AlexNet

This project intends to replicate the AlexNet CNN network by strictly following the original paper.

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1. Dataset

Currently, the project is configured to be trained on imagenette/full-size-v2, a dataset delivered by Tensorflow. From the documentation:

Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.

Link: https://github.com/fastai/imagenette

TODO: Find ImageNet 2010
Please drop me an email if you know where to find this specific dataset

2. Prerequisites

• Python 3.7.9
• Tensorflow 2.3.1

pip install tensorflow-gpu==2.3.1

3. Setup

1. Clone the repo:

git clone https://github.com/houseofai/alexnet.git

2. Install the 3rd party packages

cd alexnet/
pip install -r requirements.txt

4. Training

To train the model, launch the following command:

python train.py

To test the configuration on a smaller dataset:

python train.py --conf=test

5. Predict

To predict class probabilities on an image:

python train.py --mode=predict --image=/path/to/image

6. Bibliographies: ImageNet Classification with Deep Convolutional Neural Networks

https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf