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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.
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