mmdetection version of TinyBenchmark.

Related tags

Deep LearningTOV_mmdetection
Overview

introduction

This project is an mmdetection version of TinyBenchmark.

TODO list:

  • add TinyPerson dataset and evaluation
  • add crop and merge for image during inference
  • implement RetinaNet and Faster-FPN baseline on TinyPerson
  • add SM/MSM experiment support
  • add visDronePerson dataset support and baseline performance
  • add point localization task for TinyPerson
  • add point localization task for visDronePerson
  • add point localization task for COCO

install and setup

download project

git clone https://github.com/ucas-vg/TOV_mmdetection --recursive

install mmdetection

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab
conda install -c pytorch pytorch=1.5.0 cudatoolkit=10.2 torchvision -y  # (recommand)
# install latest pytorch prebuilt with the default prebuilt CUDA version (usually the latest)
# conda install -c pytorch pytorch torchvision -y

# install the latest mmcv
pip install mmcv-full --user
# install mmdetection

cd TOV_mmdetection
pip uninstall pycocotools
pip install -r requirements/build.txt
pip install -v -e . --user  # or "python setup.py develop"

For more detail, please refer mmdetection install to install mmdetecion.

Quickly Start

to train baseline of TinyPerson, download the mini_annotation of all annotation is enough, which can be downloaded as tiny_set/mini_annotations.tar.gz in Baidu Yun(password:pmcq) / Google Driver.

mkdir data
ln -s $Path of TinyPerson$ data/tiny_set
tar -zxvf data/tiny_set/mini_annotations.tar.gz && mv mini_annotations data/tiny_set/

# run experiment, for other config run, see exp/Baseline_TinyPerson.sh
export GPU=4 && LR=02 && CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=10000 tools/dist_train.sh configs2/TinyPerson/base/faster_rcnn_r50_fpn_1x_TinyPerson640.py $GPU \
  --work-dir ../TOV_mmdetection_cache/work_dir/TinyPerson/Base/faster_rcnn_r50_fpn_1x_TinyPerson640/old640x512_lr0${LR}_1x_${GPU}g/ \
  --cfg-options optimizer.lr=0.${LR}

performance

All train and test on 2080Ti,

  • CUDA10.1/10.2
  • python3.7, cudatookit=10.2, pytorch=1.5, torchvision=0.6

for Faster-FPN, we think the gain compare to TinyBenchmark may come from the cut and merge during inference running time and multi-gpu training.

performance 43.80(2) where 2 means the performance is mean result of running such setting for 2 time.

detector num_gpu $AP_{50}^{tiny}$ script
Faster-FPN 4 48.63(1) exp/Baseline_TinyPerson.sh:exp1.1
Adap RetainaNet 1 43.80(2) exp/Baseline_TinyPerson.sh:exp2.1
Adap RetainaNet 4 44.94(1) exp/Baseline_TinyPerson.sh:exp2.2(clip grad)
Owner
GitHub Repository https://github.com/ucas-vg/TinyBenchmark
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