OpenMMLab framework to train yolo's on VisDrone dataset
currently only support Object Detection in Images.
download this files.
- trainset (1.44 GB): GoogleDrive
- valset (0.07 GB): GoogleDrive
- testset-dev (0.28 GB): GoogleDrive
please change the names for your split folders to: train, val, test. your data stracture need to be look like this:
root/
|_train
|_images
|_annotations
|_val
|_images
|_annotations
|_test
|_images
|_annotations
git clone https://github.com/eitan159/VisDrone.git
cd VisDrone
pip install -r requirements.txt
creating COCOForamt json files for VisDrone data.
NOTE: I modified the data, I only took samples that has the specific classes that I needed. See preprocess.py for better understanding :)
python VisDrone/preprocess.py --data_root your_path
params
--data_root
: Path for your root dir that contains the data splits dirs
Please change the config files before training Run PP-YOLOE+:
python VisDrone/main.py --data_root your_path
params
--data_root
: path for your data splits directories.
--work_dir
(default exp/): dir name for all your saved ckpt, logs, etc.
--wandb_project
(default VisDrone): wandb project name.
--model_size
(default s): model version of PPYOLOE+ to train. Only supported with s/x
--lr
(default 0.001)
--epochs
(default 30)