keras_CNN models_with_cifar10
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Updated
Aug 8, 2018 - Python
keras_CNN models_with_cifar10
Implementing a neural network classifier for cifar-10
Building CIFAR10 with Keras
This repository provides experiment results for MobileNetV2 based on PyTorch.
This project is a simple implementation of a convolutional neural network (CNN) using TensorFlow to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The dataset is split into 50,000 training images and 10,000 testing images.
things about deep learning
This repository allows you to training classification models and classification inference API with GluonCV-MXNET and cifar10.
Classification using a Multi-Layer Perceptron neural network from scratch
SIFT based image classification on the CIFAR-10 dataset
Templates for transfering dataset images to TFRecords for running tensorflow code. Template for checking TFRecords contents.
base backbone model
CNN Implementation and optimization in comparison to Image-Net Pre-trained on CIFAR-10 Dataset Image Classification
Investigating alternative auxiliary tasks in image classification.
PyTorch implementation of deep CNNs
An Image Classification Group Project on the CIFAR-10 dataset
CNN application on CIFAR-10 dataset using tensorflow
Pytorch implementation of different models
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