This Python sample is available only with our Professional plan.
Training Pipeline of Object Detection
This Python script allows you to train a Pytorch Object Detection model with preprocessed HDF5 data and labeled bounding boxes.
Object Detection training result:
checkpoints (models at different training stages)
videos on test dataset
A test 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
each subfolder should contain one or multiple
h5files and their corresponding
a dictionary file named
label_map_dictionary.json, which contains all the detection categories.
To have a quick test of the pipeline, you can simply set the path of training dataset to “toy_problem”. This will train the model on Moving MNIST Dataset.
How to start
To run the script:
python3 train_detection.py /path/to/logging /path/to/dataset
python train_detection.py /path/to/logging /path/to/dataset
To find the full list of options, run:
python3 train_detection.py -h
python train_detection.py -h