Simple Implementation of many GAN models with PyTorch.

This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)" (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu).

PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017

Train a TensorFlow deep learning model to detect vehicle make/model.

Fine-Grained Visual Classification on Stanford Cars Dataset

PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)

Final project assigned for the Introduction to Image Processing (EE 475) course in the Spring 2023 semester.

The source code for Multi-Scale Kronecker-Product Relation Networks for Few-Shot Learning

Uncertainty quantification method and tool for object detection models

Deep Learning experiments for the Stanford Cars dataset

Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning

Car Classification with 89% accuracy using ResNet50 with PyTorch & FastAI.

Enhanced class label granularity of the Stanford Cars dataset.

Multi-class classification on Stanford Cars Dataset

Class Activation Map | Stanford Cars | PyTorch

A comparative analysis of state-of-the-art CNN and transformer architectures for an image classification problem.

Car Model Classifier built using PyTorch, deployed via AWS SageMaker 🚗 💨