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

This repository will help you get label for images in Stanford Cars Dataset.

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

PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)

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.

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

Multi-class classification on Stanford Cars Dataset

Enhanced class label granularity of the Stanford Cars dataset.

Class Activation Map | Stanford Cars | PyTorch

Project that detects the model of a car, between 1 and 196 models ( the 196 modelss of Stanford car file), that appears in a photograph with a success rate of more than 70% (using a test file that has not been involved in the training as a valid or training file, "unseen data") and can be implemented on a personal computer.