Svhn
Based on the CIFAR10 example on TensorFlow. Reconfiguring it to run my own images and data.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| One Pixel Attack Keras | 1,078 | 3 years ago | 4 | mit | Jupyter Notebook | |||||
| Keras implementation of "One pixel attack for fooling deep neural networks" using differential evolution on Cifar10 and ImageNet | ||||||||||
| Cifar 10 Cnn | 726 | 4 years ago | 4 | mit | Python | |||||
| Play deep learning with CIFAR datasets | ||||||||||
| Senet Tensorflow | 611 | 6 years ago | 13 | mit | Python | |||||
| Simple Tensorflow implementation of "Squeeze and Excitation Networks" using Cifar10 (ResNeXt, Inception-v4, Inception-resnet-v2) | ||||||||||
| Densenet Tensorflow | 471 | 5 years ago | 15 | mit | Python | |||||
| Simple Tensorflow implementation of Densenet using Cifar10, MNIST | ||||||||||
| Deep Learning With Tensorflow 2 And Keras | 299 | 4 months ago | 7 | mit | Jupyter Notebook | |||||
| Deep Learning with TensorFlow 2 and Keras, published by Packt | ||||||||||
| Naszilla | 269 | a year ago | 1 | November 15, 2020 | 3 | apache-2.0 | Python | |||
| Naszilla is a Python library for neural architecture search (NAS) | ||||||||||
| Awesome Tensorlayer | 212 | 4 years ago | 1 | cc0-1.0 | ||||||
| A curated list of dedicated resources and applications | ||||||||||
| Tf Vqvae | 205 | 6 years ago | 1 | Jupyter Notebook | ||||||
| Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv.org/abs/1711.00937) (VQ-VAE). | ||||||||||
| Generative_adversarial_networks_101 | 190 | 6 months ago | mit | Jupyter Notebook | ||||||
| Keras implementations of Generative Adversarial Networks. GANs, DCGAN, CGAN, CCGAN, WGAN and LSGAN models with MNIST and CIFAR-10 datasets. | ||||||||||
| Binarynet.tf | 161 | 6 years ago | 16 | Python | ||||||
| BNN implementation in tensorflow | ||||||||||
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