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Cholec80

The proposed network is implemented in PyTorch using a single Tesla V100-DGXS-32GB GPU of an NVIDIA DGX station. For the ResNet-50 part, PyTorch default ImageNet pretrained parameters are loaded for transfer learning and fine-tuned for 100 epochs on Cholec80 and Sacrocolpopexy respectively.

Data and Resources

Cite this as

Jiale Zhang, Wenfeng Huang, Xiangyun Liao, Qiong Wang (2024). Dataset: Cholec80. https://doi.org/10.57702/g9q07m7i

DOI retrieved: December 16, 2024

Additional Info

Field Value
Created December 16, 2024
Last update December 16, 2024
Defined In https://doi.org/10.48550/arXiv.2009.13411
Citation
  • https://doi.org/10.48550/arXiv.2208.00902
  • https://doi.org/10.48550/arXiv.2104.11008
  • https://doi.org/10.48550/arXiv.1806.05573
  • https://doi.org/10.1007/s11548-019-01958-6
  • https://doi.org/10.48550/arXiv.2310.17209
  • https://doi.org/10.48550/arXiv.2208.03824
  • https://doi.org/10.48550/arXiv.2205.09292
Author Jiale Zhang
More Authors
Wenfeng Huang
Xiangyun Liao
Qiong Wang
Homepage https://github.com/jlzcode/PFAN