Cifar classification model using Pytorch CNN module with ResNet9 model, with CUDA for training to archive 75% accuracy
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Updated
Aug 26, 2024 - Jupyter Notebook
Cifar classification model using Pytorch CNN module with ResNet9 model, with CUDA for training to archive 75% accuracy
Implemented Cifar-10 without using any pre-trained model with an accuracy of 75%.
A step-by-step implementation of a ResNet-18 model for image classification on the CIFAR-10 dataset
This project uses TensorFlow to classify images from the CIFAR-10 dataset. It compares the performance of an Artificial Neural Network (ANN) and a Convolutional Neural Network (CNN), covering data preprocessing, model training, evaluation, and prediction on new images.
Image classification with two CNNs using PyTorch
Contains my project code for two CNN models, one trained for binary classification while the other made for multi-class classification. It utillises the CIFAR-10 dataset.
Image classification using traditional machine learning model, ensemble model, hybrid model, and deep learning model
Tensorflow-based Object Detection on the CIFAR-10 dataset, served with FastAPI
Experiments on CIFAR-10 classification
This project evaluates the robustness of image classification models against adversarial attacks using two key metrics: Adversarial Distance and CLEVER. The study employs variants of the WideResNet model, including a standard and a corruption-trained robust model, trained on the CIFAR-10 dataset. Key insights reveal that the CLEVER Score serves as
This project uses an ensemble of CNN, RNN, and VGG16 models to enhance CIFAR-10 image classification accuracy and robustness. By combining multiple architectures, we significantly outperform single-model approaches, achieving superior classification performance.
Создание и обучение сверточной нейронной сети (CNN) для классификации изображений из набора данных CIFAR-10 с аугментацией и предотвращением переобучения
Разработка сверточной нейронной сети для классификации изображений
Variety of neural network architectures implemented for different datasets and scenarios, along with regularization techniques and hyperparameter tuning strategies.
Experience CIFAR-Net, a streamlined Python solution for classifying CIFAR-10 images with precision. Train, test, and predict effortlessly using our efficient CNN architecture and automation scripts. Dive into diverse datasets, make accurate predictions, and redefine image classification with ease! 🌟
This is an implementation of the LeNet-5 architecture on the Cifar10 and MNIST datasets.
the CIFAR10 dataset
In this project, the code snippet initialises a machine learning project for image classification.
This project encompasses a series of modules designed to facilitate the creation, training, and prediction using a PyTorch CNN Neural Network for Image classification based on the CIFAR10 dataset.
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