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https://github.com/taki0112/ResNeXt-Tensorflow
Simple Tensorflow implementation of ResNeXt using Cifar10
https://github.com/taki0112/ResNeXt-Tensorflow
Last synced: about 1 month ago
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Simple Tensorflow implementation of ResNeXt using Cifar10
- Host: GitHub
- URL: https://github.com/taki0112/ResNeXt-Tensorflow
- Owner: taki0112
- License: mit
- Created: 2017-08-23T06:16:24.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-14T04:35:00.000Z (over 6 years ago)
- Last Synced: 2024-08-01T22:49:56.546Z (4 months ago)
- Language: Python
- Homepage:
- Size: 278 KB
- Stars: 158
- Watchers: 9
- Forks: 59
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-image-classification - unofficial-tensorflow : https://github.com/taki0112/ResNeXt-Tensorflow
- awesome-image-classification - unofficial-tensorflow : https://github.com/taki0112/ResNeXt-Tensorflow
README
# ResNeXt-Tensorflow
Tensorflow implementation of [ResNeXt](https://arxiv.org/abs/1611.05431) using **Cifar10**
If you want to see the ***original author's code***, please refer to this [link](https://github.com/facebookresearch/ResNeXt)
## Requirements
* Tensorflow 1.x
* Python 3.x
* tflearn (If you are easy to use ***global average pooling***, you should install ***tflearn***)
## Issue
* If not enough GPU memory, Please edit the code
```python
with tf.Session() as sess : NO
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess : OK
```
## Compare Architecture
### ResNet
### ResNeXt
* I implemented (b)
* (b) is ***split + transform(bottleneck) + concatenate + transition + merge***
## Idea
### What is the "split" ?
```python
def split_layer(self, input_x, stride, layer_name):
with tf.name_scope(layer_name) :
layers_split = list()
for i in range(cardinality) :
splits = self.transform_layer(input_x, stride=stride, scope=layer_name + '_splitN_' + str(i))
layers_split.append(splits)
return Concatenation(layers_split)
```
* ***Cardinality*** means how many times you want to split.
### What is the "transform" ?
```python
def transform_layer(self, x, stride, scope):
with tf.name_scope(scope) :
x = conv_layer(x, filter=depth, kernel=[1,1], stride=stride, layer_name=scope+'_conv1')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
x = Relu(x)
x = conv_layer(x, filter=depth, kernel=[3,3], stride=1, layer_name=scope+'_conv2')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch2')
x = Relu(x)
return x
```
### What is the "transition" ?
```python
def transition_layer(self, x, out_dim, scope):
with tf.name_scope(scope):
x = conv_layer(x, filter=out_dim, kernel=[1,1], stride=1, layer_name=scope+'_conv1')
x = Batch_Normalization(x, training=self.training, scope=scope+'_batch1')
return x
````
## Comapre Results (ResNet, DenseNet, ResNeXt)
## Related works
* [DenseNet-Tensorflow](https://github.com/taki0112/Densenet-Tensorflow)
* [SENet-Tensorflow](https://github.com/taki0112/SENet-Tensorflow)
* [ResNet-Tensorflow](https://github.com/taki0112/ResNet-Tensorflow)
## References
* [Classification Datasets Results](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html)
## Author
Junho Kim