| 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. | |