In this repo, you can train Faster RCNN with PAA (applied to RPN):
python tools/train_net.py \
--config-file configs/COCO-Detection/faster_rcnn_R_50_FPN_iou_paa_1x.yaml
Reults:
| Model | AP (minival) | AP50 | AP75 | APs | APm | APl |
|---|---|---|---|---|---|---|
| Faster_R_50_FPN_1x | 37.989 | 58.810 | 41.314 | 22.361 | 41.522 | 49.584 |
| Faster_R_50_FPN_PAA_1x | 39.292 | 60.019 | 42.567 | 22.650 | 43.170 | 51.875 |
This repo is based on an old version of Detectron2, so the implementation of PAA is not compatible with the latest Detecton2.
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.
- It is powered by the PyTorch deep learning framework.
- Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
- Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.
- It trains much faster.
See our blog post to see more demos and learn about detectron2.
See INSTALL.md.
See GETTING_STARTED.md, or the Colab Notebook.
Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.
We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.
Detectron2 is released under the Apache 2.0 license.
If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}