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.

Designed a smaller architecture implemented from the paper Deep Residual Learning for Image Recognition and achieved 93.65% accuracy.

contains exercise solution

ConvMixer - Patches Are All You Need?

AI Nexus is a streamlined suite of AI-powered apps built with Streamlit. It includes StyleScan for fashion classification, GlycoTrack for diabetes prediction, DigitSense for digit recognition, IrisWise for iris species identification, ObjexVision for object recognition, and GradeCast for GPA prediction with detailed insights.

Implemeting SVM to classify images with hinge loss and the softmax loss.

The cifar10 classification project completed by tensorflow, including complete training, prediction, visualization, independent of each module of the project, and convenient expansion.

⭐ Make Once for All support 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

Classification of CIFAR dataset with CNN which has %91 accuracy and deployment of the model with FLASK.

A step-by-step implementation of a ResNet-18 model for image classification on the CIFAR-10 dataset

DigiPic-Classifier is a powerful image classification app built with Streamlit. It features two models: CIFAR-10 Object Recognition to classify objects like airplanes, cars, animals, and more, and MNIST Digit Classification for recognizing handwritten digits. With a sleek interface and real-time predictions, DigiPic-Classifier offers a seamless

Implemented the Deep Residual Learning for Image Recognition Paper and achieved better accuracy by customizing different parts of the architecture.

Implementing a neural network classifier for cifar-10

Deep Learning Projects

A summarization of the course Deep Learning with PyTorch at Jovian.