This directory contains a few example scripts that demonstrate features of detectron2.

• train_net.py

An example training script that's made to train builtin models of detectron2.

For usage, see GETTING_STARTED.md.

• plain_train_net.py

Similar to train_net.py, but implements a training loop instead of using Trainer. This script includes fewer features but it may be more friendly to hackers.

• benchmark.py

Benchmark the training speed, inference speed or data loading speed of a given config.

Usage:

python benchmark.py --config-file config.yaml --task train/eval/data [optional DDP flags]

• analyze_model.py

Analyze FLOPs, parameters, activations of a detectron2 model. See its --help for usage.

• visualize_json_results.py

Visualize the json instance detection/segmentation results dumped by COCOEvalutor or LVISEvaluator

Usage:

python visualize_json_results.py --input x.json --output dir/ --dataset coco_2017_val

If not using a builtin dataset, you'll need your own script or modify this script.

• visualize_data.py

Visualize ground truth raw annotations or training data (after preprocessing/augmentations).

Usage:

python visualize_data.py --config-file config.yaml --source annotation/dataloader --output-dir dir/ [--show]

NOTE: the script does not stop by itself when using --source dataloader because a training dataloader is usually infinite.