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V0.2.5-Mixup-iNaturalist2017-Weights
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@Lupin1998 Lupin1998 released this 19 Aug 14:01
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mixup-inat2017-weights
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A collection of weights and logs for mixup classification benchmark on iNaturalist-2017 (download, config). You can download all files from Baidu Cloud: iNaturalist-2017 (1e7w).

  • All compared methods adopt ResNet-18/50 and ResNeXt-101 (32x4d) architectures and are trained 100 epochs using the PyTorch training recipe. The training and testing image size is 224 with the CenterCrop ratio of 0.85. We search $\alpha$ in $Beta(\alpha, \alpha)$ for all compared methods.
  • The median of top-1 accuracy in the last 5 training epochs is reported for ResNet variants.
  • Visualization of mixed samples from AutoMix and SAMix are provided in zip files. [2022-08-22] Update MixBlock keys in AutoMix and SAMix checkpoints.
  • Test pre-trained weights with tools/dist_test.sh or fine-tune pre-trained models tools/dist_train.sh with --load_checkpoint.

Mixup Classification Benchmark on iNaturalist-2017

Backbones ResNet-18 top-1 ResNet-50 top-1 ResNeXt-101 top-1
Vanilla 51.79 60.23 63.70
MixUp [ICLR'2018] 51.40 61.22 66.27
CutMix [ICCV'2019] 51.24 62.34 67.59
ManifoldMix [ICML'2019] 51.83 61.47 66.08
SaliencyMix [ICLR'2021] 51.29 62.51 67.20
FMix [Arixv'2020] 52.01 61.90 66.64
PuzzleMix [ICML'2020] - 62.66 67.72
ResizeMix [Arixv'2020] 51.21 62.29 66.82
AutoMix [ECCV'2022] 52.84 63.08 68.03
SAMix [Arxiv'2021] 53.42 63.32 68.26
Assets 62