261 Open source projects that are alternatives of or similar to MNIST-CoreML

Largest list of models for Core ML (for iOS 11+)

This project shows how to use CoreML and Vision with a pre-trained deep learning SSD (Single Shot MultiBox Detector) model. There are many variations of SSD. The one we’re going to use is MobileNetV2 as the backbone this model also has separable convolutions for the SSD layers, also known as SSDLite. This app can find the locations of several di…

Real-time Number Recognition using Apple's CoreML 2.0 and MNIST -

This app predicts the age of a person from the picture input using camera or photos gallery. The app uses Core ML framework of iOS for the predictions. The Vision library of CoreML is used here. The trained model fed to the system is AgeNet.

Sample code for Core ML using ResNet50 provided by Apple and a custom model generated by coremltools.

Build your iOS 11+ apps with the ready-to-use Core ML models below

A Big Awesome List CoreML Models.

Handwritten digit recognizer using a feed-forward neural network and the MNIST dataset of 70,000 human-labeled handwritten digits.

This is a sample project demonstrating the use of Keras (Tensorflow) for the training of a MNIST model for handwriting recognition using CoreML on iOS 11 for inference.

🌅 iOS11 demo application for visual sentiment prediction.

A simple image classification test using Core ML and Inception V3 model in Objective-C

Recognize your own handwritten digits with Tensorflow, embedded in a PyQT5 GUI. The Neural Network was trained on MNIST.

Deep Learning with Tensorflow/Keras: Digit recognition based on mnist-dataset and convolutional neural-network on iOS with CoreML

PyTorch tutorials A to Z

A project designed to explore CNN and the effectiveness of RCNN on classifying the EMNIST dataset.

A repository for recording the machine learning code

Lingvo

Tensorflow implementation of conditional variational auto-encoder for MNIST

Label Embedding Network

Deep Learning From Scratch

Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets.

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Get started with Machine Learning in TensorFlow with a selection of good reads and implemented examples!

Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE).

Generative Adversarial Network for MNIST with tensorflow

A PyTorch implementation of the NIPS 2017 paper "Dynamic Routing Between Capsules".

The implementation of Relativistic average GAN with Keras

Comparison of Generative Models in Tensorflow

Tensorflow implementation of Generative Adversarial Networks (GAN) and Deep Convolutional Generative Adversarial Netwokrs for MNIST dataset.

A MNIST-like fashion product database. Benchmark 👇

Çeşitli kütüphaneler kullanılarak Türkçe kod açıklamalarıyla TEMEL SEVİYEDE pratik derin öğrenme uygulamaları.

🎎 InfoGAN: Interpretable Representation Learning

Simple Implementation of many GAN models with PyTorch.

An implementation for mnist center loss training and visualization

GPU Accelerated t-SNE for CUDA with Python bindings

MNIST with TensorFlow Lite on Android

Combine multiple MNIST digits to create datasets with 100/1000 classes for few-shot learning/meta-learning

The Pytorch Implementation of L-Softmax

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

A small convolution neural network deep learning framework implemented in c++.

Tensorflow implementation of conditional Generative Adversarial Networks (cGAN) and conditional Deep Convolutional Adversarial Networks (cDCGAN) for MANIST dataset.

Deep generative models implemented with TensorFlow 2.0: eg. Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Convolutional Variational Auto-Encoder (CVAE), Convolutional Generative Adversarial Network (CGAN)

Implementation of a neural network from scratch in python.

Collection of generative models in Pytorch version.

TI-pooling: transformation-invariant pooling for feature learning in Convolutional Neural Networks

tensorflow implementation of Exploring Randomly Wired Neural Networks for Image Recognition

Layers Outputs and Gradients in Keras. Made easy.

Generative Probabilistic Novelty Detection with Adversarial Autoencoders

Implementation of ews weight constraint mentioned in recent Deep Mind paper: http://www.pnas.org/content/early/2017/03/13/1611835114.full.pdf

Theano implementation of XNOR-Net

Minimalist implementation of VQ-VAE in Pytorch

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

Implement Decoupled Neural Interfaces using Synthetic Gradients in Pytorch

AoE (AI on Edge，终端智能，边缘计算) 是一个终端侧AI集成运行时环境 (IRE)，帮助开发者提升效率。

Early stopping for PyTorch

Tensorflow implementation of different GANs and their comparisions

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.