Deep-Learning-For-Computer-Vision

A series of small projects where deep neural network based approaches have been applied to computer vision problems like Image Classification. The models have been built using Tensorflow and Keras. The primary focus is on building naive deep neural networks as well as Convolutional Neural Networks for Image Classification tasks. This repository also contains a Hello World directory which touches upon basics of Computer Vision and Neural Networks with TensorFlow.

Projects

1. CIFAR-10 Image Classification
2. Covid-19 Mask Recognition
3. Cat and Dog Image Classification
4. Fashion MNIST Image Classification
5. Horse and Human Image Classification
6. MNIST Image Classification

Datasets :

• MNIST (Modified National Institute of Standards and Technology) Dataset for Handwritten Digit Recognition

  • https://www.kaggle.com/c/digit-recognizer/data

• Fashion MNIST Dataset for classifying clothing image

  • https://www.kaggle.com/zalando-research/fashionmnist

• Horse or Human Dataset by Laurence Moroney

  • https://www.kaggle.com/sanikamal/horses-or-humans-dataset

• Cats Vs Dogs Dataset

  • https://www.kaggle.com/c/dogs-vs-cats/data

• CIFAR-10 object recognition Dataset

  • https://www.cs.toronto.edu/~kriz/cifar.html

How to contribute

1. Create a directory for your project with the project name.
2. Add all your project files in that directory only.
3. Add a dedicated README file in the project directory describing your project in detail. (View other project directories for reference if necessary)
4. Append the name of your project to the project list in the common README.
5. Mention the dataset(s) used for the project in the dataset list above.

Contributors