senet-keras

KO | EN

gitlite — search

#python #typescript #go #machine-learning #linux #macos #cli #kubernetes #awesome-list #pytorch #security #ios #mcp #open-source

(★ 134)

Naive implementation of SENet in Keras

senet-keras 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 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%`

senet-keras

senet-keras 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

senet-keras

senet-keras 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README