Simple Implementation of many GAN models with PyTorch.
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Updated
Feb 22, 2023 - Jupyter Notebook
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
Uncertainty quantification method and tool for object detection models
Car Classification with 89% accuracy using ResNet50 with PyTorch & FastAI.
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.
Official implementation of the paper: Learn to aggregate global and local representations for few-shot learning
PyTorch MobileNetV2 Stanford Cars Dataset Classification (0.85 Accuracy)
Class Activation Map | Stanford Cars | PyTorch
Enhanced class label granularity of the Stanford Cars dataset.
Multi-class classification on Stanford Cars Dataset
Deep Learning experiments for the Stanford Cars dataset
The source code for Multi-Scale Kronecker-Product Relation Networks for Few-Shot Learning
Car Model Classifier built using PyTorch, deployed via AWS SageMaker 🚗 💨
Final project assigned for the Introduction to Image Processing (EE 475) course in the Spring 2023 semester.
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