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Models/CeiT-T

CeiT-T

Reported on 7 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision7 results

Image ClassificationonStanford Cars
Accuracy· 2021-03-22
90.5
best: 96.868 (efficient adaptive ensembling)
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816
Image ClassificationonCIFAR-10
Percentage correct· 2021-03-22
98.5
best: 99.5 (ViT-H/14)
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816
Image ClassificationonOxford-IIIT Pets
Accuracy· 2021-03-22
93.8
best: 97.1 (EffNet-L2 (SAM))
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816
Image ClassificationonFlowers-102
Accuracy· uses extra data· 2021-03-22
96.9
best: 99.76 (CCT-14/7x2)
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816
Image ClassificationoniNaturalist 2019
Top-1 Accuracy· uses extra data· 2021-03-22
72.8
best: 88.5 (Hiera-H (448px))
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816
Image ClassificationonCIFAR-100
Percentage correct· uses extra data· 2021-03-22
89.4
best: 96.08 (EffNet-L2 (SAM))
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816
Image ClassificationonImageNet
GFLOPs· 2021-03-22
1.2
best: 1478 (InternImage-H)
Incorporating Convolution Designs into Visual Transformers arXiv:2103.11816