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.