# senet-keras

**Repository Path**: xxuffei/senet-keras

## Basic Information

- **Project Name**: senet-keras
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-17
- **Last Updated**: 2024-07-31

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SENet (Keras implementation)

---
**New information**
- We provide a trained SEResNeXt model (training data: cifar10)  
  [Google drive](https://drive.google.com/open?id=1JlHMYe-bZdcNJeaBZAUfdanWyMTuOana)  
  You can try this model in `evaluate-cifar10.ipynb`.
---

Naive implementation of SENet models in Keras.
- Transplanting https://github.com/taki0112/SENet-Tensorflow to Keras.
- Only SE-ResNext at this stage.

## Prerequisites
- nvidia-docker environment

## Environment constuction
- Build a docker image (on the root directory of the repository)
  ```
  $ docker build -t [tag name] -f docker/Dockerfile .
  ```
- Create a container using the image
  ```
  $ nvidia-docker run -it -v $PWD:/work [tag name]
  ```

## Train a model
- Train a model with cifar10 data.
  ```
  (in the container) $ pwd
  /work
  (in the container) $ python train-cifar10.py
  ```

Note that this script is written in an insufficient way; use data generator in consideration of expansion to general image data). The training speed is slow. On a p3.2xlarge instance, it takes about 1.5 days.


## Evaluate the model
- Launch a jupyter notebook.
  ```
  (in the container) $ bash launch_notebook.sh
  ```
- Execute `evaluate-cifar10.ipynb` notebook.

## Results
- Accuracy plot of train/val. <br>
  ![result](https://github.com/yoheikikuta/senet-keras/blob/resource/images/plot-accuracy.png)
- Loss plot of train/val. <br>
  ![result](https://github.com/yoheikikuta/senet-keras/blob/resource/images/plot-loss.png)

- Accuracy for the test data. <br>
  `92.38%`