YOLOv8 implementation without DFL using PyTorch

Installation

conda create -n YOLO python=3.8
conda activate YOLO
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts
pip install opencv-python==4.5.5.64
pip install PyYAML
pip install tqdm

Train

• Configure your dataset path in main.py for training
• Run bash main.sh $ --train for training, $ is number of GPUs

Test

• Configure your dataset path in main.py for testing
• Run python main.py --test for testing

Results

Version	Epochs	Box mAP	Download
v8_n	500	37.0	model
v8_n*	500	37.3	-
v8_s	500	-	-
v8_s*	500	44.9	-
v8_m	500	-	-
v8_m*	500	50.2	-
v8_l	500	-	-
v8_l*	500	52.9	-
v8_x	500	-	-
v8_x*	500	53.9	-

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.370
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.529
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.401
Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.188
Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.408
Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.522
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.315
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.529
Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.585
Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.371
Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.646
Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.764

• * means that it is from original repository, see reference
• In the official YOLOv8 code, mask annotation information is used, which leads to higher performance

Dataset structure

├── COCO 
    ├── images
        ├── train2017
            ├── 1111.jpg
            ├── 2222.jpg
        ├── val2017
            ├── 1111.jpg
            ├── 2222.jpg
    ├── labels
        ├── train2017
            ├── 1111.txt
            ├── 2222.txt
        ├── val2017
            ├── 1111.txt
            ├── 2222.txt

Reference

• https://github.com/ultralytics/yolov5
• https://github.com/ultralytics/ultralytics