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.
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.
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》