you should update the weights and biases variables entity and project in init_logging() in train.py to match your account.
If not using Weights and Biases (not recommended) you can set logs to False in main.py
To kick off training you can run
uv run main.py

Citation

Paper Arxiv Link "Deep Residual Learning for Image Recognition"
CVPR Link

@InProceedings{He_2016_CVPR,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
title = {Deep Residual Learning for Image Recognition},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2016}
}