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pytorch implementation for paper, towards realistic predictors
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This repo constains the pytorch implementation for the paper Towards Realistic Predictors on ECCV2018.


  • python 3.6
  • pytorch = 0.4
  • other common modules


Since our code is not an end-to-end trainable model (for a improved new version, please turn to an end to end implementation for realistic predictors), please run

to get the hardness predictor after replacing 'dataset' and 'net' with 'imagenet' or 'indoor' and 'res' or 'vgg'.

Then run

to get realistic predictors.

We used the custom data load form, so before training, please first make a train and test sample list. Each item is formed as

imagepath target index


For questions, feel free to reach

Pei Wang:
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