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centernet-visdrone

Implement of CenterNet on visdrone2019 dataset. The neck is modified to fpn with deconv.
The entire project has less than 2000 lines of code.

image

Dependencies

  • Python >= 3.6
  • PyTorch >= 1.6
  • opencv-python
  • pycocotools
  • numba

Result on validation set

backbone mAP/flip AP50/flip AP75/flip inference time/flip download
resnet18 24.70/26.26 49.22/51.56 21.33/23.10 0.017s/0.027s google drive
resnet50 28.13/29.46 53.91/55.67 25.36/26.75 0.026s/0.043s google drive
res2net50 29.93/31.05 56.46/58.01 27.47/28.58 0.035s/0.055s google drive

The inference time(pure net time) is measured on a single NVIDIA Titan V GPU.
The resolution of image is 1280*960.
Flip means using flip test.

Data

The data structure would look like:

data/
    visdrone/
        annotations/
        train/
        test/
        val/

Coco format visdrone2019 download from google drive. You can also download the original dataset from http://aiskyeye.com and use the tools in src/tools to convert the format by yourself.

Train

python main.py \
--arch resnet18 \
--min_overlap 0.3 \
--gpus 0,1 \
--num_epochs 100 \
--lr_step 60,80 \
--batch_size 4 \
--lr 0.15625e-4 \
--exp_id <save_dir>

You can specify more parameters in src/opt.py.
Results(weights and logs) will default save to exp/default if you dont specify --exp_id.
Arch supports resnet18,resnet34,resnet50,resnet101,resnet152,res2net50,res2net101.
If you scale batch_size, lr should scale too.

Test

python test.py \
--arch resnet18 \
--gpus 0 \
--load_model <path/to/weight_name.pt> \
--flip_test 

Demo

python demo.py \
--arch resnet18 \
--gpus 0 \
--load_model <path/to/weight_name.pt> \
--image <path/to/your_picture.jpg>

Reference

https://github.com/xingyizhou/CenterNet
https://github.com/yjh0410/CenterNet-Lite
https://github.com/tjiiv-cprg/visdrone-det-toolkit-python
https://blog.csdn.net/mary_0830/article/details/103361664

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Simple implement of CenterNet on VisDrone dataset.

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