Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

A model based on CNN (Convolutional Neural Networks) in order to classify the CIFAR100 dataset, composed by 60,000 32x32 color images labeled over 100 categories.

Image classification using CNNs

This is for Student Research Project at Uni Hildesheim: Topics: Knowledge distillation in Encoders

Programming assignments from Deep Learning Class (CSE496/896)

Trained a multiclass classifer network using cifar100 dataset

ResNet for CIFAR with Estimator API and tf.keras.Model class

Projects for Rice Undergraduate Data Science Summer Program

A simple image classification flask app. CNN Model trained on CISNET100 data.

Implementaiton of BSC-Densenet-121 in Pytorch from research paper "Adding Binary Search Connections to Improve DenseNet Performance".

Models with variable output classes designed for CIFAR-100

Image augmentation with mixup image tag

It's a simple Convolutional Neural Network about discriminate cifar-100 datasets.

Model performance and tuning analysis conducted on the CIFAR10 and CIFAR100 datasets. Convolutional Neural Network (CNN), Gated Multilayer Perceptron (gMLP), and Vision Transformer (ViT) model architectures are utilized. The study is built using PyTorch, PyTorch Lightning for clean and concise code and Optuna for hyperparameter tuning.

Code repository for the paper titled "MIO : Mutual Information Optimization using Self-Supervised Binary Contrastive Learning"

Classification of CIFAR 100 Images - Transfer Learning - ResNet-50