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.
This project uses TensorFlow and Keras to develop and optimize CNN models for classifying CIFAR-10's 60,000 32x32 color images across 10 classes, with evaluations via accuracy metrics and hyperparameter tuning.
keras_CNN models_with_cifar10
Implementing a neural network classifier for cifar-10
Building CIFAR10 with Keras
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