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HyperAI超神経

Image Classification On Imagenet

評価指標

Hardware Burden
Number of params
Operations per network pass
Top 1 Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

比較表
モデル名Hardware BurdenNumber of paramsOperations per network passTop 1 Accuracy
xception-deep-learning-with-depthwise87G22.855952M0.838G79%
deep-residual-learning-for-image-recognition-40M-78.25%
cvt-introducing-convolutions-to-vision-20M-83%
densely-connected-convolutional-networks---77.42%
convit-improving-vision-transformers-with-10M-76.7%
when-vision-transformers-outperform-resnets-87M-79.9%
pvtv2-improved-baselines-with-pyramid-vision-25.4M-82%
metaformer-baselines-for-vision-40M-85.4%
mambavision-a-hybrid-mamba-transformer-vision-241.5M-85.3%
fbnetv5-neural-architecture-search-for---78.4%
gated-convolutional-networks-with-hybrid-12.9M-78.5%
convit-improving-vision-transformers-with-48M-82.2%
spatial-channel-token-distillation-for-vision-122.6M-82.4%
rethinking-and-improving-relative-position-22M-80.9%
bottleneck-transformers-for-visual-53.9M-84%
augmenting-sub-model-to-improve-main-model-86.6M-84.2%
maxvit-multi-axis-vision-transformer-120M-84.94%
biformer-vision-transformer-with-bi-level---84.3%
incorporating-convolution-designs-into-visual-6.4M-76.4%
automix-unveiling-the-power-of-mixup-44.6M-80.98%
resnest-split-attention-networks-27.5M-81.13%
dilated-neighborhood-attention-transformer-90M-84.4%
torchdistill-a-modular-configuration-driven---71.08%
next-vit-next-generation-vision-transformer-44.8M-83.2%
improved-multiscale-vision-transformers-for-218M-86.3%
cyclemlp-a-mlp-like-architecture-for-dense-76M-83.2%
efficientnetv2-smaller-models-and-faster-54M-86.2%
sp-vit-learning-2d-spatial-priors-for-vision---83.9%
an-improved-one-millisecond-mobile-backbone-2.1M-72.5%
high-performance-large-scale-image-193.8M-85.1%
mixpro-data-augmentation-with-maskmix-and---76.7%
co-training-2-l-submodels-for-visual---87.5%
hvt-a-comprehensive-vision-framework-for---87.4%
basisnet-two-stage-model-synthesis-for-1---80%
vitae-vision-transformer-advanced-by---75.3%
semi-supervised-recognition-under-a-noisy-and-25.58M-83.0%
モデル 37---80.91%
alphanet-improved-training-of-supernet-with---80.0%
which-transformer-to-favor-a-comparative---82.29%
localvit-bringing-locality-to-vision-22.4M-80.8%
bottleneck-transformers-for-visual---83.5%
a-convnet-for-the-2020s-350M-87.8%
an-improved-one-millisecond-mobile-backbone-4.8M-75.9
an-image-is-worth-16x16-words-transformers-1---87.76%
190409925---79.1%
shape-texture-debiased-neural-network-1-81.2
gswin-gated-mlp-vision-model-with-39.8M-83.01%
revbifpn-the-fully-reversible-bidirectional-48.7M-83%
rethinking-spatial-dimensions-of-vision-4.9M-74.6%
dilated-neighborhood-attention-transformer---86.5%
an-evolutionary-approach-to-dynamic---86.74%
2103-14899-27.4M-81.5%
exploring-target-representations-for-masked---85.7%
on-the-adequacy-of-untuned-warmup-for---72.1%
alphanet-improved-training-of-supernet-with---77.8%
multigrain-a-unified-image-embedding-for---83.2%
gswin-gated-mlp-vision-model-with-15.5M-80.32%
モデル 58---80.25%
wavemix-lite-a-resource-efficient-neural-1-32.4M-67.7%
randaugment-practical-data-augmentation-with---85.4%
maxvit-multi-axis-vision-transformer---88.32%
gtp-vit-efficient-vision-transformers-via---85.4%
an-improved-one-millisecond-mobile-backbone-14.8M-79.4%
cvt-introducing-convolutions-to-vision-32M-84.9%
splitnet-divide-and-co-training-88.6M-82.13%
tinyvit-fast-pretraining-distillation-for-21M-84.8%
performance-of-gaussian-mixture-model---84.1%
resnest-split-attention-networks-70M-83.9%
what-do-deep-networks-like-to-see---77.12%
filter-response-normalization-layer---78.95%
do-you-even-need-attention-a-stack-of-feed-74.9
repmlp-re-parameterizing-convolutions-into52.77M78.60%
scaling-vision-with-sparse-mixture-of-experts-7200M-88.36%
drop-an-octave-reducing-spatial-redundancy-in20771G66.8M2.22G82.9%
self-training-with-noisy-student-improves-9.2M-82.4%
visformer-the-vision-friendly-transformer-10.3M-78.6%
mixnet-mixed-depthwise-convolutional-kernels-4.1M-75.8%
three-things-everyone-should-know-about---83.4%
multigrain-a-unified-image-embedding-for---83.6%
dynamic-convolution-attention-over-18.6M-67.7%
token-labeling-training-a-85-5-top-1-accuracy-26M-83.3%
metaformer-baselines-for-vision-56M-85.2%
rethinking-local-perception-in-lightweight-4.2M-77%
lets-keep-it-simple-using-simple-3M-75.66
elsa-enhanced-local-self-attention-for-vision-298M-87.2%
an-improved-one-millisecond-mobile-backbone-2.1M-71.4%
bottleneck-transformers-for-visual-66.6M-82.2%
dilated-neighborhood-attention-transformer-20M-81.8%
revbifpn-the-fully-reversible-bidirectional-142.3M-84.2%
exploring-target-representations-for-masked---88.2%
fixing-the-train-test-resolution-discrepancy-2-9.2M-83.6%
kolmogorov-arnold-transformer-86.6M-81.8
モデル 93---70.54
debiformer-vision-transformer-with-deformable---83.9%
mega-moving-average-equipped-gated-attention-90M-82.4%
espnetv2-a-light-weight-power-efficient-and-5.9M-74.9%
multigrain-a-unified-image-embedding-for---83.0%
semi-supervised-recognition-under-a-noisy-and-25.58M-84.0%
when-shift-operation-meets-vision-transformer-88M-83.3%
polynomial-networks-in-deep-classifiers-11.51M-71.6%
peco-perceptual-codebook-for-bert-pre---87.5%
puzzle-mix-exploiting-saliency-and-local-1-78.76%
token-labeling-training-a-85-5-top-1-accuracy-56M-84.1%
bottleneck-transformers-for-visual---83.8%
exploring-the-limits-of-weakly-supervised-829M-85.4%
fastvit-a-fast-hybrid-vision-transformer---82.6%
training-data-efficient-image-transformers-86M-84.2%
an-image-is-worth-16x16-words-transformers-1---24%
coca-contrastive-captioners-are-image-text2100M91.0%
circumventing-outliers-of-autoaugment-with88M85.8%
contrastive-learning-rivals-masked-image-307M-89.0%
transnext-robust-foveal-visual-perception-for-12.8M-82.5%
hyenapixel-global-image-context-with---84.9%
2103-15358-39.7M-83.3%
a-dot-product-attention-free-transformer-22.6M-79.8%
tinyvit-fast-pretraining-distillation-for-11M-83.2%
torchdistill-a-modular-configuration-driven---70.09%
florence-a-new-foundation-model-for-computer-893M-90.05%
coatnet-marrying-convolution-and-attention-42M-83.3%
high-performance-large-scale-image-377.2M-86.0%
transboost-improving-the-best-imagenet-25.56M-81.15%
maxvit-multi-axis-vision-transformer-69M-84.45%
sp-vit-learning-2d-spatial-priors-for-vision---85.5%
transboost-improving-the-best-imagenet-60.19M-80.64%
efficient-self-supervised-learning-with---87.4%
revbifpn-the-fully-reversible-bidirectional-5.11M-75.9%
scarletnas-bridging-the-gap-between-6.5M-76.3%
splitnet-divide-and-co-training98M83.34%
fastervit-fast-vision-transformers-with-53.4M-83.2%
fairnas-rethinking-evaluation-fairness-of-4.6M-75.34%
dynamic-convolution-attention-over-4M-69.4%
resmlp-feedforward-networks-for-image-17.7M-78.6%
generalized-parametric-contrastive-learning---86.01%
co-training-2-l-submodels-for-visual---85.8%
mixpro-data-augmentation-with-maskmix-and---73.8%
spinenet-learning-scale-permuted-backbone-for-60.5M-79%
unicom-universal-and-compact-representation---88.3
billion-scale-semi-supervised-learning-for-193M-84.8%
efficient-multi-order-gated-aggregation-3M-77.2%
pattern-attention-transformer-with-doughnut---83.6%
fastervit-fast-vision-transformers-with-159.5M-84.9%
reproducible-scaling-laws-for-contrastive---88.5%
which-transformer-to-favor-a-comparative---82.11%
190411491-14.7M-75.7%
graph-rise-graph-regularized-image-semantic---68.29%
muxconv-information-multiplexing-in-2.4M-71.6%
mobilenets-efficient-convolutional-neural---70.6%
fractalnet-ultra-deep-neural-networks-without---75.88%
xcit-cross-covariance-image-transformers-189M-86%
understanding-gaussian-attention-bias-of---81.484%
fixing-the-train-test-resolution-discrepancy---79.1%
scaling-vision-transformers-to-22-billion-86M-88.6%
ghostnet-more-features-from-cheap-operations-13M-75%
volo-vision-outlooker-for-visual-recognition-296M-87.1%
dat-spatially-dynamic-vision-transformer-with-53M-84.6%
convit-improving-vision-transformers-with-86M-82.4%
efficientvit-enhanced-linear-attention-for-64M-85.6%
mambavision-a-hybrid-mamba-transformer-vision-227.9M-85%
greedynas-towards-fast-one-shot-nas-with6.5M77.1%
visual-attention-network-200M-86.9%
a-dot-product-attention-free-transformer-23M-80.8%
scalable-visual-transformers-with-5.74M-69.64%
on-the-performance-analysis-of-momentum---76.91%
levit-a-vision-transformer-in-convnet-s-39.4M-82.5%
fixing-the-train-test-resolution-discrepancy-25.6M-82.5%
scaling-up-your-kernels-to-31x31-revisiting-335M-87.8%
contextual-transformer-networks-for-visual-55.8M-84.6%
meta-knowledge-distillation---83.1%
involution-inverting-the-inherence-of-34M-79.3%
tokens-to-token-vit-training-vision---83.3%
rethinking-the-design-principles-of-robust-23.3M-81.9%
which-transformer-to-favor-a-comparative---83.61%
a-convnet-for-the-2020s-1827M-88.36%
clcnet-rethinking-of-ensemble-modeling-with---86.42%
going-deeper-with-image-transformers-438M-86.5%
three-things-everyone-should-know-about---85.5%
fbnetv5-neural-architecture-search-for---84.1%
sp-vit-learning-2d-spatial-priors-for-vision---85.1%
semi-supervised-recognition-under-a-noisy-and-5.47M-79.0%
augmenting-convolutional-networks-with-99.4M-83.5%
from-xception-to-nexception-new-design---82%
automix-unveiling-the-power-of-mixup-21.8M-76.1%
balanced-binary-neural-networks-with-gated---59.4%
co-training-2-l-submodels-for-visual---88.0%
transnext-robust-foveal-visual-perception-for-49.7M-86.0%
refiner-refining-self-attention-for-vision81M86.03
co-training-2-l-submodels-for-visual---86.3%
repvgg-making-vgg-style-convnets-great-again-80.31M-78.78%
firecaffe-near-linear-acceleration-of-deep---58.9%
convmlp-hierarchical-convolutional-mlps-for-42.7M-80.2%
モデル 191---78.15%
internimage-exploring-large-scale-vision-50M-84.2%
2103-15358-24.6M-82%
scaling-up-visual-and-vision-language480M88.64%
from-xception-to-nexception-new-design---81.8%
gtp-vit-efficient-vision-transformers-via---83.7%
multigrain-a-unified-image-embedding-for---79.4%
zen-nas-a-zero-shot-nas-for-high-performance-183M-83.0%
kolmogorov-arnold-transformer-86.6M-82.8
a-fast-knowledge-distillation-framework-for-5M-78.7%
hyenapixel-global-image-context-with---83.5%
gtp-vit-efficient-vision-transformers-via---81.9%
rethinking-and-improving-relative-position---81.1%
which-transformer-to-favor-a-comparative---81.33%
one-peace-exploring-one-general-1520M--
moat-alternating-mobile-convolution-and-483.2M-89.1%
sequencer-deep-lstm-for-image-classification-28M-82.3%
dilated-neighborhood-attention-transformer-51M-83.8%
tinyvit-fast-pretraining-distillation-for-11M-81.5%
モデル 210---78.75%
incorporating-convolution-designs-into-visual---82%
high-performance-large-scale-image-527M-89.2%
self-training-with-noisy-student-improves-66M-86.9%
cswin-transformer-a-general-vision-173M-87.5%
debiformer-vision-transformer-with-deformable---84.4%
cutmix-regularization-strategy-to-train---78.4%
mambavision-a-hybrid-mamba-transformer-vision-97.7M-84.2%
expeditious-saliency-guided-mix-up-through---77.39%
nasvit-neural-architecture-search-for---78.2%
volo-vision-outlooker-for-visual-recognition-59M-86%
wave-vit-unifying-wavelet-and-transformers-33.5M-84.8%
alphanet-improved-training-of-supernet-with---78.9%
nasvit-neural-architecture-search-for---81.0%
mixpro-data-augmentation-with-maskmix-and---82.9%
omnivec2-a-novel-transformer-based-network---89.3%
transboost-improving-the-best-imagenet-71.71M-82.16%
rexnet-diminishing-representational-2.7M-74.6%
next-vit-next-generation-vision-transformer-57.8M-84.7%
vision-models-are-more-robust-and-fair-when-10000M-85.8%
nasvit-neural-architecture-search-for---81.4%
masked-autoencoders-are-scalable-vision---85.9%
vitaev2-vision-transformer-advanced-by-644M-88.5%
neighborhood-attention-transformer-20M-81.8%
an-image-is-worth-16x16-words-transformers-1----
revisiting-resnets-improved-training-and192M84.4%
efficientnet-rethinking-model-scaling-for-66M-84.4%
2103-15358-55.7M-83.2%
lets-keep-it-simple-using-simple-1.5M-61.52
vision-gnn-an-image-is-worth-graph-of-nodes-10.7M-78.2%
adaptive-split-fusion-transformer-56.7M-83.9%
gtp-vit-efficient-vision-transformers-via---85.8%
uninet-unified-architecture-search-with-1-11.5M-80.8%
augmenting-convolutional-networks-with-99.4M-86.5%
mixpro-data-augmentation-with-maskmix-and---80.6%
going-deeper-with-image-transformers-68.2M-85.4%
which-transformer-to-favor-a-comparative---82.54%
モデル 247---76.3
fastvit-a-fast-hybrid-vision-transformer---84.9%
davit-dual-attention-vision-transformers-87.9M-86.9%
co-training-2-l-submodels-for-visual---87.1%
モデル 251---81.92%
firecaffe-near-linear-acceleration-of-deep---68.3%
sp-vit-learning-2d-spatial-priors-for-vision---84.9%
efficientnet-rethinking-model-scaling-for-9.2M-79.8%
efficientnet-rethinking-model-scaling-for-12M-81.1%
exploring-the-limits-of-weakly-supervised-466M-85.1%
correlated-input-dependent-label-noise-in-68.6%
not-all-images-are-worth-16x16-words-dynamic---78.48%
rethinking-and-improving-relative-position---81.4%
container-context-aggregation-network-20M-82%
self-training-with-noisy-student-improves-30M-86.1%
srm-a-style-based-recalibration-module-for---78.47%
densely-connected-convolutional-networks---74.98%
self-training-with-noisy-student-improves-19M-85.3%
モデル 265---78.36%
exploring-randomly-wired-neural-networks-for-61.5M-80.1%
mambavision-a-hybrid-mamba-transformer-vision-31.8M-82.3%
fast-vision-transformers-with-hilo-attention-28M-82%
your-diffusion-model-is-secretly-a-zero-shot---79.1%
xcit-cross-covariance-image-transformers-84M-85.8%
semi-supervised-learning-of-visual-features---75.5%
cutmix-regularization-strategy-to-train---80.53%
convit-improving-vision-transformers-with152M82.5%
res2net-a-new-multi-scale-backbone---78.59%
exploring-target-representations-for-masked---87.8%
going-deeper-with-image-transformers-271M-86.3%
rest-an-efficient-transformer-for-visual-13.66M-79.6%
resnest-split-attention-networks-27.5M-80.64%
mobilevitv3-mobile-friendly-vision-5.8M-79.3%
high-performance-large-scale-image-254.9M-85.7%
visual-attention-network-60M-86.6%
tokenmixup-efficient-attention-guided-token---82.37%
wide-residual-networks---78.1%
unsupervised-data-augmentation-1---79.04%
mobilenetv4-universal-models-for-the-mobile---79.9%
parametric-contrastive-learning---80.9%
designing-network-design-spaces-6.3M-76.3%
adversarial-autoaugment-1---79.4%
mixmim-mixed-and-masked-image-modeling-for-88M-85.1%
neighborhood-attention-transformer-28M-83.2%
mixpro-data-augmentation-with-maskmix-and---82.7%
hornet-efficient-high-order-spatial---87.7%
vitae-vision-transformer-advanced-by-13.2M-81%
going-deeper-with-image-transformers-46.9M-85.1%
unireplknet-a-universal-perception-large---87.4%
designing-bert-for-convolutional-networks-198M-86.0%
swin-transformer-hierarchical-vision-88M-86.4%
モデル 298---77.71%
quantnet-learning-to-quantize-by-learning---71.97%
adversarial-examples-improve-image-66M-85.2%
whats-hidden-in-a-randomly-weighted-neural-20.6M-73.3%
involution-inverting-the-inherence-of-15.5M-78.4%
elsa-enhanced-local-self-attention-for-vision-28M-82.7%
next-vit-next-generation-vision-transformer-31.7M-82.5%
muxconv-information-multiplexing-in-4.0M-76.6%
alphanet-improved-training-of-supernet-with---80.3%
2103-14899-44.3M-82.8%
sliced-recursive-transformer-1-21.3M-84.3%
resmlp-feedforward-networks-for-image---79.4%
mobilevit-light-weight-general-purpose-and-5.6M-78.4%
masked-image-residual-learning-for-scaling-1-96M-84.8%
neighborhood-attention-transformer-90M-84.3%
bottleneck-transformers-for-visual-49.2M-81.4%
pvtv2-improved-baselines-with-pyramid-vision-45.2M-83.2%
scalable-pre-training-of-large-autoregressive---84.0
distilled-gradual-pruning-with-pruned-fine-2.56M-73.66%
uninet-unified-architecture-search-with-1-72.9M-87%
fastvit-a-fast-hybrid-vision-transformer---80.6%
gated-convolutional-networks-with-hybrid-42.2M-80.5%
augmenting-sub-model-to-improve-main-model-632M-85.7%
fbnetv5-neural-architecture-search-for---77.2%
tiny-models-are-the-computational-saver-for---85.75
mish-a-self-regularized-non-monotonic-neural---79.8%
differentiable-model-compression-via-pseudo-82.0
graph-convolutions-enrich-the-self-attention---82.8%
model-soups-averaging-weights-of-multiple-1843M-90.94%
torchdistill-a-modular-configuration-driven---70.52%
filter-response-normalization-layer---77.21%
scarletnas-bridging-the-gap-between-6M-75.6%
cas-vit-convolutional-additive-self-attention-21.76-84.1%
lets-keep-it-simple-using-simple-9.5M-74.17
fast-autoaugment---80.6%
self-training-with-noisy-student-improves-12M-84.1%
uninet-unified-architecture-search-with-11.9M-79.1%
lets-keep-it-simple-using-simple-1.5M-69.11
involution-inverting-the-inherence-of-9.2-75.9%
not-all-images-are-worth-16x16-words-dynamic-80.43%
uninet-unified-architecture-search-with-73.5M-85.2%
omnivore-a-single-model-for-many-visual---86.0%
densely-connected-convolutional-networks---76.2%
efficientnet-rethinking-model-scaling-for-5.3M-76.3%
rethinking-local-perception-in-lightweight-12.3M-81.6%
compact-global-descriptor-for-neural-networks-4.26M-72.56%
improved-multiscale-vision-transformers-for-667M-88%
revisiting-a-knn-based-image-classification---79.8%
visual-representation-learning-from-unlabeled---85%
efficientvit-enhanced-linear-attention-for-64M-86%
billion-scale-semi-supervised-learning-for-42M-83.4%
incepformer-efficient-inception-transformer-14.0M-80.5%
fast-vision-transformers-with-hilo-attention-87M-84.7%
rexnet-diminishing-representational-34.8M-84.5%
identity-mappings-in-deep-residual-networks---79.9%
mixpro-data-augmentation-with-maskmix-and---81.2%
maxvit-multi-axis-vision-transformer---86.7%
asymmnet-towards-ultralight-convolution-3.1M-68.4%
モデル 356-62M-63.3
three-things-everyone-should-know-about---84.1%
mobilevitv3-mobile-friendly-vision-1.2M-70.98%
edgenext-efficiently-amalgamated-cnn-1.3M-71.2%
distilled-gradual-pruning-with-pruned-fine-1.03M-65.59%
sparse-mlp-for-image-recognition-is-self-65.9M-83.4%
tokens-to-token-vit-training-vision-64.4M-82.6%
bias-loss-for-mobile-neural-networks-5.5M-76.2%
metaformer-baselines-for-vision-27M-85.0%
transboost-improving-the-best-imagenet---79.03%
graph-convolutions-enrich-the-self-attention---83%
an-improved-one-millisecond-mobile-backbone-10.1M-80.0%
torchdistill-a-modular-configuration-driven---71.56%
on-the-performance-analysis-of-momentum---67.74%
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190409460---77.8%
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model-rubik-s-cube-twisting-resolution-depth-5.1M-77.7%
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