pyiqa.archs.hypernet_arch¶
HyperNet Metric
- @InProceedings{hyperiqa,
author = {Su, Shaolin and Yan, Qingsen and Zhu, Yu and Zhang, Cheng and Ge, Xin and Sun, Jinqiu and Zhang, Yanning}, title = {Blindly Assess Image Quality in the Wild Guided by a Self-Adaptive Hyper Network}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2020}
}
Ref url: https://github.com/SSL92/hyperIQA Re-implemented by: Chaofeng Chen (https://github.com/chaofengc)
Module Contents¶
- class pyiqa.archs.hypernet_arch.HyperNet(base_model_name='resnet50', num_crop=25, pretrained=True, pretrained_model_path=None, default_mean=[0.485, 0.456, 0.406], default_std=[0.229, 0.224, 0.225])[source]¶
Bases:
torch.nn.ModuleHyperIQA self-adaptive hyper network for no-reference IQA.
- Parameters:
base_model_name (str) – Backbone model name supported by
timm.num_crop (int) – Number of test-time crops for inference averaging.
pretrained (bool) – Whether to load pretrained HyperIQA weights.
pretrained_model_path (str | None) – Optional local checkpoint path.
default_mean (list[float]) – Input normalization mean.
default_std (list[float]) – Input normalization std.
- Reference:
Su, Shaolin, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang. “Blindly assess image quality in the wild guided by a self-adaptive hyper network.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3667-3676. 2020.