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Codes for the AAAI 2023 paper (Oral) "Efficient Mirror Detection via Multi-level Heterogeneous Learning" https://arxiv.org/pdf/2211.15644v1.pdf

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Efficient Mirror Detection via Multi-level Heterogeneous Learning (AAAI23, ORAL)

Authors: Ruozhen He, Jiaying Lin, and Rynson Lau [Paper Link]

HetNet

Dataset

  • Download the PMD dataset at here and MSD dataset at here.

Experimental Results

  • We provide HetNet training weights on MSD and PMD presented in the papers.
  • We provide HetNet testing maps on MSD and PMD presented in the papers.

Evaluation

Code

Pretrained weights

  • The pretrained weight can be found here: ResNeXt-101.
  • Put the downloaded weights under ./resnext/.

Train

  • Download the dataset and pretrained model.
  • Modify the path in train.py.
  • Run python train.py.

Test and Evaluate

  • Modify the path and filename.
  • Run python test.py.
  • Run python eval.py.
  • The efficiency evaluation is supported by the ptflops toolkit. To evaluate the FLOPs and Parameters, run python eval_complexity.py.

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Codes for the AAAI 2023 paper (Oral) "Efficient Mirror Detection via Multi-level Heterogeneous Learning" https://arxiv.org/pdf/2211.15644v1.pdf

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