Naive implementation of SENet models in Keras.
- Transplanting https://github.com/taki0112/SENet-Tensorflow to Keras.
- Only SE-ResNext at this stage.
- nvidia-docker environment
- 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 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.
- Launch a jupyter notebook.
(in the container) $ bash launch_notebook.sh
- Execute
evaluate-cifar10.ipynb
notebook.