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](../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.