Getting Started with Detectron2
This document provides a brief intro of the usage of builtin command-line tools in detectron2.
For a tutorial that involves actual coding with the API, see our Colab Notebook which covers how to run inference with an existing model, and how to train a builtin model on a custom dataset.
Inference Demo with Pre-trained Models
- Pick a model and its config file from
- We provide
demo.pythat is able to demo builtin configs. Run it with:
cd demo/ python demo.py --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml \ --input input1.jpg input2.jpg \ [--other-options] --opts MODEL.WEIGHTS detectron2://COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x/137849600/model_final_f10217.pkl
The configs are made for training, therefore we need to specify
MODEL.WEIGHTS to a model from model zoo for evaluation.
This command will run the inference and show visualizations in an OpenCV window.
For details of the command line arguments, see
demo.py -h or look at its source code
to understand its behavior. Some common arguments are:
- To run on your webcam, replace
- To run on a video, replace
- To run on cpu, add
- To save outputs to a directory (for images) or a file (for webcam or video), use
Training & Evaluation in Command Line
We provide two scripts in "tools/plain_train_net.py" and "tools/train_net.py", that are made to train all the configs provided in detectron2. You may want to use it as a reference to write your own training script.
Compared to "train_net.py", "plain_train_net.py" supports fewer default features. It also includes fewer abstraction, therefore is easier to add custom logic.
To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md, then run:
cd tools/ ./train_net.py --num-gpus 8 \ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml
The configs are made for 8-GPU training. To train on 1 GPU, you may need to change some parameters, e.g.:
./train_net.py \ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \ --num-gpus 1 SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025
To evaluate a model's performance, use
./train_net.py \ --config-file ../configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml \ --eval-only MODEL.WEIGHTS /path/to/checkpoint_file
For more options, see
Use Detectron2 APIs in Your Code
See our Colab Notebook to learn how to use detectron2 APIs to:
- run inference with an existing model
- train a builtin model on a custom dataset
See detectron2/projects for more ways to build your project on detectron2.