V0.2.5-Mixup-iNaturalist2017-Weights

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