PP-YOLOE-R-L.yaml 988 B

12345678910111213141516171819202122232425262728293031323334353637383940
  1. Global:
  2. model: PP-YOLOE-R-L
  3. mode: check_dataset # check_dataset/train/evaluate/predict
  4. dataset_dir: "dataset/rdet_dota_examples"
  5. device: gpu:0,1,2,3
  6. output: "output"
  7. CheckDataset:
  8. convert:
  9. enable: False
  10. src_dataset_type: null
  11. split:
  12. enable: False
  13. train_percent: null
  14. val_percent: null
  15. Train:
  16. num_classes: 15
  17. epochs_iters: 10
  18. batch_size: 1
  19. learning_rate: 0.128
  20. pretrain_weight_path: https://paddledet.bj.bcebos.com/models/pretrained/CSPResNetb_l_pretrained.pdparams
  21. warmup_steps: 100
  22. resume_path: null
  23. log_interval: 10
  24. eval_interval: 5
  25. Evaluate:
  26. weight_path: "output/best_model/best_model.pdparams"
  27. log_interval: 10
  28. Export:
  29. weight_path: https://paddledet.bj.bcebos.com/models/ppyoloe_r_crn_l_3x_dota.pdparams
  30. Predict:
  31. batch_size: 1
  32. model_dir: "output/best_model/inference"
  33. input: "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/rotated_object_detection_001.png"
  34. kernel_option:
  35. run_mode: paddle