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This repository performs Computer-Vision tasks on multiple Image Datasets using CNN based Networks.

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Hello CV

Not just a hello-world Repository with entery-level code!

In this repo I have worked to develop various Computer Vision taskes. The code uses both Pytorch & Tensorflow.

TODOs for running the code

  1. Create paths.py file (use paths.py.template as a template)
  2. Download the datasets.

Datasets

All the used datasets well-known around the deeplearning community. They include -

Data Preprocessing

  • For large datasets I have used Image Iterator, This uses less memory.

  • Image augmentation is also used to augment the images.

Image Classification Results

S. No Dataset Best Accuracy Best Model Real-Life Test
1 MNIST(Digit Recognizer) 99.3% Simple Sequential CNN
2 CIFAR-10 89.94% VGG-Like Model 5 images
3 Stanford Dogs 84.3% InceptionResNetV2 4 images

Other Computer Vision Tasks

S. No Application Name Refered Literature/Implimentation Implimented Using Metric Score Visuals
1 Neural Style Transfer A Neural Algorithm of Artistic Style,
NST With Two Style
Pytorch gif
2 Dog Breed Detection (YOLOv8) Joseph Chet's Publications, YOLOv8 Implimentation Pytorch, Ultralytics mAP50-95 0.79 Alt text



Check the branches. I have created a new branch everytime I added a new type of model.

NOTE: For Some reason different hardware give different results, I used two Machines, Macbook Air M1(8gb) and Intel i7 11700k | RTX3070. RTX3070 machine gave better results with a good margin. I even used Kaggle and Jarvis Labsto train some of the models.