Training of EB Classification Model

This Python script allows you to train a supervised classification model with recorded event streams.

The source code of this sample can be found in <install-prefix>/share/metavision/sdk/ml/python_samples/train_classification when installing Metavision SDK from installer or packages. For other deployment methods, check the page Path of Samples.

Expected Output

Training result:

  • checkpoints (models at different training stages)

  • log files

  • videos on test dataset

A training demo is shown below:

Setup & requirements

To run the script, you need:

  • path to the output folder

  • path to the training dataset:

    • a folder containing 3 sub folders, named train, val, test.

    • each subfolder should contain one or multiple h5 files and their corresponding <h5 filename>_bbox.npy labels. The label is by default set to EventBbox format, except that only the column “ts” and “class_id” are actually used, the rest can be set to a constant dummy value.

    • a dictionary file named label_map_dictionary.json, which contains all the classification categories.

How to start

To run the script:

Linux

python3 train_classification.py /path/to/logging /path/to/dataset

Windows

python train_classification.py /path/to/logging /path/to/dataset

To find the full list of options, run:

Linux

python3 train_classification.py -h

Windows

python train_classification.py -h