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A Simple and Versatile Framework for Object Detection and Instance Recognition
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README.md

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

Major Features

  • FP16 training for memory saving and up to 2.5X acceleration
  • Highly scalable distributed training available out of box
  • Full coverage of state-of-the-art models including FasterRCNN, MaskRCNN, CascadeRCNN, RetinaNet, DCNv1/v2, TridentNet, NASFPN , EfficientNet, and Kownledge Distillation
  • Extensive feature set including large batch BN, loss synchronization, automatic BN fusion, soft NMS, multi-scale train/test
  • Modular design for coding-free exploration of new experiment settings
  • Extensive 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 (2019.10.24)

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 dependency
sudo apt update && sudo apt install -y git wget make python3-dev libglib2.0-0 libsm6 libxext6 libxrender-dev unzip

# create conda env
conda create -n simpledet python=3.7
conda activate simpledet

# fetch CUDA environment
conda install cudatoolkit=10.1

# install python dependency
pip install 'matplotlib<3.1' opencv-python pytz

# download and intall pre-built wheel for CUDA 10.1
pip install https://1dv.alarge.space/mxnet_cu101-1.6.0b20190820-py2.py3-none-manylinux1_x86_64.whl

# install pycocotools
pip install 'git+https://github.com/RogerChern/cocoapi.git#subdirectory=PythonAPI'

# install mxnext, a wrapper around MXNet symbolic API
pip install 'git+https://github.com/RogerChern/mxnext#egg=mxnext'

# get simpledet
git clone https://github.com/tusimple/simpledet
cd simpledet
make

# test simpledet installation
mkdir -p experiments/faster_r50v1_fpn_1x
python 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 dir
mkdir -p data/coco/images data/src

# skip this if you have the zip files
wget -c http://images.cocodataset.org/zips/train2017.zip -O data/src/train2017.zip
wget -c http://images.cocodataset.org/zips/val2017.zip -O data/src/val2017.zip
wget -c http://images.cocodataset.org/zips/test2017.zip -O data/src/test2017.zip
wget -c http://images.cocodataset.org/annotations/annotations_trainval2017.zip -O data/src/annotations_trainval2017.zip
wget -c http://images.cocodataset.org/annotations/image_info_test2017.zip -O data/src/image_info_test2017.zip

unzip data/src/train2017.zip -d data/coco/images
unzip data/src/val2017.zip -d data/coco/images
unzip data/src/test2017.zip -d data/coco/images
unzip data/src/annotations_trainval2017.zip -d data/coco
unzip data/src/image_info_test2017.zip -d data/coco

python utils/create_coco_roidb.py --dataset coco --dataset-split train2017
python utils/create_coco_roidb.py --dataset coco --dataset-split val2017
python 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

# train
python detection_train.py --config config/faster_r50v1_fpn_1x.py

# test
python 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.py
detection_test.py
config/
    detection_config.py
core/
    detection_input.py
    detection_metric.py
    detection_module.py
models/
    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.py
experiments/
    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

Faster R-CNN

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, Yi Jiang, Naiyan Wang

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{chen2019simpledet,
  title={SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition},
  author={Chen, Yuntao and and Han, Chenxia and Li, Yanghao and Huang, Zehao and Jiang, Yi and Wang, Naiyan and Zhang, Zhaoxiang},
  journal={Journal of Machine Learning Research(156):1−8, 2019},
  year={2019}
}
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