sahgerlad / resnext-in-tensorflow

This is an implementation of ResNeXt (by Xie et al.) in tensorflow

ResNeXt in Tensorflow

This is an implementation of ResNeXt in tensorflow. The tensorboard visualization of the detailed model structure (ResNeXt-29, 4x64d as example) is shown in graph, block, and split.

I implemented the blocks with both methods in Figure 3b (split->concat).

**Figure 3c (grouped convolutions) is not available yet. **

tensorflow 1.0.0 is needed here. They re-named lots of functions, so the previous versions won't work...

Quick guide

You can run cifar10_train.py to train a ResNeXt on cifar10 and see how it works from the screen output (the code will download the data for you if you don't have them yet). It’s better to speicify a version identifier before running, since the training logs, checkpoints, and error.csv file will be saved in the folder with name logs_$version.

python cifar10_train.py --version='test'

You may alter the shape of the model via the hyper-parameters. Typically a ResNeXt is represented as 'ResNeXt-a, b*c'.

a is the total layer, which is defined by 9 * FLAGS.num_resnext_blocks + 2.

b is the cardinality, which is defined by FLAGS.cardinality.

c is the number of channels in each split, which is defined by FLAGS.block_unit_depth

To play with these hyper-parameters, you could either change inside the hyper-parameters.py or use the following commands:

python cifar10_train.py --version='test',num_resnext_blocks=3,cardinality=4

More explanations about the hyper-parameters can be found here

Files included

1. hyper-parameters.py defines the hyper-parameters related to train, ResNeXt structure, data augmentation, etc.

2. cifar10_input.py includes the data I/O, pre-processing of images and data augmentation

3. resNeXt.py is the main body of ResNeXt network

4. cifar10_train.py is responsible for the training and validation

TODO:

1. Train the model and generate the learning curve

2. Data augmentation: cv2 is not compatible w/ tensorflow 1.0.0, so I need to:

   a. Wait for a new version of opencv

   or

   b. Use the queueRunner in tensorflow as data I/O and implement data augmentaion