Tensorflow-based Object Detection on the CIFAR-10 dataset, served with FastAPI
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Updated
Nov 10, 2024 - Python
Tensorflow-based Object Detection on the CIFAR-10 dataset, served with FastAPI
Image classification on CIFAR-10 dataset using PyTorch.
Research on adversarial machine learning
Connection Reduction of DenseNet for Image Recognition
TripleNet Image Classification on CIFAR-10 by raspberrypi
ThreshNet image classification on CIFAR-10
This project implements and tests Convolutional Neural Network (CNN) models to classify images from the CIFAR-10 dataset, which includes 60,000 color images across 10 classes. The models achieve up to 90.45% accuracy, with training stability considerations and evaluation through confusion matrices and training history.
Repository for the Exposing Outlier Exposure paper
Implementing FedAvg (Federated Average) based on CIFAR-10 dataset 基于CIFAR-10数据集实现FedAvg
Convolutional neural network (CNN) implemented in PyTorch for image classification trained on CIFAR-10 dataset classifying images in 10 categories (airplanes, automobiles, dog, etc.)
Vitis AI tutorial for MNIST and CIFAR10 classification
A collection of small deep learning experiments
PyTorch implementation of a 9-layer ResNet for CIFAR-10.
sensAI: ConvNets Decomposition via Class Parallelism for Fast Inference on Live Data
Scripts for downloading, preprocessing, and numpy-ifying popular machine learning datasets
Some mini-projects using well known datasets to practice important deep learning concepts.
A Pytorch implementation of ResNet trained on cifar-10 with accuracy of 92.07%
Official code for "PubDef: Defending Against Transfer Attacks From Public Models" (ICLR 2024)
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