A C++ Convolution Neural Network Library
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Updated
Mar 29, 2018 - C++
A C++ Convolution Neural Network Library
Neural networks implementation in Java, based on Stanford cs231n
Deep robust vision methods
Varying classifier and data processing techniques for the CIFAR-10 dataset.
Transfer Learning with CIFAR-10 dataset
Estudo de técnicas de deep learning para classificação do conjunto de dados cifar-10
CIFAR-10 is an image dataset which contains 60000 tiny color images with the size of 32 by 32 pixels. The dataset consists of 10 different classes (i.e. airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck), in which each of those classes consists of 6000 images.
Cifar-10 CNN implementation using TensorFlow library
A CNN model trained on 50,000 images for classification of images on 10 different classes.
Applying Dimensionality Reduction algorithms i.e PCA, LDA, FDA on CIFAR-10, MNIST, F-MNIST dataset
classifying CIFAR-10 images using CNN in Tensorflow and Keras
Image Reconstruction and Classification with Autoencoder and SVM.
Implementing an ANN using PyTorch (under 800,000 parameters) to achieve +92% accuracy in under 100 epochs.
The code does image classification using the CIFAR-10 dataset. Two models, ANN and CNN, are trained on 32x32 color images across 10 classes. Following data preprocessing, the models are constructed and trained. Their classification performance is assessed on test images, highlighting their effectiveness in identifying objects within the dataset.
Comparative Study of Differential Privacy on MNIST, SVHN and CIFAR-10 datasets
CIFAR10 with pytorch
Multi-class classification : Representation and similarity measures using CIFAR-10
Object Recognition Using All CNN Network
WRN 40-4 training from scratch. Best test accuracy on Fashion MNIST dataset is ~96.74%; best test accuracy on Cifar-10 dataset is ~98.03%.
Implemented Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks from scratch in Python and used ResNet-34 as a feature extractor. Evaluated and compared the classification accuracy of the two networks on the CIFAR-10 dataset.
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