A One-stage Crowd ObjectDetection

YOLO for CrowdDetection

Environment Setting

• Base Enviroment

pip install -r requirements.txt

• Pytorch Install

  check https://pytorch.org/get-started/locally/ for pytorch install

• odcore Install (Providing training, evaluating, inferencing, etc backend)

  https://github.com/NuayHL/odcore

• Note: This is my private Repo

git clone https://github.com/NuayHL/odcore.git

Simple Inferencing

• Downloading Model Weight: YOLOv3_640.pth

  https://drive.google.com/file/d/1-y_pMTb3lMLTr4Clw2TTQ5pYi_g--WeP/view?usp=sharing

• Infer through following command

python infer.py --ckpt-file YOLOv3_640.pth --conf-file cfgs/yolov3 --img imgs/val_1.png

• Replace the image name to infer other images

Model Training

• Download CrowdHuman dataset

  https://www.crowdhuman.org/

• Rearrange the dataset as following structure

CrowdDetection
      |--CrowdHuman
            |--Images_train
            |      |-- All training images
            |--Images_val    
            |      |-- All val images
            |--annotation_train.odgt
            |--annotation_val.odgt

• Run utility/tran_CrowdHuman.py at root path
• Run command

python train.py --conf-file cfgs/yolox --batch-size 32 --workers 8 --device 0 --eval-interval 10 --save-interval 50

Model Evaluation

*CrowdHuman dataset required

python eval.py --conf-file cfgs/yolo_v4 --ckpt-file best_epoch.pth --workers 8 --batch-size 32 --type mip --force-eval

Replace the --ckpt-file and its correspond config yaml file for custom evaluation

If you have complete exp dir under the running_dir (exmple: running_log/rYOLOv4), you can start eval the .pth file (example: best_epoch.pth) with eval.sh

./eval.sh rYOLOv4 best_epoch mip

Evaluation type

• This repo has integrated with 3 evaluation metrics:
  • --type mip: CrowdHuman AP + MR + JI (used as final evaluation)
    • https://github.com/megvii-model/CrowdDetection/tree/master/evaluate
  • --type coco: COCO (optional used during training)
    • https://github.com/cocodataset/cocoapi
  • --type mr: COCO based MR (optional used during training)
    • https://github.com/TencentYoutuResearch/PedestrianDetection-NohNMS/blob/main/detectron2/evaluation/crowdhuman_evaluation.py

Loss visualization

python drawloss.py --loss-log exp_loss.log

• Replace the --loss-log to other _loss.log files to visualize them

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