80 lines
3.1 KiB
Markdown
80 lines
3.1 KiB
Markdown
|
## 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](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
|
||
|
|
which covers how to run inference with an
|
||
|
|
existing model, and how to train a builtin model on a custom dataset.
|
||
|
|
|
||
|
|
For more advanced tutorials, refer to our [documentation](https://detectron2.readthedocs.io/tutorials/extend.html).
|
||
|
|
|
||
|
|
|
||
|
|
### Inference Demo with Pre-trained Models
|
||
|
|
|
||
|
|
1. Pick a model and its config file from
|
||
|
|
[model zoo](MODEL_ZOO.md),
|
||
|
|
for example, `mask_rcnn_R_50_FPN_3x.yaml`.
|
||
|
|
2. We provide `demo.py` that is able to run builtin standard models. 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 `--input files` with `--webcam`.
|
||
|
|
* To run __on a video__, replace `--input files` with `--video-input video.mp4`.
|
||
|
|
* To run __on cpu__, add `MODEL.DEVICE cpu` after `--opts`.
|
||
|
|
* To save outputs to a directory (for images) or a file (for webcam or video), use `--output`.
|
||
|
|
|
||
|
|
|
||
|
|
### Training & Evaluation in Command Line
|
||
|
|
|
||
|
|
We provide a script in "tools/{,plain_}train_net.py", that is made to train
|
||
|
|
all the configs provided in detectron2.
|
||
|
|
You may want to use it as a reference to write your own training script.
|
||
|
|
|
||
|
|
To train a model with "train_net.py", first
|
||
|
|
setup the corresponding datasets following
|
||
|
|
[datasets/README.md](./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](https://arxiv.org/abs/1706.02677), 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
|
||
|
|
```
|
||
|
|
|
||
|
|
For most models, CPU training is not supported.
|
||
|
|
|
||
|
|
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 `./train_net.py -h`.
|
||
|
|
|
||
|
|
### Use Detectron2 APIs in Your Code
|
||
|
|
|
||
|
|
See our [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5)
|
||
|
|
to learn how to use detectron2 APIs to:
|
||
|
|
1. run inference with an existing model
|
||
|
|
2. train a builtin model on a custom dataset
|
||
|
|
|
||
|
|
See [detectron2/projects](https://github.com/facebookresearch/detectron2/tree/master/projects)
|
||
|
|
for more ways to build your project on detectron2.
|