ComputerVision

Overview

This repository contains implementations of main deep learning computer vision architectures using PyTorch. The goal is to provide a comprehensive resource for understanding and experimenting with the main models in computer vision.

Table of Contents

1. Introduction
2. Architectures
3. Docker
4. License

Introduction

Deep learning has revolutionized computer vision, and this repository aims to gather implementations of popular architectures, making it easy for researchers, practitioners, and enthusiasts to explore, learn, and experiment with these models.

First steps with PyTorch;

• Implementing a Neural Network in Pytorch
• Convolutional Neural Network in Pytorch

Architectures

The following architectures are currently implemented:

• AlexNet: AlexNet: ImageNet Classification with Deep Convolutional Neural Networks
• ResNet: Deep Residual Networks
• VGG: Visual Geometry Group Networks
• DenseNet: Densely Connected Convolutional Networks
• Inception: Going Deeper with Convolutions
• MobileNet: Efficient Convolutional Neural Networks for Mobile Vision Applications

Docker

1. cd into the docker folder
2. Run the command

docker build -t <image_name> --build-arg token_name=<token> . 

To build the container run:

docker run -it \
--gpus all \
--name <instance_name> \
-p 8888:8888 \
-v <Path/to/notebook/folder>:/home/Notebooks ^\
-v <Path/to/data/folder>:/home/Data \
-v <Path/to/outputs/folder>:/home/Outputs \
-v <Path/to/scripts/folder>:/home/Scripts \
<image_name>

If you are using Rancher Desktop you should map the folder paths with 'mnt/c'

License

This project is licensed under the MIT License - see the LICENSE file for details.