Performance of defined yolo models

[xignos3108]

Hi, I have tried training yolov6_s, cdpdarknet with coco dataset and yolov5_s with VOC dataset however non of them surpassed AP of 10. I know AP of well trained YOLO family for VOC is around 80. I'm having trouble finding the reason for training of the model. What would be the problem?

Here is the result of the training and the environment. Please help me train the model and sorry for the poor English.

• Results
  | AP | AP50 | AP75 | APs | APm | APl |
  | 6.120 | 15.489 | 3.370 | 1.325 | 5.173 | 7.646 |

• Environment

---

sys.platform linux
Python 3.8.15 (default, Nov 4 2022, 20:59:55) [GCC 11.2.0]
numpy 1.23.5
detectron2 0.5 @/home/monet/anaconda3/envs/yolov6/lib/python3.8/site-packages/detectron2
Compiler GCC 7.3
CUDA compiler CUDA 10.1
detectron2 arch flags 3.7, 5.0, 5.2, 6.0, 6.1, 7.0, 7.5
DETECTRON2_ENV_MODULE
PyTorch 1.8.1+cu101 @/home/monet/anaconda3/envs/yolov6/lib/python3.8/site-packages/torch
PyTorch debug build False
GPU available Yes
GPU 0,1 NVIDIA TITAN RTX (arch=7.5)
CUDA_HOME /usr/local/cuda
Pillow 9.3.0
torchvision 0.9.1+cu101 @/home/monet/anaconda3/envs/yolov6/lib/python3.8/site-packages/torchvision
torchvision arch flags 3.5, 5.0, 6.0, 7.0, 7.5
fvcore 0.1.5.post20220512
iopath 0.1.8
cv2 4.6.0

---

[lucasjinreal]

hello, please using yolox.