HXZhong1997 / detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

Code for Faster-rcnn of Chap04 of Haoxiang's Thesis

Based on Detectron2.

Step1: Preparation

Prepare dataset VOC2007, VOC2012, OVIS in ./datasets

Download ovis_occlusion_no2severe.json to ./datasets/OVIS

Download output/faster-rcnn/model_final.pth and put it at that path.

Prepare the environment as in README of Detectron

Step2: Train mask generation network (net g).

python tools/plain_train_G.py \
--config-file configs/Net-G-no2severe.yaml \
--config-det configs/COCO-Detection/faster_rcnn_R_50_FPN_1x_trained.yaml \
--num-gpus 2 \
OUTPUT_DIR output/netG_lr1e-2-no2svr

Step3: Train Faster RCNN with net g together with the update strategy

python tools/plain_train_net_wG.py \
--config-file configs/faster-rcnn-g/Net-G-lr1e-2-no2svr.yaml \
--config-det configs/faster-rcnn-g/voc_faster_rcnn_R_50_FPN.yaml \
--num-gpus 4 \
OUTPUT_DIR output/voc/fasterrcnn-glr1e_2-no2svr-ui20x5-d3-icassp-gs005-clip \
NET_G.INTERVAL 20 \
NET_G.START_ITER 10000 \
NET_G.UPDATE_START 10000 \
NET_G.UPDATE_INTERVAL 20 \
NET_G.UPDATE_MODE 'icassp' \
NET_G.ONLY_G True \
NET_G.UPDATE_TIMES 5 \
NET_G.DROP 0.3 \
NET_G.MASK_CLIP True \
NET_G.G_STEP 0.05 

Below are the original README file of Detecron2

Detectron2 is Facebook AI Research's next generation library that provides state-of-the-art detection and segmentation algorithms. It is the successor of Detectron and maskrcnn-benchmark. It supports a number of computer vision research projects and production applications in Facebook.

What's New

• Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, etc.
• Used as a library to support building research projects on top of it.
• Models can be exported to TorchScript format or Caffe2 format for deployment.
• It trains much faster.

See our blog post to see more demos and learn about detectron2.

Installation

See installation instructions.

Getting Started

See Getting Started with Detectron2, and the Colab Notebook to learn about basic usage.

Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.

License

Detectron2 is released under the Apache 2.0 license.

Citing Detectron2

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}
}