Skip to content

Latest commit

 

History

History
190 lines (152 loc) · 6.65 KB

GetStart.md

File metadata and controls

190 lines (152 loc) · 6.65 KB
Raw

Getting Started

This page provides basic usage about yolov5-obb. For installation instructions, please see install.md.

Train a model

1. Prepare custom dataset files

1.1 Make sure the labels format is [poly classname diffcult], e.g., You can set diffcult=0

  x1      y1       x2        y2       x3       y3       x4       y4       classname     diffcult

1686.0   1517.0   1695.0   1511.0   1711.0   1535.0   1700.0   1541.0   large-vehicle      1

image

1.2 Split the dataset.

cd yolov5_obb
python DOTA_devkit/ImgSplit_multi_process.py

or Use the orignal dataset.

cd yolov5_obb

1.3 Make sure your dataset structure same as:

parent
├── yolov5
└── datasets
    └── DOTAv1.5
        ├── train_split_rate1.0_subsize1024_gap200
        ├── train_split_rate1.0_subsize1024_gap200
        └── test_split_rate1.0_subsize1024_gap200
            ├── images
                 |────1.jpg
                 |────...
                 └────10000.jpg
            ├── labelTxt
                 |────1.txt
                 |────...
                 └────10000.txt

Note:

  • DOTA is a high resolution image dataset, so it needs to be splited before training/testing to get better performance.

2. Train

2.1 Train with specified GPUs. (for example with GPU=3)

python train.py --device 3

2.2 Train with multiple(4) GPUs. (DDP Mode)

python -m torch.distributed.launch --nproc_per_node 4 train.py --device 0,1,2,3

2.3 Train the orignal dataset demo.

python train.py \
  --weights 'weights/yolov5n_s_m_l_x.pt' \
  --data 'data/yolov5obb_demo.yaml' \
  --hyp 'data/hyps/obb/hyp.finetune_dota.yaml' \
  --epochs 10 \
  --batch-size 1 \
  --img 1024 \
  --device 0

2.4 Train the splited dataset demo.

python train.py \
  --weights 'weights/yolov5n_s_m_l_x.pt' \
  --data 'data/yolov5obb_demo_split.yaml' \
  --hyp 'data/hyps/obb/hyp.finetune_dota.yaml' \
  --epochs 10 \
  --batch-size 2 \
  --img 1024 \
  --device 0

Inferenece with pretrained models. (Splited Dataset)

This repo provides the validation/testing scripts to evaluate the trained model.

Examples:

Assume that you have already downloaded the checkpoints to runs/train/yolov5m_csl_dotav1.5/weights.

  1. Test yolov5-obb with single GPU. Get the HBB metrics.
python val.py \
 --data 'data/yolov5obb_demo_split.yaml' \
 --weights 'runs/train/yolov5m_csl_dotav1.5/weights/best.pt' \
 --batch-size 2 --img 1024 --task 'val' --device 0 --save-json --name 'obb_demo_split'

               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|██████████| 3/3 [00:02<00:00,  1.09it/s]                                        
                 all          6         68      0.921      0.914      0.966      0.776
               plane          6         16      0.946          1      0.995      0.934
       small-vehicle          6         35      0.928      0.741      0.916      0.599
       large-vehicle          6         17       0.89          1      0.986      0.793
Speed: .................................................... per image at shape (2, 3, 1024, 1024)
...
Evaluating pycocotools mAP... saving runs/val/obb_demo_split/best_obb_predictions.json...
---------------------The hbb and obb results has been saved in json file-----------------------
  1. Parse the results. Get the poly format results.
python tools/TestJson2VocClassTxt.py --json_path 'runs/val/obb_demo_split/best_obb_predictions.json' --save_path 'runs/val/obb_demo_split/obb_predictions_Txt'
  1. Merge the results. (If you split your dataset)
python DOTA_devkit/ResultMerge_multi_process.py \
    --scrpath 'runs/val/obb_demo_split/obb_predictions_Txt' \
    --dstpath 'runs/val/obb_demo_split/obb_predictions_Txt_Merged'
  1. Get the OBB metrics
python DOTA_devkit/dota_evaluation_task1.py \
    --detpath 'runs/val/obb_demo_split/obb_predictions_Txt_Merged/Task1_{:s}.txt' \
    --annopath 'dataset/dataset_demo/labelTxt/{:s}.txt' \
    --imagesetfile 'dataset/dataset_demo/imgnamefile.txt'

...
map: 0.6666666666666669
classaps:  [100.   0. 100.]

Inferenece with pretrained models. (Original Dataset)

We provide the validation/testing scripts to evaluate the trained model.

Examples:

Assume that you have already downloaded the checkpoints to runs/train/yolov5m_csl_dotav1.5/weights.

  1. Test yolov5-obb with single GPU. Get the HBB metrics.
python val.py \
 --data 'data/yolov5obb_demo.yaml' \
 --weights 'runs/train/yolov5m_csl_dotav1.5/weights/best.pt' \
 --batch-size 1 --img 2048 --task 'val' --device 0 --save-json --name 'obb_demo'

               Class     Images     Labels          P          R     [email protected] [email protected]:.95: 100%|██████████| 1/1 [00:00<00:00,  1.98it/s]                                        
                 all          1         56       0.97       0.85      0.953      0.752
               plane          1         11          1          1      0.995      0.944
       small-vehicle          1         34          1      0.641      0.889      0.535
       large-vehicle          1         11       0.91      0.909      0.976      0.777
Speed: .................................................... per image at shape (1, 3, 2048, 2048)
...
Evaluating pycocotools mAP... saving runs/val/obb_demo/best_obb_predictions.json...
---------------------The hbb and obb results has been saved in json file-----------------------
  1. Parse the results. Get the poly format results.
python tools/TestJson2VocClassTxt.py --json_path 'runs/val/obb_demo/best_obb_predictions.json' --save_path 'runs/val/obb_demo/obb_predictions_Txt'
  1. Get the OBB metrics
python DOTA_devkit/dota_evaluation_task1.py \
    --detpath 'runs/val/obb_demo/obb_predictions_Txt/Task1_{:s}.txt' \
    --annopath 'dataset/dataset_demo/labelTxt/{:s}.txt' \
    --imagesetfile 'dataset/dataset_demo/imgnamefile.txt'

...
map: 0.6666666666666669
classaps:  [100.   0. 100.]

Run inference on images, videos, directories, streams, etc. Then save the detection file.

  1. image demo
python detect.py --weights 'runs/train/yolov5m_csl_dotav1.5/weights/best.pt' \
  --source 'dataset/dataset_demo/images/' \
  --img 2048 --device 0 --conf-thres 0.25 --iou-thres 0.2 --hide-labels --hide-conf

*If you want to evaluate the result on DOTA test-dev, please zip the poly format results files and submit it to the evaluation server.