A PyTorch implementation of "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks".

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.

Pilot project of generative adversarial models (GAN) on CIFAR-10

Auto encoder U-Nets and DCGANs for the coulourization task of CIFAR10 images.

NNs, CNNs and Residual Networks for the classification task of CIFAR10 images.

Deep Learning Neural Network Architectures - LeNet and AlexNet, both trained on CIFAR10 and CIFAR100 Datasets

CAI NEURAL API - Pascal based deep learning neural network API optimized for AVX, AVX2 and AVX512 instruction sets plus OpenCL capable devices including AMD, Intel and NVIDIA.

Robust Transformer with Locality Inductive Bias and Feature Normalization (JESTECH 2023)

This project optimizes the CIFAR-10 dataset for improved model performance through data exploration, augmentation, and training a CNN. It includes data loading, preprocessing, exploratory data analysis (EDA), and model training in a streamlined pipeline, showcasing the importance of data preparation in achieving better classification accuracy.

CIFAR-10 trained with CreateML and a SwiftUI app for real time model testing. Kaggle Score: 0.69660

This repository includes official implementation and model weights of Data-Efficient Multi-Scale Fusion Vision Transformer.

Explored Diffusion Models in Seasons of Code - 2024, IIT Bombay.

This project demonstrates image classification using a Convolutional Neural Network (CNN) on the CIFAR-10 dataset. The model is trained to classify images into one of 10 classes.

Implemented a paper explaining Block Switching in Deep Learning on CIFAR10

Deep Learning Project on Diffusion Models for Image Generation

This repository contains a Python-based image recognition project using TensorFlow and Keras. It leverages a pre-trained Convolutional Neural Network (CNN) model on the CIFAR-10 dataset to classify objects in images. The project supports image recognition from both local files and URLs.

In this project, I implemented an active learning framework utilizing the GCN query technique. The objective was to train a ResNet18 model on CIFAR-10 with reduced data, achieving accuracy comparable to full dataset training.

This is a test project for classifying the well-known cifar10 database using a deep neural network.

Image transformations and deep learning using PyTorch

Self-training variants using PyTorch