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.

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.

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

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

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…