ResNet50_face.yaml 973 B

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  1. Global:
  2. model: ResNet50_face
  3. mode: check_dataset # check_dataset/train/evaluate/predict
  4. dataset_dir: "/paddle/dataset/paddlex/cls/face_rec_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: 995
  17. epochs_iters: 25
  18. batch_size: 64
  19. learning_rate: 0.004
  20. pretrain_weight_path: null
  21. warmup_steps: 1
  22. resume_path: null
  23. log_interval: 1
  24. eval_interval: 1
  25. save_interval: 1
  26. Evaluate:
  27. weight_path: "output/best_model/best_model.pdparams"
  28. log_interval: 1
  29. Export:
  30. weight_path: "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/foundation_models/resnet50_face.pdparams"
  31. Predict:
  32. batch_size: 1
  33. model_dir: "output/best_model/inference"
  34. input: "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/face_classification_001.jpg"
  35. kernel_option:
  36. run_mode: paddle