PP-YOLOE_plus-L.yaml 1.1 KB

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  1. Global:
  2. model: PP-YOLOE_plus-L
  3. mode: check_dataset # check_dataset/train/evaluate/predict
  4. dataset_dir: "/paddle/dataset/paddlex/det/det_coco_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: 4
  17. epochs_iters: 10
  18. batch_size: 8
  19. learning_rate: 0.0001
  20. pretrain_weight_path: https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-L_obj365_pretrained.pdparams # use object365 pretrained
  21. warmup_steps: 100
  22. resume_path: null
  23. log_interval: 10
  24. eval_interval: 1
  25. Evaluate:
  26. weight_path: "output/best_model/best_model.pdparams"
  27. log_interval: 10
  28. Export:
  29. weight_path: https://paddle-model-ecology.bj.bcebos.com/paddlex/official_pretrained_model/PP-YOLOE_plus-L_pretrained.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/general_object_detection_002.png"
  34. kernel_option:
  35. run_mode: paddle