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Replace top-X error with Acc@X and fix metric discrepancies #3360

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Feb 8, 2021
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Replace top-X error with Acc@X and fix metric discrepancies #3360

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64 changes: 33 additions & 31 deletions docs/source/models.rst
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Expand Up @@ -115,38 +115,40 @@ or `these experiments <https://github.com/pytorch/vision/pull/1965>`_.
ImageNet 1-crop error rates (224x224)

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