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Running
on
Zero
This directory contains a few scripts that use 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] | |
``` | |
* `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. | |