In this project we use a Lightweight-CNN based model to classify instruments from the Freesound audio data set. We make use of Mel-Spectrogram features from the input audio data as the input to the CNN model. To add robustness to the model, we use a novel data augmentation technique based on the Cut-Mix algorithm.

Image Classification Using Swin Transformer With RandAugment, CutMix, and MixUp

Implementation of CutMix Augmentation with Keras.

Through this project we will try to understand CutMix by implementing it on a simple problem of cat-vs-dog classification.

This repository contains the code and the report for the coursework of INFR11031 Advanced Vision, a postgraduate course offered at The University of Edinburgh. The task was to train on limited and improve the accuracy of the ResNet-50 classifier on a small subset of the ImageNet dataset containing 50K training images and 50K test images. Achieve…

Python codes to implement DeMix, a DETR assisted CutMix method for image data augmentation

Train CIFAR 10- get 85 % accuracy