Training examples with reproducible performance.
The word "reproduce" should always mean reproduce performance. With the magic of SGD, wrong deep learning code often appears to work, especially if you try it on toy datasets. Github is full of such deep learning code that "implements" but does not "reproduce" methods. See Unawareness of Deep Learning Mistakes.
We refuse toy examples. Instead of showing you 10 arbitrary networks trained on toy datasets with random final performance, tensorpack examples try to faithfully replicate experiments and performance in the paper, so you're confident that they are correct.
These are all the toy examples in tensorpack. They are supposed to be just demos.
- An illustrative MNIST example with explanation of the framework
- Tensorpack supports any symbolic libraries. See the same MNIST example written with tf.layers, tf-slim, and with weights visualizations
- A tiny Cifar ConvNet and SVHN ConvNet
- If you've used Keras, check out Keras+Tensorpack examples
- A boilerplate file to start with, for your own tasks
|Train ResNet, ShuffleNet and other models on ImageNet||reproduce paper|
|Train Faster-RCNN / Mask-RCNN on COCO||reproduce paper|
|Generative Adversarial Network(GAN) variants, including DCGAN, InfoGAN,
Conditional GAN, WGAN, BEGAN, DiscoGAN, Image to Image, CycleGAN
|DoReFa-Net: training binary / low-bitwidth CNN on ImageNet||reproduce paper|
|Fully-convolutional Network for Holistically-Nested Edge Detection(HED)||visually reproduce|
|Spatial Transformer Networks on MNIST addition||reproduce paper|
|Visualize CNN saliency maps||visually reproduce|
|Similarity learning on MNIST|
|Single-image super-resolution using EnhanceNet|
|Learn steering filters with Dynamic Filter Networks||visually reproduce|
|Load a pre-trained AlexNet, VGG, or Convolutional Pose Machines|
|Load a pre-trained FlowNet2-S, FlowNet2-C, FlowNet2|
|Deep Q-Network(DQN) variants on Atari games, including
DQN, DoubleDQN, DuelingDQN.
|Asynchronous Advantage Actor-Critic(A3C) on Atari games||reproduce paper|
Speech / NLP:
|LSTM-CTC for speech recognition||reproduce paper|
|char-rnn for fun||fun|
|LSTM language model on PennTreebank||reproduce reference code|