Paper Replications

A collection of deep learning paper implementations in PyTorch, trained and evaluated on standard benchmarks.

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Implementations

#	Paper	Dataset	Directory
1	AlexNet — ImageNet Classification with Deep Convolutional Neural Networks (Krizhevsky et al., 2012)	CIFAR-10	AlexNet/

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AlexNet

PyTorch implementation of AlexNet adapted for CIFAR-10.

Prerequisites

• Python 3.8+
• PyTorch
• TorchVision

cd AlexNet
pip install -r requirements.txt

Usage

python train.py

Command Line Arguments

Argument	Description	Default
--data-dir	Directory to store CIFAR-10 data	./data
--epochs	Number of training epochs	5
--batch-size	Batch size for training	64
--lr	Learning rate	0.01
--momentum	Momentum for SGD	0.9
--weight-decay	Weight decay for SGD	0.0005
--save-path	Path to save trained model weights	alexnet.pth

Example:

python train.py --epochs 10 --batch-size 128 --lr 0.005

Files

File	Description
train.py	AlexNet model definition and training loop
alexnet.ipynb	Jupyter notebook for interactive exploration
requirements.txt	Python dependencies
NIPS-2012-imagenet-classification-with-deep-convolutional-neural-networks-Paper.pdf	Original paper

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Contributing

Each paper lives in its own subdirectory with a self-contained train.py, requirements.txt, and the original PDF.