stanford-cars
There are 21 repositories under stanford-cars topic.
Yangyangii/GAN-Tutorial
Simple Implementation of many GAN models with PyTorch.
ZF4444/MMAL-Net
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).
lvyilin/pytorch-fgvc-dataset
PyTorch custom dataset APIs -- CUB-200-2011, Stanford Dogs, Stanford Cars, FGVC Aircraft, NABirds, Tiny ImageNet, iNaturalist2017
deanwetherby/tf_oda_stanford_cars
Train a TensorFlow deep learning model to detect vehicle make/model.
codope/aiforsea-cv-cars
Fine-Grained Visual Classification on Stanford Cars Dataset
nguyentruonglau/stanford-cars
This repository will help you get label for images in Stanford Cars Dataset.
mouniraziz/MsKPRN
The source code for Multi-Scale Kronecker-Product Relation Networks for Few-Shot Learning
haksorus/mobilenetv2-cars-classification
PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)
Simula-COMPLEX/pure
Uncertainty quantification method and tool for object detection models
amitdu6ey/stanford-cars-image-calssification-model
Car Classification with 89% accuracy using ResNet50 with PyTorch & FastAI.
DanRuta/Stanford-Cars-Deep-Learning
Deep Learning experiments for the Stanford Cars dataset
mouniraziz/AGLRs
Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning
arasgungore/car-brand-recognition
Final project assigned for the Introduction to Image Processing (EE 475) course in the Spring 2023 semester.
dimgag/stanford_car_classification
Multi-class classification on Stanford Cars Dataset
morrisfl/stanford_cars_refined
Enhanced class label granularity of the Stanford Cars dataset.
KonWski/CAM_Stanford_Cars
Class Activation Map | Stanford Cars | PyTorch
Sid1279/CarCognize-MLOps
Car Model Classifier built using PyTorch, deployed via AWS SageMaker 🚗 💨
ablanco1950/CarsModels_Resnet_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.