Easy and Efficient Object Detector

EOD (Easy and Efficient Object Detection) is a general object detection model production framework. It aim on provide two key feature about Object Detection:

• Efficient: we will focus on training VERY HIGH ACCURARY single-shot detection model, and model compress (quantization/sparsity) will be heavy address.
• Easy: easy to use, easy to add new features(backbone/head/neck), easy to deploy.
• Large-Scale Dataset Training Detail

The master branch works with PyTorch 1.8.1. Due to the pytorch version, it can not well support the 30 series graphics card hardware.

pip install -r requirments

Some example scripts are supported in scripts/.

Export eod into ROOT and PYTHONPATH

ROOT=../../
export ROOT=$ROOT
export PYTHONPATH=$ROOT:$PYTHONPATH

Step1: edit meta_file and image_dir of image_reader:

dataset:
  type: coco # dataset type
    kwargs:
      source: train
      meta_file: coco/annotations/instances_train2017.json 
      image_reader:
        type: fs_opencv
        kwargs:
          image_dir: coco/train2017
          color_mode: BGR

Step2: train

python -m eod train --config configs/yolox/yolox_tiny.yaml --nm 1 --ng 8 --launch pytorch 2>&1 | tee log.train

• --config: yamls in configs/
• --nm: machine number
• --ng: gpu number for each machine
• --launch: slurm or pytorch

Step3: fp16, add fp16 setting into runtime config

runtime:
  runner:
    type: fp16

Step1: edit config of evaluating dataset

Step2: test

python -m eod train -e --config configs/yolox/yolox_tiny.yaml --nm 1 --ng 1 --launch pytorch 2>&1 | tee log.test

Step1: add visualizer config in yaml

inference:
  visualizer:
    type: plt
    kwargs:
      class_names: ['__background__', 'person'] # class names
      thresh: 0.5

Step2: inference

python -m eod inference --config configs/yolox/yolox_tiny.yaml --ckpt ckpt_tiny.pth -i imgs -v vis_dir

• --ckpt: model for inferencing
• -i: images directory or single image
• -v: directory saving visualization results

EOD supports mpirun mode to launch task, MPI needs to be installed firstly

# download mpich
wget https://www.mpich.org/static/downloads/3.2.1/mpich-3.2.1.tar.gz # other versions: https://www.mpich.org/static/downloads/

tar -zxvf mpich-3.2.1.tar.gz
cd mpich-3.2.1
./configure  --prefix=/usr/local/mpich-3.2.1
make && make install

Launch task

mpirun -np 8 python -m eod train --config configs/yolox/yolox_tiny.yaml --launch mpi 2>&1 | tee log.train

• Add mpirun -np x; x indicates number of processes
• Mpirun is convenient to debug with pdb
• --launch: mpi

• custom dataset
• rank_dataset

• YOLOX
• YOLOV5
• RetinaNet

• Training on custom data
• Register modules
• EOD developing mode

• Learn about configs
• Datasets
• Augmentations and preprocesses
• Loss functions

• Hooks
• Visualization

• YOLOX
• YOLOv5

Thanks to all past contributors, especially opcoder,