/VGGNet-Tensorflow

VGGNet-Family (11, 13, 16 & 19) Implementation to train on ImageNet 2012 using Tensorflow 2.x

Primary LanguageJupyter NotebookGNU Affero General Public License v3.0AGPL-3.0

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VGGNet Family

Tensorflow 2.x Implementation of the original VGGNet Paper
Paper Link · Architecture · Data Loader · Model Trainer


VGGNet Architecture

Table of Contents

About The Project

VGGNet is a Deep Learning Paper published in the year 2015 by Visual Geometry Group, Department of Engineering Science, University of Oxford (Hence, the name). It was one of the first papers to dive into very deep CNN based architectures with up to 144M trainable parameters.

This implementation is a part of my learning where I take an attempt to implement Key Deep Learning Papers using Tensorflow or PyTorch.

The Original Literature Can be found here: VGGNet Paper

Quick Links

Features

  • Tf.Data - Optimized Tensorflow Data Pipelining using Tf.Data and Tensorflow Datasets
  • Multiple VGG ARchitectures - Implementation of VGGNet from VGG 11 to VGG 19 has been done
  • Mixed Precision Training - The Implementation uses FP16 - Mixed Precision Training since that uses the Tensor Cores of the Nvidia GPU with Compute Capability 7.0 or higher.

Architectures

All the VGG Architectures mentioned in the original paper has been implemented as per spec.

Hardware Requirements

Nvidia GPU for Training is recommended, However, it can work with CPUs as well (Not Recommended, ImageNet is Huge. It will probably take over a year to Train)

Hardware used for Development and Testing

  • CPU: AMD Ryzen 7 3700X - 8 Cores 16 Threads
  • GPU: Nvidia GeForce RTX 2080 Ti 11 GB
  • RAM: 32 GB DDR4 @ 3200 MHz
  • Storage: 1 TB NVMe SSD
  • OS: Ubuntu 20.04

Minimum Hardware Requirements

  • CPU: AMD/Intel 4 Core CPU (CPU will become a bottleneck here)
  • GPU: Nvidia GeForce GTX 1660 6 GB (You can go lower, but I would not recommend it)
  • RAM: 16 GB
  • Storage: Minimum of 500 GB SSD (HDD is Not Recommended)
  • OS: Any Linux Distribution

Dataset Download & Setup

Enough HDD/SSD space is required for the following:

  • Downloading Raw Dataset - 156.8 GB
  • Convert to TFRecord and Store - 155.9 GB
  • Total Storage Required - 312.7 GB

An SSD is recommended and a Mechanical HDD should be avoided since it will slow down the data loader significantly.

Dataset Download

ImageNet Download Link: Download ImageNet Dataset

  • Download Train Images (Required): ILSVRC2012_img_train.tar - Size 137.7 GB
  • Download Val Images (Required): ILSVRC2012_img_val.tar - Size 6.3 GB
  • Download Train Images (Optional): ILSVRC2012_img_test.tar - Size 12.7 GB

Raw/Source Dataset Directory Structure

Download the dataset from the above link and put it in the folder like shown:

imagenet2012/
├── ILSVRC2012_img_test.tar
├── ILSVRC2012_img_train.tar
└── ILSVRC2012_img_val.tar

Processed/Destination Dataset Directory Structure

Create another folder and create the folders data, download & extracted like shown:

imagenet/
├── data/
├── downloaded/
└── extracted/

References

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the GNU AGPL V3 License. See LICENSE for more information.

Contact

Animikh Aich