There are 2 repositories under cifar10-classification topic.
PyTorch-1.0 implementation for the adversarial training on MNIST/CIFAR-10 and visualization on robustness classifier.
Implementation of Conv-based and Vit-based networks designed for CIFAR.
The aim of this project is to train autoencoder, and use the trained weights as initialization to improve classification accuracy with cifar10 dataset.
contains exercise solution
ConvMixer - Patches Are All You Need?
Implemeting SVM to classify images with hinge loss and the softmax loss.
Classification of CIFAR dataset with CNN which has %91 accuracy and deployment of the model with FLASK.
The cifar10 classification project completed by tensorflow, including complete training, prediction, visualization, independent of each module of the project, and convenient expansion.
Implementing a neural network classifier for cifar-10
Deep Learning Projects
the CIFAR10 dataset
使用了 https://github.com/SaeedShurrab/SimSiam-pytorch 作为Simsiam backbone,添加了中文注释和简单的训练过程
Classifies the cifar-10 database by using a vgg16 network. Training, predicting and showing learned filters are included.
CapsNet models
A summarization of the course Deep Learning with PyTorch at Jovian.
This GitHub repository hosts my comprehensive CIFAR-10 image prediction project, which I completed as part of the SmartKnower program. CIFAR-10 is a widely used dataset in computer vision, consisting of 60,000 32x32 color images from 10 different classes.
CIFAR-10 Photo Classification
This project is one of the Computational Intelligence course projects in the spring of 2023, and it includes code related to training neural networks with gradient descent, training neural network using neuroevolution, Neural Architecture Search (NAS), and Self-Organizing Maps (SOM)
This repository contains code to solve different tasks related to building, training and creating adversarial examples for classification models on the MNIST and CIFAR10 datasets.
Python Basics with PyTorch Deep Learning Course
The project is based on datasets from various sectors namely finance, health, industrial, crime, education, social media, biology, product and multimedia from the UCI repository and Kaggle. Trained and evaluated 8 classification methods across 10 classification datasets, 7 regression methods across 10 regression datasets and 2 classification methods (Convolutional Neural Network and Decision Tree Classifier).
A guide on custom implementation of metric, logging, monitoring, and lr schedule callbacks in Keras
This repository consists of Lab Assignments for course Machine Learning for Data Mining.
This repository contains all the work I have done during the course Deep Learning with PyTorch : Zero to GANs under Jovian.ai.
Machine Learning
Applied K-Nearest Neighhbor (KNN) Classifier on Cifar10 Dataset
Applied Softmax Classifier on Cifar10 Dataset
Applied Support Vector Machine (SVM) Classifier on Cifar10 Dataset
PyTorch implementation of "Learning Loss for Active Learning"