Unverified Commit 17e1ec4e authored by Vasilis Vryniotis's avatar Vasilis Vryniotis Committed by GitHub
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Replace top-X error with Acc@X and fix metric discrepancies. (#3360)

parent ced96a0c
...@@ -115,38 +115,40 @@ or `these experiments <https://github.com/pytorch/vision/pull/1965>`_. ...@@ -115,38 +115,40 @@ or `these experiments <https://github.com/pytorch/vision/pull/1965>`_.
ImageNet 1-crop error rates (224x224) ImageNet 1-crop error rates (224x224)
================================ ============= ============= ================================ ============= =============
Network Top-1 error Top-5 error Model Acc@1 Acc@5
================================ ============= ============= ================================ ============= =============
AlexNet 43.45 20.91 AlexNet 56.522 79.066
VGG-11 30.98 11.37 VGG-11 69.020 88.628
VGG-13 30.07 10.75 VGG-13 69.928 89.246
VGG-16 28.41 9.62 VGG-16 71.592 90.382
VGG-19 27.62 9.12 VGG-19 72.376 90.876
VGG-11 with batch normalization 29.62 10.19 VGG-11 with batch normalization 70.370 89.810
VGG-13 with batch normalization 28.45 9.63 VGG-13 with batch normalization 71.586 90.374
VGG-16 with batch normalization 26.63 8.50 VGG-16 with batch normalization 73.360 91.516
VGG-19 with batch normalization 25.76 8.15 VGG-19 with batch normalization 74.218 91.842
ResNet-18 30.24 10.92 ResNet-18 69.758 89.078
ResNet-34 26.70 8.58 ResNet-34 73.314 91.420
ResNet-50 23.85 7.13 ResNet-50 76.130 92.862
ResNet-101 22.63 6.44 ResNet-101 77.374 93.546
ResNet-152 21.69 5.94 ResNet-152 78.312 94.046
SqueezeNet 1.0 41.90 19.58 SqueezeNet 1.0 58.092 80.420
SqueezeNet 1.1 41.81 19.38 SqueezeNet 1.1 58.178 80.624
Densenet-121 25.35 7.83 Densenet-121 74.434 91.972
Densenet-169 24.00 7.00 Densenet-169 75.600 92.806
Densenet-201 22.80 6.43 Densenet-201 76.896 93.370
Densenet-161 22.35 6.20 Densenet-161 77.138 93.560
Inception v3 22.55 6.44 Inception v3 77.294 93.450
GoogleNet 30.22 10.47 GoogleNet 69.778 89.530
ShuffleNet V2 30.64 11.68 ShuffleNet V2 x1.0 69.362 88.316
MobileNet V2 28.12 9.71 ShuffleNet V2 x0.5 60.552 81.746
MobileNet V3 Large 25.96 8.66 MobileNet V2 71.878 90.286
ResNeXt-50-32x4d 22.38 6.30 MobileNet V3 Large 74.042 91.340
ResNeXt-101-32x8d 20.69 5.47 ResNeXt-50-32x4d 77.618 93.698
Wide ResNet-50-2 21.49 5.91 ResNeXt-101-32x8d 79.312 94.526
Wide ResNet-101-2 21.16 5.72 Wide ResNet-50-2 78.468 94.086
MNASNet 1.0 26.49 8.456 Wide ResNet-101-2 78.848 94.284
MNASNet 1.0 73.456 91.510
MNASNet 0.5 67.734 87.490
================================ ============= ============= ================================ ============= =============
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