Imagenet Experiments
Installation
Download the ImageNet dataset and move validation images to labeled subfolders,
to do this, you can use the following script:
https://raw.githubusercontent.com/soumith/imagenetloader.torch/master/valprep.sh
so that you have a local ./data folder
with train and val sub-folders, which contain class folders:
data
├── train
│ ├── n01440764
│ ├── n01443537
...
│ └── n15075141
└── val
├── n01440764
├── n01443537
...
└── n15075141
Recommended way to run is via docker,
this requires https://github.com/NVIDIA/nvidia-docker.
But you can also install everything without Docker, check Dockerfile.
Build docker image:
docker build -t imagenet .
Example run command,
assuming imagenet data folder is in local ./data folder,
and mounting code folders,
so that you don't have to re-build the image too often:
./run --arch resnet34 --workers 4
All runs are recorded into ./mlruns folder, you can use ./mlflow-ui
to monitor them (use ./mlflow-ui --host 0.0.0.0 to expose it).
Note that 5000 port is hardcoded in ./mlflow-ui.
License
License is BSD-3.
Initial version of the train script is based on https://github.com/pytorch/examples/tree/master/imagenet