8. FAQ¶

  • Why are the metrics different for different cards?
  • A: Fleet is the default option for the use of PaddleClas. Each GPU card is taken as a single trainer and deals with different images, which cause the final small difference. Single card evalution is suggested to get the accurate results if you use tools/eval.py. You can also use tools/eval_multi_platform.py to evalute the models on multiple GPU cards, which is also supported on Windows and CPU.
  • Q: Why Mixup or Cutmix is not used even if I have already add the data operation in the configuration file?
  • A: When using Mixup or Cutmix, you also need to add use_mix: True in the configuration file to make it work properly.
  • Q: During evaluation and inference, pretrained model address is assgined, but the weights can not be imported. Why?
  • A: Prefix of the pretrained model is needed. For example, if the pretained weights are located in output/ResNet50_vd/19, with the filename output/ResNet50_vd/19/ppcls.pdparams, then pretrained_model in the configuration file needs to be output/ResNet50_vd/19/ppcls.
  • Q: Why are the metrics 0.3% lower than that shown in the model zoo for EfficientNet series of models?
  • A: Resize method is set as Cubic for EfficientNet(interpolation is set as 2 in OpenCV), while other models are set as Bilinear(interpolation is set as None in OpenCV). Therefore, you need to modify the interpolation explicitly in ResizeImage. Specifically, the following configuration is a demo for EfficientNet.
    batch_size: 16
    num_workers: 4
    file_list: "./dataset/ILSVRC2012/val_list.txt"
    data_dir: "./dataset/ILSVRC2012/"
    shuffle_seed: 0
        - DecodeImage:
            to_rgb: True
            to_np: False
            channel_first: False
        - ResizeImage:
            resize_short: 256
            interpolation: 2
        - CropImage:
            size: 224
        - NormalizeImage:
            scale: 1.0/255.0
            mean: [0.485, 0.456, 0.406]
            std: [0.229, 0.224, 0.225]
            order: ''
        - ToCHWImage:
  • Q: What should I do if I want to transform the weights’ format from pdparams to an earlier version(before Paddle1.7.0), which consists of the scattered files?
  • A: You can use fluid.load to load the pdparams weights and use fluid.io.save_vars to save the weights as scattered files.