SimpleDet - 用于对象检测和实例识别的简单通用框架

SimpleDet - 用于对象检测和实例识别的简单通用框架

SimpleDet - A Simple and Versatile Framework for Object Detection and Instance Recognition

Major Features

FP16 training for memory saving and up to 2.5X accelerationHighly scalable distributed training available out of boxFull coverage of state-of-the-art models including FasterRCNN, MaskRCNN, CascadeRCNN, RetinaNet, DCNv1/v2, TridentNet, NASFPN , EfficientNet, and Kownledge DistillationExtensive feature set including large batch BN, loss synchronization, automatic BN fusion, soft NMS, multi-scale train/testModular design for coding-free exploration of new experiment settingsExtensive documentations including annotated config, Fintuning Guide

Recent Updates

Add RPN test (2019.05.28)Add NASFPN (2019.06.04)Add new ResNetV1b baselines from GluonCV (2019.06.07)Add Cascade R-CNN with FPN backbone (2019.06.11)Speed up FPN up to 70% (2019.06.16)Update NASFPN to include larger models (2019.07.01)Automatic BN fusion for fixed BN training, saving up to 50% GPU memory (2019.07.04)Speed up MaskRCNN by 80% (2019.07.23)Update MaskRCNN baselines (2019.07.25)Add EfficientNet and DCN (2019.08.06)Add python wheel for easy local installation (2019.08.20)Add FitNet based Knowledge Distill (2019.08.27)Add SE and train from scratch (2019.08.30)Add a lot of docs (2019.09.03)Add support for INT8 training(contributed by Xiaotao Chen & Jingqiu Zhou) (2019.10.24)Add support for FCOS(contributed by Zhen Wei) (2019.11)Add support for Mask Scoring RCNN(contributed by Zehui Chen) (2019.12)Add support for RepPoints(contributed by Bo Ke) (2020.02)Add support for FreeAnchor (2020.03)Add support for Feature Pyramid Grids & PAFPN (2020.06)

Setup

All-in-one Script

We provide a setup script for install simpledet and preppare the coco dataset. If you use this script, you can skip to the Quick Start.

Install

We provide a conda installation here for Debian/Ubuntu system. To use a pre-built docker or singularity images, please refer to INSTALL.md for more information.

# install dependencysudo apt update && sudo apt install -y git wget make python3-dev libglib2.0-0 libsm6 libxext6 libxrender-dev unzip# create conda envconda create -n simpledet python=3.7conda activate simpledet# fetch CUDA environmentconda install cudatoolkit=10.1# install python dependencypip install 'matplotlib<3.1' opencv-python pytz# download and intall pre-built wheel for CUDA 10.1pip install https://1dv.aflat-/mxnet_cu101-1.6.0b20191214-py2.py3-none-manylinux1_x86_64.whl# install pycocotoolspip install 'git+https://github.com/RogerChern/cocoapi.git#subdirectory=PythonAPI'# install mxnext, a wrapper around MXNet symbolic APIpip install 'git+https://github.com/RogerChern/mxnext#egg=mxnext'# get simpledetgit clone https://github.com/tusimple/simpledetcd simpledetmake# test simpledet installationmkdir -p experiments/faster_r50v1_fpn_1xpython detection_infer_speed.py --config config/faster_r50v1_fpn_1x.py --shape 800 1333

If the last line execute successfully, the average running speed of Faster R-CNN R-50 FPN will be reported. And you have successfuly setup SimpleDet. Now you can head up to the next section to prepare your dataset.

Preparing Data

We provide a step by step preparation for the COCO dataset below.

cd simpledet# make data dirmkdir -p data/coco/images data/src# skip this if you have the zip fileswget -c http://images.cocodataset.org/zips/train2017.zip -O data/src/train2017.zipwget -c http://images.cocodataset.org/zips/val2017.zip -O data/src/val2017.zipwget -c http://images.cocodataset.org/zips/test2017.zip -O data/src/test2017.zipwget -c http://images.cocodataset.org/annotations/annotations_trainval2017.zip -O data/src/annotations_trainval2017.zipwget -c http://images.cocodataset.org/annotations/image_info_test2017.zip -O data/src/image_info_test2017.zipunzip data/src/train2017.zip -d data/coco/imagesunzip data/src/val2017.zip -d data/coco/imagesunzip data/src/test2017.zip -d data/coco/imagesunzip data/src/annotations_trainval2017.zip -d data/cocounzip data/src/image_info_test2017.zip -d data/cocopython utils/create_coco_roidb.py --dataset coco --dataset-split train2017python utils/create_coco_roidb.py --dataset coco --dataset-split val2017python utils/create_coco_roidb.py --dataset coco --dataset-split test-dev2017

For other datasets or your own data, please check DATASET.md for more details.

Quick Start

# trainpython detection_train.py --config config/faster_r50v1_fpn_1x.py# testpython detection_test.py --config config/faster_r50v1_fpn_1x.py

Finetune

Please check FINTUNE.md

Model Zoo

Please refer to MODEL_ZOO.md for available models

Distributed Training

Please refer to DISTRIBUTED.md

Project Organization

Code Structure

detection_train.pydetection_test.pyconfig/ detection_config.pycore/ detection_input.py detection_metric.py detection_module.pymodels/ FPN/ tridentnet/ maskrcnn/ cascade_rcnn/ retinanet/mxnext/symbol/ builder.py

Config

Everything is configurable from the config file, all the changes should be out of source.

Experiments

One experiment is a directory in experiments folder with the same name as the config file.

E.g. r50_fixbn_1x.py is the name of a config file

config/ r50_fixbn_1x.pyexperiments/ r50_fixbn_1x/ checkpoint.params log.txt coco_minival2014_result.json

Models

The models directory contains SOTA models implemented in SimpletDet.

How is Faster R-CNN built

Simpledet supports many popular detection methods and here we take Faster R-CNN as a typical example to show how a detector is built.

Preprocessing. The preprocessing methods of the detector is implemented through DetectionAugmentation. Image/bbox-related preprocessing, such as Norm2DImage and Resize2DImageBbox.Anchor generator AnchorTarget2D, which generates anchors and corresponding anchor targets for training RPN. Network Structure. The training and testing symbols of Faster-RCNN detector is defined in FasterRcnn. The key components are listed as follow: Backbone. Backbone provides interfaces to build backbone networks, e.g. ResNet and ResNext.Neck. Neck provides interfaces to build complementary feature extraction layers for backbone networks, e.g. FPNNeck builds Top-down pathway for Feature Pyramid Network.RPN head. RpnHead aims to build classification and regression layers to generate proposal outputs for RPN. Meanwhile, it also provides interplace to generate sampled proposals for the subsequent R-CNN.Roi Extractor. RoiExtractor extracts features for each roi (proposal) based on the R-CNN features generated by Backbone and Neck.Bounding Box Head. BboxHead builds the R-CNN layers for proposal refinement.

How to build a custom detector

The flexibility of simpledet framework makes it easy to build different detectors. We take TridentNet as an example to demonstrate how to build a custom detector simply based on the Faster R-CNN framework.

Preprocessing. The additional processing methods could be provided accordingly by inheriting from DetectionAugmentation. In TridentNet, a new TridentAnchorTarget2D is implemented to generate anchors for multiple branches and filter anchors for scale-aware training scheme. Network Structure. The new network structure could be constructed easily for a custom detector by modifying some required components as needed and For TridentNet, we build trident blocks in the Backbone according to the descriptions in the paper. We also provide a TridentRpnHead to generate filtered proposals in RPN to implement the scale-aware scheme. Other components are shared the same with original Faster-RCNN.

Contributors

Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Naiyan Wang, Xiaotao Chen, Jingqiu Zhou, Zhen Wei, Zehui Chen, Zhaoxiang Zhang, Bo Ke

License and Citation

This project is release under the Apache 2.0 license for non-commercial usage. For commercial usage, please contact us for another license.

If you find our project helpful, please consider cite our tech report.

@article{JMLR:v20:19-205, author = {Yuntao Chen and Chenxia Han and Yanghao Li and Zehao Huang and Yi Jiang and Naiyan Wang and Zhaoxiang Zhang}, title = {SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition}, journal = {Journal of Machine Learning Research}, year = {2019}, volume = {20}, number = {156}, pages = {1-8}, url = {http://jmlr.org/papers/v20/19-205.html}}

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