repo_id stringlengths 15 89 | file_path stringlengths 27 180 | content stringlengths 1 2.23M | __index_level_0__ int64 0 0 |
|---|---|---|---|
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/mnasnet.md | # MnasNet
**MnasNet** is a type of convolutional neural network optimized for mobile devices that is discovered through mobile neural architecture search, which explicitly incorporates model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency. The main building block is an [inverted residual block](https://paperswithcode.com/method/inverted-residual-block) (from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2)).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2019mnasnet,
title={MnasNet: Platform-Aware Neural Architecture Search for Mobile},
author={Mingxing Tan and Bo Chen and Ruoming Pang and Vijay Vasudevan and Mark Sandler and Andrew Howard and Quoc V. Le},
year={2019},
eprint={1807.11626},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: MNASNet
Paper:
Title: 'MnasNet: Platform-Aware Neural Architecture Search for Mobile'
URL: https://paperswithcode.com/paper/mnasnet-platform-aware-neural-architecture
Models:
- Name: mnasnet_100
In Collection: MNASNet
Metadata:
FLOPs: 416415488
Parameters: 4380000
File Size: 17731774
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Inverted Residual Block
- Max Pooling
- ReLU
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- RMSProp
- Weight Decay
Training Data:
- ImageNet
ID: mnasnet_100
Layers: 100
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4000
Image Size: '224'
Interpolation: bicubic
RMSProp Decay: 0.9
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L894
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.67%
Top 5 Accuracy: 92.1%
- Name: semnasnet_100
In Collection: MNASNet
Metadata:
FLOPs: 414570766
Parameters: 3890000
File Size: 15731489
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Depthwise Separable Convolution
- Dropout
- Global Average Pooling
- Inverted Residual Block
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: semnasnet_100
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L928
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.45%
Top 5 Accuracy: 92.61%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/adversarial-inception-v3.md | # Adversarial Inception v3
**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
This particular model was trained for study of adversarial examples (adversarial training).
The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/abs-1804-00097,
author = {Alexey Kurakin and
Ian J. Goodfellow and
Samy Bengio and
Yinpeng Dong and
Fangzhou Liao and
Ming Liang and
Tianyu Pang and
Jun Zhu and
Xiaolin Hu and
Cihang Xie and
Jianyu Wang and
Zhishuai Zhang and
Zhou Ren and
Alan L. Yuille and
Sangxia Huang and
Yao Zhao and
Yuzhe Zhao and
Zhonglin Han and
Junjiajia Long and
Yerkebulan Berdibekov and
Takuya Akiba and
Seiya Tokui and
Motoki Abe},
title = {Adversarial Attacks and Defences Competition},
journal = {CoRR},
volume = {abs/1804.00097},
year = {2018},
url = {http://arxiv.org/abs/1804.00097},
archivePrefix = {arXiv},
eprint = {1804.00097},
timestamp = {Thu, 31 Oct 2019 16:31:22 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-00097.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<!--
Type: model-index
Collections:
- Name: Adversarial Inception v3
Paper:
Title: Adversarial Attacks and Defences Competition
URL: https://paperswithcode.com/paper/adversarial-attacks-and-defences-competition
Models:
- Name: adv_inception_v3
In Collection: Adversarial Inception v3
Metadata:
FLOPs: 7352418880
Parameters: 23830000
File Size: 95549439
Architecture:
- 1x1 Convolution
- Auxiliary Classifier
- Average Pooling
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inception-v3 Module
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: adv_inception_v3
Crop Pct: '0.875'
Image Size: '299'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/inception_v3.py#L456
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.58%
Top 5 Accuracy: 93.74%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/rexnet.md | # RexNet
**Rank Expansion Networks** (ReXNets) follow a set of new design principles for designing bottlenecks in image classification models. Authors refine each layer by 1) expanding the input channel size of the convolution layer and 2) replacing the [ReLU6s](https://www.paperswithcode.com/method/relu6).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{han2020rexnet,
title={ReXNet: Diminishing Representational Bottleneck on Convolutional Neural Network},
author={Dongyoon Han and Sangdoo Yun and Byeongho Heo and YoungJoon Yoo},
year={2020},
eprint={2007.00992},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: RexNet
Paper:
Title: 'ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
Network'
URL: https://paperswithcode.com/paper/rexnet-diminishing-representational
Models:
- Name: rexnet_100
In Collection: RexNet
Metadata:
FLOPs: 509989377
Parameters: 4800000
File Size: 19417552
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_100
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L212
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_100-1b4dddf4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.86%
Top 5 Accuracy: 93.88%
- Name: rexnet_130
In Collection: RexNet
Metadata:
FLOPs: 848364461
Parameters: 7560000
File Size: 30508197
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_130
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L218
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_130-590d768e.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.49%
Top 5 Accuracy: 94.67%
- Name: rexnet_150
In Collection: RexNet
Metadata:
FLOPs: 1122374469
Parameters: 9730000
File Size: 39227315
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_150
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L224
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_150-bd1a6aa8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.31%
Top 5 Accuracy: 95.16%
- Name: rexnet_200
In Collection: RexNet
Metadata:
FLOPs: 1960224938
Parameters: 16370000
File Size: 65862221
Architecture:
- Batch Normalization
- Convolution
- Dropout
- ReLU6
- Residual Connection
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Linear Warmup With Cosine Annealing
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x NVIDIA V100 GPUs
ID: rexnet_200
LR: 0.5
Epochs: 400
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 512
Image Size: '224'
Weight Decay: 1.0e-05
Interpolation: bicubic
Label Smoothing: 0.1
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/rexnet.py#L230
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rexnet/rexnetv1_200-8c0b7f2d.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.63%
Top 5 Accuracy: 95.67%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/selecsls.md | # SelecSLS
**SelecSLS** uses novel selective long and short range skip connections to improve the information flow allowing for a drastically faster network without compromising accuracy.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@article{Mehta_2020,
title={XNect},
volume={39},
ISSN={1557-7368},
url={http://dx.doi.org/10.1145/3386569.3392410},
DOI={10.1145/3386569.3392410},
number={4},
journal={ACM Transactions on Graphics},
publisher={Association for Computing Machinery (ACM)},
author={Mehta, Dushyant and Sotnychenko, Oleksandr and Mueller, Franziska and Xu, Weipeng and Elgharib, Mohamed and Fua, Pascal and Seidel, Hans-Peter and Rhodin, Helge and Pons-Moll, Gerard and Theobalt, Christian},
year={2020},
month={Jul}
}
```
<!--
Type: model-index
Collections:
- Name: SelecSLS
Paper:
Title: 'XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera'
URL: https://paperswithcode.com/paper/xnect-real-time-multi-person-3d-human-pose
Models:
- Name: selecsls42b
In Collection: SelecSLS
Metadata:
FLOPs: 3824022528
Parameters: 32460000
File Size: 129948954
Architecture:
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Global Average Pooling
- ReLU
- SelecSLS Block
Tasks:
- Image Classification
Training Techniques:
- Cosine Annealing
- Random Erasing
Training Data:
- ImageNet
ID: selecsls42b
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L335
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls42b-8af30141.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.18%
Top 5 Accuracy: 93.39%
- Name: selecsls60
In Collection: SelecSLS
Metadata:
FLOPs: 4610472600
Parameters: 30670000
File Size: 122839714
Architecture:
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Global Average Pooling
- ReLU
- SelecSLS Block
Tasks:
- Image Classification
Training Techniques:
- Cosine Annealing
- Random Erasing
Training Data:
- ImageNet
ID: selecsls60
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L342
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60-bbf87526.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.83%
- Name: selecsls60b
In Collection: SelecSLS
Metadata:
FLOPs: 4657653144
Parameters: 32770000
File Size: 131252898
Architecture:
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Global Average Pooling
- ReLU
- SelecSLS Block
Tasks:
- Image Classification
Training Techniques:
- Cosine Annealing
- Random Erasing
Training Data:
- ImageNet
ID: selecsls60b
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/selecsls.py#L349
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-selecsls/selecsls60b-94e619b5.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.41%
Top 5 Accuracy: 94.18%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/csp-darknet.md | # CSP-DarkNet
**CSPDarknet53** is a convolutional neural network and backbone for object detection that uses [DarkNet-53](https://paperswithcode.com/method/darknet-53). It employs a CSPNet strategy to partition the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
This CNN is used as the backbone for [YOLOv4](https://paperswithcode.com/method/yolov4).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{bochkovskiy2020yolov4,
title={YOLOv4: Optimal Speed and Accuracy of Object Detection},
author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
year={2020},
eprint={2004.10934},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: CSP DarkNet
Paper:
Title: 'YOLOv4: Optimal Speed and Accuracy of Object Detection'
URL: https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
Models:
- Name: cspdarknet53
In Collection: CSP DarkNet
Metadata:
FLOPs: 8545018880
Parameters: 27640000
File Size: 110775135
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Mish
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- CutMix
- Label Smoothing
- Mosaic
- Polynomial Learning Rate Decay
- SGD with Momentum
- Self-Adversarial Training
- Weight Decay
Training Data:
- ImageNet
Training Resources: 1x NVIDIA RTX 2070 GPU
ID: cspdarknet53
LR: 0.1
Layers: 53
Crop Pct: '0.887'
Momentum: 0.9
Batch Size: 128
Image Size: '256'
Warmup Steps: 1000
Weight Decay: 0.0005
Interpolation: bilinear
Training Steps: 8000000
FPS (GPU RTX 2070): 66
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/cspnet.py#L441
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspdarknet53_ra_256-d05c7c21.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.05%
Top 5 Accuracy: 95.09%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/res2net.md | # Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@article{Gao_2021,
title={Res2Net: A New Multi-Scale Backbone Architecture},
volume={43},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
DOI={10.1109/tpami.2019.2938758},
number={2},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
year={2021},
month={Feb},
pages={652–662}
}
```
<!--
Type: model-index
Collections:
- Name: Res2Net
Paper:
Title: 'Res2Net: A New Multi-scale Backbone Architecture'
URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
Models:
- Name: res2net101_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 10415881200
Parameters: 45210000
File Size: 181456059
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net101_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L152
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.43%
- Name: res2net50_14w_8s
In Collection: Res2Net
Metadata:
FLOPs: 5403546768
Parameters: 25060000
File Size: 100638543
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_14w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L196
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.14%
Top 5 Accuracy: 93.86%
- Name: res2net50_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 5499974064
Parameters: 25700000
File Size: 103110087
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L141
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.85%
- Name: res2net50_26w_6s
In Collection: Res2Net
Metadata:
FLOPs: 8130156528
Parameters: 37050000
File Size: 148603239
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_6s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L163
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.57%
Top 5 Accuracy: 94.12%
- Name: res2net50_26w_8s
In Collection: Res2Net
Metadata:
FLOPs: 10760338992
Parameters: 48400000
File Size: 194085165
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L174
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.37%
- Name: res2net50_48w_2s
In Collection: Res2Net
Metadata:
FLOPs: 5375291520
Parameters: 25290000
File Size: 101421406
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_48w_2s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L185
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.53%
Top 5 Accuracy: 93.56%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/legacy-senet.md | # (Legacy) SENet
A **SENet** is a convolutional neural network architecture that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
The weights from this model were ported from Gluon.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Legacy SENet
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: legacy_senet154
In Collection: Legacy SENet
Metadata:
FLOPs: 26659556016
Parameters: 115090000
File Size: 461488402
Architecture:
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_senet154
LR: 0.6
Epochs: 100
Layers: 154
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L440
Weights: http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.33%
Top 5 Accuracy: 95.51%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/big-transfer.md | # Big Transfer (BiT)
**Big Transfer (BiT)** is a type of pretraining recipe that pre-trains on a large supervised source dataset, and fine-tunes the weights on the target task. Models are trained on the JFT-300M dataset. The finetuned models contained in this collection are finetuned on ImageNet.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{kolesnikov2020big,
title={Big Transfer (BiT): General Visual Representation Learning},
author={Alexander Kolesnikov and Lucas Beyer and Xiaohua Zhai and Joan Puigcerver and Jessica Yung and Sylvain Gelly and Neil Houlsby},
year={2020},
eprint={1912.11370},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Big Transfer
Paper:
Title: 'Big Transfer (BiT): General Visual Representation Learning'
URL: https://paperswithcode.com/paper/large-scale-learning-of-general-visual
Models:
- Name: resnetv2_101x1_bitm
In Collection: Big Transfer
Metadata:
FLOPs: 5330896
Parameters: 44540000
File Size: 178256468
Architecture:
- 1x1 Convolution
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Group Normalization
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Weight Standardization
Tasks:
- Image Classification
Training Techniques:
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPUv3-512
ID: resnetv2_101x1_bitm
LR: 0.03
Epochs: 90
Layers: 101
Crop Pct: '1.0'
Momentum: 0.9
Batch Size: 4096
Image Size: '480'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L444
Weights: https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.21%
Top 5 Accuracy: 96.47%
- Name: resnetv2_101x3_bitm
In Collection: Big Transfer
Metadata:
FLOPs: 15988688
Parameters: 387930000
File Size: 1551830100
Architecture:
- 1x1 Convolution
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Group Normalization
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Weight Standardization
Tasks:
- Image Classification
Training Techniques:
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPUv3-512
ID: resnetv2_101x3_bitm
LR: 0.03
Epochs: 90
Layers: 101
Crop Pct: '1.0'
Momentum: 0.9
Batch Size: 4096
Image Size: '480'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L451
Weights: https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.38%
Top 5 Accuracy: 97.37%
- Name: resnetv2_152x2_bitm
In Collection: Big Transfer
Metadata:
FLOPs: 10659792
Parameters: 236340000
File Size: 945476668
Architecture:
- 1x1 Convolution
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Group Normalization
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Weight Standardization
Tasks:
- Image Classification
Training Techniques:
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
ID: resnetv2_152x2_bitm
Crop Pct: '1.0'
Image Size: '480'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L458
Weights: https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.4%
Top 5 Accuracy: 97.43%
- Name: resnetv2_152x4_bitm
In Collection: Big Transfer
Metadata:
FLOPs: 21317584
Parameters: 936530000
File Size: 3746270104
Architecture:
- 1x1 Convolution
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Group Normalization
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Weight Standardization
Tasks:
- Image Classification
Training Techniques:
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPUv3-512
ID: resnetv2_152x4_bitm
Crop Pct: '1.0'
Image Size: '480'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L465
Weights: https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.95%
Top 5 Accuracy: 97.45%
- Name: resnetv2_50x1_bitm
In Collection: Big Transfer
Metadata:
FLOPs: 5330896
Parameters: 25550000
File Size: 102242668
Architecture:
- 1x1 Convolution
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Group Normalization
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Weight Standardization
Tasks:
- Image Classification
Training Techniques:
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPUv3-512
ID: resnetv2_50x1_bitm
LR: 0.03
Epochs: 90
Layers: 50
Crop Pct: '1.0'
Momentum: 0.9
Batch Size: 4096
Image Size: '480'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L430
Weights: https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.19%
Top 5 Accuracy: 95.63%
- Name: resnetv2_50x3_bitm
In Collection: Big Transfer
Metadata:
FLOPs: 15988688
Parameters: 217320000
File Size: 869321580
Architecture:
- 1x1 Convolution
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Group Normalization
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Weight Standardization
Tasks:
- Image Classification
Training Techniques:
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
- JFT-300M
Training Resources: Cloud TPUv3-512
ID: resnetv2_50x3_bitm
LR: 0.03
Epochs: 90
Layers: 50
Crop Pct: '1.0'
Momentum: 0.9
Batch Size: 4096
Image Size: '480'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/b9843f954b0457af2db4f9dea41a8538f51f5d78/timm/models/resnetv2.py#L437
Weights: https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.75%
Top 5 Accuracy: 97.12%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/csp-resnext.md | # CSP-ResNeXt
**CSPResNeXt** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNeXt](https://paperswithcode.com/method/resnext). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{wang2019cspnet,
title={CSPNet: A New Backbone that can Enhance Learning Capability of CNN},
author={Chien-Yao Wang and Hong-Yuan Mark Liao and I-Hau Yeh and Yueh-Hua Wu and Ping-Yang Chen and Jun-Wei Hsieh},
year={2019},
eprint={1911.11929},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: CSP ResNeXt
Paper:
Title: 'CSPNet: A New Backbone that can Enhance Learning Capability of CNN'
URL: https://paperswithcode.com/paper/cspnet-a-new-backbone-that-can-enhance
Models:
- Name: cspresnext50
In Collection: CSP ResNeXt
Metadata:
FLOPs: 3962945536
Parameters: 20570000
File Size: 82562887
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Polynomial Learning Rate Decay
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 1x GPU
ID: cspresnext50
LR: 0.1
Layers: 50
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 128
Image Size: '224'
Weight Decay: 0.005
Interpolation: bilinear
Training Steps: 8000000
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/cspnet.py#L430
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnext50_ra_224-648b4713.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.05%
Top 5 Accuracy: 94.94%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/dla.md | # Deep Layer Aggregation
Extending “shallow” skip connections, **Dense Layer Aggregation (DLA)** incorporates more depth and sharing. The authors introduce two structures for deep layer aggregation (DLA): iterative deep aggregation (IDA) and hierarchical deep aggregation (HDA). These structures are expressed through an architectural framework, independent of the choice of backbone, for compatibility with current and future networks.
IDA focuses on fusing resolutions and scales while HDA focuses on merging features from all modules and channels. IDA follows the base hierarchy to refine resolution and aggregate scale stage-bystage. HDA assembles its own hierarchy of tree-structured connections that cross and merge stages to aggregate different levels of representation.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{yu2019deep,
title={Deep Layer Aggregation},
author={Fisher Yu and Dequan Wang and Evan Shelhamer and Trevor Darrell},
year={2019},
eprint={1707.06484},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: DLA
Paper:
Title: Deep Layer Aggregation
URL: https://paperswithcode.com/paper/deep-layer-aggregation
Models:
- Name: dla102
In Collection: DLA
Metadata:
FLOPs: 7192952808
Parameters: 33270000
File Size: 135290579
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: dla102
LR: 0.1
Epochs: 120
Layers: 102
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L410
Weights: http://dl.yf.io/dla/models/imagenet/dla102-d94d9790.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.03%
Top 5 Accuracy: 93.95%
- Name: dla102x
In Collection: DLA
Metadata:
FLOPs: 5886821352
Parameters: 26310000
File Size: 107552695
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: dla102x
LR: 0.1
Epochs: 120
Layers: 102
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L418
Weights: http://dl.yf.io/dla/models/imagenet/dla102x-ad62be81.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.51%
Top 5 Accuracy: 94.23%
- Name: dla102x2
In Collection: DLA
Metadata:
FLOPs: 9343847400
Parameters: 41280000
File Size: 167645295
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: dla102x2
LR: 0.1
Epochs: 120
Layers: 102
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L426
Weights: http://dl.yf.io/dla/models/imagenet/dla102x2-262837b6.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.44%
Top 5 Accuracy: 94.65%
- Name: dla169
In Collection: DLA
Metadata:
FLOPs: 11598004200
Parameters: 53390000
File Size: 216547113
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x GPUs
ID: dla169
LR: 0.1
Epochs: 120
Layers: 169
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L434
Weights: http://dl.yf.io/dla/models/imagenet/dla169-0914e092.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.69%
Top 5 Accuracy: 94.33%
- Name: dla34
In Collection: DLA
Metadata:
FLOPs: 3070105576
Parameters: 15740000
File Size: 63228658
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla34
LR: 0.1
Epochs: 120
Layers: 32
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L362
Weights: http://dl.yf.io/dla/models/imagenet/dla34-ba72cf86.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.62%
Top 5 Accuracy: 92.06%
- Name: dla46_c
In Collection: DLA
Metadata:
FLOPs: 583277288
Parameters: 1300000
File Size: 5307963
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla46_c
LR: 0.1
Epochs: 120
Layers: 46
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L369
Weights: http://dl.yf.io/dla/models/imagenet/dla46_c-2bfd52c3.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 64.87%
Top 5 Accuracy: 86.29%
- Name: dla46x_c
In Collection: DLA
Metadata:
FLOPs: 544052200
Parameters: 1070000
File Size: 4387641
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla46x_c
LR: 0.1
Epochs: 120
Layers: 46
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L378
Weights: http://dl.yf.io/dla/models/imagenet/dla46x_c-d761bae7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 65.98%
Top 5 Accuracy: 86.99%
- Name: dla60
In Collection: DLA
Metadata:
FLOPs: 4256251880
Parameters: 22040000
File Size: 89560235
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla60
LR: 0.1
Epochs: 120
Layers: 60
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L394
Weights: http://dl.yf.io/dla/models/imagenet/dla60-24839fc4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.04%
Top 5 Accuracy: 93.32%
- Name: dla60_res2net
In Collection: DLA
Metadata:
FLOPs: 4147578504
Parameters: 20850000
File Size: 84886593
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla60_res2net
Layers: 60
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L346
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net_dla60_4s-d88db7f9.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.46%
Top 5 Accuracy: 94.21%
- Name: dla60_res2next
In Collection: DLA
Metadata:
FLOPs: 3485335272
Parameters: 17030000
File Size: 69639245
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla60_res2next
Layers: 60
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L354
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next_dla60_4s-d327927b.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.44%
Top 5 Accuracy: 94.16%
- Name: dla60x
In Collection: DLA
Metadata:
FLOPs: 3544204264
Parameters: 17350000
File Size: 70883139
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla60x
LR: 0.1
Epochs: 120
Layers: 60
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L402
Weights: http://dl.yf.io/dla/models/imagenet/dla60x-d15cacda.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.25%
Top 5 Accuracy: 94.02%
- Name: dla60x_c
In Collection: DLA
Metadata:
FLOPs: 593325032
Parameters: 1320000
File Size: 5454396
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- DLA Bottleneck Residual Block
- DLA Residual Block
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: dla60x_c
LR: 0.1
Epochs: 120
Layers: 60
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/dla.py#L386
Weights: http://dl.yf.io/dla/models/imagenet/dla60x_c-b870c45c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 67.91%
Top 5 Accuracy: 88.42%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/efficientnet.md | # EfficientNet
**EfficientNet** is a convolutional neural network architecture and scaling method that uniformly scales all dimensions of depth/width/resolution using a *compound coefficient*. Unlike conventional practice that arbitrary scales these factors, the EfficientNet scaling method uniformly scales network width, depth, and resolution with a set of fixed scaling coefficients. For example, if we want to use $2^N$ times more computational resources, then we can simply increase the network depth by $\alpha ^ N$, width by $\beta ^ N$, and image size by $\gamma ^ N$, where $\alpha, \beta, \gamma$ are constant coefficients determined by a small grid search on the original small model. EfficientNet uses a compound coefficient $\phi$ to uniformly scales network width, depth, and resolution in a principled way.
The compound scaling method is justified by the intuition that if the input image is bigger, then the network needs more layers to increase the receptive field and more channels to capture more fine-grained patterns on the bigger image.
The base EfficientNet-B0 network is based on the inverted bottleneck residual blocks of [MobileNetV2](https://paperswithcode.com/method/mobilenetv2), in addition to [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2020efficientnet,
title={EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks},
author={Mingxing Tan and Quoc V. Le},
year={2020},
eprint={1905.11946},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
Type: model-index
Collections:
- Name: EfficientNet
Paper:
Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
URL: https://paperswithcode.com/paper/efficientnet-rethinking-model-scaling-for
Models:
- Name: efficientnet_b0
In Collection: EfficientNet
Metadata:
FLOPs: 511241564
Parameters: 5290000
File Size: 21376743
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_b0
Layers: 18
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1002
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.71%
Top 5 Accuracy: 93.52%
- Name: efficientnet_b1
In Collection: EfficientNet
Metadata:
FLOPs: 909691920
Parameters: 7790000
File Size: 31502706
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_b1
Crop Pct: '0.875'
Image Size: '240'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1011
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.71%
Top 5 Accuracy: 94.15%
- Name: efficientnet_b2
In Collection: EfficientNet
Metadata:
FLOPs: 1265324514
Parameters: 9110000
File Size: 36788104
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_b2
Crop Pct: '0.875'
Image Size: '260'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1020
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.38%
Top 5 Accuracy: 95.08%
- Name: efficientnet_b2a
In Collection: EfficientNet
Metadata:
FLOPs: 1452041554
Parameters: 9110000
File Size: 49369973
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_b2a
Crop Pct: '1.0'
Image Size: '288'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1029
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.61%
Top 5 Accuracy: 95.32%
- Name: efficientnet_b3
In Collection: EfficientNet
Metadata:
FLOPs: 2327905920
Parameters: 12230000
File Size: 49369973
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_b3
Crop Pct: '0.904'
Image Size: '300'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1038
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.08%
Top 5 Accuracy: 96.03%
- Name: efficientnet_b3a
In Collection: EfficientNet
Metadata:
FLOPs: 2600628304
Parameters: 12230000
File Size: 49369973
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_b3a
Crop Pct: '1.0'
Image Size: '320'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1047
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.25%
Top 5 Accuracy: 96.11%
- Name: efficientnet_em
In Collection: EfficientNet
Metadata:
FLOPs: 3935516480
Parameters: 6900000
File Size: 27927309
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_em
Crop Pct: '0.882'
Image Size: '240'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1118
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.26%
Top 5 Accuracy: 94.79%
- Name: efficientnet_es
In Collection: EfficientNet
Metadata:
FLOPs: 2317181824
Parameters: 5440000
File Size: 22003339
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_es
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1110
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.09%
Top 5 Accuracy: 93.93%
- Name: efficientnet_lite0
In Collection: EfficientNet
Metadata:
FLOPs: 510605024
Parameters: 4650000
File Size: 18820005
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Connections
- Dropout
- Inverted Residual Block
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: efficientnet_lite0
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/efficientnet.py#L1163
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.5%
Top 5 Accuracy: 92.51%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/legacy-se-resnext.md | # (Legacy) SE-ResNeXt
**SE ResNeXt** is a variant of a [ResNeXt](https://www.paperswithcode.com/method/resnext) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: Legacy SE ResNeXt
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: legacy_seresnext101_32x4d
In Collection: Legacy SE ResNeXt
Metadata:
FLOPs: 10287698672
Parameters: 48960000
File Size: 196466866
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnext101_32x4d
LR: 0.6
Epochs: 100
Layers: 101
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L462
Weights: http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.23%
Top 5 Accuracy: 95.02%
- Name: legacy_seresnext26_32x4d
In Collection: Legacy SE ResNeXt
Metadata:
FLOPs: 3187342304
Parameters: 16790000
File Size: 67346327
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnext26_32x4d
LR: 0.6
Epochs: 100
Layers: 26
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L448
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnext26_32x4d-65ebdb501.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.11%
Top 5 Accuracy: 93.31%
- Name: legacy_seresnext50_32x4d
In Collection: Legacy SE ResNeXt
Metadata:
FLOPs: 5459954352
Parameters: 27560000
File Size: 110559176
Architecture:
- 1x1 Convolution
- Batch Normalization
- Convolution
- Global Average Pooling
- Grouped Convolution
- Max Pooling
- ReLU
- ResNeXt Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: legacy_seresnext50_32x4d
LR: 0.6
Epochs: 100
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/senet.py#L455
Weights: http://data.lip6.fr/cadene/pretrainedmodels/se_resnext50_32x4d-a260b3a4.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.08%
Top 5 Accuracy: 94.43%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/res2next.md | # Res2NeXt
**Res2NeXt** is an image model that employs a variation on [ResNeXt](https://paperswithcode.com/method/resnext) bottleneck residual blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@article{Gao_2021,
title={Res2Net: A New Multi-Scale Backbone Architecture},
volume={43},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
DOI={10.1109/tpami.2019.2938758},
number={2},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
year={2021},
month={Feb},
pages={652–662}
}
```
<!--
Type: model-index
Collections:
- Name: Res2NeXt
Paper:
Title: 'Res2Net: A New Multi-scale Backbone Architecture'
URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
Models:
- Name: res2next50
In Collection: Res2NeXt
Metadata:
FLOPs: 5396798208
Parameters: 24670000
File Size: 99019592
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2NeXt Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2next50
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L207
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2next50_4s-6ef7e7bf.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.24%
Top 5 Accuracy: 93.91%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/csp-resnet.md | # CSP-ResNet
**CSPResNet** is a convolutional neural network where we apply the Cross Stage Partial Network (CSPNet) approach to [ResNet](https://paperswithcode.com/method/resnet). The CSPNet partitions the feature map of the base layer into two parts and then merges them through a cross-stage hierarchy. The use of a split and merge strategy allows for more gradient flow through the network.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{wang2019cspnet,
title={CSPNet: A New Backbone that can Enhance Learning Capability of CNN},
author={Chien-Yao Wang and Hong-Yuan Mark Liao and I-Hau Yeh and Yueh-Hua Wu and Ping-Yang Chen and Jun-Wei Hsieh},
year={2019},
eprint={1911.11929},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: CSP ResNet
Paper:
Title: 'CSPNet: A New Backbone that can Enhance Learning Capability of CNN'
URL: https://paperswithcode.com/paper/cspnet-a-new-backbone-that-can-enhance
Models:
- Name: cspresnet50
In Collection: CSP ResNet
Metadata:
FLOPs: 5924992000
Parameters: 21620000
File Size: 86679303
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- Polynomial Learning Rate Decay
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: cspresnet50
LR: 0.1
Layers: 50
Crop Pct: '0.887'
Momentum: 0.9
Batch Size: 128
Image Size: '256'
Weight Decay: 0.005
Interpolation: bilinear
Training Steps: 8000000
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/cspnet.py#L415
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/cspresnet50_ra-d3e8d487.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.57%
Top 5 Accuracy: 94.71%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/densenet.md | # DenseNet
**DenseNet** is a type of convolutional neural network that utilises dense connections between layers, through [Dense Blocks](http://www.paperswithcode.com/method/dense-block), where we connect *all layers* (with matching feature-map sizes) directly with each other. To preserve the feed-forward nature, each layer obtains additional inputs from all preceding layers and passes on its own feature-maps to all subsequent layers.
The **DenseNet Blur** variant in this collection by Ross Wightman employs [Blur Pooling](http://www.paperswithcode.com/method/blur-pooling)
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@article{DBLP:journals/corr/HuangLW16a,
author = {Gao Huang and
Zhuang Liu and
Kilian Q. Weinberger},
title = {Densely Connected Convolutional Networks},
journal = {CoRR},
volume = {abs/1608.06993},
year = {2016},
url = {http://arxiv.org/abs/1608.06993},
archivePrefix = {arXiv},
eprint = {1608.06993},
timestamp = {Mon, 10 Sep 2018 15:49:32 +0200},
biburl = {https://dblp.org/rec/journals/corr/HuangLW16a.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
```
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
```
<!--
Type: model-index
Collections:
- Name: DenseNet
Paper:
Title: Densely Connected Convolutional Networks
URL: https://paperswithcode.com/paper/densely-connected-convolutional-networks
Models:
- Name: densenet121
In Collection: DenseNet
Metadata:
FLOPs: 3641843200
Parameters: 7980000
File Size: 32376726
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Block
- Dense Connections
- Dropout
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Kaiming Initialization
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
ID: densenet121
LR: 0.1
Epochs: 90
Layers: 121
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L295
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenet121_ra-50efcf5c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.56%
Top 5 Accuracy: 92.65%
- Name: densenet161
In Collection: DenseNet
Metadata:
FLOPs: 9931959264
Parameters: 28680000
File Size: 115730790
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Block
- Dense Connections
- Dropout
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Kaiming Initialization
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
ID: densenet161
LR: 0.1
Epochs: 90
Layers: 161
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L347
Weights: https://download.pytorch.org/models/densenet161-8d451a50.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.36%
Top 5 Accuracy: 93.63%
- Name: densenet169
In Collection: DenseNet
Metadata:
FLOPs: 4316945792
Parameters: 14150000
File Size: 57365526
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Block
- Dense Connections
- Dropout
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Kaiming Initialization
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
ID: densenet169
LR: 0.1
Epochs: 90
Layers: 169
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L327
Weights: https://download.pytorch.org/models/densenet169-b2777c0a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.9%
Top 5 Accuracy: 93.02%
- Name: densenet201
In Collection: DenseNet
Metadata:
FLOPs: 5514321024
Parameters: 20010000
File Size: 81131730
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Block
- Dense Connections
- Dropout
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- Kaiming Initialization
- Nesterov Accelerated Gradient
- Weight Decay
Training Data:
- ImageNet
ID: densenet201
LR: 0.1
Epochs: 90
Layers: 201
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L337
Weights: https://download.pytorch.org/models/densenet201-c1103571.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.29%
Top 5 Accuracy: 93.48%
- Name: densenetblur121d
In Collection: DenseNet
Metadata:
FLOPs: 3947812864
Parameters: 8000000
File Size: 32456500
Architecture:
- 1x1 Convolution
- Batch Normalization
- Blur Pooling
- Convolution
- Dense Block
- Dense Connections
- Dropout
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Data:
- ImageNet
ID: densenetblur121d
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L305
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/densenetblur121d_ra-100dcfbc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 76.59%
Top 5 Accuracy: 93.2%
- Name: tv_densenet121
In Collection: DenseNet
Metadata:
FLOPs: 3641843200
Parameters: 7980000
File Size: 32342954
Architecture:
- 1x1 Convolution
- Average Pooling
- Batch Normalization
- Convolution
- Dense Block
- Dense Connections
- Dropout
- Max Pooling
- ReLU
- Softmax
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
ID: tv_densenet121
LR: 0.1
Epochs: 90
Crop Pct: '0.875'
LR Gamma: 0.1
Momentum: 0.9
Batch Size: 32
Image Size: '224'
LR Step Size: 30
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/densenet.py#L379
Weights: https://download.pytorch.org/models/densenet121-a639ec97.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 74.74%
Top 5 Accuracy: 92.15%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/resnest.md | # ResNeSt
A **ResNeSt** is a variant on a [ResNet](https://paperswithcode.com/method/resnet), which instead stacks [Split-Attention blocks](https://paperswithcode.com/method/split-attention). The cardinal group representations are then concatenated along the channel dimension: $V = \text{Concat}${$V^{1},V^{2},\cdots{V}^{K}$}. As in standard residual blocks, the final output $Y$ of otheur Split-Attention block is produced using a shortcut connection: $Y=V+X$, if the input and output feature-map share the same shape. For blocks with a stride, an appropriate transformation $\mathcal{T}$ is applied to the shortcut connection to align the output shapes: $Y=V+\mathcal{T}(X)$. For example, $\mathcal{T}$ can be strided convolution or combined convolution-with-pooling.
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{zhang2020resnest,
title={ResNeSt: Split-Attention Networks},
author={Hang Zhang and Chongruo Wu and Zhongyue Zhang and Yi Zhu and Haibin Lin and Zhi Zhang and Yue Sun and Tong He and Jonas Mueller and R. Manmatha and Mu Li and Alexander Smola},
year={2020},
eprint={2004.08955},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: ResNeSt
Paper:
Title: 'ResNeSt: Split-Attention Networks'
URL: https://paperswithcode.com/paper/resnest-split-attention-networks
Models:
- Name: resnest101e
In Collection: ResNeSt
Metadata:
FLOPs: 17423183648
Parameters: 48280000
File Size: 193782911
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest101e
LR: 0.1
Epochs: 270
Layers: 101
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 4096
Image Size: '256'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L182
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 82.88%
Top 5 Accuracy: 96.31%
- Name: resnest14d
In Collection: ResNeSt
Metadata:
FLOPs: 3548594464
Parameters: 10610000
File Size: 42562639
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest14d
LR: 0.1
Epochs: 270
Layers: 14
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L148
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest14-9c8fe254.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.51%
Top 5 Accuracy: 92.52%
- Name: resnest200e
In Collection: ResNeSt
Metadata:
FLOPs: 45954387872
Parameters: 70200000
File Size: 193782911
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest200e
LR: 0.1
Epochs: 270
Layers: 200
Dropout: 0.2
Crop Pct: '0.909'
Momentum: 0.9
Batch Size: 2048
Image Size: '320'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L194
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest101-22405ba7.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.85%
Top 5 Accuracy: 96.89%
- Name: resnest269e
In Collection: ResNeSt
Metadata:
FLOPs: 100830307104
Parameters: 110930000
File Size: 445402691
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest269e
LR: 0.1
Epochs: 270
Layers: 269
Dropout: 0.2
Crop Pct: '0.928'
Momentum: 0.9
Batch Size: 2048
Image Size: '416'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L206
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest269-0cc87c48.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 84.53%
Top 5 Accuracy: 96.99%
- Name: resnest26d
In Collection: ResNeSt
Metadata:
FLOPs: 4678918720
Parameters: 17070000
File Size: 68470242
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest26d
LR: 0.1
Epochs: 270
Layers: 26
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L159
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_resnest26-50eb607c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.48%
Top 5 Accuracy: 94.3%
- Name: resnest50d
In Collection: ResNeSt
Metadata:
FLOPs: 6937106336
Parameters: 27480000
File Size: 110273258
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest50d
LR: 0.1
Epochs: 270
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L170
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50-528c19ca.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.96%
Top 5 Accuracy: 95.38%
- Name: resnest50d_1s4x24d
In Collection: ResNeSt
Metadata:
FLOPs: 5686764544
Parameters: 25680000
File Size: 103045531
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest50d_1s4x24d
LR: 0.1
Epochs: 270
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L229
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_1s4x24d-d4a4f76f.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.0%
Top 5 Accuracy: 95.33%
- Name: resnest50d_4s2x40d
In Collection: ResNeSt
Metadata:
FLOPs: 5657064720
Parameters: 30420000
File Size: 122133282
Architecture:
- 1x1 Convolution
- Convolution
- Dense Connections
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Connection
- Softmax
- Split Attention
Tasks:
- Image Classification
Training Techniques:
- AutoAugment
- DropBlock
- Label Smoothing
- Mixup
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 64x NVIDIA V100 GPUs
ID: resnest50d_4s2x40d
LR: 0.1
Epochs: 270
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 8192
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/resnest.py#L218
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-resnest/resnest50_fast_4s2x40d-41d14ed0.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 81.11%
Top 5 Accuracy: 95.55%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs/models/.templates | hf_public_repos/pytorch-image-models/docs/models/.templates/models/mixnet.md | # MixNet
**MixNet** is a type of convolutional neural network discovered via AutoML that utilises [MixConvs](https://paperswithcode.com/method/mixconv) instead of regular [depthwise convolutions](https://paperswithcode.com/method/depthwise-convolution).
{% include 'code_snippets.md' %}
## How do I train this model?
You can follow the [timm recipe scripts](https://rwightman.github.io/pytorch-image-models/scripts/) for training a new model afresh.
## Citation
```BibTeX
@misc{tan2019mixconv,
title={MixConv: Mixed Depthwise Convolutional Kernels},
author={Mingxing Tan and Quoc V. Le},
year={2019},
eprint={1907.09595},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: MixNet
Paper:
Title: 'MixConv: Mixed Depthwise Convolutional Kernels'
URL: https://paperswithcode.com/paper/mixnet-mixed-depthwise-convolutional-kernels
Models:
- Name: mixnet_l
In Collection: MixNet
Metadata:
FLOPs: 738671316
Parameters: 7330000
File Size: 29608232
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_l
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1669
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.98%
Top 5 Accuracy: 94.18%
- Name: mixnet_m
In Collection: MixNet
Metadata:
FLOPs: 454543374
Parameters: 5010000
File Size: 20298347
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_m
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1660
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.27%
Top 5 Accuracy: 93.42%
- Name: mixnet_s
In Collection: MixNet
Metadata:
FLOPs: 321264910
Parameters: 4130000
File Size: 16727982
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_s
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1651
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 75.99%
Top 5 Accuracy: 92.79%
- Name: mixnet_xl
In Collection: MixNet
Metadata:
FLOPs: 1195880424
Parameters: 11900000
File Size: 48001170
Architecture:
- Batch Normalization
- Dense Connections
- Dropout
- Global Average Pooling
- Grouped Convolution
- MixConv
- Squeeze-and-Excitation Block
- Swish
Tasks:
- Image Classification
Training Techniques:
- MNAS
Training Data:
- ImageNet
ID: mixnet_xl
Crop Pct: '0.875'
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/9a25fdf3ad0414b4d66da443fe60ae0aa14edc84/timm/models/efficientnet.py#L1678
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.47%
Top 5 Accuracy: 94.93%
-->
| 0 |
hf_public_repos/pytorch-image-models/docs | hf_public_repos/pytorch-image-models/docs/javascripts/tables.js | app.location$.subscribe(function() {
var tables = document.querySelectorAll("article table")
tables.forEach(function(table) {
new Tablesort(table)
})
}) | 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt111-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count
tinynet_e,47972.76,21.335,1024,106,2.04
mobilenetv3_small_050,42473.43,24.099,1024,224,1.59
lcnet_035,39739.31,25.756,1024,224,1.64
lcnet_050,35211.0,29.071,1024,224,1.88
mobilenetv3_small_075,31410.3,32.589,1024,224,2.04
mobilenetv3_small_100,28111.39,36.416,1024,224,2.54
tf_mobilenetv3_small_minimal_100,27538.82,37.173,1024,224,2.04
tinynet_d,26670.17,38.384,1024,152,2.34
tf_mobilenetv3_small_075,26522.93,38.597,1024,224,2.04
tf_mobilenetv3_small_100,24036.65,42.591,1024,224,2.54
lcnet_075,22451.72,45.598,1024,224,2.36
levit_128s,19963.52,51.282,1024,224,7.78
mnasnet_small,19706.27,51.952,1024,224,2.03
lcnet_100,18132.59,56.461,1024,224,2.95
mobilenetv2_035,17586.23,58.217,1024,224,1.68
ghostnet_050,16726.5,61.209,1024,224,2.59
regnetx_002,16238.56,63.048,1024,224,2.68
regnety_002,15227.23,67.235,1024,224,3.16
mnasnet_050,15022.24,68.154,1024,224,2.22
tinynet_c,14089.9,72.665,1024,184,2.46
mobilenetv2_050,14032.51,72.961,1024,224,1.97
levit_128,13679.77,74.845,1024,224,9.21
semnasnet_050,13508.98,75.79,1024,224,2.08
vit_small_patch32_224,12109.88,84.548,1024,224,22.88
mixer_s32_224,11702.15,87.494,1024,224,19.1
levit_192,11695.39,87.545,1024,224,10.95
lcnet_150,11564.86,88.533,1024,224,4.5
mobilenetv3_large_075,11407.33,89.755,1024,224,3.99
gernet_s,10837.81,94.473,1024,224,8.17
vit_tiny_r_s16_p8_224,10598.14,96.609,1024,224,6.34
mobilenetv3_rw,10164.28,100.733,1024,224,5.48
tf_mobilenetv3_large_075,10125.76,101.117,1024,224,3.99
regnetx_004,10069.8,101.678,1024,224,5.16
mobilenetv3_large_100,10017.28,102.212,1024,224,5.48
mobilenetv3_large_100_miil,10014.68,102.238,1024,224,5.48
ese_vovnet19b_slim_dw,9944.69,102.957,1024,224,1.9
hardcorenas_a,9792.24,104.561,1024,224,5.26
mnasnet_075,9774.09,104.755,1024,224,3.17
tf_mobilenetv3_large_minimal_100,9771.38,104.784,1024,224,3.92
ghostnet_100,9041.17,113.248,1024,224,5.18
hardcorenas_b,9021.74,113.492,1024,224,5.18
tinynet_b,8976.69,114.061,1024,188,3.73
swsl_resnet18,8971.65,114.125,1024,224,11.69
tf_mobilenetv3_large_100,8954.66,114.343,1024,224,5.48
gluon_resnet18_v1b,8949.74,114.406,1024,224,11.69
ssl_resnet18,8947.2,114.439,1024,224,11.69
resnet18,8927.25,114.693,1024,224,11.69
hardcorenas_c,8864.36,115.506,1024,224,5.52
mobilenetv2_075,8764.66,116.82,1024,224,2.64
mnasnet_100,8646.99,118.411,1024,224,4.38
mnasnet_b1,8646.34,118.421,1024,224,4.38
semnasnet_075,8603.57,119.009,1024,224,2.91
levit_256,8528.42,120.058,1024,224,18.89
regnety_004,8497.03,120.501,1024,224,4.34
seresnet18,8461.0,121.015,1024,224,11.78
hardcorenas_d,8306.25,123.269,1024,224,7.5
legacy_seresnet18,8213.74,124.658,1024,224,11.78
regnetx_006,8055.06,127.114,1024,224,6.2
mobilenetv2_100,7900.5,129.6,1024,224,3.5
spnasnet_100,7827.4,130.811,1024,224,4.42
semnasnet_100,7701.96,132.941,1024,224,3.89
mnasnet_a1,7678.8,133.342,1024,224,3.89
resnet18d,7478.23,136.919,1024,224,11.71
levit_256d,7357.48,139.166,1024,224,26.21
ghostnet_130,7270.82,140.824,1024,224,7.36
regnety_006,7263.32,140.971,1024,224,6.06
hardcorenas_f,7222.67,141.764,1024,224,8.2
hardcorenas_e,7174.56,142.715,1024,224,8.07
efficientnet_lite0,7057.13,145.09,1024,224,4.65
ese_vovnet19b_slim,6975.46,146.789,1024,224,3.17
tinynet_a,6918.13,148.004,1024,192,6.19
fbnetc_100,6847.55,149.531,1024,224,5.57
tf_efficientnetv2_b0,6842.85,149.633,1024,224,7.14
xcit_nano_12_p16_224_dist,6769.74,151.25,1024,224,3.05
xcit_nano_12_p16_224,6760.61,151.454,1024,224,3.05
regnetx_008,6358.56,161.031,1024,224,7.26
deit_tiny_patch16_224,6350.86,161.227,1024,224,5.72
vit_tiny_patch16_224,6346.8,161.33,1024,224,5.72
tf_efficientnet_lite0,6324.64,161.895,1024,224,4.65
deit_tiny_distilled_patch16_224,6241.01,164.064,1024,224,5.91
efficientnet_b0,6183.5,165.59,1024,224,5.29
efficientnet_b1_pruned,6044.57,169.396,1024,240,6.33
rexnet_100,6031.43,169.765,1024,224,4.8
mnasnet_140,6024.95,169.948,1024,224,7.12
rexnetr_100,5992.24,170.876,1024,224,4.88
dla46_c,5989.01,170.968,1024,224,1.3
pit_ti_distilled_224,5978.67,171.264,1024,224,5.1
mobilenetv2_110d,5966.33,171.618,1024,224,4.52
pit_ti_224,5950.27,172.082,1024,224,4.85
regnety_008,5940.31,172.37,1024,224,6.26
resnetblur18,5929.76,172.677,1024,224,11.69
tf_efficientnet_b0,5620.98,182.161,1024,224,5.29
tf_efficientnet_b0_ns,5610.83,182.491,1024,224,5.29
tf_efficientnet_b0_ap,5603.36,182.734,1024,224,5.29
skresnet18,5578.53,183.549,1024,224,11.96
regnetz_005,5482.6,186.761,1024,224,7.12
semnasnet_140,5325.69,192.263,1024,224,6.11
mobilenetv2_140,5271.81,194.229,1024,224,6.11
resnet34,5239.3,195.434,1024,224,21.8
tv_resnet34,5236.24,195.548,1024,224,21.8
gluon_resnet34_v1b,5233.7,195.644,1024,224,21.8
ese_vovnet19b_dw,5190.19,197.284,1024,224,6.54
levit_384,5177.17,197.779,1024,224,39.13
mobilevit_xxs,5154.01,198.668,1024,256,1.27
visformer_tiny,5148.77,198.871,1024,224,10.32
hrnet_w18_small,5137.51,199.307,1024,224,13.19
nf_regnet_b0,5134.01,199.442,1024,256,8.76
seresnet34,4940.74,207.245,1024,224,21.96
mixnet_s,4921.18,208.067,1024,224,4.13
gernet_m,4881.9,209.742,1024,224,21.14
efficientnet_lite1,4817.11,212.565,1024,240,5.42
legacy_seresnet34,4790.34,213.752,1024,224,21.96
selecsls42,4787.26,213.889,1024,224,30.35
selecsls42b,4772.45,214.553,1024,224,32.46
dla46x_c,4717.05,217.071,1024,224,1.07
resnet34d,4707.81,217.499,1024,224,21.82
vit_base_patch32_224,4653.29,220.048,1024,224,88.22
vit_base_patch32_224_sam,4636.98,220.822,1024,224,88.22
pit_xs_224,4628.58,221.222,1024,224,10.62
tf_mixnet_s,4615.88,221.831,1024,224,4.13
fbnetv3_b,4595.16,222.83,1024,256,8.6
rexnetr_130,4587.31,223.212,1024,224,7.61
pit_xs_distilled_224,4586.36,223.258,1024,224,11.0
resmlp_12_distilled_224,4524.79,226.297,1024,224,15.35
resmlp_12_224,4522.51,226.411,1024,224,15.35
tf_efficientnetv2_b1,4515.62,226.756,1024,240,8.14
dla60x_c,4459.55,229.607,1024,224,1.32
tf_efficientnet_lite1,4427.03,231.295,1024,240,5.42
mixer_b32_224,4423.78,231.465,1024,224,60.29
rexnet_130,4423.43,231.482,1024,224,7.56
xcit_tiny_12_p16_224_dist,4363.62,234.654,1024,224,6.72
xcit_tiny_12_p16_224,4352.75,235.24,1024,224,6.72
resnet26,4300.48,238.1,1024,224,16.0
mobilenetv2_120d,4276.4,239.441,1024,224,5.83
efficientnet_es_pruned,4244.8,241.225,1024,224,5.44
efficientnet_es,4243.52,241.298,1024,224,5.44
repvgg_b0,4216.88,242.821,1024,224,15.82
selecsls60,4146.99,246.913,1024,224,30.67
selecsls60b,4134.81,247.64,1024,224,32.77
tf_efficientnet_es,4097.34,249.906,1024,224,5.44
fbnetv3_d,4060.14,252.196,1024,256,10.31
efficientnet_b2_pruned,4051.65,252.724,1024,260,8.31
efficientnet_b0_g16_evos,3982.5,257.113,1024,224,8.11
rexnetr_150,3947.19,259.414,1024,224,9.78
crossvit_tiny_240,3922.12,261.07,1024,240,7.01
mixer_s16_224,3902.13,262.409,1024,224,18.53
resnet26d,3896.6,262.781,1024,224,16.01
dla34,3859.49,265.307,1024,224,15.74
ecaresnet50d_pruned,3854.93,265.621,1024,224,19.94
rexnet_150,3827.99,267.492,1024,224,9.73
vit_small_patch32_384,3806.52,269.0,1024,384,22.92
nf_resnet26,3804.26,269.16,1024,224,16.0
gmixer_12_224,3797.94,269.608,1024,224,12.7
efficientnet_lite2,3793.99,269.889,1024,260,6.09
gmlp_ti16_224,3724.27,274.941,1024,224,5.87
crossvit_9_240,3711.74,275.869,1024,240,8.55
regnetx_016,3640.75,281.247,1024,224,9.19
efficientnet_cc_b0_4e,3606.59,283.912,1024,224,13.31
efficientnet_cc_b0_8e,3600.63,284.383,1024,224,24.01
crossvit_9_dagger_240,3560.19,287.614,1024,240,8.78
tf_efficientnet_b1_ap,3560.01,287.626,1024,240,7.79
tf_efficientnet_b1,3559.25,287.687,1024,240,7.79
tf_efficientnet_b1_ns,3553.74,288.134,1024,240,7.79
tf_efficientnet_lite2,3505.87,292.07,1024,260,6.09
efficientnet_b1,3481.01,294.155,1024,256,7.79
poolformer_s12,3480.88,294.166,1024,224,11.92
vit_tiny_r_s16_p8_384,3451.73,148.319,512,384,6.36
tf_efficientnetv2_b2,3443.76,297.337,1024,260,10.1
tf_efficientnet_cc_b0_8e,3407.59,300.493,1024,224,24.01
tf_efficientnet_cc_b0_4e,3402.61,300.934,1024,224,13.31
mixnet_m,3369.57,303.884,1024,224,5.01
regnety_016,3343.57,306.248,1024,224,11.2
nf_seresnet26,3326.57,307.813,1024,224,17.4
nf_ecaresnet26,3308.22,309.519,1024,224,16.0
repvgg_a2,3284.74,311.731,1024,224,28.21
gernet_l,3260.13,314.086,1024,256,31.08
tf_mixnet_m,3258.23,314.269,1024,224,5.01
resnest14d,3225.43,317.465,1024,224,10.61
efficientnet_b3_pruned,3214.49,318.545,1024,300,9.86
convnext_nano_hnf,3199.89,319.999,1024,224,15.59
skresnet34,3189.47,321.044,1024,224,22.28
convit_tiny,3117.16,328.49,1024,224,5.71
resnext26ts,3098.65,330.453,1024,256,10.3
legacy_seresnext26_32x4d,3086.27,331.78,1024,224,16.79
nf_regnet_b1,3049.58,335.771,1024,288,10.22
resnet26t,3040.5,336.774,1024,256,16.01
seresnext26ts,3026.32,338.35,1024,256,10.39
eca_resnext26ts,3023.05,338.719,1024,256,10.3
gcresnext26ts,2976.22,344.049,1024,256,10.48
ecaresnet101d_pruned,2954.94,346.526,1024,224,24.88
nf_regnet_b2,2933.65,349.041,1024,272,14.31
mobilevit_xs,2913.13,175.744,512,256,2.32
seresnext26tn_32x4d,2898.81,353.236,1024,224,16.81
seresnext26t_32x4d,2897.48,353.398,1024,224,16.81
ecaresnext26t_32x4d,2893.22,353.918,1024,224,15.41
ecaresnext50t_32x4d,2891.83,354.088,1024,224,15.41
seresnext26d_32x4d,2884.91,354.937,1024,224,16.81
ecaresnetlight,2878.45,355.733,1024,224,30.16
pit_s_224,2872.59,356.46,1024,224,23.46
deit_small_patch16_224,2853.43,358.855,1024,224,22.05
pit_s_distilled_224,2851.86,359.052,1024,224,24.04
vit_small_patch16_224,2845.41,359.865,1024,224,22.05
tf_efficientnet_b2_ap,2814.51,363.814,1024,260,9.11
tf_efficientnet_b2_ns,2814.31,363.839,1024,260,9.11
coat_lite_tiny,2814.08,363.872,1024,224,5.72
tf_efficientnet_b2,2813.96,363.886,1024,260,9.11
rexnetr_200,2808.62,182.283,512,224,16.52
deit_small_distilled_patch16_224,2801.73,365.478,1024,224,22.44
tresnet_m,2787.92,367.287,1024,224,31.39
resnetv2_50,2780.22,368.303,1024,224,25.55
eca_botnext26ts_256,2766.45,370.137,1024,256,10.59
rexnet_200,2763.52,185.259,512,224,16.37
vit_base2_patch32_256,2752.1,372.066,1024,256,119.46
botnet26t_256,2750.58,372.273,1024,256,12.49
halonet26t,2727.12,375.475,1024,256,12.48
eca_halonext26ts,2721.42,376.262,1024,256,10.76
swsl_resnet50,2693.51,380.159,1024,224,25.56
tv_resnet50,2687.71,380.98,1024,224,25.56
efficientnet_b0_gn,2686.21,381.194,1024,224,5.29
ssl_resnet50,2684.8,381.394,1024,224,25.56
gluon_resnet50_v1b,2682.34,381.744,1024,224,25.56
resnet50,2681.21,381.904,1024,224,25.56
vit_small_resnet26d_224,2675.44,382.728,1024,224,63.61
efficientnet_b2a,2654.03,385.816,1024,288,9.11
efficientnet_b2,2649.9,386.418,1024,288,9.11
coat_lite_mini,2646.6,386.899,1024,224,11.01
hrnet_w18_small_v2,2638.47,388.089,1024,224,15.6
resnetv2_50t,2621.75,390.565,1024,224,25.57
vovnet39a,2620.98,390.68,1024,224,22.6
resnetv2_50d,2613.09,391.86,1024,224,25.57
bat_resnext26ts,2594.52,394.665,1024,256,10.73
resnet32ts,2591.72,395.092,1024,256,17.96
efficientnet_em,2587.13,395.792,1024,240,6.9
cspresnet50,2561.78,399.709,1024,256,21.62
mixnet_l,2560.63,199.939,512,224,7.33
resnet33ts,2550.89,401.417,1024,256,19.68
dpn68b,2548.03,401.866,1024,224,12.61
gluon_resnet50_v1c,2543.04,402.654,1024,224,25.58
ese_vovnet39b,2535.01,403.931,1024,224,24.57
eca_vovnet39b,2533.56,404.162,1024,224,22.6
cspresnext50,2530.86,404.592,1024,224,20.57
legacy_seresnet50,2527.3,405.162,1024,224,28.09
vgg11_bn,2524.72,202.784,512,224,132.87
tf_efficientnet_em,2523.81,405.723,1024,240,6.9
resnet50t,2521.66,406.069,1024,224,25.57
resnet50d,2517.37,406.76,1024,224,25.58
gluon_resnet50_v1d,2514.4,407.243,1024,224,25.58
dpn68,2513.01,407.467,1024,224,12.61
selecsls84,2490.46,411.155,1024,224,50.95
seresnet33ts,2489.53,411.308,1024,256,19.78
eca_resnet33ts,2483.09,412.378,1024,256,19.68
lambda_resnet26t,2479.99,412.893,1024,256,10.96
tf_mixnet_l,2478.99,206.524,512,224,7.33
twins_svt_small,2475.31,413.674,1024,224,24.06
gcresnet33ts,2439.7,419.711,1024,256,19.88
cspresnet50w,2418.46,423.398,1024,256,28.12
seresnet50,2407.37,425.348,1024,224,28.09
cspresnet50d,2400.89,426.492,1024,256,21.64
dla60,2376.63,430.848,1024,224,22.04
densenet121,2346.76,436.333,1024,224,7.98
resnest26d,2346.08,436.46,1024,224,17.07
tv_densenet121,2345.79,436.514,1024,224,7.98
xcit_tiny_24_p16_224_dist,2334.54,438.616,1024,224,12.12
xcit_nano_12_p16_384_dist,2332.68,438.968,1024,384,3.05
xcit_tiny_24_p16_224,2328.43,439.766,1024,224,12.12
haloregnetz_b,2320.38,441.294,1024,224,11.68
resmlp_24_224,2308.85,443.498,1024,224,30.02
resmlp_24_distilled_224,2308.13,443.636,1024,224,30.02
resnetaa50d,2295.34,446.109,1024,224,25.58
seresnet50t,2282.97,448.526,1024,224,28.1
efficientnet_cc_b1_8e,2282.2,448.676,1024,240,39.72
convnext_tiny,2276.32,449.828,1024,224,28.59
res2net50_48w_2s,2265.43,451.999,1024,224,25.29
resnetblur50,2265.02,452.08,1024,224,25.56
efficientnet_lite3,2261.44,226.393,512,300,8.2
ecaresnet50d,2260.92,452.9,1024,224,25.58
efficientnet_b0_g8_gn,2259.05,453.276,1024,224,6.56
densenet121d,2243.46,456.425,1024,224,8.0
resnetrs50,2241.6,456.804,1024,224,35.69
mobilevit_s,2240.37,228.522,512,256,5.58
regnetx_032,2201.52,465.118,1024,224,15.3
visformer_small,2194.82,466.539,1024,224,40.22
gluon_resnet50_v1s,2193.42,466.838,1024,224,25.68
tf_efficientnet_cc_b1_8e,2188.26,467.941,1024,240,39.72
vit_base_resnet26d_224,2188.18,467.954,1024,224,101.4
resnetblur50d,2150.42,476.173,1024,224,25.58
gluon_inception_v3,2150.17,476.225,1024,299,23.83
adv_inception_v3,2148.48,476.602,1024,299,23.83
vovnet57a,2148.25,476.651,1024,224,36.64
tf_inception_v3,2147.17,476.894,1024,299,23.83
inception_v3,2146.78,476.978,1024,299,23.83
densenetblur121d,2133.31,479.992,1024,224,8.0
cspresnext50_iabn,2129.31,480.895,1024,256,20.57
semobilevit_s,2105.83,243.122,512,256,5.74
seresnetaa50d,2088.02,490.403,1024,224,28.11
cspdarknet53_iabn,2087.26,490.582,1024,256,27.64
swsl_resnext50_32x4d,2080.34,492.214,1024,224,25.03
convnext_tiny_hnf,2075.56,493.349,1024,224,28.59
ese_vovnet57b,2074.63,493.57,1024,224,38.61
tf_efficientnet_lite3,2074.49,246.795,512,300,8.2
resnext50_32x4d,2073.97,493.725,1024,224,25.03
ssl_resnext50_32x4d,2073.23,493.902,1024,224,25.03
gluon_resnext50_32x4d,2072.3,494.125,1024,224,25.03
tv_resnext50_32x4d,2055.26,498.221,1024,224,25.03
res2net50_26w_4s,2037.79,502.491,1024,224,25.7
twins_pcpvt_small,2036.4,502.835,1024,224,24.11
xcit_small_12_p16_224_dist,2021.26,506.599,1024,224,26.25
tf_efficientnetv2_b3,2020.91,506.688,1024,300,14.36
xcit_small_12_p16_224,2020.4,506.814,1024,224,26.25
nf_seresnet50,2015.9,507.948,1024,224,28.09
skresnet50,2014.26,508.362,1024,224,25.8
nf_ecaresnet50,2005.54,510.572,1024,224,25.56
regnetx_040,2003.23,511.16,1024,224,22.12
efficientnetv2_rw_t,1999.8,512.038,1024,288,13.65
sehalonet33ts,1991.52,257.078,512,256,13.69
fbnetv3_g,1991.44,514.187,1024,288,16.62
dla60x,1986.19,515.547,1024,224,17.35
gcresnet50t,1982.14,516.599,1024,256,25.9
resnext50d_32x4d,1976.72,518.018,1024,224,25.05
lambda_resnet26rpt_256,1950.98,262.42,512,256,10.99
gmixer_24_224,1937.61,528.474,1024,224,24.72
gc_efficientnetv2_rw_t,1928.42,530.993,1024,288,13.68
res2net50_14w_8s,1920.35,533.22,1024,224,25.06
skresnet50d,1919.46,533.468,1024,224,25.82
densenet169,1916.46,534.305,1024,224,14.15
dla60_res2net,1907.38,536.848,1024,224,20.85
gcresnext50ts,1902.33,538.276,1024,256,15.67
seresnext50_32x4d,1902.13,538.331,1024,224,27.56
gluon_seresnext50_32x4d,1901.68,538.457,1024,224,27.56
res2next50,1896.89,539.818,1024,224,24.67
legacy_seresnext50_32x4d,1896.31,539.984,1024,224,27.56
repvgg_b1g4,1884.44,543.384,1024,224,39.97
resnest50d_1s4x24d,1879.81,544.722,1024,224,25.68
crossvit_small_240,1855.05,551.992,1024,240,26.86
nf_regnet_b3,1852.1,552.873,1024,320,18.59
darknet53,1847.62,277.102,512,256,41.61
mixnet_xl,1839.34,278.346,512,224,11.9
dla60_res2next,1829.2,559.793,1024,224,17.03
swin_tiny_patch4_window7_224,1820.74,562.394,1024,224,28.29
cspdarknet53,1804.24,283.762,512,256,27.64
poolformer_s24,1803.36,567.817,1024,224,21.39
vit_small_r26_s32_224,1799.77,568.949,1024,224,36.43
xcit_nano_12_p8_224_dist,1796.05,570.128,1024,224,3.05
xcit_nano_12_p8_224,1795.49,570.304,1024,224,3.05
regnetz_b16,1791.93,571.438,1024,288,9.72
ecaresnet26t,1786.82,573.073,1024,320,16.01
convnext_tiny_hnfd,1744.82,586.868,1024,224,28.63
gmlp_s16_224,1741.6,587.951,1024,224,19.42
sebotnet33ts_256,1718.54,223.433,384,256,13.7
crossvit_15_240,1711.44,598.314,1024,240,27.53
resnetv2_101,1693.03,604.82,1024,224,44.54
vit_base_resnet50d_224,1670.98,612.798,1024,224,110.97
swin_s3_tiny_224,1660.14,616.801,1024,224,28.33
repvgg_b1,1657.85,617.654,1024,224,57.42
tv_resnet101,1656.03,618.332,1024,224,44.55
gluon_resnet101_v1b,1653.8,619.167,1024,224,44.55
resnet101,1652.45,619.673,1024,224,44.55
tf_efficientnet_b3_ap,1649.82,310.323,512,300,12.23
tf_efficientnet_b3,1649.61,310.363,512,300,12.23
tf_efficientnet_b3_ns,1649.37,310.407,512,300,12.23
crossvit_15_dagger_240,1648.34,621.218,1024,240,28.21
lambda_resnet50ts,1639.74,624.475,1024,256,21.54
resnetv2_101d,1628.19,628.906,1024,224,44.56
efficientnet_b3,1614.3,317.152,512,320,12.23
efficientnet_b3a,1613.57,317.296,512,320,12.23
gluon_resnet101_v1c,1597.11,641.145,1024,224,44.57
resnest50d,1586.43,645.46,1024,224,27.48
gluon_resnet101_v1d,1584.97,646.054,1024,224,44.57
wide_resnet50_2,1583.06,646.835,1024,224,68.88
cait_xxs24_224,1580.68,647.808,1024,224,11.96
dla102,1573.96,650.576,1024,224,33.27
resnetv2_50x1_bit_distilled,1561.81,655.635,1024,224,25.55
res2net50_26w_6s,1556.3,657.955,1024,224,37.05
vit_large_patch32_224,1551.79,659.869,1024,224,306.54
resmlp_36_224,1549.07,661.03,1024,224,44.69
regnetx_080,1548.92,661.091,1024,224,39.57
resmlp_36_distilled_224,1548.49,661.277,1024,224,44.69
halonet50ts,1542.39,663.891,1024,256,22.73
legacy_seresnet101,1528.82,669.783,1024,224,49.33
ese_vovnet39b_evos,1523.51,672.121,1024,224,24.58
coat_lite_small,1509.49,678.361,1024,224,19.84
vgg13_bn,1504.09,340.392,512,224,133.05
xcit_tiny_12_p16_384_dist,1501.95,681.763,1024,384,6.72
resnetaa101d,1496.92,684.059,1024,224,44.57
swin_v2_cr_tiny_224,1495.36,684.765,1024,224,28.33
densenet201,1488.38,687.985,1024,224,20.01
seresnet101,1484.39,689.83,1024,224,49.33
vit_tiny_patch16_384,1479.06,692.319,1024,384,5.79
lamhalobotnet50ts_256,1474.4,694.504,1024,256,22.57
swin_v2_cr_tiny_ns_224,1471.62,695.817,1024,224,28.33
vit_base_patch32_384,1470.27,696.459,1024,384,88.3
vit_base_r26_s32_224,1467.48,697.779,1024,224,101.38
gluon_resnet101_v1s,1452.66,704.901,1024,224,44.67
regnetx_064,1450.48,352.975,512,224,26.21
mixer_b16_224,1448.87,706.746,1024,224,59.88
nf_resnet101,1448.19,707.076,1024,224,44.55
mixer_b16_224_miil,1446.14,708.08,1024,224,59.88
resnetv2_50d_frn,1444.18,709.041,1024,224,25.59
resnetblur101d,1433.31,714.414,1024,224,44.57
nf_resnet50,1425.83,718.163,1024,288,25.56
mixer_l32_224,1425.39,718.388,1024,224,206.94
ecaresnet101d,1423.43,719.375,1024,224,44.57
hrnet_w18,1416.64,722.817,1024,224,21.3
convnext_small,1412.23,725.081,1024,224,50.22
tresnet_l,1401.27,730.75,1024,224,55.99
twins_pcpvt_base,1398.04,732.441,1024,224,43.83
regnety_032,1384.84,739.421,1024,288,19.44
nest_tiny,1381.77,370.526,512,224,17.06
resnet50_gn,1377.54,743.338,1024,224,25.56
resnet51q,1366.14,749.545,1024,288,35.7
resnetv2_50d_evob,1365.49,749.897,1024,224,25.59
jx_nest_tiny,1357.18,377.241,512,224,17.06
botnet50ts_256,1354.82,377.899,512,256,22.74
xception,1347.6,379.923,512,299,22.86
dla102x,1333.01,768.169,1024,224,26.31
convit_small,1329.45,770.231,1024,224,27.78
halo2botnet50ts_256,1327.73,385.607,512,256,22.64
skresnext50_32x4d,1314.86,778.776,1024,224,27.48
swsl_resnext101_32x4d,1313.2,779.762,1024,224,44.18
ssl_resnext101_32x4d,1309.86,781.751,1024,224,44.18
gluon_resnext101_32x4d,1309.44,781.999,1024,224,44.18
resnext101_32x4d,1308.3,782.684,1024,224,44.18
repvgg_b2g4,1287.88,795.093,1024,224,61.76
res2net50_26w_8s,1277.42,801.601,1024,224,48.4
res2net101_26w_4s,1275.83,802.597,1024,224,45.21
resnest50d_4s2x40d,1267.83,807.666,1024,224,30.42
nf_seresnet101,1246.67,821.372,1024,224,49.33
twins_svt_base,1242.25,824.296,1024,224,56.07
nf_ecaresnet101,1240.12,825.715,1024,224,44.55
vgg16_bn,1239.17,413.169,512,224,138.37
resnet61q,1236.58,828.077,1024,288,36.85
eca_nfnet_l0,1234.82,829.257,1024,288,24.14
nfnet_l0,1234.39,829.546,1024,288,35.07
hrnet_w32,1225.86,835.318,1024,224,41.23
xception41p,1219.47,419.841,512,299,26.91
poolformer_s36,1217.85,840.81,1024,224,30.86
ese_vovnet99b_iabn,1214.4,843.2,1024,224,63.2
crossvit_18_240,1210.54,845.892,1024,240,43.27
regnetv_040,1210.02,423.122,512,288,20.64
regnety_040,1209.84,423.186,512,288,20.65
hrnet_w30,1208.97,846.988,1024,224,37.71
dpn92,1205.79,849.224,1024,224,37.67
gluon_seresnext101_32x4d,1198.19,854.61,1024,224,48.96
seresnext101_32x4d,1197.93,854.792,1024,224,48.96
legacy_seresnext101_32x4d,1196.55,855.783,1024,224,48.96
efficientnet_el,1181.26,433.424,512,300,10.59
efficientnet_el_pruned,1179.3,434.142,512,300,10.59
ese_vovnet99b,1178.02,869.24,1024,224,63.2
resnetv2_152,1172.64,873.226,1024,224,60.19
crossvit_18_dagger_240,1169.98,875.211,1024,240,44.27
tf_efficientnet_el,1156.16,442.832,512,300,10.59
tv_resnet152,1155.28,886.354,1024,224,60.19
resnet152,1153.46,887.752,1024,224,60.19
gluon_resnet152_v1b,1153.39,887.802,1024,224,60.19
xcit_tiny_12_p8_224_dist,1148.77,891.372,1024,224,6.71
xcit_tiny_12_p8_224,1148.55,891.542,1024,224,6.71
vit_small_resnet50d_s16_224,1140.9,897.523,1024,224,57.53
resnetv2_152d,1140.85,897.561,1024,224,60.2
mixnet_xxl,1136.05,338.002,384,224,23.96
ecaresnet50t,1133.49,903.391,1024,320,25.57
regnetz_c16,1132.12,452.237,512,320,13.46
repvgg_b2,1129.63,906.478,1024,224,89.02
gluon_resnet152_v1c,1126.87,908.694,1024,224,60.21
vit_base_patch16_224_miil,1124.8,910.37,1024,224,86.54
gluon_resnet152_v1d,1122.21,912.468,1024,224,60.21
volo_d1_224,1121.56,912.997,1024,224,26.63
swin_small_patch4_window7_224,1117.13,916.62,1024,224,49.61
regnety_040s_gn,1113.45,919.647,1024,224,20.65
inception_v4,1099.65,931.189,1024,299,42.68
vit_base_patch16_224_sam,1089.03,940.269,1024,224,86.57
xception41,1089.03,470.133,512,299,26.97
vit_base_patch16_224,1089.0,940.3,1024,224,86.57
deit_base_patch16_224,1088.05,941.117,1024,224,86.57
xcit_small_24_p16_224_dist,1079.16,948.867,1024,224,47.67
xcit_small_24_p16_224,1078.82,949.17,1024,224,47.67
densenet161,1076.84,950.914,1024,224,28.68
convmixer_1024_20_ks9_p14,1075.95,951.707,1024,224,24.38
nfnet_f0,1075.06,952.487,1024,256,71.49
deit_base_distilled_patch16_224,1073.74,953.659,1024,224,87.34
dla169,1071.22,955.906,1024,224,53.39
vgg19_bn,1060.19,482.919,512,224,143.68
cait_xxs36_224,1058.79,967.13,1024,224,17.3
tnt_s_patch16_224,1056.28,969.422,1024,224,23.76
legacy_seresnet152,1054.79,970.791,1024,224,66.82
gluon_resnet152_v1s,1052.41,972.991,1024,224,60.32
regnetx_120,1048.48,488.312,512,224,46.11
seresnet152,1033.07,991.203,1024,224,66.82
tresnet_xl,1032.49,991.76,1024,224,78.44
efficientnet_lite4,1029.33,373.047,384,380,13.01
beit_base_patch16_224,1003.86,1020.053,1024,224,86.53
regnety_120,1003.65,510.126,512,224,51.82
repvgg_b3g4,997.42,1026.637,1024,224,83.83
twins_pcpvt_large,995.78,1028.325,1024,224,60.99
convnext_base,984.44,1040.164,1024,224,88.59
convnext_base_in22ft1k,984.35,1040.256,1024,224,88.59
coat_tiny,976.27,1048.875,1024,224,5.5
tf_efficientnet_lite4,967.59,396.848,384,380,13.01
pit_b_224,954.91,536.164,512,224,73.76
dm_nfnet_f0,948.25,1079.874,1024,256,71.49
pit_b_distilled_224,947.46,540.381,512,224,74.79
vit_small_patch16_36x1_224,919.98,1113.057,1024,224,64.67
wide_resnet101_2,917.07,1116.581,1024,224,126.89
swin_v2_cr_small_224,915.96,1117.937,1024,224,49.7
dla102x2,910.31,562.434,512,224,41.28
resnetv2_50d_gn,909.47,1125.915,1024,288,25.57
efficientnetv2_s,905.47,1130.894,1024,384,21.46
vit_small_patch16_18x2_224,899.06,1138.948,1024,224,64.67
tf_efficientnetv2_s_in21ft1k,889.42,1151.294,1024,384,21.46
tf_efficientnetv2_s,889.32,1151.431,1024,384,21.46
xception65p,886.31,577.66,512,299,39.82
nest_small,881.75,580.652,512,224,38.35
twins_svt_large,880.33,1163.185,1024,224,99.27
repvgg_b3,878.37,1165.779,1024,224,123.09
resnetrs101,877.57,1166.842,1024,288,63.62
jx_nest_small,871.53,587.458,512,224,38.35
efficientnetv2_rw_s,866.51,1181.735,1024,384,23.94
dpn98,862.25,1187.578,1024,224,61.57
ens_adv_inception_resnet_v2,856.37,1195.737,1024,299,55.84
inception_resnet_v2,855.57,1196.844,1024,299,55.84
nf_regnet_b4,854.46,1198.403,1024,384,30.21
regnetz_b16_evos,853.4,599.942,512,288,9.74
regnetx_160,848.43,603.455,512,224,54.28
regnetz_d8,845.88,1210.552,1024,320,23.37
cait_s24_224,834.81,1226.608,1024,224,46.92
gluon_resnext101_64x4d,834.02,1227.777,1024,224,83.46
resnet200,828.19,1236.414,1024,224,64.67
regnetz_040,826.88,464.381,384,320,27.12
regnetz_040h,823.24,466.438,384,320,28.94
efficientnet_b4,820.41,468.046,384,384,19.34
swin_s3_small_224,817.53,626.263,512,224,49.74
hrnet_w40,816.49,1254.128,1024,224,57.56
poolformer_m36,815.39,1255.826,1024,224,56.17
swsl_resnext101_32x8d,805.27,1271.611,1024,224,88.79
regnety_064,803.23,637.411,512,288,30.58
ssl_resnext101_32x8d,802.86,1275.43,1024,224,88.79
xcit_tiny_24_p16_384_dist,802.73,1275.631,1024,384,12.12
ig_resnext101_32x8d,802.17,1276.521,1024,224,88.79
resnext101_32x8d,802.06,1276.704,1024,224,88.79
regnetv_064,800.43,639.64,512,288,30.58
resnetv2_50d_evos,798.97,640.813,512,288,25.59
gluon_xception65,797.15,642.277,512,299,39.92
resnet101d,796.13,1286.203,1024,320,44.57
xception65,795.21,643.84,512,299,39.92
resnest101e,791.92,646.513,512,256,48.28
swin_base_patch4_window7_224,791.65,1293.482,1024,224,87.77
gluon_seresnext101_64x4d,787.65,1300.055,1024,224,88.23
coat_mini,785.23,1304.064,1024,224,10.34
tf_efficientnet_b4_ap,782.92,490.459,384,380,19.34
tf_efficientnet_b4,782.13,490.953,384,380,19.34
tf_efficientnet_b4_ns,782.09,490.976,384,380,19.34
regnety_080,767.29,667.271,512,288,39.18
hrnet_w44,759.24,1348.7,1024,224,67.06
crossvit_base_240,756.03,677.208,512,240,105.03
xcit_medium_24_p16_224_dist,748.3,1368.42,1024,224,84.4
xcit_medium_24_p16_224,747.93,1369.089,1024,224,84.4
gmlp_b16_224,744.43,1375.538,1024,224,73.08
hrnet_w48,733.55,1395.939,1024,224,77.47
tresnet_m_448,727.54,1407.473,1024,448,31.39
vit_large_r50_s32_224,726.03,1410.402,1024,224,328.99
regnetz_d32,721.75,1418.755,1024,320,27.58
vit_small_patch16_384,684.33,748.167,512,384,22.2
tnt_b_patch16_224,677.0,1512.541,1024,224,65.41
xcit_small_12_p16_384_dist,675.76,1515.313,1024,384,26.25
convit_base,673.78,1519.763,1024,224,86.54
swin_s3_base_224,663.17,1544.087,1024,224,71.13
swin_v2_cr_base_224,651.94,1570.69,1024,224,87.88
densenet264d_iabn,647.21,1582.161,1024,224,72.74
efficientnet_b3_gn,646.96,395.683,256,320,11.73
dpn131,635.27,1611.889,1024,224,79.25
densenet264,628.46,1629.36,1024,224,72.69
nest_base,627.45,815.992,512,224,67.72
volo_d2_224,626.17,1635.329,1024,224,58.68
jx_nest_base,620.43,825.215,512,224,67.72
poolformer_m48,615.67,1663.217,1024,224,73.47
vit_small_r26_s32_384,611.26,628.196,384,384,36.47
xcit_nano_12_p8_384_dist,610.06,1678.498,1024,384,3.05
xcit_tiny_24_p8_224,603.0,1698.15,1024,224,12.11
xcit_tiny_24_p8_224_dist,602.22,1700.353,1024,224,12.11
xception71,600.16,853.086,512,299,42.34
hrnet_w64,599.02,1709.451,1024,224,128.06
vit_base_r50_s16_224,598.25,1711.642,1024,224,98.66
legacy_senet154,582.07,1759.212,1024,224,115.09
senet154,582.01,1759.392,1024,224,115.09
gluon_senet154,581.94,1759.609,1024,224,115.09
dpn107,578.97,1768.627,1024,224,86.92
eca_nfnet_l1,575.61,1778.965,1024,320,41.41
seresnet200d,562.84,1819.333,1024,256,71.86
resnet152d,561.29,1824.359,1024,320,60.21
ecaresnet200d,559.73,1829.437,1024,256,64.69
convnext_large_in22ft1k,546.71,936.5,512,224,197.77
convnext_large,546.09,937.561,512,224,197.77
regnetz_c16_evos,539.7,948.669,512,320,13.49
regnety_320,534.72,957.496,512,224,145.05
regnety_160,523.72,733.2,384,288,83.59
efficientnet_b3_g8_gn,520.23,492.079,256,320,14.25
xcit_small_12_p8_224,514.94,1988.547,1024,224,26.21
xcit_small_12_p8_224_dist,514.63,1989.761,1024,224,26.21
halonet_h1,507.89,504.03,256,256,8.1
resnext101_64x4d,505.79,1012.259,512,288,83.46
seresnet152d,503.07,2035.474,1024,320,66.84
resnetrs152,499.61,2049.599,1024,320,86.62
vit_large_patch32_384,494.68,2070.028,1024,384,306.63
regnetx_320,468.44,819.734,384,224,107.81
mixer_l16_224,464.51,2204.436,1024,224,208.2
seresnext101_32x8d,461.67,1109.006,512,288,93.57
swin_large_patch4_window7_224,454.97,1125.326,512,224,196.53
regnetz_e8,451.54,1133.877,512,320,57.7
efficientnetv2_m,442.19,2315.716,1024,416,54.14
seresnet269d,440.45,2324.895,1024,256,113.67
volo_d3_224,438.95,2332.847,1024,224,86.33
xcit_large_24_p16_224,432.87,2365.575,1024,224,189.1
xcit_large_24_p16_224_dist,432.7,2366.505,1024,224,189.1
efficientnet_b5,411.07,622.743,256,456,30.39
efficientnetv2_rw_m,406.88,1258.338,512,416,53.24
resnet200d,404.01,2534.588,1024,320,64.69
tf_efficientnet_b5,395.61,647.082,256,456,30.39
tf_efficientnet_b5_ap,395.52,647.242,256,456,30.39
tf_efficientnet_b5_ns,395.47,647.322,256,456,30.39
resnetv2_50x1_bitm,392.67,1303.884,512,448,25.55
xcit_tiny_12_p8_384_dist,390.32,2623.487,1024,384,6.71
swin_v2_cr_large_224,385.91,1326.718,512,224,196.68
swsl_resnext101_32x16d,375.78,1362.484,512,224,194.03
ig_resnext101_32x16d,373.86,1369.478,512,224,194.03
ssl_resnext101_32x16d,373.64,1370.28,512,224,194.03
regnetz_d8_evos,373.09,1372.306,512,320,23.46
swin_v2_cr_tiny_384,364.64,702.039,256,384,28.33
tresnet_l_448,361.5,2832.633,1024,448,55.99
convnext_xlarge_in22ft1k,360.86,1418.822,512,224,350.2
xcit_small_24_p16_384_dist,360.65,2839.301,1024,384,47.67
resnetrs200,358.31,2857.856,1024,320,93.21
nfnet_f1,357.09,2867.576,1024,320,132.63
vit_large_patch16_224,356.78,2870.107,1024,224,304.33
crossvit_15_dagger_408,354.9,721.306,256,408,28.5
vit_base_patch16_18x2_224,348.19,2940.87,1024,224,256.73
tf_efficientnetv2_m_in21ft1k,347.38,1473.892,512,480,54.14
tf_efficientnetv2_m,346.86,1476.074,512,480,54.14
dm_nfnet_f1,342.33,1495.606,512,320,132.63
convnext_base_384_in22ft1k,336.37,1141.59,384,384,88.59
beit_large_patch16_224,330.46,3098.668,1024,224,304.43
volo_d1_384,293.12,1746.724,512,384,26.78
convmixer_768_32,290.99,3518.992,1024,224,21.11
eca_nfnet_l2,282.42,1812.878,512,384,56.72
volo_d4_224,281.7,3635.003,1024,224,192.96
resnetv2_152x2_bit_teacher,280.73,1823.773,512,224,236.34
vit_base_patch16_384,280.43,1369.296,384,384,86.86
deit_base_patch16_384,280.4,1369.456,384,384,86.86
deit_base_distilled_patch16_384,276.56,1388.495,384,384,87.63
xcit_small_24_p8_224,269.73,3796.43,1024,224,47.63
tresnet_xl_448,269.71,1898.302,512,448,78.44
xcit_small_24_p8_224_dist,269.67,3797.157,1024,224,47.63
resnest200e,265.14,1931.055,512,320,70.2
cait_xxs24_384,264.07,3877.737,1024,384,12.03
vit_large_patch14_224,262.13,3906.369,1024,224,304.2
crossvit_18_dagger_408,260.17,983.968,256,408,44.61
xcit_medium_24_p16_384_dist,254.44,2012.271,512,384,84.4
nasnetalarge,254.21,1510.535,384,331,88.75
pnasnet5large,251.02,1529.719,384,331,86.06
resnetv2_101x1_bitm,246.55,2076.684,512,448,44.54
beit_base_patch16_384,241.2,1592.025,384,384,86.74
vit_large_r50_s32_384,240.58,1596.142,384,384,329.09
efficientnet_b6,239.36,534.745,128,528,43.04
ecaresnet269d,233.52,4385.094,1024,352,102.09
tf_efficientnet_b6_ns,231.37,553.209,128,528,43.04
tf_efficientnet_b6,231.23,553.545,128,528,43.04
tf_efficientnet_b6_ap,231.04,553.998,128,528,43.04
resnetrs270,227.52,4500.663,1024,352,129.86
swin_v2_cr_small_384,224.2,1141.845,256,384,49.7
swin_base_patch4_window12_384,211.77,906.647,192,384,87.9
resmlp_big_24_224,209.16,4895.869,1024,224,129.14
resmlp_big_24_distilled_224,208.94,4900.863,1024,224,129.14
resmlp_big_24_224_in22ft1k,208.92,4901.346,1024,224,129.14
xcit_tiny_24_p8_384_dist,204.37,5010.586,1024,384,12.11
nfnet_f2,200.76,5100.492,1024,352,193.78
tf_efficientnetv2_l,199.73,1922.595,384,480,118.52
efficientnetv2_l,198.4,2580.63,512,480,118.52
tf_efficientnetv2_l_in21ft1k,196.98,2599.257,512,480,118.52
dm_nfnet_f2,194.75,2628.942,512,352,193.78
xcit_medium_24_p8_224,189.81,2697.371,512,224,84.32
xcit_medium_24_p8_224_dist,189.81,2697.462,512,224,84.32
volo_d5_224,187.06,5474.175,1024,224,295.46
convnext_large_384_in22ft1k,186.8,1370.399,256,384,197.77
cait_xs24_384,184.72,2771.758,512,384,26.67
vit_base_patch8_224,183.25,1396.951,256,224,86.58
cait_xxs36_384,176.62,5797.903,1024,384,17.37
vit_base_r50_s16_384,174.09,2205.77,384,384,98.95
vit_base_resnet50_384,174.05,2206.21,384,384,98.95
xcit_small_12_p8_384_dist,173.11,2957.56,512,384,26.21
swin_v2_cr_huge_224,170.28,2255.094,384,224,657.83
convmixer_1536_20,167.4,6117.1,1024,224,51.63
volo_d2_384,164.47,2334.792,384,384,58.87
swin_v2_cr_base_384,160.01,1199.906,192,384,87.88
eca_nfnet_l3,159.42,3211.652,512,448,72.04
resnetrs350,151.88,3371.146,512,384,163.96
xcit_large_24_p16_384_dist,147.05,3481.712,512,384,189.1
ig_resnext101_32x32d,146.83,1743.474,256,224,468.53
cait_s24_384,142.26,3598.927,512,384,47.06
vit_huge_patch14_224,141.6,7231.567,1024,224,632.05
efficientnet_b7,139.66,687.357,96,600,66.35
tf_efficientnet_b7,135.86,706.608,96,600,66.35
tf_efficientnet_b7_ap,135.79,706.955,96,600,66.35
tf_efficientnet_b7_ns,135.74,707.2,96,600,66.35
efficientnetv2_xl,127.77,3005.388,384,512,208.12
tf_efficientnetv2_xl_in21ft1k,127.07,3021.991,384,512,208.12
swin_large_patch4_window12_384,125.28,1021.736,128,384,196.74
resnest269e,123.52,3108.719,384,416,110.93
convnext_xlarge_384_in22ft1k,123.3,1557.136,192,384,350.2
xcit_large_24_p8_224,110.05,4652.475,512,224,188.93
xcit_large_24_p8_224_dist,109.94,4656.907,512,224,188.93
nfnet_f3,109.46,4677.461,512,416,254.92
resnetrs420,108.78,4706.738,512,416,191.89
dm_nfnet_f3,98.93,5175.208,512,416,254.92
swin_v2_cr_large_384,98.1,1304.756,128,384,196.68
resnetv2_152x2_bit_teacher_384,97.22,2633.19,256,384,236.34
cait_s36_384,95.27,5374.225,512,384,68.37
vit_large_patch16_384,94.74,2702.22,256,384,304.72
resnetv2_50x3_bitm,94.62,1352.761,128,448,217.32
vit_giant_patch14_224,92.89,5511.692,512,224,1012.61
xcit_small_24_p8_384_dist,90.84,4227.257,384,384,47.63
ig_resnext101_32x48d,87.18,2202.253,192,224,828.41
efficientnet_b8,83.91,1144.078,96,672,87.41
beit_large_patch16_384,82.64,3097.749,256,384,305.0
tf_efficientnet_b8_ap,82.27,1166.925,96,672,87.41
tf_efficientnet_b8,82.25,1167.133,96,672,87.41
volo_d3_448,72.26,2657.14,192,448,86.63
resnetv2_152x2_bitm,71.6,2681.389,192,448,236.34
xcit_medium_24_p8_384_dist,64.15,3990.849,256,384,84.32
nfnet_f4,60.24,6374.659,384,512,316.07
dm_nfnet_f4,59.09,4332.344,256,512,316.07
resnetv2_101x3_bitm,58.2,2199.411,128,448,387.93
vit_gigantic_patch14_224,56.14,9120.818,512,224,1844.44
volo_d4_448,52.76,2425.859,128,448,193.41
swin_v2_cr_giant_224,49.04,2610.23,128,224,2598.76
tf_efficientnet_l2_ns_475,46.41,1378.993,64,475,480.31
nfnet_f5,44.14,5799.172,256,544,377.21
swin_v2_cr_huge_384,43.59,1468.203,64,384,657.94
dm_nfnet_f5,39.84,6426.216,256,544,377.21
xcit_large_24_p8_384_dist,36.78,5220.859,192,384,188.93
volo_d5_448,36.57,3500.029,128,448,295.91
nfnet_f6,34.59,7401.234,256,576,438.36
beit_large_patch16_512,33.3,2882.954,96,512,305.67
cait_m36_384,31.36,8162.98,256,384,271.22
dm_nfnet_f6,31.17,8214.193,256,576,438.36
nfnet_f7,26.91,9514.003,256,608,499.5
volo_d5_512,25.64,4991.614,128,512,296.09
resnetv2_152x4_bitm,18.58,3444.83,64,480,936.53
efficientnet_l2,16.55,1450.295,24,800,480.31
tf_efficientnet_l2_ns,16.36,1467.093,24,800,480.31
swin_v2_cr_giant_384,13.63,1760.856,24,384,2598.76
cait_m48_448,13.35,9584.614,128,448,356.46
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt111-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,10725.36,46.047,512,106,2.04
mobilenetv3_small_050,9864.52,50.786,512,224,1.59
lcnet_035,9593.72,52.888,512,224,1.64
lcnet_050,8283.82,61.296,512,224,1.88
tf_mobilenetv3_small_minimal_100,8178.73,62.055,512,224,2.04
tinynet_d,7987.22,63.336,512,152,2.34
mobilenetv3_small_075,7734.29,65.482,512,224,2.04
mobilenetv3_small_100,7481.49,67.702,512,224,2.54
tf_mobilenetv3_small_075,7093.89,71.455,512,224,2.04
tf_mobilenetv3_small_100,6879.11,73.705,512,224,2.54
levit_128s,6303.14,80.293,512,224,7.78
lcnet_075,5742.95,88.676,512,224,2.36
lcnet_100,5331.75,95.531,512,224,2.95
mixer_s32_224,4714.36,108.029,512,224,19.1
mnasnet_small,4652.19,109.156,512,224,2.03
mnasnet_050,4534.41,112.14,512,224,2.22
levit_128,4434.56,114.332,512,224,9.21
vit_small_patch32_224,4334.06,117.284,512,224,22.88
mobilenetv2_035,4197.24,121.203,512,224,1.68
tinynet_c,4165.97,121.921,512,184,2.46
gernet_s,4117.74,123.649,512,224,8.17
semnasnet_050,4027.14,126.223,512,224,2.08
vit_tiny_r_s16_p8_224,3857.49,131.88,512,224,6.34
levit_192,3823.94,132.765,512,224,10.95
lcnet_150,3663.02,139.3,512,224,4.5
resnet18,3584.19,142.504,512,224,11.69
gluon_resnet18_v1b,3584.07,142.508,512,224,11.69
swsl_resnet18,3583.72,142.531,512,224,11.69
ssl_resnet18,3558.1,143.543,512,224,11.69
mobilenetv2_050,3541.93,143.76,512,224,1.97
mobilenetv3_large_075,3343.71,152.255,512,224,3.99
ese_vovnet19b_slim_dw,3243.14,157.395,512,224,1.9
tf_mobilenetv3_large_minimal_100,3227.09,157.922,512,224,3.92
seresnet18,3222.19,158.398,512,224,11.78
legacy_seresnet18,3130.77,163.021,512,224,11.78
tf_mobilenetv3_large_075,3109.15,163.824,512,224,3.99
mnasnet_075,3102.76,164.235,512,224,3.17
ghostnet_050,3069.54,165.437,512,224,2.59
mobilenetv3_rw,3020.36,168.644,512,224,5.48
mobilenetv3_large_100,2997.0,169.969,512,224,5.48
mobilenetv3_large_100_miil,2996.51,169.991,512,224,5.48
levit_256,2923.52,174.041,512,224,18.89
hardcorenas_a,2875.54,177.351,512,224,5.26
resnet18d,2830.02,180.547,512,224,11.71
mnasnet_b1,2826.42,180.324,512,224,4.38
mnasnet_100,2810.01,181.39,512,224,4.38
tf_mobilenetv3_large_100,2800.51,181.961,512,224,5.48
tinynet_b,2773.33,183.58,512,188,3.73
hardcorenas_b,2665.39,191.158,512,224,5.18
semnasnet_075,2649.51,192.342,512,224,2.91
hardcorenas_c,2643.17,192.764,512,224,5.52
ese_vovnet19b_slim,2613.26,195.551,512,224,3.17
mobilenetv2_075,2538.45,200.896,512,224,2.64
tf_efficientnetv2_b0,2507.59,202.986,512,224,7.14
spnasnet_100,2504.82,203.411,512,224,4.42
levit_256d,2485.6,204.456,512,224,26.21
hardcorenas_d,2483.8,204.983,512,224,7.5
semnasnet_100,2411.15,211.44,512,224,3.89
mnasnet_a1,2396.96,212.676,512,224,3.89
mobilenetv2_100,2381.65,214.209,512,224,3.5
regnetx_002,2371.97,215.169,512,224,2.68
tinynet_a,2274.6,223.875,512,192,6.19
regnety_002,2255.16,226.095,512,224,3.16
ghostnet_100,2251.56,226.062,512,224,5.18
fbnetc_100,2248.09,226.804,512,224,5.57
deit_tiny_patch16_224,2233.8,228.385,512,224,5.72
vit_tiny_patch16_224,2229.44,228.819,512,224,5.72
efficientnet_lite0,2209.28,231.003,512,224,4.65
hardcorenas_f,2207.45,230.839,512,224,8.2
deit_tiny_distilled_patch16_224,2193.62,232.567,512,224,5.91
hardcorenas_e,2183.35,233.392,512,224,8.07
xcit_nano_12_p16_224_dist,2148.4,236.588,512,224,3.05
xcit_nano_12_p16_224,2147.35,236.626,512,224,3.05
tv_resnet34,2081.13,245.45,512,224,21.8
resnet34,2080.93,245.474,512,224,21.8
gluon_resnet34_v1b,2070.75,246.674,512,224,21.8
pit_ti_distilled_224,2069.0,246.563,512,224,5.1
pit_ti_224,2067.14,246.798,512,224,4.85
tf_efficientnet_lite0,2051.57,248.83,512,224,4.65
skresnet18,2011.18,253.956,512,224,11.96
resnet26,1965.66,259.994,512,224,16.0
resnetblur18,1945.89,262.773,512,224,11.69
gernet_m,1920.15,265.947,512,224,21.14
ese_vovnet19b_dw,1905.39,268.216,512,224,6.54
nf_resnet26,1877.61,272.201,512,224,16.0
hrnet_w18_small,1858.6,274.127,512,224,13.19
seresnet34,1854.8,275.134,512,224,21.96
mnasnet_140,1835.61,278.136,512,224,7.12
legacy_seresnet34,1814.57,281.284,512,224,21.96
efficientnet_b0,1800.57,212.181,384,224,5.29
levit_384,1799.63,283.374,512,224,39.13
resnet34d,1799.03,284.0,512,224,21.82
mobilenetv2_110d,1768.64,216.1,384,224,4.52
rexnetr_100,1759.23,217.141,384,224,4.88
selecsls42,1754.3,291.22,512,224,30.35
selecsls42b,1748.59,292.176,512,224,32.46
mixer_b32_224,1728.02,295.478,512,224,60.29
tf_efficientnet_b0_ns,1702.0,224.532,384,224,5.29
tf_efficientnet_b0_ap,1700.69,224.751,384,224,5.29
tf_efficientnet_b0,1700.24,224.78,384,224,5.29
mixer_s16_224,1649.18,309.899,512,224,18.53
semnasnet_140,1640.99,311.119,512,224,6.11
tf_efficientnet_es,1622.88,314.744,512,224,5.44
efficientnet_es,1618.83,315.528,512,224,5.44
efficientnet_es_pruned,1616.07,316.054,512,224,5.44
vit_base_patch32_224_sam,1613.76,316.429,512,224,88.22
vit_base_patch32_224,1612.96,316.587,512,224,88.22
resnet26d,1609.37,317.63,512,224,16.01
tf_efficientnetv2_b1,1607.05,237.525,384,240,8.14
ghostnet_130,1600.19,318.63,512,224,7.36
pit_xs_distilled_224,1591.2,320.859,512,224,11.0
pit_xs_224,1589.39,321.258,512,224,10.62
repvgg_b0,1586.15,321.714,512,224,15.82
resmlp_12_224,1552.02,329.099,512,224,15.35
resmlp_12_distilled_224,1551.98,329.119,512,224,15.35
gmixer_12_224,1551.47,329.195,512,224,12.7
mobilenetv2_140,1539.46,248.646,384,224,6.11
mobilevit_xxs,1515.36,252.293,384,256,1.27
selecsls60,1486.56,343.544,512,224,30.67
selecsls60b,1480.82,344.867,512,224,32.77
nf_seresnet26,1471.47,347.302,512,224,17.4
xcit_tiny_12_p16_224,1422.37,358.218,512,224,6.72
xcit_tiny_12_p16_224_dist,1420.76,358.558,512,224,6.72
efficientnet_lite1,1418.67,179.483,256,240,5.42
vit_small_patch32_384,1414.68,361.045,512,384,22.92
efficientnet_b1_pruned,1384.32,368.407,512,240,6.33
gmlp_ti16_224,1378.39,276.995,384,224,5.87
dla46_c,1367.99,373.533,512,224,1.3
nf_ecaresnet26,1361.11,375.605,512,224,16.0
poolformer_s12,1359.83,375.831,512,224,11.92
rexnetr_130,1350.37,188.438,256,224,7.61
tf_efficientnet_lite1,1343.78,189.557,256,240,5.42
crossvit_tiny_240,1320.33,386.159,512,240,7.01
mobilenetv2_120d,1309.71,194.276,256,224,5.83
resnetv2_50,1296.0,394.281,512,224,25.55
gernet_l,1283.32,398.106,512,256,31.08
rexnet_100,1277.92,299.324,384,224,4.8
crossvit_9_240,1236.5,309.16,384,240,8.55
resnet26t,1227.85,416.475,512,256,16.01
ssl_resnet50,1223.5,417.643,512,224,25.56
resnet50,1222.61,417.956,512,224,25.56
tv_resnet50,1222.03,418.147,512,224,25.56
swsl_resnet50,1222.0,418.189,512,224,25.56
gluon_resnet50_v1b,1221.97,418.181,512,224,25.56
crossvit_9_dagger_240,1195.59,319.731,384,240,8.78
vit_tiny_r_s16_p8_384,1193.45,320.912,384,384,6.36
rexnetr_150,1185.63,214.799,256,224,9.78
fbnetv3_b,1184.86,322.487,384,256,8.6
botnet26t_256,1168.73,327.971,384,256,12.49
tf_efficientnetv2_b2,1157.41,219.649,256,260,10.1
regnetx_004,1150.82,443.834,512,224,5.16
repvgg_a2,1142.14,447.42,512,224,28.21
skresnet34,1140.48,447.803,512,224,22.28
resnetv2_50t,1134.35,450.541,512,224,25.57
fbnetv3_d,1133.65,223.966,256,256,10.31
resnetv2_50d,1131.93,451.499,512,224,25.57
gluon_resnet50_v1c,1131.41,338.54,384,224,25.58
halonet26t,1122.34,341.57,384,256,12.48
convit_tiny,1108.09,461.02,512,224,5.71
efficientnet_lite2,1096.26,232.544,256,260,6.09
dla34,1094.33,467.287,512,224,15.74
convnext_nano_hnf,1075.54,356.227,384,224,15.59
resnet50d,1070.21,357.929,384,224,25.58
gluon_resnet50_v1d,1070.13,357.997,384,224,25.58
resnet50t,1068.62,358.508,384,224,25.57
mixnet_s,1051.2,485.837,512,224,4.13
legacy_seresnext26_32x4d,1051.18,486.414,512,224,16.79
tf_efficientnet_lite2,1045.58,243.879,256,260,6.09
vit_small_resnet26d_224,1042.37,367.395,384,224,63.61
deit_small_patch16_224,1032.62,371.017,384,224,22.05
vit_small_patch16_224,1027.63,372.823,384,224,22.05
regnety_004,1027.25,497.255,512,224,4.34
tf_efficientnet_b1_ns,1026.16,247.955,256,240,7.79
tf_efficientnet_b1_ap,1026.11,248.006,256,240,7.79
tf_efficientnet_b1,1025.11,248.193,256,240,7.79
resnet32ts,1021.77,249.96,256,256,17.96
deit_small_distilled_patch16_224,1010.79,379.058,384,224,22.44
resnet33ts,1009.39,252.998,256,256,19.68
res2net50_48w_2s,1006.25,380.82,384,224,25.29
vovnet39a,1004.46,509.092,512,224,22.6
seresnext26d_32x4d,1002.27,382.471,384,224,16.81
seresnext26t_32x4d,1001.74,382.665,384,224,16.81
seresnext26tn_32x4d,1001.47,382.779,384,224,16.81
legacy_seresnet50,993.86,385.256,384,224,28.09
tf_efficientnet_em,979.63,260.356,256,240,6.9
efficientnet_em,978.46,260.687,256,240,6.9
dla46x_c,973.77,525.047,512,224,1.07
eca_resnet33ts,964.39,264.788,256,256,19.68
pit_s_224,961.0,265.507,256,224,23.46
pit_s_distilled_224,960.14,265.718,256,224,24.04
tf_mixnet_s,958.59,532.877,512,224,4.13
seresnet50,956.87,400.165,384,224,28.09
efficientnet_b1,954.91,266.596,256,256,7.79
seresnet33ts,954.85,267.313,256,256,19.78
ecaresnetlight,952.86,536.422,512,224,30.16
vit_base2_patch32_256,950.48,537.853,512,256,119.46
ese_vovnet39b,947.73,539.578,512,224,24.57
ecaresnext50t_32x4d,947.69,404.65,384,224,15.41
ecaresnext26t_32x4d,947.22,404.844,384,224,15.41
dla60,945.45,405.196,384,224,22.04
gluon_resnet50_v1s,943.31,406.227,384,224,25.68
resnetaa50d,941.94,406.82,384,224,25.58
eca_vovnet39b,939.18,544.514,512,224,22.6
vgg11,930.6,550.023,512,224,132.86
gcresnet33ts,927.58,274.995,256,256,19.88
lambda_resnet26rpt_256,921.55,207.755,192,256,10.99
dla60x_c,921.01,554.951,512,224,1.32
ecaresnet50d_pruned,911.85,560.565,512,224,19.94
resnetblur50,909.58,421.362,384,224,25.56
mobilevit_xs,909.51,210.0,192,256,2.32
cspresnet50,906.79,422.612,384,256,21.62
rexnetr_200,896.75,212.966,192,224,16.52
coat_lite_tiny,890.79,430.193,384,224,5.72
nf_seresnet50,886.81,431.807,384,224,28.09
dpn68b,878.29,436.039,384,224,12.61
selecsls84,872.56,585.545,512,224,50.95
twins_svt_small,868.85,440.366,384,224,24.06
hrnet_w18_small_v2,867.42,587.948,512,224,15.6
seresnet50t,865.51,442.504,384,224,28.1
cspresnext50,862.61,444.309,384,224,20.57
resnetrs50,861.96,444.354,384,224,35.69
cspresnet50w,860.12,445.567,384,256,28.12
cspresnet50d,849.09,451.363,384,256,21.64
densenet121,845.28,301.077,256,224,7.98
tv_densenet121,845.28,301.063,256,224,7.98
rexnet_150,842.23,302.82,256,224,9.73
tv_resnext50_32x4d,836.18,458.41,384,224,25.03
swsl_resnext50_32x4d,836.09,458.464,384,224,25.03
res2net50_26w_4s,835.77,458.208,384,224,25.7
coat_lite_mini,833.77,459.672,384,224,11.01
vit_base_resnet26d_224,833.37,459.491,384,224,101.4
resnext50_32x4d,832.87,460.244,384,224,25.03
ssl_resnext50_32x4d,832.34,460.521,384,224,25.03
dpn68,831.95,460.44,384,224,12.61
gluon_resnext50_32x4d,831.77,460.799,384,224,25.03
vovnet57a,828.64,616.994,512,224,36.64
efficientnet_b2_pruned,825.77,308.457,256,260,8.31
resnetblur50d,818.48,311.928,256,224,25.58
skresnet50,810.12,472.628,384,224,25.8
tf_efficientnet_b2_ap,809.72,235.646,192,260,9.11
tf_efficientnet_b2_ns,809.32,235.717,192,260,9.11
tf_efficientnet_b2,809.06,235.843,192,260,9.11
vgg11_bn,805.38,476.555,384,224,132.87
densenet121d,805.31,316.045,256,224,8.0
nf_ecaresnet50,804.89,476.102,384,224,25.56
rexnet_130,801.42,318.304,256,224,7.56
ecaresnet50d,793.07,483.245,384,224,25.58
ese_vovnet57b,790.94,484.567,384,224,38.61
regnetx_006,790.56,646.782,512,224,6.2
gcresnet50t,788.09,323.372,256,256,25.9
regnety_006,787.76,648.873,512,224,6.06
convnext_tiny,781.17,326.737,256,224,28.59
tf_inception_v3,774.78,494.217,384,299,23.83
gluon_inception_v3,774.48,494.404,384,299,23.83
seresnetaa50d,773.82,329.682,256,224,28.11
inception_v3,772.99,495.38,384,299,23.83
adv_inception_v3,769.85,497.351,384,299,23.83
resmlp_24_distilled_224,767.92,331.893,256,224,30.02
resnext50d_32x4d,765.61,333.516,256,224,25.05
resmlp_24_224,762.25,334.383,256,224,30.02
gmixer_24_224,759.72,335.461,256,224,24.72
resnetv2_101,757.49,336.449,256,224,44.54
res2net50_14w_8s,756.34,336.303,256,224,25.06
xcit_nano_12_p16_384_dist,754.95,337.338,256,384,3.05
sehalonet33ts,749.52,340.746,256,256,13.69
densenetblur121d,739.98,344.167,256,224,8.0
dla60_res2net,736.45,346.205,256,224,20.85
skresnet50d,736.23,346.324,256,224,25.82
mobilevit_s,734.66,260.236,192,256,5.58
gluon_resnet101_v1b,731.02,348.601,256,224,44.55
tv_resnet101,727.65,350.31,256,224,44.55
resnet101,727.5,350.369,256,224,44.55
xcit_tiny_24_p16_224,726.25,349.143,256,224,12.12
xcit_tiny_24_p16_224_dist,725.28,349.489,256,224,12.12
efficientnet_b2,724.21,263.654,192,288,9.11
twins_pcpvt_small,724.17,351.883,256,224,24.11
efficientnet_b2a,723.56,263.856,192,288,9.11
ecaresnet101d_pruned,714.19,715.116,512,224,24.88
nf_resnet50,710.87,539.322,384,288,25.56
nf_resnet101,707.69,540.968,384,224,44.55
seresnext50_32x4d,706.91,361.01,256,224,27.56
gluon_seresnext50_32x4d,706.58,361.114,256,224,27.56
efficientnet_b0_gn,705.84,361.599,256,224,5.29
legacy_seresnext50_32x4d,704.96,362.028,256,224,27.56
nf_regnet_b0,703.19,726.933,512,256,8.76
darknet53,701.97,363.892,256,256,41.61
resnetv2_101d,699.92,364.177,256,224,44.56
densenet169,698.35,364.121,256,224,14.15
gluon_resnet101_v1c,697.85,365.313,256,224,44.57
dla60x,694.54,367.645,256,224,17.35
poolformer_s24,690.11,369.675,256,224,21.39
semobilevit_s,684.71,279.143,192,256,5.74
efficientnetv2_rw_t,684.48,278.424,192,288,13.65
vit_small_r26_s32_224,682.34,373.878,256,224,36.43
convnext_tiny_hnf,681.83,374.501,256,224,28.59
gluon_resnet101_v1d,674.08,378.237,256,224,44.57
tf_efficientnetv2_b3,670.7,284.467,192,300,14.36
xcit_small_12_p16_224,670.22,380.208,256,224,26.25
xcit_small_12_p16_224_dist,669.97,380.26,256,224,26.25
sebotnet33ts_256,666.38,191.3,128,256,13.7
rexnet_200,665.99,287.162,192,224,16.37
vgg13,663.21,578.818,384,224,133.05
regnety_008,661.82,772.637,512,224,6.26
wide_resnet50_2,661.04,580.088,384,224,68.88
dla102,650.38,392.047,256,224,33.27
gmlp_s16_224,648.9,294.316,192,224,19.42
vit_base_resnet50d_224,646.39,394.444,256,224,110.97
swin_tiny_patch4_window7_224,639.35,399.428,256,224,28.29
ecaresnet26t,628.75,406.586,256,320,16.01
repvgg_b1,627.31,815.089,512,224,57.42
gluon_resnet101_v1s,624.3,408.497,256,224,44.67
crossvit_small_240,624.04,408.587,256,240,26.86
resnetaa101d,621.23,410.532,256,224,44.57
eca_botnext26ts_256,618.03,413.613,256,256,10.59
resnext26ts,615.62,623.252,384,256,10.3
gc_efficientnetv2_rw_t,605.04,314.593,192,288,13.68
eca_halonext26ts,604.41,422.932,256,256,10.76
seresnext26ts,598.53,427.051,256,256,10.39
eca_resnext26ts,598.25,427.346,256,256,10.3
convnext_tiny_hnfd,592.58,430.992,256,224,28.63
regnetx_008,591.8,864.35,512,224,7.26
resnetv2_50x1_bit_distilled,589.64,324.794,192,224,25.55
legacy_seresnet101,588.41,432.899,256,224,49.33
gcresnext26ts,585.17,436.656,256,256,10.48
halonet50ts,584.39,327.573,192,256,22.73
xcit_nano_12_p8_224,583.42,437.05,256,224,3.05
cait_xxs24_224,582.93,436.673,256,224,11.96
xcit_nano_12_p8_224_dist,581.01,438.848,256,224,3.05
mixer_b16_224,580.44,440.268,256,224,59.88
mixer_b16_224_miil,579.94,440.653,256,224,59.88
swin_s3_tiny_224,578.76,441.339,256,224,28.33
seresnet101,574.05,443.691,256,224,49.33
mixnet_m,573.23,891.634,512,224,5.01
res2net50_26w_6s,569.28,447.908,256,224,37.05
vgg13_bn,568.79,449.813,256,224,133.05
crossvit_15_240,568.46,335.957,192,240,27.53
resnetblur101d,567.71,449.352,256,224,44.57
cspdarknet53,565.64,451.547,256,256,27.64
efficientnet_lite3,563.01,226.244,128,300,8.2
tf_efficientnet_lite3,561.05,227.067,128,300,8.2
crossvit_15_dagger_240,549.96,347.22,192,240,28.21
resnext101_32x4d,548.6,465.031,256,224,44.18
tf_mixnet_m,547.03,700.438,384,224,5.01
swsl_resnext101_32x4d,546.13,467.192,256,224,44.18
gluon_resnext101_32x4d,545.31,467.899,256,224,44.18
ssl_resnext101_32x4d,545.31,467.944,256,224,44.18
densenet201,539.32,353.029,192,224,20.01
vgg16,536.49,715.548,384,224,138.36
bat_resnext26ts,533.44,478.688,256,256,10.73
nf_seresnet101,532.2,478.733,256,224,49.33
vit_base_r26_s32_224,528.57,361.981,192,224,101.38
resnetv2_152,524.49,485.916,256,224,60.19
botnet50ts_256,524.41,243.109,128,256,22.74
res2net101_26w_4s,523.46,486.536,256,224,45.21
efficientnet_b3_pruned,519.43,491.117,256,300,9.86
vit_base_patch32_384,517.32,494.013,256,384,88.3
vit_large_patch32_224,511.41,498.976,256,224,306.54
halo2botnet50ts_256,510.64,375.017,192,256,22.64
mixer_l32_224,510.1,374.908,192,224,206.94
swin_v2_cr_tiny_224,506.89,377.55,192,224,28.33
resmlp_36_distilled_224,505.72,377.435,192,224,44.69
resmlp_36_224,505.39,377.735,192,224,44.69
vit_tiny_patch16_384,503.94,253.174,128,384,5.79
res2next50,502.84,507.818,256,224,24.67
dla102x,501.98,380.938,192,224,26.31
swin_v2_cr_tiny_ns_224,501.36,381.688,192,224,28.33
resnet152,499.83,381.783,192,224,60.19
gluon_resnet152_v1b,499.79,381.933,192,224,60.19
tv_resnet152,496.47,384.401,192,224,60.19
xception,494.06,258.29,128,299,22.86
visformer_tiny,491.83,1040.332,512,224,10.32
mixnet_l,488.23,784.993,384,224,7.33
gluon_resnet152_v1c,485.58,393.139,192,224,60.21
resnet50_gn,484.73,395.256,192,224,25.56
resnetv2_152d,484.59,393.938,192,224,60.2
twins_pcpvt_base,480.18,397.102,192,224,43.83
nest_tiny,477.31,267.267,128,224,17.06
res2net50_26w_8s,476.93,534.581,256,224,48.4
ecaresnet101d,475.58,536.508,256,224,44.57
convnext_small,473.89,403.445,192,224,50.22
gluon_resnet152_v1d,473.52,403.216,192,224,60.21
tf_mixnet_l,470.86,542.123,256,224,7.33
jx_nest_tiny,470.71,271.015,128,224,17.06
vgg16_bn,469.97,544.386,256,224,138.37
nf_ecaresnet101,465.87,547.628,256,224,44.55
coat_lite_small,463.34,412.922,192,224,19.84
poolformer_s36,458.24,417.134,192,224,30.86
efficientnet_el,455.42,279.993,128,300,10.59
efficientnet_el_pruned,454.32,280.643,128,300,10.59
vgg19,451.86,849.574,384,224,143.67
convit_small,450.19,425.458,192,224,27.78
fbnetv3_g,449.02,283.045,128,288,16.62
ese_vovnet99b,448.97,568.658,256,224,63.2
seresnext101_32x4d,448.16,426.163,192,224,48.96
gluon_seresnext101_32x4d,447.49,426.94,192,224,48.96
gluon_resnet152_v1s,446.73,427.49,192,224,60.32
legacy_seresnext101_32x4d,446.01,428.263,192,224,48.96
nf_regnet_b3,442.03,577.378,256,320,18.59
tf_efficientnet_el,441.61,288.785,128,300,10.59
ese_vovnet39b_evos,439.23,290.493,128,224,24.58
dla60_res2next,437.89,583.208,256,224,17.03
volo_d1_224,437.25,437.756,192,224,26.63
dla169,436.98,436.866,192,224,53.39
skresnext50_32x4d,435.09,586.972,256,224,27.48
hrnet_w32,433.46,438.263,192,224,41.23
vit_small_resnet50d_s16_224,432.94,442.239,192,224,57.53
twins_svt_base,429.83,444.617,192,224,56.07
hrnet_w18,419.32,605.846,256,224,21.3
crossvit_18_240,400.15,317.858,128,240,43.27
vgg19_bn,399.74,640.035,256,224,143.68
ecaresnet50t,399.44,319.549,128,320,25.57
inception_v4,397.78,480.469,192,299,42.68
tf_efficientnet_b3,393.44,323.695,128,300,12.23
swin_small_patch4_window7_224,393.38,486.272,192,224,49.61
legacy_seresnet152,392.87,485.406,192,224,66.82
tf_efficientnet_b3_ap,392.08,324.783,128,300,12.23
vit_base_patch16_224_miil,391.87,489.169,192,224,86.54
tf_efficientnet_b3_ns,391.61,325.176,128,300,12.23
crossvit_18_dagger_240,387.95,327.891,128,240,44.27
vit_base_patch16_224,385.23,497.575,192,224,86.57
vit_base_patch16_224_sam,384.98,497.891,192,224,86.57
deit_base_patch16_224,384.15,498.956,192,224,86.57
cait_xxs36_224,380.29,501.012,192,224,17.3
repvgg_b2,380.03,1346.183,512,224,89.02
regnetx_016,379.21,1349.264,512,224,9.19
deit_base_distilled_patch16_224,378.28,506.739,192,224,87.34
densenet161,376.13,337.907,128,224,28.68
haloregnetz_b,375.52,680.247,256,224,11.68
xcit_tiny_12_p8_224,374.86,339.659,128,224,6.71
xcit_tiny_12_p8_224_dist,374.8,339.672,128,224,6.71
seresnet152,372.93,339.954,128,224,66.82
dla102x2,362.92,351.15,128,224,41.28
wide_resnet101_2,360.46,531.121,192,224,126.89
gluon_resnext101_64x4d,357.83,356.147,128,224,83.46
efficientnet_b3a,354.6,359.335,128,320,12.23
resnet200,354.57,357.998,128,224,64.67
xception41p,353.88,360.846,128,299,26.91
regnety_016,353.36,1447.082,512,224,11.2
efficientnet_b3,353.18,360.796,128,320,12.23
beit_base_patch16_224,352.29,543.93,192,224,86.53
resnest14d,349.84,1463.045,512,224,10.61
hrnet_w30,346.82,733.381,256,224,37.71
ens_adv_inception_resnet_v2,341.5,558.819,192,299,55.84
inception_resnet_v2,341.03,559.722,192,299,55.84
xcit_small_24_p16_224_dist,341.01,371.894,128,224,47.67
tnt_s_patch16_224,340.3,562.32,192,224,23.76
xcit_small_24_p16_224,339.02,373.952,128,224,47.67
efficientnet_lite4,334.87,189.779,64,380,13.01
dpn92,332.25,769.002,256,224,37.67
nf_regnet_b1,330.04,1549.911,512,288,10.22
twins_pcpvt_large,327.73,386.698,128,224,60.99
resnet101d,327.41,389.353,128,320,44.57
convnext_small_in22ft1k,327.31,389.301,128,224,88.59
convnext_base,327.3,389.374,128,224,88.59
convnext_base_in22ft1k,326.61,390.177,128,224,88.59
convnext_tiny_in22ft1k,324.94,392.181,128,224,88.59
tf_efficientnet_lite4,322.62,197.041,64,380,13.01
resnetrs101,321.74,395.61,128,288,63.62
pit_b_224,318.92,400.391,128,224,73.76
pit_b_distilled_224,318.09,401.455,128,224,74.79
gcresnext50ts,316.73,604.706,192,256,15.67
repvgg_b3,315.56,1215.795,384,224,123.09
gluon_seresnext101_64x4d,313.23,406.424,128,224,88.23
regnetz_d8,311.3,204.013,64,320,23.37
poolformer_m36,307.82,413.95,128,224,56.17
xception41,305.16,418.228,128,299,26.97
resnetv2_50d_gn,304.62,419.347,128,288,25.57
coat_tiny,304.05,418.955,128,224,5.5
vit_small_patch16_36x1_224,302.22,420.928,128,224,64.67
swin_v2_cr_small_224,302.05,421.43,128,224,49.7
cait_s24_224,301.09,422.497,128,224,46.92
mixnet_xl,300.81,849.169,256,224,11.9
vit_small_patch16_18x2_224,299.9,424.102,128,224,64.67
resnetv2_50d_frn,299.64,426.047,128,224,25.59
efficientnetv2_s,299.05,318.819,96,384,21.46
twins_svt_large,298.59,426.599,128,224,99.27
tf_efficientnetv2_s,297.45,320.544,96,384,21.46
tf_efficientnetv2_s_in21ft1k,295.84,322.238,96,384,21.46
efficientnetv2_rw_s,295.58,214.343,64,384,23.94
nest_small,295.19,323.571,96,224,38.35
jx_nest_small,293.04,325.974,96,224,38.35
hrnet_w40,291.68,653.529,192,224,57.56
regnetz_005,286.8,1783.819,512,224,7.12
nf_regnet_b2,284.19,1799.964,512,272,14.31
dpn98,283.21,450.397,128,224,61.57
gluon_xception65,280.82,339.911,96,299,39.92
xception65,279.18,341.922,96,299,39.92
resnet51q,277.93,689.969,192,288,35.7
nf_regnet_b4,277.28,459.47,128,384,30.21
swin_s3_small_224,277.05,344.678,96,224,49.74
swin_base_patch4_window7_224,275.83,462.244,128,224,87.77
xception65p,274.95,464.241,128,299,39.82
gmlp_b16_224,270.89,352.851,96,224,73.08
hrnet_w48,266.5,475.648,128,224,77.47
resnest26d,258.35,1485.604,384,224,17.07
resnest50d_1s4x24d,258.11,990.508,256,224,25.68
xcit_tiny_24_p16_384_dist,251.56,378.207,96,384,12.12
regnetz_c16,250.16,510.248,128,320,13.46
crossvit_base_240,249.99,382.366,96,240,105.03
coat_mini,247.34,515.482,128,224,10.34
xcit_medium_24_p16_224,244.65,519.851,128,224,84.4
xcit_medium_24_p16_224_dist,244.08,521.069,128,224,84.4
hrnet_w44,241.77,789.352,192,224,67.06
efficientnet_b4,241.47,262.953,64,384,19.34
volo_d2_224,238.72,400.292,96,224,58.68
tf_efficientnet_b4,236.52,268.528,64,380,19.34
tf_efficientnet_b4_ap,236.39,268.69,64,380,19.34
tf_efficientnet_b4_ns,236.1,269.028,64,380,19.34
vit_small_patch16_384,235.52,270.897,64,384,22.2
resnetv2_50d_evob,235.19,406.951,96,224,25.59
tresnet_m,234.84,2177.505,512,224,31.39
nfnet_l0,233.22,1096.449,256,288,35.07
visformer_small,232.72,1649.37,384,224,40.22
xcit_small_12_p16_384_dist,230.87,414.048,96,384,26.25
vit_large_r50_s32_224,228.88,417.083,96,224,328.99
convit_base,228.65,558.76,128,224,86.54
eca_nfnet_l0,226.92,1127.142,256,288,24.14
resnetv2_50d_evos,223.6,285.051,64,288,25.59
tnt_b_patch16_224,222.54,573.324,128,224,65.41
vit_small_r26_s32_384,221.41,287.746,64,384,36.47
densenet264,220.0,432.388,96,224,72.69
swin_s3_base_224,219.07,435.557,96,224,71.13
hrnet_w64,218.91,579.973,128,224,128.06
resnext101_64x4d,217.22,440.378,96,288,83.46
resnet152d,216.09,441.927,96,320,60.21
xception71,215.62,294.681,64,299,42.34
swin_v2_cr_base_224,215.23,443.662,96,224,87.88
dpn131,211.54,603.038,128,224,79.25
nest_base,210.35,302.564,64,224,67.72
vit_base_r50_s16_224,208.87,457.959,96,224,98.66
jx_nest_base,208.37,305.48,64,224,67.72
resnet61q,207.91,614.63,128,288,36.85
mixnet_xxl,202.79,629.315,128,224,23.96
xcit_nano_12_p8_384_dist,196.18,324.445,64,384,3.05
poolformer_m48,193.91,492.638,96,224,73.47
xcit_tiny_24_p8_224,190.8,499.85,96,224,12.11
xcit_tiny_24_p8_224_dist,190.62,500.3,96,224,12.11
seresnet200d,190.31,500.165,96,256,71.86
ecaresnet200d,182.23,523.49,96,256,64.69
regnetz_b16,181.6,1055.83,192,288,9.72
convnext_large_in22ft1k,180.86,529.028,96,224,197.77
convnext_large,180.64,529.707,96,224,197.77
convmixer_768_32,179.25,534.249,96,224,21.11
repvgg_b1g4,178.41,2868.637,512,224,39.97
regnety_032,178.01,1436.656,256,288,19.44
regnetx_032,177.68,2160.015,384,224,15.3
resnest50d,177.64,1439.786,256,224,27.48
gluon_senet154,177.29,538.24,96,224,115.09
senet154,176.81,539.67,96,224,115.09
halonet_h1,175.85,362.526,64,256,8.1
legacy_senet154,175.45,543.752,96,224,115.09
xcit_small_12_p8_224,174.99,363.954,64,224,26.21
xcit_small_12_p8_224_dist,174.85,364.256,64,224,26.21
seresnet152d,173.74,364.967,64,320,66.84
dpn107,173.23,552.482,96,224,86.92
mixer_l16_224,172.58,554.751,96,224,208.2
resnetrs152,171.14,370.433,64,320,86.62
resnest50d_4s2x40d,167.22,1529.64,256,224,30.42
resnet200d,166.19,382.131,64,320,64.69
volo_d3_224,162.25,391.709,64,224,86.33
regnetx_040,161.87,2371.182,384,224,22.12
vit_large_patch32_384,161.83,591.666,96,384,306.63
efficientnet_b3_gn,160.22,397.781,64,320,11.73
swin_large_patch4_window7_224,151.35,420.987,64,224,196.53
regnetx_080,150.02,2558.515,384,224,39.57
regnety_040s_gn,149.61,853.983,128,224,20.65
efficientnetv2_m,149.31,318.263,48,416,54.14
ssl_resnext101_32x8d,147.46,866.477,128,224,88.79
resnext101_32x8d,147.32,867.312,128,224,88.79
swsl_resnext101_32x8d,147.16,868.264,128,224,88.79
ig_resnext101_32x8d,146.91,869.691,128,224,88.79
regnetz_e8,146.27,326.163,48,320,57.7
resnetv2_50x1_bitm,140.77,340.162,48,448,25.55
seresnet269d,137.16,460.633,64,256,113.67
xcit_large_24_p16_224,136.77,464.553,64,224,189.1
xcit_large_24_p16_224_dist,136.73,464.738,64,224,189.1
xcit_tiny_12_p8_384_dist,128.06,373.098,48,384,6.71
efficientnetv2_rw_m,126.21,250.013,32,416,53.24
regnetx_064,124.13,2061.422,256,224,26.21
resnetrs200,123.75,383.587,48,320,93.21
dm_nfnet_f0,121.58,2104.417,256,256,71.49
swin_v2_cr_large_224,120.98,394.327,48,224,196.68
regnety_040,120.25,1595.17,192,288,20.65
nfnet_f0,119.63,2138.729,256,256,71.49
regnetv_040,118.92,1074.873,128,288,20.64
ese_vovnet99b_iabn,118.27,3243.951,384,224,63.2
xcit_small_24_p16_384_dist,117.41,405.393,48,384,47.67
regnetz_b16_evos,117.22,544.08,64,288,9.74
crossvit_15_dagger_408,116.43,273.026,32,408,28.5
efficientnet_b0_g8_gn,115.43,2216.717,256,224,6.56
vit_large_patch16_224,115.39,553.095,64,224,304.33
regnetz_c16_evos,115.15,414.978,48,320,13.49
vit_base_patch16_18x2_224,114.12,558.126,64,224,256.73
convnext_xlarge_in22ft1k,114.03,559.475,64,224,350.2
convnext_tiny_384_in22ft1k,112.53,424.859,48,384,88.59
convnext_small_384_in22ft1k,112.49,424.983,48,384,88.59
convnext_base_384_in22ft1k,112.43,425.187,48,384,88.59
swin_v2_cr_tiny_384,111.07,286.85,32,384,28.33
tf_efficientnetv2_m,109.5,289.071,32,480,54.14
tf_efficientnetv2_m_in21ft1k,109.37,289.435,32,480,54.14
beit_large_patch16_224,106.59,598.365,64,224,304.43
volo_d1_384,104.96,303.527,32,384,26.78
tresnet_l,104.79,4882.686,512,224,55.99
repvgg_b2g4,102.33,5002.104,512,224,61.76
eca_nfnet_l1,101.26,1262.246,128,320,41.41
volo_d4_224,101.17,471.916,48,224,192.96
cspdarknet53_iabn,98.65,3890.155,384,256,27.64
cait_xxs24_384,98.52,484.701,48,384,12.03
efficientnet_b5,97.28,326.485,32,456,30.39
tf_efficientnet_b5,95.75,331.792,32,456,30.39
tf_efficientnet_b5_ns,95.73,331.833,32,456,30.39
vit_base_patch16_384,95.69,333.568,32,384,86.86
deit_base_patch16_384,95.67,333.658,32,384,86.86
regnetz_d8_evos,95.66,332.456,32,320,23.46
tf_efficientnet_b5_ap,95.45,332.725,32,456,30.39
regnetz_040,94.56,674.98,64,320,27.12
regnetz_040h,94.14,678.01,64,320,28.94
deit_base_distilled_patch16_384,93.65,340.87,32,384,87.63
tresnet_xl,90.76,4227.401,384,224,78.44
cspresnext50_iabn,89.98,4265.102,384,256,20.57
resnest101e,89.71,1424.366,128,256,48.28
crossvit_18_dagger_408,87.99,361.633,32,408,44.61
xcit_small_24_p8_224,87.73,361.414,32,224,47.63
xcit_small_24_p8_224_dist,87.7,361.441,32,224,47.63
resnetv2_101x1_bitm,86.89,366.637,32,448,44.54
nf_regnet_b5,86.53,736.892,64,456,49.74
resnetv2_152x2_bit_teacher,86.4,368.012,32,224,236.34
repvgg_b3g4,84.75,4530.071,384,224,83.83
vit_large_patch14_224,84.3,567.814,48,224,304.2
beit_base_patch16_384,82.73,385.724,32,384,86.74
seresnext101_32x8d,81.65,781.61,64,288,93.57
xcit_medium_24_p16_384_dist,81.08,391.168,32,384,84.4
ecaresnet269d,77.88,406.425,32,352,102.09
regnetx_120,77.54,3300.773,256,224,46.11
pnasnet5large,76.44,414.721,32,331,86.06
vit_large_r50_s32_384,75.76,419.984,32,384,329.09
regnety_120,75.34,2547.007,192,224,51.82
resnetrs270,75.27,419.128,32,352,129.86
swin_base_patch4_window12_384,73.61,432.836,32,384,87.9
regnety_064,72.69,1759.173,128,288,30.58
regnetz_d32,72.18,885.049,64,320,27.58
regnetv_064,71.81,1780.738,128,288,30.58
resmlp_big_24_224,68.34,466.717,32,224,129.14
resmlp_big_24_224_in22ft1k,68.31,466.955,32,224,129.14
resmlp_big_24_distilled_224,68.26,467.278,32,224,129.14
regnety_320,67.49,1895.092,128,224,145.05
nasnetalarge,66.93,473.19,32,331,88.75
swin_v2_cr_small_384,66.26,359.874,24,384,49.7
cait_xs24_384,65.98,482.447,32,384,26.67
regnety_080,65.45,1954.421,128,288,39.18
regnetx_160,65.01,2952.336,192,224,54.28
xcit_tiny_24_p8_384_dist,64.61,492.002,32,384,12.11
volo_d5_224,64.26,494.733,32,224,295.46
cait_xxs36_384,63.75,498.023,32,384,17.37
vit_base_patch8_224,62.96,380.293,24,224,86.58
xcit_medium_24_p8_224,62.71,506.97,32,224,84.32
xcit_medium_24_p8_224_dist,62.63,507.407,32,224,84.32
efficientnet_b3_g8_gn,62.43,1023.448,64,320,14.25
convnext_large_384_in22ft1k,61.7,516.836,32,384,197.77
convmixer_1024_20_ks9_p14,61.37,4170.722,256,224,24.38
efficientnet_b0_g16_evos,60.46,6350.16,384,224,8.11
tf_efficientnetv2_l_in21ft1k,60.25,261.19,16,480,118.52
xcit_small_12_p8_384_dist,60.04,398.02,24,384,26.21
efficientnetv2_l,59.19,265.957,16,480,118.52
vit_base_resnet50_384,59.0,405.136,24,384,98.95
tf_efficientnetv2_l,58.87,267.382,16,480,118.52
vit_base_r50_s16_384,58.85,406.196,24,384,98.95
volo_d2_384,58.21,273.125,16,384,58.87
tresnet_m_448,53.55,3582.526,192,448,31.39
cait_s24_384,49.8,479.385,24,384,47.06
regnety_160,48.14,1993.0,96,288,83.59
ig_resnext101_32x16d,47.52,2018.737,96,224,194.03
swsl_resnext101_32x16d,47.43,2022.391,96,224,194.03
xcit_large_24_p16_384_dist,47.39,502.961,24,384,189.1
ssl_resnext101_32x16d,47.29,2028.601,96,224,194.03
resnetrs350,47.22,500.442,24,384,163.96
swin_v2_cr_base_384,47.19,336.677,16,384,87.88
swin_v2_cr_huge_224,46.26,343.404,16,224,657.83
regnetx_320,46.16,2771.769,128,224,107.81
eca_nfnet_l2,44.82,1425.204,64,384,56.72
efficientnet_b6,42.73,371.597,16,528,43.04
tf_efficientnet_b6,41.54,382.29,16,528,43.04
tf_efficientnet_b6_ns,41.5,382.621,16,528,43.04
tf_efficientnet_b6_ap,41.36,384.088,16,528,43.04
swin_large_patch4_window12_384,41.02,388.265,16,384,196.74
nfnet_f1,40.44,2371.478,96,320,132.63
vit_huge_patch14_224,39.64,401.543,16,224,632.05
dm_nfnet_f1,38.26,1670.645,64,320,132.63
convnext_xlarge_384_in22ft1k,37.04,430.195,16,384,350.2
efficientnet_b7,36.68,214.625,8,600,66.35
efficientnetv2_xl,36.54,322.755,12,512,208.12
tf_efficientnetv2_xl_in21ft1k,36.36,324.371,12,512,208.12
tf_efficientnet_b7_ap,36.21,217.402,8,600,66.35
tf_efficientnet_b7,36.05,218.422,8,600,66.35
tf_efficientnet_b7_ns,35.41,221.975,8,600,66.35
xcit_large_24_p8_224,34.96,454.269,16,224,188.93
xcit_large_24_p8_224_dist,34.92,454.696,16,224,188.93
resnetrs420,32.2,487.619,16,416,191.89
cait_s36_384,32.09,494.814,16,384,68.37
resnest200e,32.0,1494.763,48,320,70.2
densenet264d_iabn,31.93,4003.992,128,224,72.74
resnetv2_50x3_bitm,31.81,502.194,16,448,217.32
xcit_small_24_p8_384_dist,29.97,396.971,12,384,47.63
resnetv2_152x2_bit_teacher_384,29.92,398.698,12,384,236.34
vit_large_patch16_384,28.85,414.372,12,384,304.72
swin_v2_cr_large_384,28.47,419.18,12,384,196.68
tresnet_l_448,25.64,4988.984,128,448,55.99
beit_large_patch16_384,25.11,475.839,12,384,305.0
volo_d3_448,24.79,320.233,8,448,86.63
eca_nfnet_l3,24.19,1319.468,32,448,72.04
tresnet_xl_448,23.17,4140.191,96,448,78.44
nfnet_f2,22.36,2143.929,48,352,193.78
vit_giant_patch14_224,22.25,356.944,8,224,1012.61
dm_nfnet_f2,22.12,2166.49,48,352,193.78
efficientnet_cc_b0_8e,21.78,44.083,1,224,24.01
resnetv2_152x2_bitm,21.74,365.619,8,448,236.34
tf_efficientnet_cc_b0_4e,21.44,44.859,1,224,13.31
tf_efficientnet_cc_b0_8e,20.98,45.875,1,224,24.01
xcit_medium_24_p8_384_dist,20.42,388.21,8,384,84.32
efficientnet_cc_b0_4e,20.36,47.276,1,224,13.31
ig_resnext101_32x32d,18.17,1760.063,32,224,468.53
volo_d4_448,17.6,338.311,6,448,193.41
resnetv2_101x3_bitm,17.53,454.801,8,448,387.93
tf_efficientnet_cc_b1_8e,17.15,56.006,1,240,39.72
efficientnet_cc_b1_8e,16.21,59.398,1,240,39.72
resnest269e,13.09,1826.778,24,416,110.93
tf_efficientnet_b8_ap,12.18,488.771,6,672,87.41
efficientnet_b8,12.12,491.31,6,672,87.41
nfnet_f3,12.08,1982.52,24,416,254.92
xcit_large_24_p8_384_dist,11.91,500.622,6,384,188.93
tf_efficientnet_b8,11.9,500.516,6,672,87.41
cait_m36_384,11.87,501.638,6,384,271.22
dm_nfnet_f3,11.74,2040.434,24,416,254.92
volo_d5_448,11.52,343.952,4,448,295.91
swin_v2_cr_huge_384,10.9,364.423,4,384,657.94
convmixer_1536_20,9.63,4981.706,48,224,51.63
beit_large_patch16_512,9.42,422.773,4,512,305.67
tf_efficientnet_l2_ns_475,9.0,327.787,3,475,480.31
ig_resnext101_32x48d,8.5,1880.808,16,224,828.41
volo_d5_512,8.05,369.448,3,512,296.09
nfnet_f4,6.47,1849.874,12,512,316.07
dm_nfnet_f4,6.2,1929.065,12,512,316.07
cait_m48_448,4.75,415.897,2,448,356.46
nfnet_f5,4.63,1719.792,8,544,377.21
resnetv2_152x4_bitm,4.49,443.185,2,480,936.53
dm_nfnet_f5,4.47,1782.137,8,544,377.21
nfnet_f6,3.5,1707.387,6,576,438.36
dm_nfnet_f6,3.39,1759.608,6,576,438.36
nfnet_f7,2.67,1489.771,4,608,499.5
efficientnet_l2,2.09,473.733,1,800,480.31
tf_efficientnet_l2_ns,2.09,474.031,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt210-cu121-rtx3090.csv | model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count
tinynet_e,106,1024.0,50604.03,20.225,0.03,0.69,2.04
mobilenetv3_small_050,224,1024.0,46069.42,22.217,0.03,0.92,1.59
lcnet_035,224,1024.0,41190.64,24.85,0.03,1.04,1.64
lcnet_050,224,1024.0,37663.82,27.178,0.05,1.26,1.88
mobilenetv3_small_075,224,1024.0,33398.64,30.649,0.05,1.3,2.04
efficientvit_m0,224,1024.0,32179.13,31.812,0.08,0.91,2.35
mobilenetv3_small_100,224,1024.0,29653.41,34.522,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,224,1024.0,28352.57,36.106,0.06,1.41,2.04
tinynet_d,152,1024.0,27612.87,37.074,0.05,1.42,2.34
tf_mobilenetv3_small_075,224,1024.0,27505.95,37.218,0.05,1.3,2.04
tf_mobilenetv3_small_100,224,1024.0,24859.95,41.18,0.06,1.42,2.54
efficientvit_m1,224,1024.0,24836.87,41.219,0.17,1.33,2.98
lcnet_075,224,1024.0,24184.78,42.33,0.1,1.99,2.36
efficientvit_m2,224,1024.0,21907.95,46.731,0.2,1.47,4.19
mnasnet_small,224,1024.0,20764.95,49.303,0.07,2.16,2.03
levit_128s,224,1024.0,20669.44,49.531,0.31,1.88,7.78
lcnet_100,224,1024.0,19774.93,51.772,0.16,2.52,2.95
regnetx_002,224,1024.0,18945.55,54.04,0.2,2.16,2.68
resnet10t,176,1024.0,18840.28,54.342,0.7,1.51,5.44
efficientvit_m3,224,1024.0,18627.14,54.963,0.27,1.62,6.9
mobilenetv2_035,224,1024.0,18464.78,55.447,0.07,2.86,1.68
ghostnet_050,224,1024.0,17741.46,57.707,0.05,1.77,2.59
resnet18,160,1024.0,17592.15,58.198,0.93,1.27,11.69
regnety_002,224,1024.0,17571.32,58.267,0.2,2.17,3.16
levit_conv_128s,224,1024.0,17529.9,58.404,0.31,1.88,7.78
efficientvit_m4,224,1024.0,17446.52,58.683,0.3,1.7,8.8
repghostnet_050,224,1024.0,17090.91,59.904,0.05,2.02,2.31
efficientvit_b0,224,1024.0,16784.26,60.999,0.1,2.87,3.41
vit_tiny_r_s16_p8_224,224,1024.0,16479.31,62.128,0.43,1.85,6.34
vit_small_patch32_224,224,1024.0,15974.78,64.091,1.12,2.09,22.88
mnasnet_050,224,1024.0,15859.35,64.557,0.11,3.07,2.22
mobilenetv2_050,224,1024.0,14885.11,68.783,0.1,3.64,1.97
tinynet_c,184,1024.0,14726.2,69.525,0.11,2.87,2.46
pit_ti_224,224,1024.0,14628.51,69.989,0.5,2.75,4.85
pit_ti_distilled_224,224,1024.0,14546.3,70.385,0.51,2.77,5.1
semnasnet_050,224,1024.0,14351.42,71.341,0.11,3.44,2.08
levit_128,224,1024.0,14192.78,72.139,0.41,2.71,9.21
repghostnet_058,224,1024.0,13482.93,75.937,0.07,2.59,2.55
mixer_s32_224,224,1024.0,13082.53,78.262,1.0,2.28,19.1
cs3darknet_focus_s,256,1024.0,12838.86,79.748,0.69,2.7,3.27
regnetx_004,224,1024.0,12620.59,81.127,0.4,3.14,5.16
levit_conv_128,224,1024.0,12584.5,81.359,0.41,2.71,9.21
cs3darknet_s,256,1024.0,12531.56,81.703,0.72,2.97,3.28
lcnet_150,224,1024.0,12510.06,81.844,0.34,3.79,4.5
regnetx_004_tv,224,1024.0,12294.91,83.276,0.42,3.17,5.5
efficientvit_m5,224,1024.0,12067.16,84.847,0.53,2.41,12.47
mobilenetv3_large_075,224,1024.0,12041.45,85.029,0.16,4.0,3.99
levit_192,224,1024.0,11986.94,85.416,0.66,3.2,10.95
resnet10t,224,1024.0,11963.05,85.587,1.1,2.43,5.44
gernet_s,224,1024.0,11809.29,86.701,0.75,2.65,8.17
ese_vovnet19b_slim_dw,224,1024.0,11618.32,88.126,0.4,5.28,1.9
vit_tiny_patch16_224,224,1024.0,11270.42,90.846,1.08,4.12,5.72
deit_tiny_patch16_224,224,1024.0,11259.37,90.936,1.08,4.12,5.72
deit_tiny_distilled_patch16_224,224,1024.0,11217.54,91.275,1.09,4.15,5.91
repghostnet_080,224,1024.0,11079.58,92.412,0.1,3.22,3.28
mobilenetv3_rw,224,1024.0,10908.78,93.859,0.23,4.41,5.48
levit_conv_192,224,1024.0,10768.96,95.077,0.66,3.2,10.95
mobilenetv3_large_100,224,1024.0,10731.24,95.412,0.23,4.41,5.48
hardcorenas_a,224,1024.0,10620.31,96.408,0.23,4.38,5.26
tf_mobilenetv3_large_075,224,1024.0,10495.83,97.552,0.16,4.0,3.99
resnet14t,176,1024.0,10451.45,97.965,1.07,3.61,10.08
mnasnet_075,224,1024.0,10423.24,98.231,0.23,4.77,3.17
tf_mobilenetv3_large_minimal_100,224,1024.0,10369.07,98.745,0.22,4.4,3.92
resnet34,160,1024.0,10330.89,99.109,1.87,1.91,21.8
regnety_004,224,1024.0,9931.33,103.097,0.41,3.89,4.34
nf_regnet_b0,192,1024.0,9884.05,103.59,0.37,3.15,8.76
regnetx_006,224,1024.0,9823.29,104.232,0.61,3.98,6.2
hardcorenas_b,224,1024.0,9755.67,104.953,0.26,5.09,5.18
hardcorenas_c,224,1024.0,9572.88,106.958,0.28,5.01,5.52
ghostnet_100,224,1024.0,9528.83,107.453,0.15,3.55,5.18
tf_mobilenetv3_large_100,224,1024.0,9484.05,107.96,0.23,4.41,5.48
tinynet_b,188,1024.0,9358.37,109.409,0.21,4.44,3.73
mnasnet_100,224,1024.0,9357.9,109.416,0.33,5.46,4.38
tf_efficientnetv2_b0,192,1024.0,9316.15,109.906,0.54,3.51,7.14
repghostnet_100,224,1024.0,9303.14,110.06,0.15,3.98,4.07
mobilenetv2_075,224,1024.0,9280.78,110.325,0.22,5.86,2.64
resnet18,224,1024.0,9222.44,111.023,1.82,2.48,11.69
pit_xs_distilled_224,224,1024.0,9172.76,111.624,1.11,4.15,11.0
semnasnet_075,224,1024.0,9145.4,111.959,0.23,5.54,2.91
pit_xs_224,224,1024.0,9134.12,112.096,1.1,4.12,10.62
regnety_006,224,1024.0,9106.78,112.433,0.61,4.33,6.06
convnext_atto,224,1024.0,8993.29,113.851,0.55,3.81,3.7
hardcorenas_d,224,1024.0,8915.53,114.845,0.3,4.93,7.5
levit_256,224,1024.0,8893.96,115.124,1.13,4.23,18.89
seresnet18,224,1024.0,8718.39,117.442,1.82,2.49,11.78
convnext_atto_ols,224,1024.0,8549.03,119.769,0.58,4.11,3.7
mobilenetv2_100,224,1024.0,8479.08,120.757,0.31,6.68,3.5
legacy_seresnet18,224,1024.0,8452.0,121.144,1.82,2.49,11.78
spnasnet_100,224,1024.0,8438.72,121.334,0.35,6.03,4.42
repghostnet_111,224,1024.0,8382.7,122.146,0.18,4.38,4.54
semnasnet_100,224,1024.0,8351.88,122.597,0.32,6.23,3.89
dla46_c,224,1024.0,8209.51,124.721,0.58,4.5,1.3
repvgg_a0,224,1024.0,8124.8,126.024,1.52,3.59,9.11
levit_conv_256,224,1024.0,7997.32,128.032,1.13,4.23,18.89
edgenext_xx_small,256,1024.0,7955.06,128.711,0.26,3.33,1.33
regnetx_008,224,1024.0,7889.15,129.787,0.81,5.15,7.26
resnet18d,224,1024.0,7873.83,130.041,2.06,3.29,11.71
convnext_femto,224,1024.0,7867.13,130.151,0.79,4.57,5.22
ese_vovnet19b_slim,224,1024.0,7834.56,130.693,1.69,3.52,3.17
mobilevit_xxs,256,1024.0,7818.95,130.953,0.34,5.74,1.27
hardcorenas_f,224,1024.0,7811.68,131.075,0.35,5.57,8.2
hardcorenas_e,224,1024.0,7751.65,132.09,0.35,5.65,8.07
efficientnet_lite0,224,1024.0,7716.09,132.699,0.4,6.74,4.65
xcit_nano_12_p16_224,224,1024.0,7711.63,132.776,0.56,4.17,3.05
ghostnet_130,224,1024.0,7680.26,133.318,0.24,4.6,7.36
levit_256d,224,1024.0,7643.23,133.964,1.4,4.93,26.21
tf_efficientnetv2_b0,224,1024.0,7637.19,134.07,0.73,4.77,7.14
repghostnet_130,224,1024.0,7550.55,135.609,0.25,5.24,5.48
convnext_femto_ols,224,1024.0,7514.81,136.254,0.82,4.87,5.23
regnety_008,224,1024.0,7508.88,136.361,0.81,5.25,6.26
tinynet_a,192,1024.0,7458.0,137.291,0.35,5.41,6.19
fbnetc_100,224,1024.0,7362.02,139.082,0.4,6.51,5.57
tf_efficientnetv2_b1,192,1024.0,7241.64,141.394,0.76,4.59,8.14
crossvit_tiny_240,240,1024.0,7093.57,144.345,1.3,5.67,7.01
regnety_008_tv,224,1024.0,7067.28,144.882,0.84,5.42,6.43
mobilevitv2_050,256,1024.0,7057.9,145.075,0.48,8.04,1.37
crossvit_9_240,240,1024.0,6964.15,147.028,1.55,5.59,8.55
dla46x_c,224,1024.0,6837.04,149.761,0.54,5.66,1.07
tf_efficientnet_lite0,224,1024.0,6819.73,150.142,0.4,6.74,4.65
efficientnet_b0,224,1024.0,6721.47,152.337,0.4,6.75,5.29
rexnet_100,224,1024.0,6689.15,153.073,0.41,7.44,4.8
rexnetr_100,224,1024.0,6646.85,154.047,0.43,7.72,4.88
levit_conv_256d,224,1024.0,6618.0,154.719,1.4,4.93,26.21
repvit_m1,224,1024.0,6591.52,155.339,0.83,7.45,5.49
efficientnet_b1_pruned,240,1024.0,6583.2,155.537,0.4,6.21,6.33
repghostnet_150,224,1024.0,6564.41,155.982,0.32,6.0,6.58
mnasnet_140,224,1024.0,6559.1,156.108,0.6,7.71,7.12
efficientvit_b1,224,1024.0,6458.82,158.532,0.53,7.25,9.1
visformer_tiny,224,1024.0,6456.3,158.594,1.27,5.72,10.32
crossvit_9_dagger_240,240,1024.0,6436.13,159.091,1.68,6.03,8.78
resnet14t,224,1024.0,6404.13,159.886,1.69,5.8,10.08
dla60x_c,224,1024.0,6404.11,159.885,0.59,6.01,1.32
mobilenetv2_110d,224,1024.0,6387.15,160.311,0.45,8.71,4.52
ghostnetv2_100,224,1024.0,6375.73,160.599,0.18,4.55,6.16
regnetz_005,224,1024.0,6372.66,160.676,0.52,5.86,7.12
repvit_m0_9,224,1024.0,6295.33,162.649,0.83,7.45,5.49
edgenext_xx_small,288,1024.0,6241.41,164.053,0.33,4.21,1.33
fbnetv3_b,224,1024.0,6166.1,166.058,0.42,6.97,8.6
convnext_pico,224,1024.0,6145.95,166.603,1.37,6.1,9.05
cs3darknet_focus_m,256,1024.0,6145.46,166.616,1.98,4.89,9.3
pvt_v2_b0,224,1024.0,6126.38,167.135,0.53,7.01,3.67
tf_efficientnet_b0,224,1024.0,6026.91,169.894,0.4,6.75,5.29
nf_regnet_b0,256,1024.0,5970.36,171.503,0.64,5.58,8.76
resnetblur18,224,1024.0,5963.74,171.694,2.34,3.39,11.69
ese_vovnet19b_dw,224,1024.0,5956.2,171.911,1.34,8.25,6.54
hrnet_w18_small,224,1024.0,5950.21,172.083,1.61,5.72,13.19
resnet50,160,1024.0,5943.32,172.284,2.1,5.67,25.56
repvgg_a1,224,1024.0,5891.09,173.812,2.64,4.74,14.09
cs3darknet_m,256,1024.0,5871.36,174.395,2.08,5.28,9.31
convnext_pico_ols,224,1024.0,5852.38,174.961,1.43,6.5,9.06
vit_base_patch32_clip_224,224,1024.0,5768.1,177.517,4.37,4.19,88.22
tf_efficientnetv2_b2,208,1024.0,5753.76,177.96,1.06,6.0,10.1
vit_base_patch32_224,224,1024.0,5748.7,178.117,4.37,4.19,88.22
semnasnet_140,224,1024.0,5744.77,178.239,0.6,8.87,6.11
skresnet18,224,1024.0,5740.29,178.378,1.82,3.24,11.96
vit_tiny_r_s16_p8_384,384,1024.0,5663.72,180.79,1.25,5.39,6.36
resnet50d,160,1024.0,5651.35,181.185,2.22,6.08,25.58
resnet18,288,1024.0,5636.85,181.651,3.01,4.11,11.69
mobilenetv2_140,224,1024.0,5629.57,181.886,0.6,9.57,6.11
vit_small_patch32_384,384,1024.0,5499.31,186.195,3.26,6.07,22.92
convnext_atto,288,1024.0,5487.38,186.599,0.91,6.3,3.7
efficientnet_b0_gn,224,1024.0,5481.83,186.788,0.42,6.75,5.29
selecsls42,224,1024.0,5458.22,187.596,2.94,4.62,30.35
efficientnet_lite1,240,1024.0,5452.84,187.782,0.62,10.14,5.42
fbnetv3_d,224,1024.0,5449.6,187.893,0.52,8.5,10.31
pit_s_224,224,1024.0,5438.08,188.291,2.42,6.18,23.46
selecsls42b,224,1024.0,5414.81,189.1,2.98,4.62,32.46
resnet34,224,1024.0,5413.46,189.147,3.67,3.74,21.8
pit_s_distilled_224,224,1024.0,5407.14,189.368,2.45,6.22,24.04
efficientvit_b1,256,1024.0,5391.26,189.926,0.69,9.46,9.1
seresnet18,288,1024.0,5348.84,191.432,3.01,4.11,11.78
tf_efficientnetv2_b1,240,1024.0,5293.37,193.439,1.21,7.34,8.14
levit_384,224,1024.0,5286.23,193.7,2.36,6.26,39.13
convnextv2_atto,224,1024.0,5265.85,194.45,0.55,3.81,3.71
repvit_m1_0,224,1024.0,5259.32,194.683,1.13,8.69,7.3
seresnet50,160,1024.0,5236.4,195.543,2.1,5.69,28.09
convnext_atto_ols,288,1024.0,5201.4,196.86,0.96,6.8,3.7
gernet_m,224,1024.0,5195.05,197.1,3.02,5.24,21.14
fbnetv3_b,256,1024.0,5178.49,197.729,0.55,9.1,8.6
mixnet_s,224,1024.0,5129.76,199.608,0.25,6.25,4.13
repghostnet_200,224,1024.0,5125.91,199.759,0.54,7.96,9.8
vit_base_patch32_clip_quickgelu_224,224,1024.0,5125.16,199.787,4.37,4.19,87.85
seresnet34,224,1024.0,5104.13,200.612,3.67,3.74,21.96
repvit_m2,224,1024.0,5098.16,200.845,1.36,9.43,8.8
rexnetr_130,224,1024.0,5082.35,201.471,0.68,9.81,7.61
efficientnet_b0_g16_evos,224,1024.0,5016.04,204.134,1.01,7.42,8.11
ghostnetv2_130,224,1024.0,5011.79,204.307,0.28,5.9,8.96
edgenext_x_small,256,1024.0,4992.08,205.112,0.54,5.93,2.34
ecaresnet50t,160,1024.0,4989.39,205.225,2.21,6.04,25.57
tiny_vit_5m_224,224,1024.0,4963.53,206.293,1.18,9.32,12.08
rexnet_130,224,1024.0,4939.41,207.301,0.68,9.71,7.56
legacy_seresnet34,224,1024.0,4938.49,207.34,3.67,3.74,21.96
eva02_tiny_patch14_224,224,1024.0,4931.19,207.646,1.4,6.17,5.5
resnet34d,224,1024.0,4924.89,207.912,3.91,4.54,21.82
tf_efficientnet_lite1,240,1024.0,4918.8,208.17,0.62,10.14,5.42
mixer_b32_224,224,1024.0,4917.45,208.227,3.24,6.29,60.29
resnet50,176,1024.0,4914.58,208.348,2.62,6.92,25.56
resnetrs50,160,1024.0,4904.24,208.788,2.29,6.2,35.69
xcit_tiny_12_p16_224,224,1024.0,4900.19,208.961,1.24,6.29,6.72
repvit_m1_1,224,1024.0,4858.32,210.759,1.36,9.43,8.8
levit_conv_384,224,1024.0,4851.29,211.066,2.36,6.26,39.13
efficientnet_es_pruned,224,1024.0,4832.02,211.909,1.81,8.73,5.44
efficientnet_es,224,1024.0,4828.47,212.065,1.81,8.73,5.44
dla34,224,1024.0,4823.61,212.277,3.07,5.02,15.74
resnet26,224,1024.0,4806.46,213.036,2.36,7.35,16.0
resnet18d,288,1024.0,4806.17,213.049,3.41,5.43,11.71
resnext50_32x4d,160,1024.0,4797.48,213.435,2.17,7.35,25.03
tf_mixnet_s,224,1024.0,4783.68,214.05,0.25,6.25,4.13
convnext_femto,288,1024.0,4774.19,214.475,1.3,7.56,5.22
efficientnet_b1,224,1024.0,4707.45,217.516,0.59,9.36,7.79
gmlp_ti16_224,224,1024.0,4694.71,218.108,1.34,7.55,5.87
cs3darknet_focus_m,288,1024.0,4686.36,218.495,2.51,6.19,9.3
mobilenetv2_120d,224,1024.0,4673.25,219.108,0.69,11.97,5.83
selecsls60,224,1024.0,4656.74,219.885,3.59,5.52,30.67
selecsls60b,224,1024.0,4628.67,221.219,3.63,5.52,32.77
tf_efficientnet_es,224,1024.0,4617.85,221.737,1.81,8.73,5.44
resmlp_12_224,224,1024.0,4607.73,222.224,3.01,5.5,15.35
vit_small_patch16_224,224,1024.0,4586.65,223.246,4.25,8.25,22.05
deit_small_patch16_224,224,1024.0,4584.29,223.359,4.25,8.25,22.05
fbnetv3_d,256,1024.0,4567.33,224.19,0.68,11.1,10.31
gmixer_12_224,224,1024.0,4565.4,224.285,2.67,7.26,12.7
deit_small_distilled_patch16_224,224,1024.0,4564.97,224.306,4.27,8.29,22.44
convnext_femto_ols,288,1024.0,4561.96,224.454,1.35,8.06,5.23
efficientnet_b0_g8_gn,224,1024.0,4561.27,224.488,0.66,6.75,6.56
efficientnet_cc_b0_8e,224,1024.0,4542.29,225.426,0.42,9.42,24.01
efficientnet_cc_b0_4e,224,1024.0,4540.5,225.515,0.41,9.42,13.31
repvgg_b0,224,1024.0,4526.99,226.188,3.41,6.15,15.82
mixer_s16_224,224,1024.0,4518.8,226.598,3.79,5.97,18.53
cs3darknet_m,288,1024.0,4513.42,226.868,2.63,6.69,9.31
convnextv2_femto,224,1024.0,4509.16,227.082,0.79,4.57,5.23
regnetx_016,224,1024.0,4476.6,228.734,1.62,7.93,9.19
nf_regnet_b1,256,1024.0,4444.68,230.377,0.82,7.27,10.22
vit_base_patch32_clip_256,256,1024.0,4442.76,230.476,5.68,5.44,87.86
mobilevitv2_075,256,1024.0,4419.22,231.704,1.05,12.06,2.87
rexnetr_150,224,1024.0,4415.72,231.888,0.89,11.13,9.78
darknet17,256,1024.0,4402.14,232.603,3.26,7.18,14.3
resnet26d,224,1024.0,4396.77,232.887,2.6,8.15,16.01
resnetaa34d,224,1024.0,4381.9,233.677,4.43,5.07,21.82
efficientnet_b2_pruned,260,1024.0,4356.91,235.018,0.73,9.13,8.31
convnext_nano,224,1024.0,4340.39,235.913,2.46,8.37,15.59
ecaresnet50d_pruned,224,1024.0,4337.48,236.07,2.53,6.43,19.94
efficientformer_l1,224,1024.0,4271.29,239.728,1.3,5.53,12.29
nf_resnet26,224,1024.0,4216.31,242.856,2.41,7.35,16.0
deit3_small_patch16_224,224,1024.0,4203.29,243.607,4.25,8.25,22.06
nf_regnet_b2,240,1024.0,4197.9,243.92,0.97,7.23,14.31
tf_efficientnet_cc_b0_4e,224,1024.0,4196.5,244.002,0.41,9.42,13.31
tf_efficientnet_cc_b0_8e,224,1024.0,4190.23,244.367,0.42,9.42,24.01
regnety_016,224,1024.0,4161.97,246.026,1.63,8.04,11.2
rexnet_150,224,1024.0,4147.2,246.903,0.9,11.21,9.73
ghostnetv2_160,224,1024.0,4116.92,248.718,0.42,7.23,12.39
tiny_vit_11m_224,224,1024.0,4086.56,250.566,1.9,10.73,20.35
poolformer_s12,224,1024.0,4071.24,251.51,1.82,5.53,11.92
regnetz_005,288,1024.0,4056.8,252.404,0.86,9.68,7.12
efficientnet_lite2,260,1024.0,4046.71,253.034,0.89,12.9,6.09
darknet21,256,1024.0,4001.6,255.887,3.93,7.47,20.86
efficientvit_b1,288,1024.0,3997.55,256.145,0.87,11.96,9.1
resnext50_32x4d,176,1024.0,3992.51,256.47,2.71,8.97,25.03
edgenext_x_small,288,1024.0,3965.96,258.184,0.68,7.5,2.34
efficientnet_b1,256,1024.0,3961.36,258.486,0.77,12.22,7.79
convnext_nano_ols,224,1024.0,3944.64,259.582,2.65,9.38,15.65
resnest14d,224,1024.0,3932.19,260.404,2.76,7.33,10.61
tf_efficientnet_b1,240,1024.0,3922.37,261.055,0.71,10.88,7.79
flexivit_small,240,1024.0,3913.54,261.645,4.88,9.46,22.06
mobilevit_xs,256,768.0,3904.8,196.672,0.93,13.62,2.32
regnetz_b16,224,1024.0,3893.58,262.986,1.45,9.95,9.72
sedarknet21,256,1024.0,3874.2,264.302,3.93,7.47,20.95
resnext26ts,256,1024.0,3832.52,267.176,2.43,10.52,10.3
mobileone_s1,224,1024.0,3826.99,267.562,0.86,9.67,4.83
tf_efficientnetv2_b2,260,1024.0,3817.93,268.197,1.72,9.84,10.1
edgenext_small,256,1024.0,3770.23,271.588,1.26,9.07,5.59
convnext_pico,288,1024.0,3731.48,274.411,2.27,10.08,9.05
gernet_l,256,1024.0,3727.69,274.69,4.57,8.0,31.08
seresnext26ts,256,1024.0,3724.62,274.916,2.43,10.52,10.39
eca_resnext26ts,256,1024.0,3723.07,275.031,2.43,10.52,10.3
dpn48b,224,1024.0,3716.75,275.497,1.69,8.92,9.13
tf_efficientnet_lite2,260,1024.0,3695.32,277.096,0.89,12.9,6.09
gcresnext26ts,256,1024.0,3691.17,277.409,2.43,10.53,10.48
efficientnet_b2,256,1024.0,3671.26,278.912,0.89,12.81,9.11
nf_ecaresnet26,224,1024.0,3640.87,281.24,2.41,7.36,16.0
resnetblur18,288,1024.0,3639.91,281.314,3.87,5.6,11.69
nf_seresnet26,224,1024.0,3637.43,281.506,2.41,7.36,17.4
resnet101,160,1024.0,3616.15,283.164,4.0,8.28,44.55
vit_relpos_small_patch16_224,224,1024.0,3590.52,285.183,4.24,9.38,21.98
resnet26t,256,1024.0,3578.9,286.111,3.35,10.52,16.01
vit_srelpos_small_patch16_224,224,1024.0,3572.97,286.585,4.23,8.49,21.97
convnext_pico_ols,288,1024.0,3558.03,287.789,2.37,10.74,9.06
cs3darknet_focus_l,256,1024.0,3544.69,288.872,4.66,8.03,21.15
tf_efficientnetv2_b3,240,1024.0,3543.38,288.978,1.93,9.95,14.36
legacy_seresnext26_32x4d,224,1024.0,3516.72,291.169,2.49,9.39,16.79
pvt_v2_b1,224,1024.0,3507.87,291.903,2.04,14.01,14.01
repvit_m3,224,1024.0,3501.61,292.425,1.89,13.94,10.68
repvgg_a2,224,1024.0,3495.75,292.916,5.7,6.26,28.21
efficientnetv2_rw_t,224,1024.0,3486.59,293.686,1.93,9.94,13.65
ecaresnet101d_pruned,224,1024.0,3483.13,293.977,3.48,7.69,24.88
ese_vovnet19b_dw,288,1024.0,3478.51,294.369,2.22,13.63,6.54
mixnet_m,224,1024.0,3474.22,294.731,0.36,8.19,5.01
edgenext_small_rw,256,1024.0,3458.08,296.106,1.58,9.51,7.83
convnextv2_pico,224,1024.0,3458.0,296.113,1.37,6.1,9.07
gc_efficientnetv2_rw_t,224,1024.0,3445.15,297.218,1.94,9.97,13.68
cs3darknet_l,256,1024.0,3414.99,299.845,4.86,8.55,21.16
efficientnet_b3_pruned,300,1024.0,3412.19,300.09,1.04,11.86,9.86
nf_regnet_b1,288,1024.0,3373.08,303.57,1.02,9.2,10.22
tf_mixnet_m,224,1024.0,3353.29,305.361,0.36,8.19,5.01
convit_tiny,224,1024.0,3342.83,306.316,1.26,7.94,5.71
eca_botnext26ts_256,256,1024.0,3341.38,306.449,2.46,11.6,10.59
ecaresnext50t_32x4d,224,1024.0,3327.77,307.703,2.7,10.09,15.41
ecaresnext26t_32x4d,224,1024.0,3321.66,308.269,2.7,10.09,15.41
resnet34,288,1024.0,3320.08,308.416,6.07,6.18,21.8
seresnext26t_32x4d,224,1024.0,3319.26,308.491,2.7,10.09,16.81
vit_tiny_patch16_384,384,1024.0,3311.59,309.206,3.16,12.08,5.79
vit_base_patch32_plus_256,256,1024.0,3301.22,310.177,7.7,6.35,119.48
seresnext26d_32x4d,224,1024.0,3300.83,310.214,2.73,10.19,16.81
skresnet34,224,1024.0,3294.57,310.803,3.67,5.13,22.28
mobilevitv2_100,256,768.0,3290.58,233.384,1.84,16.08,4.9
vit_relpos_small_patch16_rpn_224,224,1024.0,3279.29,312.245,4.24,9.38,21.97
eca_halonext26ts,256,1024.0,3270.39,313.1,2.44,11.46,10.76
coatnet_pico_rw_224,224,1024.0,3250.74,314.993,1.96,12.91,10.85
rexnetr_200,224,768.0,3238.38,237.146,1.59,15.11,16.52
ecaresnet26t,256,1024.0,3228.23,317.19,3.35,10.53,16.01
ecaresnetlight,224,1024.0,3222.96,317.708,4.11,8.42,30.16
coatnext_nano_rw_224,224,1024.0,3218.47,318.153,2.36,10.68,14.7
cs3sedarknet_l,256,1024.0,3218.11,318.188,4.86,8.56,21.91
coat_lite_tiny,224,1024.0,3216.35,318.362,1.6,11.65,5.72
nf_regnet_b2,272,1024.0,3205.43,319.447,1.22,9.27,14.31
convnextv2_atto,288,1024.0,3199.9,319.999,0.91,6.3,3.71
vit_small_r26_s32_224,224,1024.0,3174.89,322.52,3.54,9.44,36.43
botnet26t_256,256,1024.0,3173.81,322.63,3.32,11.98,12.49
resnetv2_50,224,1024.0,3170.95,322.919,4.11,11.11,25.55
fastvit_t8,256,1024.0,3164.9,323.538,0.7,8.63,4.03
crossvit_small_240,240,1024.0,3164.86,323.541,5.09,11.34,26.86
bat_resnext26ts,256,1024.0,3139.26,326.18,2.53,12.51,10.73
seresnet34,288,1024.0,3136.77,326.439,6.07,6.18,21.96
halonet26t,256,1024.0,3132.55,326.879,3.19,11.69,12.48
lambda_resnet26t,256,1024.0,3123.88,327.786,3.02,11.87,10.96
rexnet_200,224,768.0,3120.89,246.073,1.56,14.91,16.37
vit_small_resnet26d_224,224,1024.0,3106.26,329.645,5.04,10.65,63.61
hrnet_w18_small_v2,224,1024.0,3095.42,330.8,2.62,9.65,15.6
mobileone_s2,224,1024.0,3085.91,331.82,1.34,11.55,7.88
vit_relpos_base_patch32_plus_rpn_256,256,1024.0,3081.88,332.247,7.59,6.63,119.42
tresnet_m,224,1024.0,3073.78,333.129,5.75,7.31,31.39
resnet32ts,256,1024.0,3072.91,333.224,4.63,11.58,17.96
coatnet_nano_cc_224,224,1024.0,3066.72,333.896,2.13,13.1,13.76
resnet101,176,1024.0,3047.24,336.031,4.92,10.08,44.55
resnet33ts,256,1024.0,3032.6,337.653,4.76,11.66,19.68
efficientvit_b2,224,1024.0,3030.14,337.927,1.6,14.62,24.33
resnet50,224,1024.0,3021.24,338.922,4.11,11.11,25.56
coat_lite_mini,224,1024.0,3021.22,338.925,2.0,12.25,11.01
resnet34d,288,1024.0,3013.98,339.739,6.47,7.51,21.82
cspresnet50,256,1024.0,3012.57,339.898,4.54,11.5,21.62
resnetv2_50t,224,1024.0,3011.73,339.991,4.32,11.82,25.57
dpn68b,224,1024.0,3008.58,340.347,2.35,10.47,12.61
coatnet_nano_rw_224,224,1024.0,3001.39,341.165,2.29,13.29,15.14
dpn68,224,1024.0,3001.33,341.17,2.35,10.47,12.61
resnetv2_50d,224,1024.0,2992.98,342.12,4.35,11.92,25.57
convnext_tiny,224,1024.0,2986.71,342.841,4.47,13.44,28.59
levit_512,224,1024.0,2974.0,344.305,5.64,10.22,95.17
dla60,224,1024.0,2959.44,345.999,4.26,10.16,22.04
fbnetv3_g,240,1024.0,2957.87,346.184,1.28,14.87,16.62
tf_efficientnet_b2,260,1024.0,2957.04,346.28,1.02,13.83,9.11
efficientnet_em,240,1024.0,2948.76,347.254,3.04,14.34,6.9
crossvit_15_240,240,1024.0,2948.65,347.266,5.17,12.01,27.53
eca_resnet33ts,256,1024.0,2945.18,347.676,4.76,11.66,19.68
seresnet33ts,256,1024.0,2940.4,348.24,4.76,11.66,19.78
regnetx_032,224,1024.0,2932.49,349.18,3.2,11.37,15.3
gcresnet33ts,256,1024.0,2919.42,350.744,4.76,11.68,19.88
mobileone_s0,224,1024.0,2911.68,351.675,1.09,15.48,5.29
resnet50t,224,1024.0,2893.61,353.872,4.32,11.82,25.57
resnet50c,224,1024.0,2893.38,353.9,4.35,11.92,25.58
repvit_m1_5,224,1024.0,2891.53,354.126,2.31,15.7,14.64
selecsls84,224,1024.0,2891.52,354.128,5.9,7.57,50.95
efficientnet_cc_b1_8e,240,1024.0,2883.89,355.064,0.75,15.44,39.72
haloregnetz_b,224,1024.0,2883.33,355.134,1.97,11.94,11.68
vgg11,224,1024.0,2881.16,355.4,7.61,7.44,132.86
resnet50d,224,1024.0,2872.03,356.53,4.35,11.92,25.58
resnest26d,224,1024.0,2863.53,357.59,3.64,9.97,17.07
tf_efficientnet_em,240,1024.0,2860.98,357.908,3.04,14.34,6.9
visformer_small,224,1024.0,2837.73,360.841,4.88,11.43,40.22
cspresnet50w,256,1024.0,2834.78,361.216,5.04,12.19,28.12
vovnet39a,224,1024.0,2834.5,361.252,7.09,6.73,22.6
wide_resnet50_2,176,1024.0,2833.12,361.428,7.29,8.97,68.88
cspresnet50d,256,1024.0,2828.94,361.963,4.86,12.55,21.64
resnet26,288,1024.0,2826.83,362.233,3.9,12.15,16.0
resnext26ts,288,1024.0,2826.2,362.312,3.07,13.31,10.3
efficientnet_b2,288,1024.0,2822.88,362.739,1.12,16.2,9.11
regnetv_040,224,1024.0,2785.35,367.627,4.0,12.29,20.64
levit_512d,224,1024.0,2784.75,367.707,5.85,11.3,92.5
levit_conv_512,224,1024.0,2781.3,368.162,5.64,10.22,95.17
deit3_medium_patch16_224,224,1024.0,2780.75,368.235,7.53,10.99,38.85
crossvit_15_dagger_240,240,1024.0,2776.34,368.82,5.5,12.68,28.21
regnety_040,224,1024.0,2768.62,369.849,4.0,12.29,20.65
legacy_seresnet50,224,1024.0,2766.98,370.066,3.88,10.6,28.09
eca_resnext26ts,288,1024.0,2756.51,371.473,3.07,13.32,10.3
seresnext26ts,288,1024.0,2751.54,372.144,3.07,13.32,10.39
regnety_032,224,1024.0,2744.75,373.065,3.2,11.26,19.44
convnext_tiny_hnf,224,1024.0,2744.61,373.082,4.47,13.44,28.59
convnextv2_femto,288,1024.0,2744.25,373.131,1.3,7.56,5.23
eca_vovnet39b,224,1024.0,2742.23,373.408,7.09,6.74,22.6
resnetv2_50x1_bit,224,1024.0,2741.57,373.497,4.23,11.11,25.55
gcresnext26ts,288,1024.0,2728.39,375.302,3.07,13.33,10.48
resnetaa50,224,1024.0,2728.16,375.334,5.15,11.64,25.56
densenet121,224,1024.0,2725.3,375.726,2.87,6.9,7.98
ese_vovnet39b,224,1024.0,2723.97,375.912,7.09,6.74,24.57
mixnet_l,224,1024.0,2712.93,377.44,0.58,10.84,7.33
tf_efficientnet_cc_b1_8e,240,1024.0,2710.75,377.745,0.75,15.44,39.72
mobilevit_s,256,768.0,2698.84,284.557,1.86,17.03,5.58
cs3darknet_focus_l,288,1024.0,2695.52,379.878,5.9,10.16,21.15
seresnet50,224,1024.0,2693.22,380.203,4.11,11.13,28.09
xcit_nano_12_p16_384,384,1024.0,2679.82,382.104,1.64,12.14,3.05
resnetaa34d,288,1024.0,2675.02,382.79,7.33,8.38,21.82
twins_svt_small,224,1024.0,2670.35,383.458,2.82,10.7,24.06
ecaresnet50d_pruned,288,1024.0,2662.19,384.634,4.19,10.61,19.94
convnext_nano,288,1024.0,2634.79,388.635,4.06,13.84,15.59
resnet50_gn,224,1024.0,2631.91,389.06,4.14,11.11,25.56
resnetv2_50d_gn,224,1024.0,2623.43,390.317,4.38,11.92,25.57
xcit_tiny_24_p16_224,224,1024.0,2616.39,391.368,2.34,11.82,12.12
tf_mixnet_l,224,1024.0,2615.89,391.443,0.58,10.84,7.33
res2net50_48w_2s,224,1024.0,2611.06,392.166,4.18,11.72,25.29
gcvit_xxtiny,224,1024.0,2608.34,392.574,2.14,15.36,12.0
cs3darknet_l,288,1024.0,2607.33,392.728,6.16,10.83,21.16
resnetaa50d,224,1024.0,2596.72,394.332,5.39,12.44,25.58
vgg11_bn,224,1024.0,2590.27,395.315,7.62,7.44,132.87
vit_base_resnet26d_224,224,1024.0,2580.41,396.822,6.93,12.34,101.4
vit_relpos_medium_patch16_cls_224,224,1024.0,2579.62,396.946,7.55,13.3,38.76
ecaresnet50t,224,1024.0,2579.62,396.946,4.32,11.83,25.57
coatnet_rmlp_nano_rw_224,224,1024.0,2579.38,396.984,2.51,18.21,15.15
davit_tiny,224,1024.0,2578.68,397.091,4.47,17.08,28.36
seresnet50t,224,1024.0,2574.91,397.672,4.32,11.83,28.1
resnet26d,288,1024.0,2569.96,398.438,4.29,13.48,16.01
mobilevitv2_125,256,768.0,2568.23,299.03,2.86,20.1,7.48
nf_regnet_b3,288,1024.0,2563.17,399.494,1.67,11.84,18.59
ecaresnet50d,224,1024.0,2560.76,399.87,4.35,11.93,25.58
levit_conv_512d,224,1024.0,2557.63,400.359,5.85,11.3,92.5
resnet152,160,1024.0,2531.48,404.495,5.9,11.51,60.19
efficientvit_b2,256,1024.0,2531.18,404.544,2.09,19.03,24.33
mobileone_s3,224,1024.0,2513.71,407.355,1.94,13.85,10.17
resnetrs50,224,1024.0,2512.05,407.624,4.48,12.14,35.69
twins_pcpvt_small,224,1024.0,2506.77,408.482,3.68,15.51,24.11
resnetblur50,224,1024.0,2495.43,410.338,5.16,12.02,25.56
poolformerv2_s12,224,1024.0,2489.38,411.337,1.83,5.53,11.89
convnextv2_nano,224,1024.0,2480.83,412.755,2.46,8.37,15.62
regnetx_040,224,1024.0,2478.03,413.222,3.99,12.2,22.12
eca_nfnet_l0,224,1024.0,2476.91,413.407,4.35,10.47,24.14
gcresnext50ts,256,1024.0,2473.39,413.995,3.75,15.46,15.67
nfnet_l0,224,1024.0,2472.84,414.088,4.36,10.47,35.07
tiny_vit_21m_224,224,1024.0,2468.7,414.781,4.08,15.96,33.22
cs3sedarknet_l,288,1024.0,2463.79,415.609,6.16,10.83,21.91
resnet50s,224,1024.0,2456.52,416.838,5.47,13.52,25.68
dla60x,224,1024.0,2437.95,420.012,3.54,13.8,17.35
densenetblur121d,224,1024.0,2433.6,420.765,3.11,7.9,8.0
edgenext_small,320,1024.0,2424.08,422.414,1.97,14.16,5.59
resnext50_32x4d,224,1024.0,2410.12,424.862,4.26,14.4,25.03
inception_next_tiny,224,1024.0,2404.04,425.937,4.19,11.98,28.06
convnext_nano_ols,288,1024.0,2397.01,427.188,4.38,15.5,15.65
vit_relpos_medium_patch16_224,224,1024.0,2394.54,427.629,7.5,12.13,38.75
efficientnet_lite3,300,512.0,2392.78,213.967,1.65,21.85,8.2
vit_srelpos_medium_patch16_224,224,1024.0,2386.54,429.062,7.49,11.32,38.74
regnetz_c16,256,1024.0,2383.36,429.635,2.51,16.57,13.46
resnetblur50d,224,1024.0,2382.64,429.765,5.4,12.82,25.58
vit_base_r26_s32_224,224,1024.0,2381.88,429.901,6.76,11.54,101.38
gcresnet50t,256,1024.0,2372.96,431.518,5.42,14.67,25.9
regnety_040_sgn,224,1024.0,2371.57,431.77,4.03,12.29,20.65
res2net50_26w_4s,224,1024.0,2359.62,433.957,4.28,12.61,25.7
vovnet57a,224,1024.0,2357.12,434.416,8.95,7.52,36.64
resmlp_24_224,224,1024.0,2350.19,435.697,5.96,10.91,30.02
maxvit_pico_rw_256,256,768.0,2346.84,327.238,1.68,18.77,7.46
inception_v3,299,1024.0,2346.46,436.391,5.73,8.97,23.83
maxvit_rmlp_pico_rw_256,256,768.0,2343.0,327.774,1.69,21.32,7.52
seresnetaa50d,224,1024.0,2333.21,438.87,5.4,12.46,28.11
focalnet_tiny_srf,224,1024.0,2331.81,439.132,4.42,16.32,28.43
cspresnext50,256,1024.0,2330.62,439.358,4.05,15.86,20.57
res2net50_14w_8s,224,1024.0,2327.89,439.871,4.21,13.28,25.06
dla60_res2net,224,1024.0,2327.26,439.99,4.15,12.34,20.85
coatnet_0_rw_224,224,1024.0,2319.62,441.438,4.23,15.1,27.44
regnetz_b16,288,1024.0,2318.51,441.651,2.39,16.43,9.72
gmixer_24_224,224,1024.0,2315.73,442.182,5.28,14.45,24.72
resnext50d_32x4d,224,1024.0,2305.65,444.116,4.5,15.2,25.05
lambda_resnet26rpt_256,256,768.0,2282.36,336.484,3.16,11.87,10.99
ese_vovnet57b,224,1024.0,2279.9,449.132,8.95,7.52,38.61
resnest50d_1s4x24d,224,1024.0,2278.75,449.357,4.43,13.57,25.68
dla60_res2next,224,1024.0,2268.77,451.333,3.49,13.17,17.03
sehalonet33ts,256,1024.0,2262.52,452.582,3.55,14.7,13.69
res2net50d,224,1024.0,2256.17,453.855,4.52,13.41,25.72
vit_medium_patch16_gap_240,240,1024.0,2253.27,454.439,8.6,12.57,44.4
res2next50,224,1024.0,2251.4,454.817,4.2,13.71,24.67
resnet32ts,288,1024.0,2244.87,456.139,5.86,14.65,17.96
edgenext_base,256,1024.0,2239.63,457.204,3.85,15.58,18.51
efficientvit_l1,224,1024.0,2235.54,458.043,5.27,15.85,52.65
skresnet50,224,1024.0,2226.66,459.87,4.11,12.5,25.8
nfnet_f0,192,1024.0,2226.44,459.916,7.21,10.16,71.49
tf_efficientnetv2_b3,300,1024.0,2226.35,459.935,3.04,15.74,14.36
efficientnetv2_rw_t,288,1024.0,2225.5,460.11,3.19,16.42,13.65
nf_ecaresnet50,224,1024.0,2219.3,461.395,4.21,11.13,25.56
darknetaa53,256,1024.0,2219.0,461.459,7.97,12.39,36.02
densenet169,224,1024.0,2218.3,461.604,3.4,7.3,14.15
nf_seresnet50,224,1024.0,2217.49,461.772,4.21,11.13,28.09
edgenext_small_rw,320,1024.0,2214.15,462.468,2.46,14.85,7.83
resnet33ts,288,1024.0,2214.09,462.482,6.02,14.75,19.68
xcit_small_12_p16_224,224,1024.0,2207.67,463.826,4.82,12.57,26.25
focalnet_tiny_lrf,224,1024.0,2205.41,464.301,4.49,17.76,28.65
resnet51q,256,1024.0,2195.84,466.325,6.38,16.55,35.7
repvgg_b1g4,224,1024.0,2195.75,466.344,8.15,10.64,39.97
seresnext50_32x4d,224,1024.0,2188.04,467.986,4.26,14.42,27.56
vit_relpos_medium_patch16_rpn_224,224,1024.0,2187.29,468.147,7.5,12.13,38.73
cs3darknet_focus_x,256,1024.0,2185.7,468.489,8.03,10.69,35.02
legacy_seresnext50_32x4d,224,1024.0,2184.4,468.766,4.26,14.42,27.56
tf_efficientnet_lite3,300,512.0,2178.27,235.039,1.65,21.85,8.2
resnet26t,320,1024.0,2173.03,471.22,5.24,16.44,16.01
gc_efficientnetv2_rw_t,288,1024.0,2170.84,471.696,3.2,16.45,13.68
gmlp_s16_224,224,1024.0,2161.42,473.752,4.42,15.1,19.42
seresnet33ts,288,1024.0,2156.33,474.868,6.02,14.76,19.78
eca_resnet33ts,288,1024.0,2152.27,475.765,6.02,14.76,19.68
fastvit_t12,256,1024.0,2151.9,475.846,1.42,12.42,7.55
nf_regnet_b3,320,1024.0,2148.66,476.564,2.05,14.61,18.59
eva02_small_patch14_224,224,1024.0,2144.78,477.426,5.53,12.34,21.62
resnet152,176,1024.0,2139.0,478.716,7.22,13.99,60.19
vit_medium_patch16_reg4_gap_256,256,1024.0,2137.51,479.051,9.93,14.51,38.87
gcresnet33ts,288,1024.0,2134.49,479.728,6.02,14.78,19.88
skresnet50d,224,1024.0,2133.34,479.986,4.36,13.31,25.82
ecaresnet101d_pruned,288,1024.0,2128.45,481.09,5.75,12.71,24.88
fbnetv3_g,288,1024.0,2127.74,481.25,1.77,21.09,16.62
vit_medium_patch16_reg4_256,256,1024.0,2119.83,483.047,9.97,14.56,38.87
eva02_tiny_patch14_336,336,1024.0,2106.54,486.094,3.14,13.85,5.76
convnextv2_pico,288,1024.0,2101.04,487.367,2.27,10.08,9.07
nf_resnet50,256,1024.0,2100.31,487.536,5.46,14.52,25.56
resnetrs101,192,1024.0,2100.21,487.558,6.04,12.7,63.62
poolformer_s24,224,1024.0,2099.97,487.615,3.41,10.68,21.39
pvt_v2_b2,224,1024.0,2099.92,487.626,3.9,24.96,25.36
efficientnet_b3,288,512.0,2089.91,244.977,1.63,21.49,12.23
cs3sedarknet_xdw,256,1024.0,2078.01,492.768,5.97,17.18,21.6
darknet53,256,1024.0,2077.03,493.0,9.31,12.39,41.61
ecaresnet50t,256,1024.0,2076.41,493.149,5.64,15.45,25.57
cs3darknet_x,256,1024.0,2060.02,497.071,8.38,11.35,35.05
xcit_nano_12_p8_224,224,1024.0,2059.06,497.302,2.16,15.71,3.05
mobilevitv2_150,256,512.0,2058.61,248.702,4.09,24.11,10.59
rexnetr_300,224,1024.0,2042.01,501.455,3.39,22.16,34.81
lambda_resnet50ts,256,1024.0,2041.61,501.552,5.07,17.48,21.54
fastvit_s12,256,1024.0,2028.81,504.718,1.82,13.67,9.47
coatnet_rmlp_0_rw_224,224,1024.0,2024.25,505.855,4.52,21.26,27.45
gcvit_xtiny,224,1024.0,2023.42,506.063,2.93,20.26,19.98
fastvit_sa12,256,1024.0,2022.28,506.347,1.96,13.83,11.58
crossvit_18_240,240,1024.0,2014.44,508.318,8.21,16.14,43.27
vit_medium_patch16_gap_256,256,1024.0,1996.45,512.899,9.78,14.29,38.86
resnet61q,256,1024.0,1996.22,512.958,7.8,17.01,36.85
coatnet_bn_0_rw_224,224,1024.0,1985.64,515.69,4.48,18.41,27.44
vit_base_patch32_384,384,1024.0,1984.44,516.005,12.67,12.14,88.3
vit_base_patch32_clip_384,384,1024.0,1981.44,516.784,12.67,12.14,88.3
cspdarknet53,256,1024.0,1981.04,516.888,6.57,16.81,27.64
sebotnet33ts_256,256,512.0,1977.98,258.841,3.89,17.46,13.7
ecaresnet26t,320,1024.0,1973.79,518.786,5.24,16.44,16.01
vit_base_resnet50d_224,224,1024.0,1971.35,519.428,8.68,16.1,110.97
cs3sedarknet_x,256,1024.0,1962.3,521.825,8.38,11.35,35.4
regnetx_080,224,1024.0,1962.04,521.894,8.02,14.06,39.57
seresnext26t_32x4d,288,1024.0,1950.77,524.91,4.46,16.68,16.81
mixnet_xl,224,1024.0,1948.29,525.576,0.93,14.57,11.9
resnest50d,224,1024.0,1945.36,526.368,5.4,14.36,27.48
seresnext26d_32x4d,288,1024.0,1940.04,527.813,4.51,16.85,16.81
coatnet_0_224,224,512.0,1939.29,264.004,4.43,21.14,25.04
swin_tiny_patch4_window7_224,224,1024.0,1938.74,528.165,4.51,17.06,28.29
resnetv2_101,224,1024.0,1935.15,529.146,7.83,16.23,44.54
regnetx_064,224,1024.0,1933.12,529.703,6.49,16.37,26.21
dla102,224,1024.0,1924.77,531.998,7.19,14.18,33.27
crossvit_18_dagger_240,240,1024.0,1921.19,532.991,8.65,16.91,44.27
rexnetr_200,288,512.0,1914.7,267.396,2.62,24.96,16.52
rexnet_300,224,1024.0,1911.46,535.706,3.44,22.4,34.71
nest_tiny,224,1024.0,1908.27,536.601,5.24,14.75,17.06
dm_nfnet_f0,192,1024.0,1907.3,536.873,7.21,10.16,71.49
ecaresnetlight,288,1024.0,1897.75,539.574,6.79,13.91,30.16
maxxvit_rmlp_nano_rw_256,256,768.0,1897.05,404.83,4.17,21.53,16.78
resnet101,224,1024.0,1885.15,543.183,7.83,16.23,44.55
nest_tiny_jx,224,1024.0,1884.26,543.437,5.24,14.75,17.06
pvt_v2_b2_li,224,1024.0,1882.78,543.863,3.77,25.04,22.55
vit_large_patch32_224,224,1024.0,1869.82,547.632,15.27,11.11,305.51
vgg13,224,1024.0,1868.34,548.068,11.31,12.25,133.05
resnetv2_101d,224,1024.0,1865.75,548.827,8.07,17.04,44.56
efficientformer_l3,224,1024.0,1865.63,548.865,3.93,12.01,31.41
resnetv2_50,288,1024.0,1863.99,549.347,6.79,18.37,25.55
mobileone_s4,224,1024.0,1856.33,551.615,3.04,17.74,14.95
res2net50_26w_6s,224,1024.0,1853.01,552.603,6.33,15.28,37.05
efficientvit_b2,288,1024.0,1851.14,553.16,2.64,24.03,24.33
lamhalobotnet50ts_256,256,1024.0,1841.89,555.938,5.02,18.44,22.57
maxvit_nano_rw_256,256,768.0,1833.65,418.827,4.26,25.76,15.45
maxvit_rmlp_nano_rw_256,256,768.0,1832.13,419.175,4.28,27.4,15.5
convnext_small,224,1024.0,1829.72,559.636,8.71,21.56,50.22
resnet101c,224,1024.0,1824.57,561.217,8.08,17.04,44.57
convnext_tiny,288,1024.0,1817.02,563.549,7.39,22.21,28.59
resnet101d,224,1024.0,1816.61,563.677,8.08,17.04,44.57
gcresnext50ts,288,1024.0,1802.21,568.181,4.75,19.57,15.67
efficientnetv2_s,288,1024.0,1800.9,568.595,4.75,20.13,21.46
pit_b_distilled_224,224,1024.0,1798.47,569.363,10.63,16.67,74.79
resnet50,288,1024.0,1790.94,571.757,6.8,18.37,25.56
twins_pcpvt_base,224,1024.0,1774.55,577.037,6.46,21.35,43.83
halonet50ts,256,1024.0,1772.89,577.576,5.3,19.2,22.73
dpn68b,288,1024.0,1770.85,578.24,3.89,17.3,12.61
pit_b_224,224,1024.0,1769.93,578.542,10.56,16.6,73.76
hrnet_w18_ssld,224,1024.0,1769.77,578.594,4.32,16.31,21.3
swin_s3_tiny_224,224,1024.0,1768.18,579.114,4.64,19.13,28.33
efficientvit_l2,224,1024.0,1765.89,579.866,6.97,19.58,63.71
hrnet_w18,224,1024.0,1763.75,580.57,4.32,16.31,21.3
coat_lite_small,224,1024.0,1746.27,586.38,3.96,22.09,19.84
repvgg_b1,224,1024.0,1745.5,586.64,13.16,10.64,57.42
wide_resnet50_2,224,1024.0,1744.59,586.947,11.43,14.4,68.88
efficientnet_b3,320,512.0,1740.17,294.213,2.01,26.52,12.23
gcresnet50t,288,1024.0,1734.6,590.328,6.86,18.57,25.9
densenet201,224,1024.0,1731.46,591.397,4.34,7.85,20.01
tresnet_v2_l,224,1024.0,1730.52,591.717,8.85,16.34,46.17
tf_efficientnet_b3,300,512.0,1724.68,296.856,1.87,23.83,12.23
efficientnetv2_rw_s,288,1024.0,1722.48,594.481,4.91,21.41,23.94
darknetaa53,288,1024.0,1719.51,595.509,10.08,15.68,36.02
maxxvitv2_nano_rw_256,256,768.0,1706.28,450.091,6.12,19.66,23.7
resnetaa101d,224,1024.0,1701.55,601.792,9.12,17.56,44.57
xcit_tiny_12_p16_384,384,1024.0,1700.55,602.144,3.64,18.25,6.72
cait_xxs24_224,224,1024.0,1698.66,602.815,2.53,20.29,11.96
resnet50t,288,1024.0,1694.77,604.2,7.14,19.53,25.57
legacy_seresnet101,224,1024.0,1693.62,604.611,7.61,15.74,49.33
cs3edgenet_x,256,1024.0,1692.79,604.907,11.53,12.92,47.82
resnet50d,288,1024.0,1684.01,608.061,7.19,19.7,25.58
mobilevitv2_175,256,512.0,1675.38,305.592,5.54,28.13,14.25
regnetv_064,224,1024.0,1674.09,611.663,6.39,16.41,30.58
resnetv2_101x1_bit,224,1024.0,1672.61,612.204,8.04,16.23,44.54
efficientnet_b3_gn,288,512.0,1669.75,306.623,1.74,23.35,11.73
ese_vovnet39b,288,768.0,1667.87,460.459,11.71,11.13,24.57
regnety_032,288,1024.0,1666.89,614.307,5.29,18.61,19.44
seresnet101,224,1024.0,1666.33,614.509,7.84,16.27,49.33
regnety_064,224,1024.0,1666.11,614.593,6.39,16.41,30.58
convnext_tiny_hnf,288,1024.0,1663.94,615.393,7.39,22.21,28.59
regnetv_040,288,1024.0,1658.56,617.391,6.6,20.3,20.64
regnety_040,288,1024.0,1648.75,621.064,6.61,20.3,20.65
regnety_080,224,1024.0,1645.74,622.202,8.0,17.97,39.18
resnet101s,224,1024.0,1640.53,624.176,9.19,18.64,44.67
mixer_b16_224,224,1024.0,1627.76,629.075,12.62,14.53,59.88
dla102x,224,1024.0,1623.56,630.698,5.89,19.42,26.31
nf_resnet101,224,1024.0,1622.48,631.12,8.01,16.23,44.55
swinv2_cr_tiny_224,224,1024.0,1621.28,631.59,4.66,28.45,28.33
ecaresnet101d,224,1024.0,1619.0,632.477,8.08,17.07,44.57
convnextv2_tiny,224,1024.0,1618.49,632.676,4.47,13.44,28.64
darknet53,288,1024.0,1615.64,633.795,11.78,15.68,41.61
wide_resnet101_2,176,1024.0,1615.25,633.945,14.31,13.18,126.89
repvit_m2_3,224,1024.0,1614.73,634.149,4.57,26.21,23.69
resnetaa50,288,1024.0,1610.23,635.923,8.52,19.24,25.56
resnetblur101d,224,1024.0,1609.76,636.109,9.12,17.94,44.57
efficientvit_b3,224,1024.0,1609.54,636.196,3.99,26.9,48.65
regnetz_d32,256,1024.0,1603.03,638.779,5.98,23.74,27.58
regnetz_b16_evos,224,1024.0,1602.47,639.001,1.43,9.95,9.74
ese_vovnet39b_evos,224,1024.0,1599.88,640.036,7.07,6.74,24.58
davit_small,224,1024.0,1599.81,640.066,8.69,27.54,49.75
seresnet50,288,1024.0,1595.89,641.637,6.8,18.39,28.09
cs3se_edgenet_x,256,1024.0,1593.53,642.587,11.53,12.94,50.72
nf_regnet_b4,320,1024.0,1592.57,642.975,3.29,19.88,30.21
swinv2_cr_tiny_ns_224,224,1024.0,1590.7,643.731,4.66,28.45,28.33
sequencer2d_s,224,1024.0,1586.65,645.372,4.96,11.31,27.65
tf_efficientnetv2_s,300,1024.0,1583.75,646.555,5.35,22.73,21.46
densenet121,288,1024.0,1581.16,647.615,4.74,11.41,7.98
resnet51q,288,1024.0,1581.05,647.659,8.07,20.94,35.7
regnetz_d8,256,1024.0,1580.57,647.855,3.97,23.74,23.37
resmlp_36_224,224,1024.0,1577.5,649.116,8.91,16.33,44.69
mixer_l32_224,224,1024.0,1577.26,649.215,11.27,19.86,206.94
regnetz_040,256,1024.0,1574.58,650.32,4.06,24.19,27.12
vit_base_patch16_224_miil,224,1024.0,1574.06,650.535,16.88,16.5,94.4
botnet50ts_256,256,512.0,1573.5,325.38,5.54,22.23,22.74
resnet50_gn,288,1024.0,1570.23,652.122,6.85,18.37,25.56
vit_base_patch16_clip_224,224,1024.0,1569.93,652.248,16.87,16.49,86.57
cs3darknet_x,288,1024.0,1569.68,652.352,10.6,14.36,35.05
deit_base_distilled_patch16_224,224,1024.0,1568.26,652.942,16.95,16.58,87.34
vit_base_patch16_224,224,1024.0,1568.03,653.038,16.87,16.49,86.57
deit_base_patch16_224,224,1024.0,1567.8,653.131,16.87,16.49,86.57
regnetz_040_h,256,1024.0,1564.2,654.638,4.12,24.29,28.94
resnetv2_50d_gn,288,1024.0,1555.81,658.164,7.24,19.7,25.57
resnetv2_50d_frn,224,1024.0,1553.07,659.326,4.33,11.92,25.59
tresnet_l,224,1024.0,1528.92,669.739,10.9,11.9,55.99
regnety_080_tv,224,1024.0,1528.54,669.91,8.51,19.73,39.38
resnetaa50d,288,1024.0,1524.48,671.692,8.92,20.57,25.58
nf_resnet50,288,1024.0,1524.41,671.724,6.88,18.37,25.56
caformer_s18,224,1024.0,1522.76,672.449,3.9,15.18,26.34
resnext101_32x8d,176,1024.0,1521.82,672.868,10.33,19.37,88.79
seresnet50t,288,1024.0,1518.59,674.299,7.14,19.55,28.1
ecaresnet50t,288,1024.0,1518.21,674.465,7.14,19.55,25.57
mvitv2_tiny,224,1024.0,1518.01,674.556,4.7,21.16,24.17
resnet101d,256,1024.0,1517.18,674.926,10.55,22.25,44.57
pvt_v2_b3,224,1024.0,1516.27,675.326,6.71,33.8,45.24
maxvit_tiny_rw_224,224,768.0,1513.7,507.357,4.93,28.54,29.06
ecaresnet50d,288,1024.0,1510.36,677.975,7.19,19.72,25.58
convnextv2_nano,288,768.0,1503.98,510.637,4.06,13.84,15.62
halo2botnet50ts_256,256,1024.0,1499.3,682.975,5.02,21.78,22.64
cs3sedarknet_x,288,1024.0,1498.9,683.158,10.6,14.37,35.4
res2net50_26w_8s,224,1024.0,1498.8,683.201,8.37,17.95,48.4
resnext101_32x4d,224,1024.0,1496.35,684.32,8.01,21.23,44.18
deit3_base_patch16_224,224,1024.0,1488.08,688.122,16.87,16.49,86.59
regnetz_c16,320,1024.0,1478.43,692.615,3.92,25.88,13.46
resnest50d_4s2x40d,224,1024.0,1478.06,692.785,4.4,17.94,30.42
resnetblur50,288,1024.0,1477.0,693.285,8.52,19.87,25.56
skresnext50_32x4d,224,1024.0,1470.18,696.502,4.5,17.18,27.48
efficientvit_l2,256,1024.0,1466.16,698.41,9.09,25.49,63.71
eca_nfnet_l0,288,1024.0,1463.28,699.787,7.12,17.29,24.14
mobilevitv2_200,256,768.0,1462.66,525.062,7.22,32.15,18.45
nfnet_l0,288,1024.0,1461.21,700.775,7.13,17.29,35.07
resnet61q,288,1024.0,1460.17,701.277,9.87,21.52,36.85
vit_base_patch32_clip_448,448,1024.0,1456.81,702.892,17.21,16.49,88.34
vit_small_patch16_36x1_224,224,1024.0,1454.45,704.036,12.63,24.59,64.67
vit_small_resnet50d_s16_224,224,1024.0,1451.55,705.439,13.0,21.12,57.53
beit_base_patch16_224,224,1024.0,1443.54,709.354,16.87,16.49,86.53
res2net101_26w_4s,224,1024.0,1442.54,709.848,8.1,18.45,45.21
vit_base_patch16_siglip_224,224,1024.0,1439.5,711.343,17.02,16.71,92.88
vit_base_patch16_gap_224,224,1024.0,1436.45,712.857,16.78,16.41,86.57
regnety_040_sgn,288,1024.0,1436.16,712.999,6.67,20.3,20.65
beitv2_base_patch16_224,224,1024.0,1436.01,713.075,16.87,16.49,86.53
convit_small,224,1024.0,1431.38,715.383,5.76,17.87,27.78
edgenext_base,320,1024.0,1423.6,719.289,6.01,24.32,18.51
convformer_s18,224,1024.0,1421.81,720.197,3.96,15.82,26.77
focalnet_small_srf,224,1024.0,1419.82,721.204,8.62,26.26,49.89
densenetblur121d,288,1024.0,1416.47,722.914,5.14,13.06,8.0
poolformer_s36,224,1024.0,1415.39,723.463,5.0,15.82,30.86
resnetv2_50d_evos,224,1024.0,1415.09,723.614,4.33,11.92,25.59
coatnet_rmlp_1_rw_224,224,1024.0,1413.05,724.664,7.44,28.08,41.69
res2net101d,224,1024.0,1406.68,727.943,8.35,19.25,45.23
legacy_xception,299,1024.0,1405.99,728.302,8.4,35.83,22.86
vit_small_patch16_18x2_224,224,1024.0,1405.24,728.689,12.63,24.59,64.67
resnetblur50d,288,1024.0,1403.3,729.695,8.92,21.19,25.58
resnext50_32x4d,288,1024.0,1402.5,730.115,7.04,23.81,25.03
inception_next_small,224,1024.0,1397.1,732.931,8.36,19.27,49.37
repvgg_b2g4,224,1024.0,1392.83,735.183,12.63,12.9,61.76
gcvit_tiny,224,1024.0,1390.57,736.376,4.79,29.82,28.22
vit_relpos_base_patch16_clsgap_224,224,1024.0,1386.7,738.433,16.88,17.72,86.43
vit_base_patch16_clip_quickgelu_224,224,1024.0,1384.47,739.621,16.87,16.49,86.19
vit_relpos_base_patch16_cls_224,224,1024.0,1384.18,739.775,16.88,17.72,86.43
dpn92,224,1024.0,1380.04,741.995,6.54,18.21,37.67
seresnetaa50d,288,1024.0,1379.8,742.125,8.92,20.59,28.11
vit_small_patch16_384,384,1024.0,1379.23,742.429,12.45,24.15,22.2
nf_ecaresnet101,224,1024.0,1375.27,744.569,8.01,16.27,44.55
nf_seresnet101,224,1024.0,1370.83,746.983,8.02,16.27,49.33
efficientnet_b3_gn,320,384.0,1366.12,281.077,2.14,28.83,11.73
vgg16_bn,224,1024.0,1361.56,752.067,15.5,13.56,138.37
flexivit_base,240,1024.0,1360.19,752.822,19.35,18.92,86.59
efficientformerv2_s0,224,1024.0,1357.83,754.133,0.41,5.3,3.6
resnetv2_152,224,1024.0,1356.74,754.735,11.55,22.56,60.19
seresnext101_32x4d,224,1024.0,1356.08,755.105,8.02,21.26,48.96
legacy_seresnext101_32x4d,224,1024.0,1355.29,755.543,8.02,21.26,48.96
efficientnet_b3_g8_gn,288,768.0,1342.01,572.264,2.59,23.35,14.25
efficientvit_b3,256,768.0,1340.35,572.972,5.2,35.01,48.65
efficientnet_b4,320,512.0,1338.46,382.52,3.13,34.76,19.34
nfnet_f0,256,1024.0,1336.25,766.311,12.62,18.05,71.49
resnext50d_32x4d,288,1024.0,1335.71,766.62,7.44,25.13,25.05
focalnet_small_lrf,224,1024.0,1333.55,767.863,8.74,28.61,50.34
resnet152,224,1024.0,1331.42,769.094,11.56,22.56,60.19
ese_vovnet99b,224,1024.0,1328.91,770.544,16.51,11.27,63.2
resnetv2_152d,224,1024.0,1322.45,774.307,11.8,23.36,60.2
regnetx_120,224,1024.0,1317.68,777.11,12.13,21.37,46.11
hrnet_w32,224,1024.0,1308.75,782.414,8.97,22.02,41.23
xception41p,299,512.0,1308.08,391.403,9.25,39.86,26.91
vit_relpos_base_patch16_224,224,1024.0,1306.59,783.71,16.8,17.63,86.43
xcit_tiny_12_p8_224,224,1024.0,1306.3,783.883,4.81,23.6,6.71
coatnet_1_rw_224,224,1024.0,1303.02,785.857,7.63,27.22,41.72
resnet152c,224,1024.0,1301.97,786.489,11.8,23.36,60.21
coatnet_rmlp_1_rw2_224,224,1024.0,1300.63,787.299,7.71,32.74,41.72
twins_pcpvt_large,224,1024.0,1297.56,789.162,9.53,30.21,60.99
maxvit_tiny_tf_224,224,768.0,1297.26,592.007,5.42,31.21,30.92
resnet152d,224,1024.0,1296.94,789.538,11.8,23.36,60.21
cs3edgenet_x,288,1024.0,1296.8,789.626,14.59,16.36,47.82
vit_base_patch16_xp_224,224,1024.0,1295.7,790.295,16.85,16.49,86.51
poolformerv2_s24,224,1024.0,1287.82,795.129,3.42,10.68,21.34
dla169,224,1024.0,1280.41,799.732,11.6,20.2,53.39
efficientnet_el_pruned,300,1024.0,1280.32,799.789,8.0,30.7,10.59
efficientnet_el,300,1024.0,1279.02,800.603,8.0,30.7,10.59
seresnext50_32x4d,288,1024.0,1276.82,801.978,7.04,23.82,27.56
hrnet_w30,224,1024.0,1276.63,802.098,8.15,21.21,37.71
deit3_small_patch16_384,384,1024.0,1274.41,803.494,12.45,24.15,22.21
ecaresnet50t,320,1024.0,1274.01,803.751,8.82,24.13,25.57
maxxvit_rmlp_tiny_rw_256,256,768.0,1269.37,605.011,6.36,32.69,29.64
volo_d1_224,224,1024.0,1269.05,806.894,6.94,24.43,26.63
vgg19,224,1024.0,1264.63,809.714,19.63,14.86,143.67
convnext_base,224,1024.0,1259.04,813.306,15.38,28.75,88.59
rexnetr_300,288,512.0,1257.05,407.293,5.59,36.61,34.81
vit_base_patch16_rpn_224,224,1024.0,1255.24,815.771,16.78,16.41,86.54
densenet161,224,1024.0,1254.96,815.95,7.79,11.06,28.68
efficientformerv2_s1,224,1024.0,1251.09,818.477,0.67,7.66,6.19
regnety_120,224,1024.0,1250.69,818.739,12.14,21.38,51.82
twins_svt_base,224,1024.0,1249.89,819.258,8.36,20.42,56.07
tf_efficientnet_el,300,1024.0,1249.79,819.323,8.0,30.7,10.59
sequencer2d_m,224,1024.0,1238.3,826.927,6.55,14.26,38.31
nest_small,224,1024.0,1229.99,832.512,9.41,22.88,38.35
maxvit_tiny_rw_256,256,768.0,1229.06,624.855,6.44,37.27,29.07
maxvit_rmlp_tiny_rw_256,256,768.0,1228.3,625.245,6.47,39.84,29.15
repvgg_b2,224,1024.0,1219.54,839.651,20.45,12.9,89.02
nest_small_jx,224,1024.0,1219.36,839.775,9.41,22.88,38.35
mixnet_xxl,224,768.0,1211.88,633.716,2.04,23.43,23.96
resnet152s,224,1024.0,1205.05,849.747,12.92,24.96,60.32
swin_small_patch4_window7_224,224,1024.0,1202.25,851.724,8.77,27.47,49.61
inception_v4,299,1024.0,1191.21,859.617,12.28,15.09,42.68
swinv2_tiny_window8_256,256,1024.0,1191.2,859.622,5.96,24.57,28.35
legacy_seresnet152,224,1024.0,1187.19,862.527,11.33,22.08,66.82
coatnet_1_224,224,512.0,1184.08,432.392,8.28,31.3,42.23
xcit_small_24_p16_224,224,1024.0,1178.16,869.138,9.1,23.63,47.67
vit_relpos_base_patch16_rpn_224,224,1024.0,1177.44,869.665,16.8,17.63,86.41
eca_nfnet_l1,256,1024.0,1175.13,871.38,9.62,22.04,41.41
seresnet152,224,1024.0,1173.43,872.64,11.57,22.61,66.82
maxvit_tiny_pm_256,256,768.0,1169.83,656.496,6.31,40.82,30.09
crossvit_base_240,240,1024.0,1165.77,878.374,20.13,22.67,105.03
efficientnet_lite4,380,384.0,1155.38,332.349,4.04,45.66,13.01
xception41,299,512.0,1153.48,443.864,9.28,39.86,26.97
regnetx_160,224,1024.0,1153.37,887.82,15.99,25.52,54.28
vgg19_bn,224,1024.0,1151.34,889.391,19.66,14.86,143.68
cait_xxs36_224,224,1024.0,1139.1,898.942,3.77,30.34,17.3
tresnet_xl,224,1024.0,1138.98,899.04,15.2,15.34,78.44
tnt_s_patch16_224,224,1024.0,1134.46,902.62,5.24,24.37,23.76
davit_base,224,1024.0,1133.31,903.534,15.36,36.72,87.95
dm_nfnet_f0,256,1024.0,1132.28,904.361,12.62,18.05,71.49
resnetv2_101,288,1024.0,1131.44,905.029,12.94,26.83,44.54
mvitv2_small_cls,224,1024.0,1129.19,906.833,7.04,28.17,34.87
mvitv2_small,224,1024.0,1128.19,907.64,7.0,28.08,34.87
coat_tiny,224,1024.0,1126.07,909.345,4.35,27.2,5.5
convmixer_1024_20_ks9_p14,224,1024.0,1123.31,911.577,5.55,5.51,24.38
vit_base_patch16_reg8_gap_256,256,1024.0,1115.77,917.744,22.6,22.09,86.62
fastvit_sa24,256,1024.0,1114.43,918.841,3.79,23.92,21.55
repvgg_b3g4,224,1024.0,1113.37,919.717,17.89,15.1,83.83
convnext_small,288,1024.0,1110.94,921.731,14.39,35.65,50.22
vit_base_patch16_siglip_256,256,1024.0,1108.01,924.168,22.23,21.83,92.93
resnet101,288,1024.0,1104.31,927.267,12.95,26.83,44.55
dla102x2,224,1024.0,1104.21,927.342,9.34,29.91,41.28
pvt_v2_b4,224,1024.0,1101.67,929.481,9.83,48.14,62.56
vit_large_r50_s32_224,224,1024.0,1091.33,938.289,19.45,22.22,328.99
eva02_base_patch16_clip_224,224,1024.0,1090.31,939.167,16.9,18.91,86.26
vgg13_bn,224,1024.0,1090.15,939.306,11.33,12.25,133.05
resnet152d,256,1024.0,1089.57,939.806,15.41,30.51,60.21
nf_regnet_b4,384,1024.0,1089.51,939.86,4.7,28.61,30.21
efficientnet_b3_g8_gn,320,768.0,1085.43,707.541,3.2,28.83,14.25
vit_small_r26_s32_384,384,1024.0,1083.82,944.797,10.24,27.67,36.47
efficientvit_l2,288,1024.0,1083.69,944.906,11.51,32.19,63.71
efficientnetv2_s,384,1024.0,1081.44,946.869,8.44,35.77,21.46
tf_efficientnet_lite4,380,384.0,1073.72,357.628,4.04,45.66,13.01
pvt_v2_b5,224,1024.0,1068.28,958.536,11.39,44.23,81.96
hrnet_w18_ssld,288,1024.0,1066.01,960.575,7.14,26.96,21.3
tf_efficientnetv2_s,384,1024.0,1054.1,971.431,8.44,35.77,21.46
regnety_160,224,1024.0,1046.76,978.242,15.96,23.04,83.59
samvit_base_patch16_224,224,1024.0,1027.37,996.713,16.83,17.2,86.46
convnext_tiny,384,768.0,1026.31,748.299,13.14,39.48,28.59
wide_resnet50_2,288,1024.0,1025.91,998.129,18.89,23.81,68.88
efficientnetv2_rw_s,384,1024.0,1024.66,999.343,8.72,38.03,23.94
vgg16,224,1024.0,1020.44,1003.475,15.47,13.56,138.36
cs3se_edgenet_x,320,1024.0,1009.45,1014.397,18.01,20.21,50.72
vit_base_patch16_plus_240,240,1024.0,1002.7,1021.234,26.31,22.07,117.56
swinv2_cr_small_224,224,1024.0,1001.72,1022.232,9.07,50.27,49.7
dpn98,224,1024.0,998.61,1025.406,11.73,25.2,61.57
efficientvit_b3,288,768.0,996.43,770.744,6.58,44.2,48.65
resnetaa101d,288,1024.0,996.18,1027.911,15.07,29.03,44.57
wide_resnet101_2,224,1024.0,994.0,1030.164,22.8,21.23,126.89
regnetz_d32,320,1024.0,994.0,1030.165,9.33,37.08,27.58
swinv2_cr_small_ns_224,224,1024.0,991.13,1033.149,9.08,50.27,49.7
focalnet_base_srf,224,1024.0,990.91,1033.385,15.28,35.01,88.15
convnextv2_small,224,1024.0,989.67,1034.674,8.71,21.56,50.32
resnet200,224,1024.0,987.28,1037.18,15.07,32.19,64.67
convnextv2_tiny,288,768.0,983.87,780.578,7.39,22.21,28.64
seresnet101,288,1024.0,983.64,1041.016,12.95,26.87,49.33
vit_small_patch8_224,224,1024.0,981.8,1042.968,16.76,32.86,21.67
regnetz_d8,320,1024.0,980.9,1043.922,6.19,37.08,23.37
regnety_080,288,1024.0,977.86,1047.177,13.22,29.69,39.18
inception_next_base,224,1024.0,977.1,1047.988,14.85,25.69,86.67
vit_base_r50_s16_224,224,1024.0,974.47,1050.816,20.94,27.88,97.89
resnest101e,256,1024.0,968.0,1057.838,13.38,28.66,48.28
convnext_base,256,1024.0,965.93,1060.101,20.09,37.55,88.59
regnetz_c16_evos,256,768.0,965.5,795.429,2.48,16.57,13.49
regnetz_040,320,512.0,964.02,531.096,6.35,37.78,27.12
poolformer_m36,224,1024.0,963.9,1062.337,8.8,22.02,56.17
regnetz_b16_evos,288,768.0,961.28,798.923,2.36,16.43,9.74
inception_resnet_v2,299,1024.0,958.82,1067.962,13.18,25.06,55.84
regnetz_040_h,320,512.0,958.46,534.182,6.43,37.94,28.94
seresnet152d,256,1024.0,956.44,1070.629,15.42,30.56,66.84
ecaresnet101d,288,1024.0,951.62,1076.05,13.35,28.19,44.57
regnety_064,288,1024.0,949.24,1078.741,10.56,27.11,30.58
resnetrs152,256,1024.0,948.32,1079.798,15.59,30.83,86.62
resnext101_64x4d,224,1024.0,947.79,1080.397,15.52,31.21,83.46
regnetv_064,288,1024.0,947.23,1081.038,10.55,27.11,30.58
xception65p,299,512.0,944.43,542.118,13.91,52.48,39.82
resnetblur101d,288,1024.0,942.52,1086.438,15.07,29.65,44.57
resnetrs101,288,1024.0,941.79,1087.277,13.56,28.53,63.62
focalnet_base_lrf,224,1024.0,941.31,1087.831,15.43,38.13,88.75
resnext101_32x8d,224,1024.0,939.44,1090.002,16.48,31.21,88.79
repvgg_b3,224,1024.0,933.91,1096.448,29.16,15.1,123.09
hrnet_w40,224,1024.0,931.96,1098.75,12.75,25.29,57.56
nfnet_f1,224,1024.0,924.88,1107.159,17.87,22.94,132.63
eva02_small_patch14_336,336,1024.0,923.99,1108.223,12.41,27.7,22.13
resnet101d,320,1024.0,923.18,1109.193,16.48,34.77,44.57
xcit_tiny_24_p16_384,384,1024.0,910.96,1124.082,6.87,34.29,12.12
efficientnet_b4,384,384.0,908.88,422.486,4.51,50.04,19.34
cait_s24_224,224,1024.0,904.24,1132.424,9.35,40.58,46.92
mobilevitv2_150,384,256.0,899.17,284.697,9.2,54.25,10.59
maxvit_rmlp_small_rw_224,224,768.0,898.81,854.449,10.48,42.44,64.9
coat_mini,224,1024.0,894.78,1144.406,6.82,33.68,10.34
coat_lite_medium,224,1024.0,892.4,1147.459,9.81,40.06,44.57
efficientnetv2_m,320,1024.0,889.26,1151.505,11.01,39.97,54.14
seresnext101_64x4d,224,1024.0,888.73,1152.196,15.53,31.25,88.23
gmlp_b16_224,224,1024.0,884.5,1157.706,15.78,30.21,73.08
seresnext101_32x8d,224,1024.0,883.56,1158.934,16.48,31.25,93.57
swin_s3_small_224,224,768.0,879.87,872.841,9.43,37.84,49.74
vit_relpos_base_patch16_plus_240,240,1024.0,875.04,1170.215,26.21,23.41,117.38
efficientformer_l7,224,1024.0,873.11,1172.808,10.17,24.45,82.23
nest_base,224,1024.0,870.02,1176.974,16.71,30.51,67.72
poolformerv2_s36,224,1024.0,869.16,1178.141,5.01,15.82,30.79
maxvit_small_tf_224,224,512.0,868.0,589.85,11.39,46.31,68.93
seresnext101d_32x8d,224,1024.0,866.35,1181.949,16.72,32.05,93.59
nest_base_jx,224,1024.0,862.67,1187.001,16.71,30.51,67.72
levit_384_s8,224,512.0,854.68,599.045,9.98,35.86,39.12
regnetz_e8,256,1024.0,853.36,1199.952,9.91,40.94,57.7
swin_base_patch4_window7_224,224,1024.0,852.78,1200.762,15.47,36.63,87.77
coatnet_2_rw_224,224,512.0,852.23,600.767,14.55,39.37,73.87
tf_efficientnet_b4,380,384.0,851.5,450.956,4.49,49.49,19.34
gcvit_small,224,1024.0,841.82,1216.401,8.57,41.61,51.09
convnextv2_nano,384,512.0,841.68,608.3,7.22,24.61,15.62
resnetv2_50d_evos,288,1024.0,840.21,1218.735,7.15,19.7,25.59
levit_conv_384_s8,224,512.0,839.77,609.68,9.98,35.86,39.12
xception65,299,512.0,839.39,609.953,13.96,52.48,39.92
hrnet_w44,224,1024.0,835.38,1225.779,14.94,26.92,67.06
crossvit_15_dagger_408,408,1024.0,833.7,1228.252,16.07,37.0,28.5
tiny_vit_21m_384,384,512.0,827.46,618.747,11.94,46.84,21.23
twins_svt_large,224,1024.0,824.23,1242.353,14.84,27.23,99.27
seresnextaa101d_32x8d,224,1024.0,820.77,1247.602,17.25,34.16,93.59
xcit_medium_24_p16_224,224,1024.0,820.51,1247.988,16.13,31.71,84.4
eva02_base_patch14_224,224,1024.0,819.51,1249.51,22.0,24.67,85.76
coatnet_rmlp_2_rw_224,224,512.0,814.13,628.885,14.64,44.94,73.88
hrnet_w48_ssld,224,1024.0,812.33,1260.551,17.34,28.56,77.47
hrnet_w48,224,1024.0,811.26,1262.228,17.34,28.56,77.47
caformer_s36,224,1024.0,810.13,1263.986,7.55,29.29,39.3
tresnet_m,448,1024.0,809.9,1264.343,22.99,29.21,31.39
resnet200d,256,1024.0,803.17,1274.938,20.0,43.09,64.69
sequencer2d_l,224,1024.0,802.78,1275.557,9.74,22.12,54.3
maxxvit_rmlp_small_rw_256,256,768.0,801.57,958.106,14.21,47.76,66.01
swinv2_base_window12_192,192,1024.0,799.54,1280.724,11.9,39.72,109.28
dm_nfnet_f1,224,1024.0,798.67,1282.118,17.87,22.94,132.63
coatnet_2_224,224,512.0,796.89,642.486,15.94,42.41,74.68
vit_medium_patch16_gap_384,384,1024.0,795.07,1287.922,22.01,32.15,39.03
mvitv2_base_cls,224,1024.0,791.15,1294.298,10.23,40.65,65.44
mvitv2_base,224,1024.0,785.87,1303.007,10.16,40.5,51.47
efficientnetv2_rw_m,320,1024.0,785.27,1303.997,12.72,47.14,53.24
resnet152,288,1024.0,781.77,1309.827,19.11,37.28,60.19
swinv2_tiny_window16_256,256,512.0,775.64,660.087,6.68,39.02,28.35
fastvit_sa36,256,1024.0,768.44,1332.545,5.62,34.02,31.53
xcit_small_12_p16_384,384,1024.0,764.7,1339.074,14.14,36.5,26.25
convnext_base,288,1024.0,763.36,1341.427,25.43,47.53,88.59
convformer_s36,224,1024.0,754.92,1356.424,7.67,30.5,40.01
regnety_120,288,768.0,738.36,1040.13,20.06,35.34,51.82
swinv2_small_window8_256,256,1024.0,737.99,1387.548,11.58,40.14,49.73
dpn131,224,1024.0,732.6,1397.744,16.09,32.97,79.25
swinv2_cr_small_ns_256,256,1024.0,731.79,1399.291,12.07,76.21,49.7
mobilevitv2_175,384,256.0,731.75,349.838,12.47,63.29,14.25
convit_base,224,1024.0,730.43,1401.91,17.52,31.77,86.54
resnetv2_50x1_bit,448,512.0,729.61,701.734,16.62,44.46,25.55
poolformer_m48,224,1024.0,727.01,1408.491,11.59,29.17,73.47
maxvit_rmlp_small_rw_256,256,768.0,724.69,1059.745,13.69,55.48,64.9
tnt_b_patch16_224,224,1024.0,721.67,1418.912,14.09,39.01,65.41
eca_nfnet_l1,320,1024.0,720.22,1421.77,14.92,34.42,41.41
swinv2_cr_base_224,224,1024.0,716.89,1428.383,15.86,59.66,87.88
swin_s3_base_224,224,1024.0,715.81,1430.534,13.69,48.26,71.13
volo_d2_224,224,1024.0,711.4,1439.408,14.34,41.34,58.68
swinv2_cr_base_ns_224,224,1024.0,711.07,1440.068,15.86,59.66,87.88
convnextv2_base,224,768.0,708.71,1083.64,15.38,28.75,88.72
densenet264d,224,1024.0,697.85,1467.348,13.57,14.0,72.74
ecaresnet200d,256,1024.0,697.3,1468.506,20.0,43.15,64.69
seresnet200d,256,1024.0,696.92,1469.301,20.01,43.15,71.86
nf_regnet_b5,384,1024.0,694.76,1473.879,7.95,42.9,49.74
seresnet152,288,1024.0,693.47,1476.616,19.11,37.34,66.82
resnetrs200,256,1024.0,693.26,1477.057,20.18,43.42,93.21
coat_small,224,1024.0,689.68,1484.732,12.61,44.25,21.69
convnext_large,224,1024.0,686.69,1491.207,34.4,43.13,197.77
xcit_tiny_24_p8_224,224,1024.0,684.2,1496.615,9.21,45.38,12.11
efficientvit_l3,224,1024.0,667.4,1534.307,27.62,39.16,246.04
dpn107,224,1024.0,666.43,1536.527,18.38,33.46,86.92
resnet152d,320,1024.0,664.6,1540.768,24.08,47.67,60.21
senet154,224,1024.0,664.59,1540.791,20.77,38.69,115.09
legacy_senet154,224,1024.0,663.62,1543.045,20.77,38.69,115.09
efficientformerv2_s2,224,1024.0,658.11,1555.962,1.27,11.77,12.71
maxxvitv2_rmlp_base_rw_224,224,768.0,650.48,1180.654,23.88,54.39,116.09
xcit_nano_12_p8_384,384,1024.0,649.92,1575.56,6.34,46.06,3.05
xception71,299,512.0,649.47,788.325,18.09,69.92,42.34
vit_large_patch32_384,384,1024.0,643.51,1591.268,44.28,32.22,306.63
mobilevitv2_200,384,256.0,640.82,399.48,16.24,72.34,18.45
davit_large,224,1024.0,630.01,1625.361,34.37,55.08,196.81
hrnet_w64,224,1024.0,629.26,1627.299,28.97,35.09,128.06
convnext_small,384,768.0,628.81,1221.341,25.58,63.37,50.22
regnetz_d8_evos,256,1024.0,626.83,1633.604,4.5,24.92,23.46
regnety_160,288,768.0,626.54,1225.759,26.37,38.07,83.59
convnext_base,320,768.0,617.04,1244.641,31.39,58.68,88.59
fastvit_ma36,256,1024.0,615.75,1662.995,7.85,40.39,44.07
tf_efficientnetv2_m,384,1024.0,614.24,1667.09,15.85,57.52,54.14
gcvit_base,224,1024.0,612.92,1670.669,14.87,55.48,90.32
regnety_320,224,1024.0,612.34,1672.272,32.34,30.26,145.05
efficientvit_l2,384,768.0,610.03,1258.949,20.45,57.01,63.71
poolformerv2_m36,224,1024.0,609.2,1680.886,8.81,22.02,56.08
regnetz_c16_evos,320,512.0,608.23,841.78,3.86,25.88,13.49
resnetv2_50x3_bit,224,768.0,585.49,1311.719,37.06,33.34,217.32
seresnet152d,320,1024.0,585.32,1749.453,24.09,47.72,66.84
xcit_small_12_p8_224,224,1024.0,584.75,1751.159,18.69,47.19,26.21
resnet200,288,1024.0,584.49,1751.952,24.91,53.21,64.67
resnetrs152,320,1024.0,580.71,1763.336,24.34,48.14,86.62
caformer_m36,224,1024.0,580.7,1763.373,12.75,40.61,56.2
resnext101_64x4d,288,1024.0,579.65,1766.578,25.66,51.59,83.46
levit_conv_512_s8,224,256.0,579.33,441.879,21.82,52.28,74.05
crossvit_18_dagger_408,408,1024.0,578.67,1769.56,25.31,49.38,44.61
levit_512_s8,224,256.0,564.15,453.77,21.82,52.28,74.05
convnextv2_tiny,384,384.0,553.95,693.189,13.14,39.48,28.64
convformer_m36,224,1024.0,546.86,1872.507,12.89,42.05,57.05
efficientnet_b5,416,256.0,546.68,468.268,8.27,80.68,30.39
seresnet269d,256,1024.0,545.35,1877.679,26.59,53.6,113.67
efficientvit_l3,256,768.0,542.99,1414.373,36.06,50.98,246.04
seresnext101_32x8d,288,1024.0,537.9,1903.669,27.24,51.63,93.57
efficientnetv2_m,416,1024.0,531.24,1927.549,18.6,67.5,54.14
resnetrs270,256,1024.0,529.33,1934.515,27.06,55.84,129.86
maxvit_rmlp_base_rw_224,224,768.0,529.1,1451.502,22.63,79.3,116.14
swinv2_base_window8_256,256,1024.0,528.71,1936.775,20.37,52.59,87.92
regnetz_e8,320,768.0,528.46,1453.264,15.46,63.94,57.7
seresnext101d_32x8d,288,1024.0,527.36,1941.726,27.64,52.95,93.59
convnext_large_mlp,256,768.0,525.72,1460.834,44.94,56.33,200.13
nfnet_f2,256,1024.0,524.14,1953.657,33.76,41.85,193.78
halonet_h1,256,256.0,522.84,489.621,3.0,51.17,8.1
regnetx_320,224,1024.0,522.6,1959.408,31.81,36.3,107.81
mixer_l16_224,224,1024.0,520.22,1968.376,44.6,41.69,208.2
resnext101_32x16d,224,1024.0,519.8,1969.975,36.27,51.18,194.03
eca_nfnet_l2,320,1024.0,509.51,2009.758,20.95,47.43,56.72
ecaresnet200d,288,1024.0,503.74,2032.793,25.31,54.59,64.69
seresnet200d,288,1024.0,503.36,2034.329,25.32,54.6,71.86
caformer_s18,384,512.0,501.38,1021.162,11.45,44.61,26.34
volo_d3_224,224,1024.0,497.87,2056.757,20.78,60.09,86.33
resnet200d,320,1024.0,493.82,2073.621,31.25,67.33,64.69
swin_large_patch4_window7_224,224,768.0,492.35,1559.852,34.53,54.94,196.53
vit_base_patch16_18x2_224,224,1024.0,492.32,2079.918,50.37,49.17,256.73
deit_base_patch16_384,384,1024.0,491.82,2082.046,49.4,48.3,86.86
vit_base_patch16_clip_384,384,1024.0,491.74,2082.405,49.41,48.3,86.86
vit_base_patch16_384,384,1024.0,491.42,2083.727,49.4,48.3,86.86
deit_base_distilled_patch16_384,384,1024.0,491.32,2084.164,49.49,48.39,87.63
hrnet_w48_ssld,288,1024.0,490.92,2085.876,28.66,47.21,77.47
eva_large_patch14_196,196,1024.0,490.45,2087.863,59.66,43.77,304.14
maxvit_base_tf_224,224,512.0,488.88,1047.285,23.52,81.67,119.47
efficientnet_b5,448,256.0,488.83,523.691,9.59,93.56,30.39
vit_large_patch16_224,224,1024.0,488.5,2096.219,59.7,43.77,304.33
swinv2_small_window16_256,256,512.0,486.59,1052.215,12.82,66.29,49.73
swinv2_large_window12_192,192,768.0,485.58,1581.6,26.17,56.53,228.77
convformer_s18,384,512.0,484.08,1057.663,11.63,46.49,26.77
seresnextaa101d_32x8d,288,1024.0,479.96,2133.497,28.51,56.44,93.59
coatnet_3_rw_224,224,256.0,478.44,535.067,32.63,59.07,181.81
coatnet_rmlp_3_rw_224,224,256.0,477.75,535.833,32.75,64.7,165.15
xcit_large_24_p16_224,224,1024.0,472.07,2169.166,35.86,47.26,189.1
vit_small_patch14_dinov2,518,1024.0,469.29,2181.987,29.46,57.34,22.06
deit3_base_patch16_384,384,1024.0,466.88,2193.286,49.4,48.3,86.88
deit3_large_patch16_224,224,1024.0,466.56,2194.777,59.7,43.77,304.37
efficientnetv2_rw_m,416,768.0,466.5,1646.281,21.49,79.62,53.24
nfnet_f1,320,1024.0,466.35,2195.774,35.97,46.77,132.63
nf_regnet_b5,456,768.0,464.5,1653.385,11.7,61.95,49.74
coatnet_3_224,224,256.0,464.1,551.594,35.72,63.61,166.97
vit_small_patch14_reg4_dinov2,518,1024.0,460.4,2224.119,29.55,57.51,22.06
poolformerv2_m48,224,1024.0,459.37,2229.113,11.59,29.17,73.35
beitv2_large_patch16_224,224,1024.0,452.16,2264.697,59.7,43.77,304.43
beit_large_patch16_224,224,1024.0,452.15,2264.716,59.7,43.77,304.43
resnetv2_101x1_bit,448,512.0,451.35,1134.365,31.65,64.93,44.54
dm_nfnet_f2,256,1024.0,451.22,2269.395,33.76,41.85,193.78
vit_base_patch16_siglip_384,384,1024.0,448.34,2283.991,50.0,49.11,93.18
resnetv2_152x2_bit,224,1024.0,441.5,2319.35,46.95,45.11,236.34
convnext_xlarge,224,768.0,435.62,1762.988,60.98,57.5,350.2
maxvit_tiny_tf_384,384,256.0,434.99,588.503,16.0,94.22,30.98
efficientformerv2_l,224,1024.0,431.02,2375.769,2.59,18.54,26.32
convnext_base,384,512.0,430.72,1188.698,45.21,84.49,88.59
convnextv2_base,288,512.0,429.59,1191.832,25.43,47.53,88.72
resnetrs200,320,1024.0,428.05,2392.217,31.51,67.81,93.21
flexivit_large,240,1024.0,424.67,2411.279,68.48,50.22,304.36
convnextv2_large,224,512.0,423.49,1208.977,34.4,43.13,197.96
xcit_tiny_12_p8_384,384,1024.0,423.2,2419.661,14.12,69.12,6.71
swinv2_cr_large_224,224,768.0,422.05,1819.675,35.1,78.42,196.68
caformer_b36,224,768.0,419.19,1832.111,22.5,54.14,98.75
swinv2_cr_tiny_384,384,256.0,419.04,610.909,15.34,161.01,28.33
tf_efficientnet_b5,456,256.0,418.1,612.278,10.46,98.86,30.39
convnext_large,288,512.0,415.42,1232.482,56.87,71.29,197.77
davit_huge,224,512.0,410.45,1247.402,60.93,73.44,348.92
maxxvitv2_rmlp_large_rw_224,224,768.0,409.41,1875.861,43.69,75.4,215.42
tiny_vit_21m_512,512,384.0,408.26,940.575,21.23,83.26,21.27
xcit_small_24_p16_384,384,1024.0,408.08,2509.308,26.72,68.57,47.67
tf_efficientnetv2_m,480,768.0,405.02,1896.185,24.76,89.84,54.14
tresnet_l,448,1024.0,403.56,2537.407,43.59,47.56,55.99
beit_base_patch16_384,384,1024.0,401.76,2548.786,49.4,48.3,86.74
convformer_b36,224,768.0,396.81,1935.431,22.69,56.06,99.88
regnetz_d8_evos,320,768.0,395.82,1940.285,7.03,38.92,23.46
seresnextaa101d_32x8d,320,1024.0,395.0,2592.386,35.19,69.67,93.59
seresnet269d,288,1024.0,393.84,2600.059,33.65,67.81,113.67
dm_nfnet_f1,320,1024.0,393.6,2601.642,35.97,46.77,132.63
regnety_160,384,384.0,378.47,1014.589,46.87,67.67,83.59
vit_large_r50_s32_384,384,1024.0,372.96,2745.589,56.4,64.88,329.09
regnety_640,224,768.0,362.45,2118.906,64.16,42.5,281.38
eca_nfnet_l2,384,768.0,361.66,2123.504,30.05,68.28,56.72
vit_large_patch14_224,224,1024.0,359.79,2846.069,77.83,57.11,304.2
vit_large_patch14_clip_224,224,1024.0,359.08,2851.744,77.83,57.11,304.2
swinv2_base_window12to16_192to256,256,384.0,358.35,1071.569,22.02,84.71,87.92
swinv2_base_window16_256,256,384.0,358.25,1071.869,22.02,84.71,87.92
vit_large_patch16_siglip_256,256,1024.0,351.53,2912.942,78.12,57.42,315.96
vit_base_patch8_224,224,1024.0,350.95,2917.813,66.87,65.71,86.58
efficientvit_l3,320,512.0,346.1,1479.341,56.32,79.34,246.04
efficientnetv2_l,384,1024.0,342.83,2986.92,36.1,101.16,118.52
tf_efficientnetv2_l,384,1024.0,338.97,3020.897,36.1,101.16,118.52
ecaresnet269d,320,1024.0,337.13,3037.39,41.53,83.69,102.09
resnest200e,320,1024.0,336.33,3044.627,35.69,82.78,70.2
maxvit_large_tf_224,224,384.0,336.26,1141.954,42.99,109.57,211.79
convnext_large_mlp,320,512.0,336.03,1523.669,70.21,88.02,200.13
inception_next_base,384,512.0,335.9,1524.27,43.64,75.48,86.67
resnetv2_101x3_bit,224,768.0,334.56,2295.509,71.23,48.7,387.93
eca_nfnet_l3,352,768.0,328.62,2337.043,32.57,73.12,72.04
vit_large_patch14_clip_quickgelu_224,224,1024.0,324.15,3159.023,77.83,57.11,303.97
repvgg_d2se,320,1024.0,320.2,3197.943,74.57,46.82,133.33
vit_base_r50_s16_384,384,1024.0,317.01,3230.175,61.29,81.77,98.95
volo_d4_224,224,1024.0,317.0,3230.22,44.34,80.22,192.96
volo_d1_384,384,512.0,314.1,1630.023,22.75,108.55,26.78
vit_large_patch14_xp_224,224,1024.0,309.84,3304.92,77.77,57.11,304.06
convmixer_768_32,224,1024.0,308.6,3318.227,19.55,25.95,21.11
xcit_small_24_p8_224,224,1024.0,305.72,3349.464,35.81,90.77,47.63
resnetrs350,288,1024.0,304.48,3363.098,43.67,87.09,163.96
nasnetalarge,331,384.0,300.79,1276.642,23.89,90.56,88.75
coat_lite_medium_384,384,512.0,299.62,1708.831,28.73,116.7,44.57
tresnet_xl,448,768.0,296.15,2593.304,60.77,61.31,78.44
maxvit_small_tf_384,384,192.0,288.16,666.295,33.58,139.86,69.02
pnasnet5large,331,384.0,287.26,1336.778,25.04,92.89,86.06
xcit_medium_24_p16_384,384,1024.0,282.76,3621.451,47.39,91.63,84.4
ecaresnet269d,352,1024.0,281.17,3641.867,50.25,101.25,102.09
coatnet_4_224,224,256.0,280.04,914.128,60.81,98.85,275.43
cait_xxs24_384,384,1024.0,277.04,3696.16,9.63,122.65,12.03
coatnet_rmlp_2_rw_384,384,192.0,273.87,701.059,43.04,132.57,73.88
resnetrs270,352,1024.0,271.91,3765.914,51.13,105.48,129.86
nfnet_f2,352,768.0,270.88,2835.244,63.22,79.06,193.78
caformer_s36,384,512.0,266.29,1922.686,22.2,86.08,39.3
convnext_xlarge,288,512.0,263.75,1941.25,100.8,95.05,350.2
swinv2_cr_small_384,384,256.0,258.42,990.618,29.7,298.03,49.7
efficientnet_b6,528,128.0,257.57,496.944,19.4,167.39,43.04
convformer_s36,384,512.0,257.36,1989.401,22.54,89.62,40.01
convnextv2_large,288,256.0,256.91,996.448,56.87,71.29,197.96
eva02_large_patch14_224,224,1024.0,256.79,3987.739,77.9,65.52,303.27
eva02_large_patch14_clip_224,224,1024.0,253.51,4039.312,77.93,65.52,304.11
resnext101_32x32d,224,512.0,253.0,2023.672,87.29,91.12,468.53
maxvit_tiny_tf_512,512,192.0,249.39,769.864,28.66,172.66,31.05
tf_efficientnet_b6,528,128.0,247.44,517.29,19.4,167.39,43.04
nfnet_f3,320,1024.0,247.37,4139.575,68.77,83.93,254.92
mvitv2_large_cls,224,768.0,246.55,3114.926,42.17,111.69,234.58
vit_so400m_patch14_siglip_224,224,1024.0,246.49,4154.292,106.18,70.45,427.68
efficientnetv2_xl,384,1024.0,244.46,4188.739,52.81,139.2,208.12
mvitv2_large,224,512.0,242.6,2110.485,43.87,112.02,217.99
convnextv2_base,384,256.0,242.26,1056.699,45.21,84.49,88.72
vit_base_patch16_siglip_512,512,512.0,241.2,2122.705,88.89,87.3,93.52
convnext_large,384,384.0,234.69,1636.209,101.1,126.74,197.77
convnext_large_mlp,384,384.0,234.65,1636.476,101.11,126.74,200.13
dm_nfnet_f2,352,768.0,234.38,3276.685,63.22,79.06,193.78
tf_efficientnetv2_xl,384,1024.0,230.18,4448.679,52.81,139.2,208.12
efficientnetv2_l,480,512.0,229.94,2226.68,56.4,157.99,118.52
tf_efficientnetv2_l,480,512.0,227.38,2251.742,56.4,157.99,118.52
swin_base_patch4_window12_384,384,256.0,226.65,1129.483,47.19,134.78,87.9
regnety_320,384,384.0,225.95,1699.504,95.0,88.87,145.05
resnetrs420,320,1024.0,221.8,4616.729,64.2,126.56,191.89
xcit_tiny_24_p8_384,384,1024.0,221.03,4632.753,27.05,132.94,12.11
efficientvit_l3,384,384.0,220.15,1744.25,81.08,114.02,246.04
swinv2_large_window12to16_192to256,256,256.0,218.91,1169.41,47.81,121.53,196.74
maxxvitv2_rmlp_base_rw_384,384,384.0,215.87,1778.825,70.18,160.22,116.09
resmlp_big_24_224,224,1024.0,214.65,4770.604,100.23,87.31,129.14
dm_nfnet_f3,320,1024.0,212.33,4822.62,68.77,83.93,254.92
volo_d5_224,224,1024.0,212.3,4823.349,72.4,118.11,295.46
xcit_medium_24_p8_224,224,1024.0,210.35,4868.038,63.52,121.22,84.32
seresnextaa201d_32x8d,320,1024.0,207.05,4945.752,70.22,138.71,149.39
eca_nfnet_l3,448,512.0,204.74,2500.737,52.55,118.4,72.04
xcit_small_12_p8_384,384,512.0,195.78,2615.134,54.92,138.25,26.21
cait_xs24_384,384,768.0,193.45,3970.037,19.28,183.98,26.67
caformer_m36,384,256.0,191.51,1336.728,37.45,119.33,56.2
focalnet_huge_fl3,224,384.0,190.45,2016.221,118.26,104.8,745.28
eva02_base_patch14_448,448,512.0,189.13,2707.053,87.74,98.4,87.12
maxvit_xlarge_tf_224,224,256.0,188.97,1354.682,96.49,164.37,506.99
convformer_m36,384,384.0,186.96,2053.847,37.87,123.56,57.05
cait_xxs36_384,384,1024.0,185.14,5531.038,14.35,183.7,17.37
swinv2_cr_base_384,384,256.0,184.66,1386.338,50.57,333.68,87.88
resnetrs350,384,1024.0,184.39,5553.562,77.59,154.74,163.96
regnety_1280,224,512.0,182.89,2799.45,127.66,71.58,644.81
swinv2_cr_huge_224,224,384.0,181.27,2118.357,115.97,121.08,657.83
vit_huge_patch14_clip_224,224,1024.0,179.25,5712.71,161.99,95.07,632.05
vit_huge_patch14_224,224,1024.0,179.24,5713.082,161.99,95.07,630.76
volo_d2_384,384,384.0,177.67,2161.247,46.17,184.51,58.87
maxvit_rmlp_base_rw_384,384,384.0,177.21,2166.875,66.51,233.79,116.14
vit_base_patch14_dinov2,518,512.0,175.93,2910.275,117.11,114.68,86.58
vit_huge_patch14_gap_224,224,1024.0,175.35,5839.715,161.36,94.7,630.76
vit_base_patch14_reg4_dinov2,518,512.0,175.34,2920.066,117.45,115.02,86.58
convnextv2_huge,224,256.0,174.19,1469.676,115.0,79.07,660.29
deit3_huge_patch14_224,224,1024.0,172.49,5936.531,161.99,95.07,632.13
convmixer_1536_20,224,1024.0,172.27,5944.074,48.68,33.03,51.63
vit_huge_patch14_clip_quickgelu_224,224,1024.0,165.12,6201.386,161.99,95.07,632.08
maxvit_small_tf_512,512,96.0,163.95,585.546,60.02,256.36,69.13
maxvit_base_tf_384,384,192.0,162.75,1179.72,69.34,247.75,119.65
xcit_large_24_p16_384,384,1024.0,162.01,6320.659,105.34,137.15,189.1
resnetv2_152x2_bit,384,384.0,160.06,2399.153,136.16,132.56,236.34
vit_huge_patch14_xp_224,224,1024.0,159.21,6431.544,161.88,95.07,631.8
resnest269e,416,512.0,159.04,3219.278,77.69,171.98,110.93
eva_large_patch14_336,336,768.0,155.41,4941.906,174.74,128.21,304.53
vit_large_patch14_clip_336,336,768.0,155.09,4951.819,174.74,128.21,304.53
vit_large_patch16_384,384,768.0,154.94,4956.737,174.85,128.21,304.72
convnext_xxlarge,256,384.0,152.35,2520.42,198.09,124.45,846.47
davit_giant,224,384.0,151.56,2533.626,192.34,138.2,1406.47
resnetv2_50x3_bit,448,192.0,150.44,1276.251,145.7,133.37,217.32
coatnet_5_224,224,192.0,149.61,1283.336,142.72,143.69,687.47
efficientnetv2_xl,512,512.0,149.15,3432.877,93.85,247.32,208.12
cait_s24_384,384,512.0,148.91,3438.219,32.17,245.3,47.06
convnext_xlarge,384,256.0,148.61,1722.573,179.2,168.99,350.2
tf_efficientnetv2_xl,512,512.0,148.0,3459.525,93.85,247.32,208.12
efficientnet_b7,600,96.0,147.91,649.053,38.33,289.94,66.35
deit3_large_patch16_384,384,1024.0,147.79,6928.856,174.85,128.21,304.76
seresnextaa201d_32x8d,384,768.0,147.05,5222.537,101.11,199.72,149.39
nfnet_f3,416,512.0,146.71,3489.974,115.58,141.78,254.92
vit_giant_patch16_gap_224,224,1024.0,145.38,7043.632,198.14,103.64,1011.37
convnextv2_large,384,192.0,144.92,1324.86,101.1,126.74,197.96
resnetv2_152x4_bit,224,512.0,144.91,3533.266,186.9,90.22,936.53
vit_large_patch16_siglip_384,384,768.0,144.23,5324.878,175.76,129.18,316.28
tf_efficientnet_b7,600,96.0,143.48,669.058,38.33,289.94,66.35
nfnet_f4,384,768.0,142.67,5383.101,122.14,147.57,316.07
vit_large_patch14_clip_quickgelu_336,336,768.0,140.95,5448.604,174.74,128.21,304.29
caformer_b36,384,256.0,138.42,1849.458,66.12,159.11,98.75
swin_large_patch4_window12_384,384,128.0,135.49,944.717,104.08,202.16,196.74
convformer_b36,384,256.0,135.29,1892.221,66.67,164.75,99.88
resnetrs420,416,1024.0,130.11,7870.213,108.45,213.79,191.89
beit_large_patch16_384,384,768.0,129.31,5939.365,174.84,128.21,305.0
dm_nfnet_f3,416,512.0,127.57,4013.328,115.58,141.78,254.92
regnety_640,384,256.0,126.8,2018.836,188.47,124.83,281.38
dm_nfnet_f4,384,768.0,123.05,6241.189,122.14,147.57,316.07
focalnet_huge_fl4,224,512.0,122.81,4169.023,118.9,113.34,686.46
xcit_large_24_p8_224,224,512.0,120.1,4263.036,141.22,181.53,188.93
resnetv2_152x2_bit,448,256.0,117.91,2171.109,184.99,180.43,236.34
eva_giant_patch14_224,224,1024.0,116.71,8773.739,259.74,135.89,1012.56
eva_giant_patch14_clip_224,224,1024.0,116.64,8779.464,259.74,135.89,1012.59
vit_giant_patch14_224,224,1024.0,114.18,8968.21,259.74,135.89,1012.61
vit_giant_patch14_clip_224,224,1024.0,114.09,8975.383,259.74,135.89,1012.65
swinv2_cr_large_384,384,128.0,112.81,1134.666,108.96,404.96,196.68
maxvit_large_tf_384,384,128.0,111.17,1151.411,126.61,332.3,212.03
eva02_large_patch14_clip_336,336,1024.0,110.28,9285.405,174.97,147.1,304.43
mvitv2_huge_cls,224,384.0,107.61,3568.518,120.67,243.63,694.8
convnextv2_huge,288,128.0,105.35,1214.957,190.1,130.7,660.29
xcit_small_24_p8_384,384,512.0,102.73,4983.926,105.23,265.87,47.63
nfnet_f5,416,512.0,100.11,5114.164,170.71,204.56,377.21
cait_s36_384,384,512.0,99.61,5140.29,47.99,367.39,68.37
swinv2_base_window12to24_192to384,384,96.0,96.35,996.364,55.25,280.36,87.92
efficientnet_b8,672,96.0,95.78,1002.248,63.48,442.89,87.41
focalnet_large_fl3,384,384.0,94.47,4064.948,105.06,168.04,239.13
tf_efficientnet_b8,672,96.0,93.18,1030.252,63.48,442.89,87.41
maxvit_base_tf_512,512,96.0,92.2,1041.169,123.93,456.26,119.88
focalnet_large_fl4,384,256.0,90.17,2839.222,105.2,181.78,239.32
resnetv2_101x3_bit,448,192.0,87.88,2184.819,280.33,194.78,387.93
dm_nfnet_f5,416,512.0,86.64,5909.833,170.71,204.56,377.21
nfnet_f4,512,384.0,81.51,4711.211,216.26,262.26,316.07
volo_d3_448,448,192.0,76.74,2501.831,96.33,446.83,86.63
vit_so400m_patch14_siglip_384,384,512.0,75.92,6743.556,302.34,200.62,428.23
nfnet_f6,448,512.0,75.59,6773.482,229.7,273.62,438.36
vit_huge_patch14_clip_336,336,768.0,75.49,10173.683,363.7,213.44,632.46
xcit_medium_24_p8_384,384,384.0,71.15,5396.903,186.67,354.69,84.32
dm_nfnet_f4,512,384.0,69.56,5520.408,216.26,262.26,316.07
vit_gigantic_patch14_224,224,512.0,66.18,7736.423,473.4,204.12,1844.44
vit_gigantic_patch14_clip_224,224,512.0,66.18,7735.92,473.41,204.12,1844.91
focalnet_xlarge_fl3,384,256.0,66.07,3874.786,185.61,223.99,408.79
dm_nfnet_f6,448,512.0,65.28,7842.994,229.7,273.62,438.36
maxvit_large_tf_512,512,64.0,63.68,1005.087,225.96,611.85,212.33
focalnet_xlarge_fl4,384,192.0,63.39,3028.979,185.79,242.31,409.03
maxvit_xlarge_tf_384,384,96.0,63.2,1518.995,283.86,498.45,475.32
regnety_1280,384,128.0,62.14,2059.919,374.99,210.2,644.81
beit_large_patch16_512,512,256.0,61.47,4164.41,310.6,227.76,305.67
convnextv2_huge,384,96.0,60.73,1580.79,337.96,232.35,660.29
swinv2_large_window12to24_192to384,384,48.0,60.6,792.119,116.15,407.83,196.74
eva02_large_patch14_448,448,512.0,59.6,8591.147,310.69,261.32,305.08
tf_efficientnet_l2,475,128.0,59.14,2164.439,172.11,609.89,480.31
nfnet_f5,544,384.0,58.55,6558.595,290.97,349.71,377.21
vit_huge_patch14_clip_378,378,512.0,58.17,8801.788,460.13,270.04,632.68
volo_d4_448,448,192.0,57.2,3356.883,197.13,527.35,193.41
nfnet_f7,480,384.0,57.05,6730.663,300.08,355.86,499.5
vit_large_patch14_dinov2,518,384.0,56.81,6759.458,414.89,304.42,304.37
vit_large_patch14_reg4_dinov2,518,384.0,56.51,6795.142,416.1,305.31,304.37
vit_huge_patch14_clip_quickgelu_378,378,384.0,53.9,7123.722,460.13,270.04,632.68
swinv2_cr_giant_224,224,192.0,52.42,3662.593,483.85,309.15,2598.76
dm_nfnet_f5,544,384.0,50.82,7555.977,290.97,349.71,377.21
eva_giant_patch14_336,336,512.0,49.6,10322.486,583.14,305.1,1013.01
swinv2_cr_huge_384,384,64.0,48.85,1310.056,352.04,583.18,657.94
nfnet_f6,576,256.0,45.99,5566.397,378.69,452.2,438.36
xcit_large_24_p8_384,384,256.0,40.54,6315.135,415.0,531.74,188.93
volo_d5_448,448,192.0,39.97,4803.918,315.06,737.92,295.91
dm_nfnet_f6,576,256.0,39.68,6452.4,378.69,452.2,438.36
nfnet_f7,608,256.0,35.92,7127.91,480.39,570.85,499.5
maxvit_xlarge_tf_512,512,48.0,35.73,1343.449,505.95,917.77,475.77
regnety_2560,384,96.0,35.19,2728.299,747.83,296.49,1282.6
convnextv2_huge,512,48.0,34.07,1408.989,600.81,413.07,660.29
cait_m36_384,384,256.0,32.53,7868.895,173.11,734.79,271.22
resnetv2_152x4_bit,480,128.0,32.31,3961.512,844.84,414.26,936.53
volo_d5_512,512,96.0,27.94,3435.72,425.09,1105.37,296.09
samvit_base_patch16,1024,12.0,23.01,521.487,371.55,403.08,89.67
efficientnet_l2,800,32.0,22.53,1420.616,479.12,1707.39,480.31
tf_efficientnet_l2,800,32.0,22.12,1446.454,479.12,1707.39,480.31
vit_giant_patch14_dinov2,518,192.0,17.14,11200.639,1553.56,871.89,1136.48
vit_giant_patch14_reg4_dinov2,518,128.0,17.05,7505.847,1558.09,874.43,1136.48
swinv2_cr_giant_384,384,32.0,15.01,2131.256,1450.71,1394.86,2598.76
eva_giant_patch14_560,560,192.0,15.01,12792.976,1618.04,846.56,1014.45
cait_m48_448,448,128.0,13.76,9299.464,329.4,1708.21,356.46
samvit_large_patch16,1024,8.0,10.25,780.237,1317.08,1055.58,308.28
samvit_huge_patch16,1024,6.0,6.31,950.475,2741.59,1727.57,637.03
eva02_enormous_patch14_clip_224,224,,,,1132.46,497.58,4350.56
vit_huge_patch16_gap_448,448,,,,544.7,636.83,631.67
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/generate_csv_results.py | import numpy as np
import pandas as pd
results = {
'results-imagenet.csv': [
'results-imagenet-real.csv',
'results-imagenetv2-matched-frequency.csv',
'results-sketch.csv'
],
'results-imagenet-a-clean.csv': [
'results-imagenet-a.csv',
],
'results-imagenet-r-clean.csv': [
'results-imagenet-r.csv',
],
}
def diff(base_df, test_csv):
base_models = base_df['model'].values
test_df = pd.read_csv(test_csv)
test_models = test_df['model'].values
rank_diff = np.zeros_like(test_models, dtype='object')
top1_diff = np.zeros_like(test_models, dtype='object')
top5_diff = np.zeros_like(test_models, dtype='object')
for rank, model in enumerate(test_models):
if model in base_models:
base_rank = int(np.where(base_models == model)[0])
top1_d = test_df['top1'][rank] - base_df['top1'][base_rank]
top5_d = test_df['top5'][rank] - base_df['top5'][base_rank]
# rank_diff
if rank == base_rank:
rank_diff[rank] = f'0'
elif rank > base_rank:
rank_diff[rank] = f'-{rank - base_rank}'
else:
rank_diff[rank] = f'+{base_rank - rank}'
# top1_diff
if top1_d >= .0:
top1_diff[rank] = f'+{top1_d:.3f}'
else:
top1_diff[rank] = f'-{abs(top1_d):.3f}'
# top5_diff
if top5_d >= .0:
top5_diff[rank] = f'+{top5_d:.3f}'
else:
top5_diff[rank] = f'-{abs(top5_d):.3f}'
else:
rank_diff[rank] = ''
top1_diff[rank] = ''
top5_diff[rank] = ''
test_df['top1_diff'] = top1_diff
test_df['top5_diff'] = top5_diff
test_df['rank_diff'] = rank_diff
test_df['param_count'] = test_df['param_count'].map('{:,.2f}'.format)
test_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True)
test_df.to_csv(test_csv, index=False, float_format='%.3f')
for base_results, test_results in results.items():
base_df = pd.read_csv(base_results)
base_df.sort_values(['top1', 'top5', 'model'], ascending=[False, False, True], inplace=True)
for test_csv in test_results:
diff(base_df, test_csv)
base_df['param_count'] = base_df['param_count'].map('{:,.2f}'.format)
base_df.to_csv(base_results, index=False, float_format='%.3f')
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-real.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,91.129,8.871,98.713,1.287,305.08,448,1.000,bicubic,+1.077,-0.335,0
eva_giant_patch14_336.clip_ft_in1k,91.058,8.942,98.602,1.399,"1,013.01",336,1.000,bicubic,+1.592,-0.224,+5
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,91.022,8.978,98.683,1.317,305.08,448,1.000,bicubic,+1.052,-0.329,-1
eva_giant_patch14_560.m30m_ft_in22k_in1k,90.969,9.031,98.672,1.328,"1,014.45",560,1.000,bicubic,+1.183,-0.320,-1
eva02_large_patch14_448.mim_in22k_ft_in1k,90.920,9.080,98.685,1.315,305.08,448,1.000,bicubic,+1.298,-0.265,-1
eva_giant_patch14_336.m30m_ft_in22k_in1k,90.907,9.093,98.661,1.339,"1,013.01",336,1.000,bicubic,+1.341,-0.291,0
eva_large_patch14_336.in22k_ft_in1k,90.905,9.095,98.785,1.215,304.53,336,1.000,bicubic,+2.235,+0.063,+6
eva_giant_patch14_224.clip_ft_in1k,90.900,9.100,98.680,1.319,"1,012.56",224,0.900,bicubic,+2.020,+0.000,+1
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,90.896,9.104,98.802,1.198,87.12,448,1.000,bicubic,+2.206,+0.078,+2
eva02_large_patch14_448.mim_m38m_ft_in1k,90.890,9.110,98.653,1.347,305.08,448,1.000,bicubic,+1.316,-0.271,-5
eva_large_patch14_336.in22k_ft_in22k_in1k,90.862,9.138,98.715,1.285,304.53,336,1.000,bicubic,+1.656,-0.139,-3
eva02_base_patch14_448.mim_in22k_ft_in1k,90.800,9.200,98.742,1.258,87.12,448,1.000,bicubic,+2.548,+0.178,+14
caformer_b36.sail_in22k_ft_in1k_384,90.781,9.219,98.860,1.140,98.75,384,1.000,bicubic,+2.723,+0.278,+21
beit_large_patch16_512.in22k_ft_in22k_in1k,90.687,9.313,98.753,1.247,305.67,512,1.000,bicubic,+2.091,+0.097,+1
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,90.678,9.322,98.813,1.187,200.13,384,1.000,bicubic,+2.372,+0.231,+8
regnety_1280.swag_ft_in1k,90.657,9.343,98.819,1.181,644.81,384,1.000,bicubic,+2.427,+0.133,+12
convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.642,9.358,98.806,1.194,846.47,256,1.000,bicubic,+2.038,+0.099,-3
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,90.633,9.367,98.755,1.245,200.13,384,1.000,bicubic,+2.785,+0.309,+24
volo_d5_512.sail_in1k,90.614,9.386,98.698,1.302,296.09,512,1.150,bicubic,+3.556,+0.728,+49
beit_large_patch16_384.in22k_ft_in22k_in1k,90.606,9.394,98.766,1.234,305.00,384,1.000,bicubic,+2.204,+0.158,-1
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,90.584,9.416,98.617,1.383,116.14,384,1.000,bicubic,+2.756,+0.245,+22
volo_d5_448.sail_in1k,90.580,9.420,98.685,1.315,295.91,448,1.150,bicubic,+3.628,+0.747,+53
tf_efficientnet_l2.ns_jft_in1k,90.561,9.439,98.775,1.226,480.31,800,0.960,bicubic,+2.209,+0.127,-3
maxvit_base_tf_512.in21k_ft_in1k,90.561,9.439,98.702,1.298,119.88,512,1.000,bicubic,+2.341,+0.172,+7
eva_large_patch14_196.in22k_ft_in22k_in1k,90.557,9.443,98.698,1.302,304.14,196,1.000,bicubic,+1.983,+0.040,-8
convnextv2_huge.fcmae_ft_in22k_in1k_512,90.542,9.458,98.710,1.290,660.29,512,1.000,bicubic,+1.684,-0.038,-16
vit_large_patch14_clip_336.openai_ft_in12k_in1k,90.542,9.458,98.687,1.313,304.53,336,1.000,bicubic,+2.274,+0.161,-3
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,90.540,9.460,98.806,1.194,200.13,320,1.000,bicubic,+2.582,+0.331,+8
tf_efficientnet_l2.ns_jft_in1k_475,90.537,9.463,98.710,1.290,480.31,475,0.936,bicubic,+2.303,+0.164,-2
eva_large_patch14_196.in22k_ft_in1k,90.535,9.465,98.770,1.230,304.14,196,1.000,bicubic,+2.603,+0.272,+7
volo_d4_448.sail_in1k,90.512,9.488,98.591,1.409,193.41,448,1.150,bicubic,+3.720,+0.707,+53
maxvit_xlarge_tf_512.in21k_ft_in1k,90.503,9.497,98.576,1.424,475.77,512,1.000,bicubic,+1.965,-0.068,-14
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,90.501,9.499,98.631,1.369,632.46,336,1.000,bicubic,+1.909,-0.031,-17
convnextv2_huge.fcmae_ft_in22k_in1k_384,90.497,9.503,98.695,1.304,660.29,384,1.000,bicubic,+1.827,-0.043,-22
convnext_xlarge.fb_in22k_ft_in1k_384,90.495,9.505,98.768,1.232,350.20,384,1.000,bicubic,+2.743,+0.212,+9
caformer_m36.sail_in22k_ft_in1k_384,90.460,9.540,98.668,1.332,56.20,384,1.000,bicubic,+3.014,+0.360,+18
convformer_b36.sail_in22k_ft_in1k_384,90.448,9.552,98.772,1.228,99.88,384,1.000,bicubic,+2.846,+0.338,+10
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,90.444,9.556,98.749,1.251,116.09,384,1.000,bicubic,+2.980,+0.375,+14
caformer_b36.sail_in22k_ft_in1k,90.431,9.569,98.764,1.236,98.75,224,1.000,bicubic,+3.011,+0.436,+16
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,90.428,9.572,98.642,1.358,304.53,336,1.000,bicubic,+2.248,+0.070,-8
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,90.418,9.582,98.644,1.356,632.05,224,1.000,bicubic,+2.162,+0.092,-16
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,90.407,9.593,98.734,1.266,87.92,384,1.000,bicubic,+3.311,+0.500,+24
vit_large_patch14_clip_224.openai_ft_in12k_in1k,90.382,9.618,98.659,1.341,304.20,224,1.000,bicubic,+2.207,+0.113,-10
maxvit_xlarge_tf_384.in21k_ft_in1k,90.379,9.621,98.582,1.418,475.32,384,1.000,bicubic,+2.065,+0.038,-22
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,90.377,9.623,98.648,1.351,73.88,384,1.000,bicubic,+2.995,+0.337,+12
beit_base_patch16_384.in22k_ft_in22k_in1k,90.367,9.633,98.725,1.275,86.74,384,1.000,bicubic,+3.567,+0.589,+36
convnextv2_large.fcmae_ft_in22k_in1k_384,90.367,9.633,98.663,1.337,197.96,384,1.000,bicubic,+2.169,+0.135,-16
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,90.362,9.638,98.734,1.266,149.39,384,1.000,bicubic,+3.074,+0.400,+11
maxvit_large_tf_512.in21k_ft_in1k,90.362,9.638,98.642,1.358,212.33,512,1.000,bicubic,+2.138,+0.044,-19
maxvit_base_tf_384.in21k_ft_in1k,90.360,9.640,98.683,1.317,119.65,384,1.000,bicubic,+2.438,+0.139,-13
convnextv2_base.fcmae_ft_in22k_in1k_384,90.360,9.640,98.670,1.330,88.72,384,1.000,bicubic,+2.716,+0.254,-4
beitv2_large_patch16_224.in1k_ft_in22k_in1k,90.354,9.646,98.587,1.413,304.43,224,0.950,bicubic,+1.960,-0.011,-32
vit_large_patch14_clip_336.laion2b_ft_in1k,90.332,9.668,98.597,1.403,304.53,336,1.000,bicubic,+2.476,+0.229,-13
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,90.330,9.670,98.770,1.230,88.59,384,1.000,bicubic,+3.196,+0.548,+10
maxvit_large_tf_384.in21k_ft_in1k,90.315,9.685,98.687,1.313,212.03,384,1.000,bicubic,+2.329,+0.119,-20
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,90.311,9.689,98.668,1.332,304.20,224,1.000,bicubic,+2.417,+0.260,-17
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,90.309,9.691,98.651,1.349,200.13,256,1.000,bicubic,+2.973,+0.433,+1
vit_large_patch14_clip_224.openai_ft_in1k,90.307,9.693,98.640,1.360,304.20,224,1.000,bicubic,+2.453,+0.214,-17
caformer_s36.sail_in22k_ft_in1k_384,90.305,9.695,98.794,1.206,39.30,384,1.000,bicubic,+3.447,+0.582,+19
convnext_base.fb_in22k_ft_in1k_384,90.283,9.717,98.800,1.200,88.59,384,1.000,bicubic,+3.487,+0.536,+23
convnext_large.fb_in22k_ft_in1k_384,90.279,9.721,98.659,1.341,197.77,384,1.000,bicubic,+2.807,+0.273,-10
deit3_large_patch16_384.fb_in22k_ft_in1k,90.247,9.753,98.625,1.375,304.76,384,1.000,bicubic,+2.527,+0.113,-17
dm_nfnet_f6.dm_in1k,90.241,9.759,98.625,1.375,438.36,576,0.956,bicubic,+3.879,+0.729,+44
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,90.226,9.774,98.766,1.234,93.59,320,1.000,bicubic,+3.502,+0.590,+22
deit3_huge_patch14_224.fb_in22k_ft_in1k,90.226,9.774,98.640,1.360,632.13,224,1.000,bicubic,+3.040,+0.380,-1
regnety_320.swag_ft_in1k,90.213,9.787,98.764,1.236,145.05,384,1.000,bicubic,+3.379,+0.402,+14
vit_large_patch16_384.augreg_in21k_ft_in1k,90.213,9.787,98.661,1.339,304.72,384,1.000,bicubic,+3.129,+0.359,0
vit_base_patch16_clip_384.laion2b_ft_in1k,90.211,9.789,98.702,1.298,86.86,384,1.000,bicubic,+3.593,+0.694,+21
convformer_m36.sail_in22k_ft_in1k_384,90.204,9.796,98.651,1.349,57.05,384,1.000,bicubic,+3.312,+0.535,+8
convnextv2_huge.fcmae_ft_in1k,90.204,9.796,98.548,1.452,660.29,288,1.000,bicubic,+3.624,+0.576,+22
tf_efficientnetv2_l.in21k_ft_in1k,90.202,9.798,98.719,1.281,118.52,480,1.000,bicubic,+3.400,+0.583,+10
vit_base_patch16_clip_384.openai_ft_in12k_in1k,90.200,9.800,98.648,1.351,86.86,384,0.950,bicubic,+3.174,+0.466,-2
convformer_b36.sail_in22k_ft_in1k,90.189,9.811,98.695,1.304,99.88,224,1.000,bicubic,+3.191,+0.523,-3
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,90.189,9.811,98.585,1.415,86.86,384,1.000,bicubic,+2.983,+0.550,-13
cait_m48_448.fb_dist_in1k,90.189,9.811,98.484,1.516,356.46,448,1.000,bicubic,+3.697,+0.732,+25
vit_huge_patch14_clip_224.laion2b_ft_in1k,90.181,9.819,98.544,1.456,632.05,224,1.000,bicubic,+2.593,+0.326,-28
volo_d3_448.sail_in1k,90.177,9.823,98.550,1.450,86.63,448,1.000,bicubic,+3.675,+0.840,+20
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,90.157,9.843,98.614,1.386,196.74,384,1.000,bicubic,+2.693,+0.364,-25
beit_large_patch16_224.in22k_ft_in22k_in1k,90.149,9.851,98.725,1.275,304.43,224,0.900,bicubic,+2.671,+0.421,-29
beitv2_large_patch16_224.in1k_ft_in1k,90.100,9.900,98.439,1.561,304.43,224,0.950,bicubic,+2.688,+0.205,-24
tf_efficientnet_b7.ns_jft_in1k,90.098,9.902,98.614,1.386,66.35,600,0.949,bicubic,+3.258,+0.522,-2
vit_large_patch14_clip_224.laion2b_ft_in1k,90.095,9.905,98.555,1.445,304.20,224,1.000,bicubic,+2.809,+0.311,-21
convnextv2_base.fcmae_ft_in22k_in1k,90.068,9.932,98.676,1.324,88.72,288,1.000,bicubic,+3.070,+0.508,-11
convnext_xlarge.fb_in22k_ft_in1k,90.066,9.934,98.619,1.381,350.20,288,1.000,bicubic,+2.736,+0.291,-25
caformer_b36.sail_in1k_384,90.063,9.937,98.514,1.486,98.75,384,1.000,bicubic,+3.655,+0.700,+19
cait_m36_384.fb_dist_in1k,90.051,9.949,98.495,1.505,271.22,384,1.000,bicubic,+3.993,+0.765,+41
convnextv2_large.fcmae_ft_in22k_in1k,90.034,9.966,98.629,1.371,197.96,288,1.000,bicubic,+2.550,+0.273,-38
seresnextaa101d_32x8d.sw_in12k_ft_in1k,90.029,9.971,98.685,1.315,93.59,288,1.000,bicubic,+3.545,+0.655,+11
tiny_vit_21m_512.dist_in22k_ft_in1k,90.029,9.971,98.493,1.507,21.27,512,1.000,bicubic,+3.571,+0.609,+12
tf_efficientnetv2_m.in21k_ft_in1k,90.025,9.975,98.666,1.334,54.14,480,1.000,bicubic,+4.033,+0.722,+42
convformer_s36.sail_in22k_ft_in1k_384,90.023,9.977,98.619,1.381,40.01,384,1.000,bicubic,+3.645,+0.635,+14
swin_large_patch4_window12_384.ms_in22k_ft_in1k,90.019,9.981,98.661,1.339,196.74,384,1.000,bicubic,+2.887,+0.427,-27
deit3_large_patch16_224.fb_in22k_ft_in1k,90.008,9.992,98.659,1.341,304.37,224,1.000,bicubic,+3.026,+0.423,-20
convnext_base.clip_laiona_augreg_ft_in1k_384,90.001,9.998,98.548,1.452,88.59,384,1.000,bicubic,+3.499,+0.580,+2
swin_base_patch4_window12_384.ms_in22k_ft_in1k,89.997,10.003,98.700,1.300,87.90,384,1.000,bicubic,+3.559,+0.634,+8
vit_base_patch16_384.augreg_in21k_ft_in1k,89.989,10.011,98.678,1.322,86.86,384,1.000,bicubic,+3.995,+0.676,+35
maxvit_base_tf_512.in1k,89.974,10.026,98.433,1.567,119.88,512,1.000,bicubic,+3.372,+0.515,-7
caformer_m36.sail_in1k_384,89.933,10.067,98.454,1.546,56.20,384,1.000,bicubic,+3.767,+0.634,+20
convnextv2_large.fcmae_ft_in1k,89.931,10.069,98.559,1.441,197.96,288,1.000,bicubic,+3.813,+0.737,+22
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,89.927,10.073,98.414,1.586,116.14,224,0.950,bicubic,+3.033,+0.400,-24
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,89.925,10.075,98.505,1.494,196.74,256,0.900,bicubic,+2.973,+0.400,-27
regnety_160.swag_ft_in1k,89.918,10.082,98.644,1.356,83.59,384,1.000,bicubic,+3.898,+0.592,+27
convnext_small.fb_in22k_ft_in1k_384,89.916,10.084,98.685,1.315,50.22,384,1.000,bicubic,+4.138,+0.795,+43
efficientnet_b5.sw_in12k_ft_in1k,89.912,10.088,98.565,1.435,30.39,448,1.000,bicubic,+4.016,+0.829,+33
deit3_base_patch16_384.fb_in22k_ft_in1k,89.891,10.110,98.602,1.399,86.88,384,1.000,bicubic,+3.151,+0.486,-19
xcit_large_24_p8_384.fb_dist_in1k,89.886,10.114,98.384,1.616,188.93,384,1.000,bicubic,+3.890,+0.694,+24
volo_d5_224.sail_in1k,89.880,10.120,98.490,1.510,295.46,224,0.960,bicubic,+3.810,+0.915,+19
convnext_large.fb_in22k_ft_in1k,89.876,10.124,98.593,1.407,197.77,288,1.000,bicubic,+2.850,+0.389,-39
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,89.867,10.133,98.644,1.356,87.92,256,0.900,bicubic,+3.599,+0.762,+2
tiny_vit_21m_384.dist_in22k_ft_in1k,89.863,10.137,98.499,1.501,21.23,384,1.000,bicubic,+3.755,+0.789,+12
convnext_base.fb_in22k_ft_in1k,89.858,10.142,98.691,1.309,88.59,288,1.000,bicubic,+3.584,+0.599,-1
caformer_m36.sail_in22k_ft_in1k,89.852,10.148,98.585,1.415,56.20,224,1.000,bicubic,+3.258,+0.561,-21
cait_s36_384.fb_dist_in1k,89.835,10.165,98.427,1.573,68.37,384,1.000,bicubic,+4.381,+0.949,+52
coatnet_2_rw_224.sw_in12k_ft_in1k,89.831,10.169,98.527,1.473,73.87,224,0.950,bicubic,+3.267,+0.631,-21
convformer_m36.sail_in22k_ft_in1k,89.818,10.182,98.548,1.452,57.05,224,1.000,bicubic,+3.670,+0.698,+5
xcit_medium_24_p8_384.fb_dist_in1k,89.816,10.184,98.365,1.635,84.32,384,1.000,bicubic,+4.000,+0.773,+25
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,89.811,10.188,98.668,1.332,88.59,256,1.000,bicubic,+3.441,+0.684,-11
volo_d4_224.sail_in1k,89.811,10.188,98.427,1.573,192.96,224,0.960,bicubic,+3.939,+0.955,+20
maxvit_large_tf_512.in1k,89.799,10.201,98.330,1.670,212.33,512,1.000,bicubic,+3.273,+0.450,-25
vit_large_r50_s32_384.augreg_in21k_ft_in1k,89.792,10.208,98.522,1.478,329.09,384,1.000,bicubic,+3.610,+0.600,-4
swin_large_patch4_window7_224.ms_in22k_ft_in1k,89.786,10.214,98.640,1.360,196.53,224,0.900,bicubic,+3.474,+0.738,-13
tf_efficientnet_b6.ns_jft_in1k,89.786,10.214,98.508,1.492,43.04,528,0.942,bicubic,+3.328,+0.618,-20
volo_d2_384.sail_in1k,89.784,10.216,98.403,1.597,58.87,384,1.000,bicubic,+3.742,+0.829,+5
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,89.775,10.225,98.469,1.531,73.88,224,0.950,bicubic,+3.271,+0.575,-29
tf_efficientnetv2_xl.in21k_ft_in1k,89.773,10.227,98.288,1.712,208.12,512,1.000,bicubic,+3.025,+0.274,-40
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,89.752,10.248,98.484,1.516,116.09,224,0.950,bicubic,+3.110,+0.464,-38
beitv2_base_patch16_224.in1k_ft_in22k_in1k,89.743,10.257,98.585,1.415,86.53,224,0.900,bicubic,+3.269,+0.532,-27
dm_nfnet_f4.dm_in1k,89.741,10.259,98.409,1.591,316.07,512,0.951,bicubic,+3.905,+0.591,+12
dm_nfnet_f5.dm_in1k,89.737,10.263,98.441,1.559,377.21,544,0.954,bicubic,+3.637,+0.753,-6
xcit_small_24_p8_384.fb_dist_in1k,89.735,10.265,98.420,1.580,47.63,384,1.000,bicubic,+4.181,+0.850,+28
regnety_160.lion_in12k_ft_in1k,89.726,10.274,98.608,1.392,83.59,288,1.000,bicubic,+3.738,+0.774,+2
caformer_s18.sail_in22k_ft_in1k_384,89.720,10.280,98.578,1.422,26.34,384,1.000,bicubic,+4.306,+0.876,+36
vit_base_patch8_224.augreg2_in21k_ft_in1k,89.718,10.283,98.510,1.490,86.58,224,0.900,bicubic,+3.499,+0.678,-21
convformer_m36.sail_in1k_384,89.718,10.283,98.435,1.565,57.05,384,1.000,bicubic,+4.138,+0.893,+24
vit_base_patch16_clip_384.openai_ft_in1k,89.707,10.293,98.508,1.492,86.86,384,1.000,bicubic,+3.501,+0.632,-21
caformer_s36.sail_in22k_ft_in1k,89.701,10.300,98.638,1.362,39.30,224,1.000,bicubic,+3.910,+0.812,+8
volo_d1_384.sail_in1k,89.698,10.302,98.290,1.710,26.78,384,1.000,bicubic,+4.454,+1.096,+45
deit3_large_patch16_384.fb_in1k,89.688,10.312,98.390,1.610,304.76,384,1.000,bicubic,+3.876,+0.792,+4
caformer_s36.sail_in1k_384,89.679,10.321,98.324,1.676,39.30,384,1.000,bicubic,+3.937,+0.652,+9
xcit_large_24_p16_384.fb_dist_in1k,89.662,10.338,98.403,1.597,189.10,384,1.000,bicubic,+3.908,+0.865,+7
convformer_b36.sail_in1k_384,89.658,10.342,98.379,1.621,99.88,384,1.000,bicubic,+3.918,+0.855,+8
tf_efficientnet_b5.ns_jft_in1k,89.651,10.349,98.486,1.514,30.39,456,0.934,bicubic,+3.563,+0.730,-18
dm_nfnet_f3.dm_in1k,89.647,10.353,98.463,1.537,254.92,416,0.940,bicubic,+3.961,+0.893,+9
convnext_base.clip_laion2b_augreg_ft_in1k,89.645,10.355,98.463,1.537,88.59,256,1.000,bicubic,+3.487,+0.783,-25
regnety_2560.seer_ft_in1k,89.632,10.368,98.399,1.601,"1,282.60",384,1.000,bicubic,+4.482,+0.961,+48
convnext_small.in12k_ft_in1k_384,89.598,10.402,98.480,1.520,50.22,384,1.000,bicubic,+3.416,+0.558,-31
maxvit_base_tf_384.in1k,89.589,10.411,98.320,1.680,119.65,384,1.000,bicubic,+3.287,+0.522,-38
tf_efficientnet_b8.ap_in1k,89.579,10.421,98.305,1.695,87.41,672,0.954,bicubic,+4.215,+1.013,+28
regnety_160.sw_in12k_ft_in1k,89.570,10.430,98.546,1.454,83.59,288,1.000,bicubic,+3.584,+0.712,-15
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,89.566,10.434,98.420,1.580,86.57,224,0.950,bicubic,+3.396,+0.664,-33
maxvit_tiny_tf_512.in1k,89.562,10.438,98.335,1.665,31.05,512,1.000,bicubic,+3.898,+0.751,+2
convformer_s18.sail_in22k_ft_in1k_384,89.553,10.447,98.531,1.469,26.77,384,1.000,bicubic,+4.555,+0.961,+57
volo_d3_224.sail_in1k,89.553,10.447,98.373,1.627,86.33,224,0.960,bicubic,+4.139,+1.097,+16
maxvit_large_tf_384.in1k,89.553,10.447,98.185,1.815,212.03,384,1.000,bicubic,+3.323,+0.497,-41
tf_efficientnetv2_l.in1k,89.540,10.460,98.339,1.661,118.52,480,1.000,bicubic,+3.876,+0.865,-1
flexivit_large.1200ep_in1k,89.534,10.466,98.414,1.586,304.36,240,0.950,bicubic,+3.890,+0.874,-1
xcit_large_24_p8_224.fb_dist_in1k,89.517,10.483,98.217,1.783,188.93,224,1.000,bicubic,+4.115,+0.815,+14
flexivit_large.600ep_in1k,89.510,10.490,98.392,1.608,304.36,240,0.950,bicubic,+3.970,+0.904,+1
xcit_small_12_p8_384.fb_dist_in1k,89.508,10.492,98.307,1.693,26.21,384,1.000,bicubic,+4.430,+1.025,+43
cait_s24_384.fb_dist_in1k,89.504,10.496,98.362,1.638,47.06,384,1.000,bicubic,+4.456,+1.016,+45
convformer_s36.sail_in22k_ft_in1k,89.496,10.505,98.454,1.546,40.01,224,1.000,bicubic,+4.082,+0.886,+8
convnextv2_tiny.fcmae_ft_in22k_in1k_384,89.485,10.515,98.484,1.516,28.64,384,1.000,bicubic,+4.379,+0.856,+34
convformer_s36.sail_in1k_384,89.481,10.520,98.369,1.631,40.01,384,1.000,bicubic,+4.103,+0.893,+10
xcit_medium_24_p16_384.fb_dist_in1k,89.481,10.520,98.296,1.704,84.40,384,1.000,bicubic,+4.056,+0.966,+2
convnext_tiny.in12k_ft_in1k_384,89.472,10.528,98.505,1.494,28.59,384,1.000,bicubic,+4.350,+0.900,+30
inception_next_base.sail_in1k_384,89.453,10.547,98.345,1.655,86.67,384,1.000,bicubic,+4.251,+0.931,+23
regnety_120.sw_in12k_ft_in1k,89.446,10.554,98.537,1.462,51.82,288,1.000,bicubic,+4.046,+0.956,+5
vit_base_patch16_224.augreg2_in21k_ft_in1k,89.446,10.554,98.441,1.559,86.57,224,0.900,bicubic,+4.352,+0.911,+31
deit3_base_patch16_224.fb_in22k_ft_in1k,89.444,10.556,98.557,1.443,86.59,224,1.000,bicubic,+3.744,+0.811,-18
maxvit_small_tf_512.in1k,89.444,10.556,98.354,1.646,69.13,512,1.000,bicubic,+3.360,+0.590,-45
vit_base_patch8_224.augreg_in21k_ft_in1k,89.436,10.564,98.484,1.516,86.58,224,0.900,bicubic,+3.638,+0.694,-28
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,89.436,10.564,98.401,1.599,88.34,448,1.000,bicubic,+3.656,+0.763,-27
deit_base_distilled_patch16_384.fb_in1k,89.434,10.566,98.439,1.561,87.63,384,1.000,bicubic,+4.010,+1.033,-6
vit_base_patch16_clip_224.laion2b_ft_in1k,89.431,10.569,98.469,1.531,86.57,224,1.000,bicubic,+3.961,+0.893,-11
tf_efficientnet_b7.ap_in1k,89.431,10.569,98.345,1.655,66.35,600,0.949,bicubic,+4.307,+1.093,+20
convnextv2_base.fcmae_ft_in1k,89.421,10.579,98.360,1.640,88.72,288,1.000,bicubic,+3.947,+0.976,-13
caformer_b36.sail_in1k,89.408,10.592,98.222,1.778,98.75,224,1.000,bicubic,+3.904,+0.912,-15
vit_base_patch16_clip_224.openai_ft_in12k_in1k,89.404,10.596,98.394,1.606,86.57,224,0.950,bicubic,+3.462,+0.666,-42
hrnet_w48_ssld.paddle_in1k,89.401,10.598,98.382,1.618,77.47,288,1.000,bilinear,+4.921,+1.148,+69
beit_base_patch16_224.in22k_ft_in22k_in1k,89.395,10.605,98.529,1.471,86.53,224,0.900,bicubic,+4.183,+0.871,+7
regnetz_e8.ra3_in1k,89.378,10.622,98.459,1.542,57.70,320,1.000,bicubic,+4.344,+1.186,+25
deit3_small_patch16_384.fb_in22k_ft_in1k,89.363,10.637,98.386,1.614,22.21,384,1.000,bicubic,+4.539,+0.900,+39
tf_efficientnetv2_m.in1k,89.350,10.650,98.326,1.674,54.14,480,1.000,bicubic,+4.146,+0.962,+5
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,89.348,10.652,98.495,1.505,39.03,384,0.950,bicubic,+3.818,+0.859,-24
tf_efficientnet_b8.ra_in1k,89.348,10.652,98.305,1.695,87.41,672,0.954,bicubic,+3.980,+0.911,-10
tf_efficientnet_b6.ap_in1k,89.344,10.656,98.283,1.717,43.04,528,0.942,bicubic,+4.556,+1.145,+38
volo_d2_224.sail_in1k,89.335,10.665,98.213,1.787,58.68,224,0.960,bicubic,+4.133,+1.023,+3
eva02_small_patch14_336.mim_in22k_ft_in1k,89.333,10.667,98.377,1.623,22.13,336,1.000,bicubic,+3.615,+0.743,-38
caformer_s18.sail_in1k_384,89.331,10.669,98.294,1.706,26.34,384,1.000,bicubic,+4.305,+0.936,+18
vit_large_patch16_224.augreg_in21k_ft_in1k,89.318,10.682,98.390,1.610,304.33,224,0.900,bicubic,+3.482,+0.726,-51
flexivit_large.300ep_in1k,89.310,10.690,98.324,1.676,304.36,240,0.950,bicubic,+4.022,+0.924,-12
tf_efficientnet_b4.ns_jft_in1k,89.301,10.699,98.345,1.655,19.34,380,0.922,bicubic,+4.141,+0.877,0
convnext_small.fb_in22k_ft_in1k,89.299,10.701,98.358,1.642,50.22,288,1.000,bicubic,+4.037,+0.676,-12
xcit_small_24_p16_384.fb_dist_in1k,89.295,10.705,98.326,1.674,47.67,384,1.000,bicubic,+4.205,+1.014,+6
xcit_medium_24_p8_224.fb_dist_in1k,89.293,10.707,98.185,1.815,84.32,224,1.000,bicubic,+4.219,+0.935,+8
beitv2_base_patch16_224.in1k_ft_in1k,89.271,10.729,98.262,1.738,86.53,224,0.900,bicubic,+3.677,+0.756,-40
deit3_huge_patch14_224.fb_in1k,89.218,10.782,98.164,1.836,632.13,224,0.900,bicubic,+3.994,+0.804,-12
coat_lite_medium_384.in1k,89.209,10.791,98.219,1.781,44.57,384,1.000,bicubic,+4.331,+0.847,+19
xcit_small_24_p8_224.fb_dist_in1k,89.199,10.801,98.243,1.757,47.63,224,1.000,bicubic,+4.331,+1.053,+19
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,89.197,10.803,98.358,1.642,88.30,384,1.000,bicubic,+3.831,+0.698,-25
dm_nfnet_f2.dm_in1k,89.194,10.806,98.228,1.772,193.78,352,0.920,bicubic,+4.002,+0.882,-10
xcit_small_12_p16_384.fb_dist_in1k,89.194,10.806,98.217,1.783,26.25,384,1.000,bicubic,+4.482,+1.099,+25
swin_base_patch4_window7_224.ms_in22k_ft_in1k,89.173,10.827,98.433,1.567,87.77,224,0.900,bicubic,+3.901,+0.869,-23
vit_base_patch16_clip_224.openai_ft_in1k,89.171,10.829,98.269,1.732,86.57,224,0.900,bicubic,+3.879,+0.832,-26
eca_nfnet_l2.ra3_in1k,89.147,10.852,98.315,1.685,56.72,384,1.000,bicubic,+4.447,+1.049,+23
cait_xs24_384.fb_dist_in1k,89.147,10.852,98.292,1.708,26.67,384,1.000,bicubic,+5.085,+1.408,+87
fastvit_ma36.apple_dist_in1k,89.124,10.876,98.142,1.857,44.07,256,0.950,bicubic,+4.526,+1.141,+27
convformer_s18.sail_in1k_384,89.124,10.876,98.130,1.870,26.77,384,1.000,bicubic,+4.722,+1.018,+57
maxvit_tiny_tf_384.in1k,89.111,10.889,98.211,1.789,30.98,384,1.000,bicubic,+4.011,+0.833,-12
maxvit_small_tf_384.in1k,89.109,10.891,98.164,1.836,69.02,384,1.000,bicubic,+3.569,+0.702,-50
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,89.107,10.893,98.189,1.810,468.53,224,0.875,bilinear,+4.009,+0.751,-13
tf_efficientnet_b7.ra_in1k,89.081,10.919,98.185,1.815,66.35,600,0.949,bicubic,+4.149,+0.977,+1
tiny_vit_21m_224.dist_in22k_ft_in1k,89.077,10.923,98.236,1.764,21.20,224,0.950,bicubic,+3.991,+0.870,-12
ecaresnet269d.ra2_in1k,89.071,10.929,98.234,1.766,102.09,352,1.000,bicubic,+4.103,+1.012,-3
regnety_1280.seer_ft_in1k,89.064,10.936,98.157,1.843,644.81,384,1.000,bicubic,+4.632,+1.065,+41
vit_base_patch32_clip_384.openai_ft_in12k_in1k,89.045,10.955,98.281,1.719,88.30,384,0.950,bicubic,+3.831,+0.877,-30
xcit_large_24_p16_224.fb_dist_in1k,89.045,10.955,98.059,1.941,189.10,224,1.000,bicubic,+4.129,+0.931,-2
convnext_small.in12k_ft_in1k,89.024,10.976,98.243,1.757,50.22,288,1.000,bicubic,+3.694,+0.697,-41
resmlp_big_24_224.fb_in22k_ft_in1k,89.019,10.981,98.215,1.785,129.14,224,0.875,bicubic,+4.621,+1.103,+46
dm_nfnet_f1.dm_in1k,89.017,10.983,98.256,1.744,132.63,320,0.910,bicubic,+4.315,+1.074,+8
xcit_small_12_p8_224.fb_dist_in1k,89.009,10.991,98.076,1.924,26.21,224,1.000,bicubic,+4.773,+1.206,+56
convnext_large.fb_in1k,88.994,11.006,98.040,1.960,197.77,288,1.000,bicubic,+4.148,+0.826,-2
efficientnetv2_rw_m.agc_in1k,88.985,11.015,98.219,1.781,53.24,416,1.000,bicubic,+4.175,+1.067,-1
caformer_m36.sail_in1k,88.985,11.015,98.016,1.984,56.20,224,1.000,bicubic,+3.753,+0.816,-39
regnety_1280.swag_lc_in1k,88.955,11.045,98.228,1.772,644.81,224,0.965,bicubic,+2.973,+0.378,-90
regnetz_d8_evos.ch_in1k,88.951,11.049,98.181,1.819,23.46,320,1.000,bicubic,+4.825,+1.169,+60
regnetz_040_h.ra3_in1k,88.949,11.051,98.209,1.791,28.94,320,1.000,bicubic,+4.457,+1.451,+19
edgenext_base.in21k_ft_in1k,88.942,11.057,98.279,1.721,18.51,320,1.000,bicubic,+4.888,+1.083,+66
tf_efficientnet_b5.ap_in1k,88.942,11.057,98.166,1.834,30.39,456,0.934,bicubic,+4.684,+1.192,+44
mvitv2_large.fb_in1k,88.936,11.064,97.965,2.035,217.99,224,0.900,bicubic,+3.692,+0.751,-47
caformer_s18.sail_in22k_ft_in1k,88.932,11.068,98.311,1.689,26.34,224,1.000,bicubic,+4.858,+1.113,+60
deit3_base_patch16_384.fb_in1k,88.923,11.077,98.042,1.958,86.88,384,1.000,bicubic,+3.849,+0.768,-28
deit3_medium_patch16_224.fb_in22k_ft_in1k,88.919,11.081,98.296,1.704,38.85,224,1.000,bicubic,+4.369,+1.108,+4
volo_d1_224.sail_in1k,88.915,11.085,98.027,1.973,26.63,224,0.960,bicubic,+4.753,+1.251,+50
convnext_tiny.in12k_ft_in1k,88.906,11.094,98.309,1.691,28.59,288,1.000,bicubic,+4.456,+0.969,+15
tf_efficientnetv2_s.in21k_ft_in1k,88.898,11.102,98.275,1.725,21.46,384,1.000,bicubic,+4.612,+1.023,+34
vit_base_patch16_224.augreg_in21k_ft_in1k,88.868,11.132,98.232,1.768,86.57,224,0.900,bicubic,+4.336,+0.938,+3
convnext_tiny.fb_in22k_ft_in1k_384,88.855,11.145,98.296,1.704,28.59,384,1.000,bicubic,+4.767,+1.152,+52
regnetz_d8.ra3_in1k,88.853,11.147,98.189,1.810,23.37,320,1.000,bicubic,+4.801,+1.194,+56
convformer_b36.sail_in1k,88.851,11.149,97.878,2.122,99.88,224,1.000,bicubic,+4.033,+0.932,-18
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,88.842,11.158,97.869,2.131,41.72,224,0.950,bicubic,+3.932,+0.911,-25
resnetrs420.tf_in1k,88.838,11.162,98.031,1.968,191.89,416,1.000,bicubic,+3.834,+0.907,-34
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,88.834,11.166,98.051,1.949,194.03,224,0.875,bilinear,+4.668,+0.853,+40
resnetrs270.tf_in1k,88.829,11.171,98.136,1.864,129.86,352,1.000,bicubic,+4.401,+1.168,+14
vit_small_r26_s32_384.augreg_in21k_ft_in1k,88.821,11.179,98.341,1.659,36.47,384,1.000,bicubic,+4.773,+1.013,+52
swin_small_patch4_window7_224.ms_in22k_ft_in1k,88.817,11.184,98.328,1.672,49.61,224,0.900,bicubic,+5.519,+1.364,+133
vit_base_r50_s16_384.orig_in21k_ft_in1k,88.806,11.194,98.232,1.768,98.95,384,1.000,bicubic,+3.830,+0.942,-37
xcit_medium_24_p16_224.fb_dist_in1k,88.806,11.194,98.038,1.962,84.40,224,1.000,bicubic,+4.520,+1.106,+23
tf_efficientnet_b7.aa_in1k,88.804,11.196,98.057,1.943,66.35,600,0.949,bicubic,+4.388,+1.149,+13
maxxvit_rmlp_small_rw_256.sw_in1k,88.795,11.205,98.064,1.937,66.01,256,0.950,bicubic,+4.171,+0.996,-18
fastvit_sa36.apple_dist_in1k,88.789,11.211,98.096,1.905,31.53,256,0.900,bicubic,+4.763,+1.242,+47
seresnet152d.ra2_in1k,88.787,11.213,98.172,1.828,66.84,320,1.000,bicubic,+4.427,+1.132,+14
xcit_tiny_24_p8_384.fb_dist_in1k,88.787,11.213,98.155,1.845,12.11,384,1.000,bicubic,+5.041,+1.755,+74
convformer_m36.sail_in1k,88.787,11.213,97.769,2.231,57.05,224,1.000,bicubic,+4.293,+0.903,-7
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,88.782,11.218,98.151,1.849,88.79,224,0.875,bilinear,+4.480,+0.975,+14
resnetrs200.tf_in1k,88.763,11.237,98.115,1.885,93.21,320,1.000,bicubic,+4.319,+1.273,-5
convnext_base.fb_in1k,88.763,11.237,97.920,2.080,88.59,288,1.000,bicubic,+4.335,+0.952,+1
deit3_large_patch16_224.fb_in1k,88.763,11.237,97.920,2.080,304.37,224,0.900,bicubic,+3.989,+0.884,-32
tf_efficientnet_b6.aa_in1k,88.759,11.241,98.066,1.934,43.04,528,0.942,bicubic,+4.647,+1.182,+29
resnetrs350.tf_in1k,88.757,11.243,98.031,1.968,163.96,384,1.000,bicubic,+4.043,+1.039,-34
rexnetr_300.sw_in12k_ft_in1k,88.746,11.254,98.339,1.661,34.81,288,1.000,bicubic,+4.200,+1.083,-23
caformer_s36.sail_in1k,88.746,11.254,98.023,1.977,39.30,224,1.000,bicubic,+4.240,+1.027,-17
convnextv2_tiny.fcmae_ft_in22k_in1k,88.744,11.256,98.194,1.806,28.64,288,1.000,bicubic,+4.328,+0.934,-2
vit_base_patch16_224_miil.in21k_ft_in1k,88.742,11.258,98.027,1.973,86.54,224,0.875,bilinear,+4.476,+1.223,+8
edgenext_base.usi_in1k,88.735,11.265,98.147,1.853,18.51,320,1.000,bicubic,+4.777,+1.377,+38
regnetz_040.ra3_in1k,88.731,11.269,98.091,1.909,27.12,320,1.000,bicubic,+4.491,+1.159,+10
convformer_s18.sail_in22k_ft_in1k,88.727,11.273,98.194,1.806,26.77,224,1.000,bicubic,+4.989,+1.146,+65
resnetv2_152x2_bit.goog_in21k_ft_in1k,88.725,11.275,98.311,1.689,236.34,448,1.000,bilinear,+4.215,+0.877,-26
regnety_160.deit_in1k,88.703,11.297,98.068,1.932,83.59,288,1.000,bicubic,+5.013,+1.288,+69
davit_base.msft_in1k,88.701,11.299,97.874,2.127,87.95,224,0.950,bicubic,+4.059,+0.854,-39
regnety_640.seer_ft_in1k,88.674,11.326,98.166,1.834,281.38,384,1.000,bicubic,+4.766,+1.244,+35
regnetz_d32.ra3_in1k,88.652,11.348,98.078,1.922,27.58,320,0.950,bicubic,+4.630,+1.210,+27
vit_small_patch16_384.augreg_in21k_ft_in1k,88.648,11.352,98.230,1.770,22.20,384,1.000,bicubic,+4.844,+1.130,+47
flexivit_base.1200ep_in1k,88.648,11.352,97.935,2.065,86.59,240,0.950,bicubic,+3.972,+0.941,-43
mvitv2_base.fb_in1k,88.644,11.356,97.826,2.174,51.47,224,0.900,bicubic,+4.194,+0.968,-24
regnety_080.ra3_in1k,88.635,11.365,97.965,2.035,39.18,288,1.000,bicubic,+4.709,+1.075,+28
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,88.633,11.367,98.189,1.810,38.86,256,0.950,bicubic,+4.187,+0.980,-25
davit_small.msft_in1k,88.631,11.369,97.953,2.047,49.75,224,0.950,bicubic,+4.379,+1.013,-4
eca_nfnet_l1.ra2_in1k,88.622,11.378,98.132,1.868,41.41,320,1.000,bicubic,+4.610,+1.106,+22
repvgg_d2se.rvgg_in1k,88.599,11.401,97.984,2.015,133.33,320,1.000,bilinear,+5.039,+1.326,+69
maxvit_base_tf_224.in1k,88.588,11.412,97.850,2.150,119.47,224,0.950,bicubic,+3.728,+0.862,-62
swinv2_base_window16_256.ms_in1k,88.586,11.414,97.908,2.092,87.92,256,0.900,bicubic,+3.986,+0.818,-48
resnetaa101d.sw_in12k_ft_in1k,88.573,11.427,98.076,1.924,44.57,288,1.000,bicubic,+4.449,+0.970,+4
regnety_320.seer_ft_in1k,88.571,11.429,98.106,1.894,145.05,384,1.000,bicubic,+5.243,+1.398,+93
efficientvit_b3.r288_in1k,88.562,11.438,97.707,2.293,48.65,288,1.000,bicubic,+4.409,+0.971,0
resnetv2_152x4_bit.goog_in21k_ft_in1k,88.554,11.446,98.192,1.808,936.53,480,1.000,bilinear,+3.638,+0.754,-73
resnet200d.ra2_in1k,88.554,11.446,97.961,2.039,64.69,320,1.000,bicubic,+4.590,+1.135,+16
xcit_small_24_p16_224.fb_dist_in1k,88.545,11.455,98.004,1.996,47.67,224,1.000,bicubic,+4.671,+1.268,+22
flexivit_base.600ep_in1k,88.545,11.455,97.933,2.067,86.59,240,0.950,bicubic,+4.021,+0.997,-47
seresnextaa101d_32x8d.ah_in1k,88.537,11.463,98.002,1.998,93.59,288,1.000,bicubic,+3.971,+0.926,-54
maxvit_rmlp_small_rw_224.sw_in1k,88.522,11.478,97.773,2.227,64.90,224,0.900,bicubic,+4.030,+0.763,-44
resnest269e.in1k,88.520,11.480,98.029,1.971,110.93,416,0.928,bicubic,+4.012,+1.039,-49
coatnet_rmlp_2_rw_224.sw_in1k,88.513,11.487,97.566,2.434,73.88,224,0.950,bicubic,+3.905,+0.826,-60
efficientformerv2_l.snap_dist_in1k,88.509,11.491,97.963,2.037,26.32,224,0.950,bicubic,+4.877,+1.405,+49
repvit_m2_3.dist_300e_in1k,88.503,11.497,97.942,2.058,23.69,224,0.950,bicubic,+4.999,+1.438,+57
gcvit_base.in1k,88.501,11.499,97.769,2.231,90.32,224,0.875,bicubic,+4.056,+0.687,-42
swinv2_base_window8_256.ms_in1k,88.498,11.502,97.891,2.109,87.92,256,0.900,bicubic,+4.248,+0.967,-22
seresnext101_32x8d.ah_in1k,88.494,11.506,97.884,2.116,93.57,288,1.000,bicubic,+4.308,+1.010,-16
repvit_m2_3.dist_450e_in1k,88.488,11.512,98.059,1.941,23.69,224,0.950,bicubic,+4.746,+1.415,+31
convformer_s36.sail_in1k,88.486,11.514,97.763,2.237,40.01,224,1.000,bicubic,+4.426,+1.017,-7
flexivit_base.300ep_in1k,88.479,11.521,97.846,2.154,86.59,240,0.950,bicubic,+4.073,+0.962,-38
crossvit_18_dagger_408.in1k,88.473,11.527,97.897,2.103,44.61,408,1.000,bicubic,+4.271,+1.079,-22
efficientnetv2_rw_s.ra2_in1k,88.469,11.531,97.978,2.022,23.94,384,1.000,bicubic,+4.663,+1.246,+17
resnetv2_101x3_bit.goog_in21k_ft_in1k,88.466,11.534,98.157,1.843,387.93,448,1.000,bilinear,+4.028,+0.775,-49
fastvit_sa24.apple_dist_in1k,88.460,11.540,97.957,2.043,21.55,256,0.900,bicubic,+5.118,+1.405,+69
maxvit_large_tf_224.in1k,88.460,11.540,97.809,2.191,211.79,224,0.950,bicubic,+3.518,+0.839,-94
maxvit_small_tf_224.in1k,88.456,11.544,97.880,2.120,68.93,224,0.950,bicubic,+4.030,+1.056,-48
resnetv2_50x3_bit.goog_in21k_ft_in1k,88.447,11.553,98.200,1.800,217.32,448,1.000,bilinear,+4.427,+1.074,-8
cait_s24_224.fb_dist_in1k,88.443,11.557,97.961,2.039,46.92,224,1.000,bicubic,+5.001,+1.387,+52
resmlp_big_24_224.fb_distilled_in1k,88.441,11.559,97.938,2.062,129.14,224,0.875,bicubic,+4.849,+1.287,+37
regnetv_064.ra3_in1k,88.436,11.563,98.061,1.939,30.58,288,1.000,bicubic,+4.721,+1.319,+23
resnest200e.in1k,88.434,11.566,98.040,1.960,70.20,320,0.909,bicubic,+4.590,+1.156,+1
vit_large_r50_s32_224.augreg_in21k_ft_in1k,88.432,11.568,98.083,1.917,328.99,224,0.900,bicubic,+4.014,+0.911,-54
inception_next_base.sail_in1k,88.432,11.568,97.773,2.227,86.67,224,0.950,bicubic,+4.340,+0.977,-24
tf_efficientnet_b3.ns_jft_in1k,88.428,11.572,98.031,1.968,12.23,300,0.904,bicubic,+4.376,+1.113,-20
seresnext101d_32x8d.ah_in1k,88.428,11.572,97.961,2.039,93.59,288,1.000,bicubic,+4.070,+1.041,-47
swin_base_patch4_window12_384.ms_in1k,88.426,11.574,97.803,2.197,87.90,384,1.000,bicubic,+3.950,+0.911,-68
tf_efficientnetv2_s.in1k,88.409,11.591,97.927,2.073,21.46,384,1.000,bicubic,+4.511,+1.231,-11
convnext_small.fb_in1k,88.407,11.593,98.012,1.988,50.22,288,1.000,bicubic,+4.707,+1.204,+18
tf_efficientnet_b5.aa_in1k,88.407,11.593,97.931,2.069,30.39,456,0.934,bicubic,+4.719,+1.219,+19
regnety_320.swag_lc_in1k,88.398,11.602,98.115,1.885,145.05,224,0.965,bicubic,+3.850,+0.673,-83
vit_base_patch16_384.orig_in21k_ft_in1k,88.392,11.608,98.160,1.840,86.86,384,1.000,bicubic,+4.192,+0.942,-41
regnetz_c16_evos.ch_in1k,88.381,11.619,98.040,1.960,13.49,320,0.950,bicubic,+5.745,+1.566,+121
tresnet_v2_l.miil_in21k_ft_in1k,88.375,11.625,97.925,2.075,46.17,224,0.875,bilinear,+4.481,+1.435,-16
swinv2_small_window16_256.ms_in1k,88.372,11.628,97.848,2.152,49.73,256,0.900,bicubic,+4.148,+1.070,-47
efficientnet_b4.ra2_in1k,88.366,11.634,97.961,2.039,19.34,384,1.000,bicubic,+4.952,+1.363,+38
resnet152d.ra2_in1k,88.362,11.638,97.931,2.069,60.21,320,1.000,bicubic,+4.678,+1.193,+13
fastvit_ma36.apple_in1k,88.358,11.643,97.923,2.077,44.07,256,0.950,bicubic,+4.475,+1.181,-18
maxvit_rmlp_tiny_rw_256.sw_in1k,88.355,11.645,97.820,2.180,29.15,256,0.950,bicubic,+4.131,+0.952,-50
tf_efficientnet_b4.ap_in1k,88.347,11.653,97.891,2.109,19.34,380,0.922,bicubic,+5.097,+1.495,+52
deit3_small_patch16_224.fb_in22k_ft_in1k,88.338,11.662,98.132,1.868,22.06,224,1.000,bicubic,+5.262,+1.356,+71
regnety_064.ra3_in1k,88.323,11.677,97.865,2.135,30.58,288,1.000,bicubic,+4.603,+1.143,+1
efficientvit_b3.r256_in1k,88.319,11.681,97.560,2.440,48.65,256,1.000,bicubic,+4.517,+1.044,-11
convnextv2_nano.fcmae_ft_in22k_in1k_384,88.315,11.685,97.938,2.062,15.62,384,1.000,bicubic,+4.941,+1.194,+37
tf_efficientnet_b5.ra_in1k,88.313,11.687,97.914,2.086,30.39,456,0.934,bicubic,+4.499,+1.162,-17
crossvit_15_dagger_408.in1k,88.313,11.687,97.874,2.127,28.50,408,1.000,bicubic,+4.473,+1.096,-21
cs3se_edgenet_x.c2ns_in1k,88.300,11.700,97.933,2.067,50.72,320,1.000,bicubic,+4.754,+1.263,+13
deit3_small_patch16_384.fb_in1k,88.296,11.704,97.888,2.112,22.21,384,1.000,bicubic,+4.868,+1.214,+24
pvt_v2_b4.in1k,88.285,11.715,97.816,2.184,62.56,224,0.900,bicubic,+4.573,+1.146,-4
efficientformer_l7.snap_dist_in1k,88.278,11.722,97.882,2.118,82.23,224,0.950,bicubic,+4.896,+1.346,+30
mvitv2_small.fb_in1k,88.264,11.736,97.692,2.308,34.87,224,0.900,bicubic,+4.494,+1.116,-16
inception_next_small.sail_in1k,88.253,11.747,97.816,2.184,49.37,224,0.875,bicubic,+4.675,+1.218,+6
resnetrs152.tf_in1k,88.249,11.751,97.737,2.263,86.62,320,1.000,bicubic,+4.547,+1.125,-7
deit3_base_patch16_224.fb_in1k,88.242,11.758,97.818,2.182,86.59,224,0.900,bicubic,+4.456,+1.232,-21
xcit_small_12_p16_224.fb_dist_in1k,88.240,11.760,97.841,2.159,26.25,224,1.000,bicubic,+4.912,+1.425,+33
gcvit_small.in1k,88.223,11.777,97.788,2.212,51.09,224,0.875,bicubic,+4.331,+1.130,-37
deit_base_distilled_patch16_224.fb_in1k,88.210,11.790,97.910,2.090,87.34,224,0.900,bicubic,+4.820,+1.422,+21
regnetv_040.ra3_in1k,88.208,11.792,97.972,2.028,20.64,288,1.000,bicubic,+5.018,+1.314,+38
xception65p.ra3_in1k,88.189,11.811,97.788,2.212,39.82,299,0.940,bicubic,+5.063,+1.306,+45
swinv2_small_window8_256.ms_in1k,88.172,11.828,97.777,2.223,49.73,256,0.900,bicubic,+4.318,+1.133,-38
caformer_s18.sail_in1k,88.163,11.837,97.728,2.272,26.34,224,1.000,bicubic,+4.509,+1.210,-9
xcit_tiny_24_p16_384.fb_dist_in1k,88.161,11.839,97.942,2.058,12.12,384,1.000,bicubic,+5.591,+1.666,+108
xcit_large_24_p8_224.fb_in1k,88.159,11.841,97.391,2.609,188.93,224,1.000,bicubic,+3.765,+0.727,-87
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,88.148,11.852,98.051,1.949,236.34,384,1.000,bicubic,+4.312,+0.925,-38
tiny_vit_21m_224.in1k,88.146,11.854,97.850,2.150,21.20,224,0.950,bicubic,+4.892,+1.258,+26
coat_lite_medium.in1k,88.144,11.856,97.899,2.101,44.57,224,0.900,bicubic,+4.544,+1.171,-11
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,88.144,11.856,97.859,2.142,88.79,224,0.875,bilinear,+5.446,+1.715,+77
cait_xxs36_384.fb_dist_in1k,88.140,11.860,97.903,2.097,17.37,384,1.000,bicubic,+5.936,+1.759,+149
dm_nfnet_f0.dm_in1k,88.133,11.867,97.903,2.097,71.49,256,0.900,bicubic,+4.647,+1.335,-2
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,88.112,11.888,97.965,2.035,44.18,224,0.875,bilinear,+4.886,+1.205,+24
pit_b_distilled_224.in1k,88.110,11.890,97.654,2.346,74.79,224,0.900,bicubic,+4.344,+1.186,-35
xcit_tiny_12_p8_384.fb_dist_in1k,88.103,11.897,97.925,2.075,6.71,384,1.000,bicubic,+5.715,+1.705,+116
tiny_vit_11m_224.dist_in22k_ft_in1k,88.103,11.897,97.790,2.210,11.00,224,0.950,bicubic,+4.875,+1.160,+20
pvt_v2_b5.in1k,88.103,11.897,97.694,2.306,81.96,224,0.900,bicubic,+4.363,+1.058,-31
rexnetr_200.sw_in12k_ft_in1k,88.099,11.901,98.006,1.994,16.52,288,1.000,bicubic,+4.961,+1.370,+25
pvt_v2_b3.in1k,88.099,11.901,97.777,2.223,45.24,224,0.900,bicubic,+4.981,+1.221,+32
fastvit_sa36.apple_in1k,88.088,11.912,97.790,2.210,31.53,256,0.900,bicubic,+4.588,+1.160,-13
hrnet_w18_ssld.paddle_in1k,88.067,11.933,97.824,2.176,21.30,288,1.000,bilinear,+6.019,+1.574,+157
xception65.ra3_in1k,88.067,11.933,97.750,2.250,39.92,299,0.940,bicubic,+4.887,+1.158,+18
efficientvit_b3.r224_in1k,88.065,11.935,97.566,2.434,48.65,224,0.950,bicubic,+4.605,+1.236,-11
swin_s3_base_224.ms_in1k,88.046,11.954,97.662,2.338,71.13,224,0.900,bicubic,+4.126,+0.990,-66
xcit_tiny_24_p8_224.fb_dist_in1k,88.037,11.963,97.818,2.182,12.11,224,1.000,bicubic,+5.471,+1.760,+89
convnextv2_tiny.fcmae_ft_in1k,88.035,11.965,97.859,2.142,28.64,288,1.000,bicubic,+4.571,+1.141,-15
regnety_160.swag_lc_in1k,88.033,11.967,98.042,1.958,83.59,224,0.965,bicubic,+4.251,+0.762,-51
resnet152.a1h_in1k,88.033,11.967,97.696,2.304,60.19,288,1.000,bicubic,+4.583,+1.158,-16
focalnet_base_srf.ms_in1k,88.033,11.967,97.656,2.344,88.15,224,0.900,bicubic,+4.213,+0.976,-56
maxvit_tiny_tf_224.in1k,88.027,11.973,97.816,2.184,30.92,224,0.950,bicubic,+4.625,+1.226,-12
focalnet_base_lrf.ms_in1k,88.003,11.997,97.609,2.391,88.75,224,0.900,bicubic,+4.165,+1.001,-63
gcvit_tiny.in1k,88.001,11.999,97.722,2.278,28.22,224,0.875,bicubic,+4.617,+1.324,-10
eca_nfnet_l0.ra2_in1k,87.980,12.020,97.871,2.129,24.14,288,1.000,bicubic,+5.402,+1.379,+77
tf_efficientnet_b5.in1k,87.975,12.025,97.933,2.067,30.39,456,0.934,bicubic,+4.799,+1.397,+6
cs3sedarknet_x.c2ns_in1k,87.975,12.025,97.794,2.205,35.40,288,1.000,bicubic,+5.317,+1.445,+60
nfnet_l0.ra2_in1k,87.967,12.033,97.867,2.133,35.07,288,1.000,bicubic,+5.217,+1.351,+46
efficientformer_l3.snap_dist_in1k,87.963,12.037,97.711,2.289,31.41,224,0.950,bicubic,+5.415,+1.461,+79
tf_efficientnet_b4.aa_in1k,87.958,12.042,97.739,2.261,19.34,380,0.922,bicubic,+4.940,+1.439,+23
xcit_small_24_p8_224.fb_in1k,87.956,12.044,97.581,2.419,47.63,224,1.000,bicubic,+4.122,+0.949,-69
regnetz_c16.ra3_in1k,87.952,12.048,97.779,2.220,13.46,320,1.000,bicubic,+5.320,+1.462,+58
regnety_032.ra_in1k,87.950,12.050,97.897,2.103,19.44,288,1.000,bicubic,+5.224,+1.481,+43
coatnet_1_rw_224.sw_in1k,87.943,12.057,97.453,2.547,41.72,224,0.950,bicubic,+4.347,+1.071,-43
resnet101d.ra2_in1k,87.937,12.063,97.910,2.090,44.57,320,1.000,bicubic,+4.917,+1.458,+17
regnety_040.ra3_in1k,87.933,12.067,97.880,2.120,20.65,288,1.000,bicubic,+4.889,+1.378,+13
mobilevitv2_200.cvnets_in22k_ft_in1k_384,87.933,12.067,97.822,2.178,18.45,384,1.000,bicubic,+4.533,+1.240,-25
swinv2_cr_small_ns_224.sw_in1k,87.933,12.067,97.668,2.332,49.70,224,0.900,bicubic,+4.435,+1.184,-38
focalnet_small_lrf.ms_in1k,87.930,12.069,97.696,2.304,50.34,224,0.900,bicubic,+4.436,+1.116,-38
repvit_m1_5.dist_450e_in1k,87.928,12.072,97.703,2.297,14.64,224,0.950,bicubic,+5.416,+1.591,+72
resnetv2_101.a1h_in1k,87.924,12.076,97.651,2.349,44.54,288,1.000,bicubic,+4.924,+1.197,+13
efficientvit_b2.r288_in1k,87.920,12.080,97.600,2.400,24.33,288,1.000,bicubic,+4.820,+1.296,+3
vit_base_patch32_384.augreg_in21k_ft_in1k,87.909,12.091,98.010,1.990,88.30,384,1.000,bicubic,+4.557,+1.170,-25
sequencer2d_l.in1k,87.907,12.093,97.703,2.297,54.30,224,0.875,bicubic,+4.513,+1.207,-32
twins_svt_large.in1k,87.903,12.097,97.581,2.419,99.27,224,0.900,bicubic,+4.225,+0.993,-59
coatnet_rmlp_1_rw_224.sw_in1k,87.892,12.108,97.624,2.376,41.69,224,0.950,bicubic,+4.530,+1.174,-29
twins_pcpvt_large.in1k,87.869,12.131,97.859,2.142,60.99,224,0.900,bicubic,+4.739,+1.255,-9
swin_base_patch4_window7_224.ms_in1k,87.866,12.134,97.564,2.436,87.77,224,0.900,bicubic,+4.260,+1.112,-59
maxvit_tiny_rw_224.sw_in1k,87.856,12.144,97.643,2.357,29.06,224,0.950,bicubic,+4.352,+1.129,-51
swin_s3_small_224.ms_in1k,87.849,12.151,97.431,2.568,49.74,224,0.900,bicubic,+4.093,+0.980,-78
convformer_s18.sail_in1k,87.845,12.155,97.551,2.449,26.77,224,1.000,bicubic,+4.859,+1.301,+5
deit_base_patch16_384.fb_in1k,87.845,12.155,97.508,2.492,86.86,384,1.000,bicubic,+4.741,+1.140,-8
mobilevitv2_175.cvnets_in22k_ft_in1k_384,87.841,12.159,97.728,2.272,14.25,384,1.000,bicubic,+4.903,+1.302,+6
ecaresnet101d.miil_in1k,87.839,12.161,97.899,2.101,44.57,288,0.950,bicubic,+4.855,+1.357,+3
convnext_nano.in12k_ft_in1k,87.837,12.164,97.888,2.112,15.59,288,1.000,bicubic,+4.975,+1.332,+9
xcit_small_12_p8_224.fb_in1k,87.822,12.178,97.568,2.432,26.21,224,1.000,bicubic,+4.488,+1.086,-35
flexivit_small.600ep_in1k,87.811,12.189,97.577,2.423,22.06,240,0.950,bicubic,+5.449,+1.493,+72
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,87.809,12.191,97.756,2.244,88.22,224,0.900,bicubic,+4.513,+1.228,-35
flexivit_small.1200ep_in1k,87.809,12.191,97.613,2.387,22.06,240,0.950,bicubic,+5.283,+1.487,+51
focalnet_small_srf.ms_in1k,87.809,12.191,97.575,2.425,49.89,224,0.900,bicubic,+4.393,+1.137,-51
tf_efficientnetv2_b3.in21k_ft_in1k,87.807,12.193,97.895,2.105,14.36,300,0.900,bicubic,+5.137,+1.269,+21
maxxvit_rmlp_nano_rw_256.sw_in1k,87.807,12.193,97.752,2.248,16.78,256,0.950,bicubic,+4.765,+1.402,-11
deit3_medium_patch16_224.fb_in1k,87.807,12.193,97.654,2.346,38.85,224,0.900,bicubic,+4.721,+1.360,-15
tresnet_xl.miil_in1k_448,87.796,12.204,97.459,2.541,78.44,448,0.875,bilinear,+4.738,+1.287,-15
resnetv2_50x1_bit.goog_distilled_in1k,87.790,12.210,97.899,2.101,25.55,224,0.875,bicubic,+4.966,+1.381,+1
repvit_m1_5.dist_300e_in1k,87.772,12.227,97.649,2.351,14.64,224,0.950,bicubic,+5.396,+1.619,+61
convnext_tiny.fb_in1k,87.770,12.230,97.585,2.415,28.59,288,1.000,bicubic,+5.072,+0.953,+13
regnety_320.tv2_in1k,87.743,12.257,97.673,2.327,145.05,224,0.965,bicubic,+4.581,+1.259,-34
resnext101_64x4d.tv_in1k,87.740,12.259,97.592,2.408,83.46,224,0.875,bilinear,+4.748,+1.348,-14
convnextv2_nano.fcmae_ft_in22k_in1k,87.732,12.268,97.886,2.114,15.62,288,1.000,bicubic,+5.068,+1.366,+15
twins_pcpvt_base.in1k,87.730,12.270,97.728,2.272,43.83,224,0.900,bicubic,+5.016,+1.382,+6
tresnet_m.miil_in21k_ft_in1k,87.725,12.274,97.517,2.483,31.39,224,0.875,bilinear,+4.656,+1.407,-24
mvitv2_tiny.fb_in1k,87.719,12.281,97.553,2.447,24.17,224,0.900,bicubic,+5.309,+1.401,+48
gc_efficientnetv2_rw_t.agc_in1k,87.717,12.283,97.803,2.197,13.68,288,1.000,bicubic,+5.261,+1.507,+45
maxvit_rmlp_nano_rw_256.sw_in1k,87.715,12.285,97.577,2.423,15.50,256,0.950,bicubic,+4.761,+1.311,-17
resnetv2_101x1_bit.goog_in21k_ft_in1k,87.683,12.317,97.940,2.060,44.54,448,1.000,bilinear,+5.341,+1.420,+57
rexnet_300.nav_in1k,87.683,12.317,97.611,2.389,34.71,224,0.875,bicubic,+4.909,+1.373,-5
swin_small_patch4_window7_224.ms_in1k,87.662,12.338,97.568,2.432,49.61,224,0.900,bicubic,+4.454,+1.252,-48
efficientnetv2_rw_t.ra2_in1k,87.651,12.349,97.690,2.310,13.65,288,1.000,bicubic,+5.301,+1.498,+53
twins_svt_base.in1k,87.651,12.349,97.525,2.474,56.07,224,0.900,bicubic,+4.531,+1.111,-39
mobilevitv2_150.cvnets_in22k_ft_in1k_384,87.649,12.351,97.647,2.353,10.59,384,1.000,bicubic,+5.063,+1.333,+18
coat_small.in1k,87.647,12.354,97.530,2.470,21.69,224,0.900,bicubic,+5.285,+1.322,+46
fastvit_sa24.apple_in1k,87.638,12.362,97.726,2.274,21.55,256,0.900,bicubic,+4.960,+1.454,-1
maxxvitv2_nano_rw_256.sw_in1k,87.638,12.362,97.525,2.474,23.70,256,0.950,bicubic,+4.528,+1.201,-42
efficientvit_b2.r256_in1k,87.638,12.362,97.453,2.547,24.33,256,1.000,bicubic,+4.948,+1.359,-1
pnasnet5large.tf_in1k,87.636,12.364,97.487,2.513,86.06,331,0.911,bicubic,+4.854,+1.447,-16
resnet101.a1h_in1k,87.634,12.366,97.558,2.442,44.55,288,1.000,bicubic,+4.856,+1.248,-16
resnetaa50d.sw_in12k_ft_in1k,87.623,12.377,97.803,2.197,25.58,288,1.000,bicubic,+5.023,+1.305,+7
cs3edgenet_x.c2_in1k,87.621,12.379,97.654,2.346,47.82,288,1.000,bicubic,+4.913,+1.284,-11
flexivit_small.300ep_in1k,87.621,12.379,97.613,2.387,22.06,240,0.950,bicubic,+5.443,+1.575,+66
xcit_medium_24_p8_224.fb_in1k,87.612,12.388,97.201,2.799,84.32,224,1.000,bicubic,+3.866,+0.491,-117
swinv2_tiny_window16_256.ms_in1k,87.610,12.390,97.560,2.440,28.35,256,0.900,bicubic,+4.806,+1.324,-23
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,87.606,12.394,97.816,2.184,194.03,224,0.875,bilinear,+4.270,+0.970,-73
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,87.600,12.400,97.649,2.351,25.03,224,0.875,bilinear,+5.428,+1.425,+63
nest_base_jx.goog_in1k,87.597,12.403,97.521,2.479,67.72,224,0.875,bicubic,+4.063,+1.147,-99
maxvit_nano_rw_256.sw_in1k,87.595,12.405,97.519,2.481,15.45,256,0.950,bicubic,+4.667,+1.299,-36
tf_efficientnet_b4.in1k,87.585,12.415,97.602,2.398,19.34,380,0.922,bicubic,+4.977,+1.850,-4
davit_tiny.msft_in1k,87.565,12.435,97.585,2.415,28.36,224,0.950,bicubic,+4.869,+1.311,-17
levit_384.fb_dist_in1k,87.563,12.437,97.538,2.462,39.13,224,0.900,bicubic,+4.967,+1.520,-3
levit_conv_384.fb_dist_in1k,87.561,12.439,97.538,2.462,39.13,224,0.900,bicubic,+4.971,+1.522,-2
sequencer2d_m.in1k,87.559,12.441,97.581,2.419,38.31,224,0.875,bicubic,+4.747,+1.307,-34
convnextv2_nano.fcmae_ft_in1k,87.553,12.447,97.666,2.334,15.62,288,1.000,bicubic,+5.067,+1.440,+13
tf_efficientnet_b2.ns_jft_in1k,87.553,12.447,97.626,2.374,9.11,260,0.890,bicubic,+5.175,+1.372,+23
ecaresnet50t.ra2_in1k,87.544,12.456,97.649,2.351,25.57,320,0.950,bicubic,+5.192,+1.509,+27
regnetx_320.tv2_in1k,87.538,12.462,97.564,2.436,107.81,224,0.965,bicubic,+4.728,+1.356,-37
vit_base_patch32_clip_224.laion2b_ft_in1k,87.529,12.471,97.547,2.453,88.22,224,0.900,bicubic,+4.947,+1.347,-5
efficientformerv2_s2.snap_dist_in1k,87.516,12.484,97.615,2.385,12.71,224,0.950,bicubic,+5.350,+1.705,+51
inception_next_tiny.sail_in1k,87.516,12.484,97.549,2.451,28.06,224,0.875,bicubic,+5.038,+1.527,+9
coatnet_bn_0_rw_224.sw_in1k,87.508,12.492,97.551,2.449,27.44,224,0.950,bicubic,+5.108,+1.365,+13
vit_base_patch16_rpn_224.sw_in1k,87.504,12.496,97.489,2.511,86.54,224,0.900,bicubic,+5.302,+1.493,+43
pvt_v2_b2_li.in1k,87.501,12.499,97.470,2.530,22.55,224,0.900,bicubic,+5.307,+1.378,+44
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,87.495,12.505,97.818,2.182,236.34,224,0.875,bicubic,+4.619,+1.236,-49
edgenext_small.usi_in1k,87.493,12.507,97.587,2.413,5.59,320,1.000,bicubic,+5.929,+1.875,+96
nest_small_jx.goog_in1k,87.493,12.507,97.513,2.487,38.35,224,0.875,bicubic,+4.369,+1.193,-74
vit_relpos_base_patch16_clsgap_224.sw_in1k,87.471,12.529,97.525,2.474,86.43,224,0.900,bicubic,+4.711,+1.353,-42
vit_relpos_base_patch16_224.sw_in1k,87.467,12.533,97.558,2.442,86.43,224,0.900,bicubic,+4.971,+1.419,-2
regnety_080_tv.tv2_in1k,87.465,12.535,97.636,2.364,39.38,224,0.965,bicubic,+4.871,+1.388,-20
fbnetv3_g.ra2_in1k,87.446,12.554,97.545,2.455,16.62,288,0.950,bilinear,+5.406,+1.485,+53
resnet61q.ra2_in1k,87.439,12.561,97.598,2.402,36.85,288,1.000,bicubic,+4.915,+1.468,-9
wide_resnet50_2.racm_in1k,87.437,12.563,97.543,2.458,68.88,288,0.950,bicubic,+5.157,+1.479,+24
poolformerv2_m48.sail_in1k,87.435,12.565,97.421,2.579,73.35,224,1.000,bicubic,+4.817,+1.349,-29
efficientnet_b3.ra2_in1k,87.433,12.567,97.679,2.321,12.23,320,1.000,bicubic,+5.187,+1.561,+24
regnetx_160.tv2_in1k,87.433,12.567,97.444,2.556,54.28,224,0.965,bicubic,+4.867,+1.272,-16
resnext101_64x4d.c1_in1k,87.433,12.567,97.444,2.556,83.46,288,1.000,bicubic,+4.277,+1.070,-89
cait_xxs24_384.fb_dist_in1k,87.414,12.586,97.621,2.378,12.03,384,1.000,bicubic,+6.442,+1.981,+149
resnet51q.ra2_in1k,87.397,12.603,97.583,2.417,35.70,288,1.000,bilinear,+5.037,+1.397,+4
cs3darknet_x.c2ns_in1k,87.395,12.605,97.611,2.389,35.05,288,1.000,bicubic,+5.173,+1.381,+22
cs3sedarknet_l.c2ns_in1k,87.395,12.605,97.570,2.430,21.91,288,0.950,bicubic,+5.611,+1.606,+66
coat_lite_small.in1k,87.390,12.610,97.374,2.626,19.84,224,0.900,bicubic,+5.078,+1.524,+9
pvt_v2_b2.in1k,87.382,12.618,97.519,2.481,25.36,224,0.900,bicubic,+5.298,+1.563,+36
tresnet_l.miil_in1k_448,87.382,12.618,97.487,2.513,55.99,448,0.875,bilinear,+5.106,+1.509,+14
resnetv2_50d_gn.ah_in1k,87.380,12.620,97.536,2.464,25.57,288,1.000,bicubic,+5.422,+1.608,+47
sequencer2d_s.in1k,87.377,12.623,97.387,2.613,27.65,224,0.875,bicubic,+5.037,+1.359,+1
swinv2_cr_small_224.sw_in1k,87.377,12.623,97.346,2.654,49.70,224,0.900,bicubic,+4.242,+1.238,-97
xcit_tiny_24_p8_224.fb_in1k,87.373,12.627,97.626,2.374,12.11,224,1.000,bicubic,+5.481,+1.656,+49
fastvit_sa12.apple_dist_in1k,87.363,12.637,97.493,2.507,11.58,256,0.900,bicubic,+5.509,+1.783,+51
vit_relpos_medium_patch16_cls_224.sw_in1k,87.363,12.637,97.451,2.549,38.76,224,0.900,bicubic,+4.791,+1.383,-33
nasnetalarge.tf_in1k,87.360,12.640,97.417,2.583,88.75,331,0.911,bicubic,+4.734,+1.375,-47
seresnext50_32x4d.racm_in1k,87.356,12.644,97.617,2.383,27.56,288,0.950,bicubic,+5.160,+1.469,+15
crossvit_18_dagger_240.in1k,87.350,12.650,97.453,2.547,44.27,240,0.875,bicubic,+4.832,+1.385,-29
repvit_m3.dist_in1k,87.326,12.674,97.481,2.519,10.68,224,0.950,bicubic,+5.824,+1.913,+77
ecaresnet50d.miil_in1k,87.322,12.678,97.666,2.334,25.58,288,0.950,bicubic,+5.672,+1.784,+59
wide_resnet101_2.tv2_in1k,87.322,12.678,97.404,2.596,126.89,224,0.965,bilinear,+4.820,+1.388,-30
resnext101_32x8d.tv2_in1k,87.318,12.682,97.560,2.440,88.79,224,0.965,bilinear,+4.486,+1.328,-80
crossvit_18_240.in1k,87.318,12.682,97.487,2.513,43.27,240,0.875,bicubic,+4.918,+1.427,-21
focalnet_tiny_srf.ms_in1k,87.301,12.699,97.414,2.586,28.43,224,0.900,bicubic,+5.163,+1.446,+16
resnest101e.in1k,87.288,12.712,97.562,2.438,48.28,256,0.875,bilinear,+4.404,+1.240,-85
gcvit_xtiny.in1k,87.279,12.721,97.478,2.522,19.98,224,0.875,bicubic,+5.325,+1.513,+32
tiny_vit_11m_224.in1k,87.277,12.723,97.487,2.513,11.00,224,0.950,bicubic,+5.785,+1.625,+69
coatnet_rmlp_nano_rw_224.sw_in1k,87.277,12.723,97.447,2.554,15.15,224,0.900,bicubic,+5.227,+1.569,+20
ecaresnet101d_pruned.miil_in1k,87.269,12.731,97.713,2.287,24.88,288,0.950,bicubic,+5.271,+1.553,+23
vit_relpos_medium_patch16_rpn_224.sw_in1k,87.269,12.731,97.447,2.554,38.73,224,0.900,bicubic,+4.959,+1.475,-13
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,87.249,12.751,97.782,2.218,28.29,224,0.900,bicubic,+6.281,+1.768,+123
resnetrs101.tf_in1k,87.241,12.759,97.455,2.545,63.62,288,0.940,bicubic,+4.957,+1.441,-11
coatnext_nano_rw_224.sw_in1k,87.239,12.761,97.543,2.458,14.70,224,0.900,bicubic,+5.297,+1.627,+26
poolformer_m48.sail_in1k,87.237,12.763,97.318,2.682,73.47,224,0.950,bicubic,+4.755,+1.352,-40
regnety_160.tv2_in1k,87.232,12.768,97.461,2.539,83.59,224,0.965,bicubic,+4.586,+1.247,-70
mixer_b16_224.miil_in21k_ft_in1k,87.230,12.770,97.414,2.586,59.88,224,0.875,bilinear,+4.924,+1.694,-18
tresnet_xl.miil_in1k,87.230,12.770,97.397,2.603,78.44,224,0.875,bilinear,+5.156,+1.469,+8
xcit_tiny_12_p8_224.fb_dist_in1k,87.224,12.776,97.449,2.551,6.71,224,1.000,bicubic,+6.012,+1.847,+91
xcit_tiny_12_p16_384.fb_dist_in1k,87.209,12.791,97.466,2.534,6.72,384,1.000,bicubic,+6.271,+2.052,+117
convit_base.fb_in1k,87.209,12.791,97.284,2.716,86.54,224,0.875,bicubic,+4.919,+1.348,-20
resnetv2_50d_evos.ah_in1k,87.205,12.795,97.421,2.579,25.59,288,1.000,bicubic,+5.203,+1.521,+10
resnet152.tv2_in1k,87.188,12.812,97.387,2.613,60.19,224,0.965,bilinear,+4.901,+1.383,-22
tf_efficientnet_b3.ap_in1k,87.188,12.812,97.382,2.618,12.23,300,0.904,bicubic,+5.368,+1.756,+25
vit_base_patch32_clip_224.openai_ft_in1k,87.179,12.821,97.466,2.534,88.22,224,0.900,bicubic,+5.249,+1.500,+16
visformer_small.in1k,87.179,12.821,97.323,2.677,40.22,224,0.900,bicubic,+5.073,+1.445,-2
vit_srelpos_medium_patch16_224.sw_in1k,87.177,12.823,97.310,2.690,38.74,224,0.900,bicubic,+4.937,+1.368,-21
focalnet_tiny_lrf.ms_in1k,87.175,12.825,97.370,2.630,28.65,224,0.900,bicubic,+5.021,+1.422,-10
crossvit_15_dagger_240.in1k,87.166,12.834,97.431,2.568,28.21,240,0.875,bicubic,+4.836,+1.475,-34
convnext_tiny_hnf.a2h_in1k,87.147,12.853,97.280,2.720,28.59,288,1.000,bicubic,+4.563,+1.272,-71
vit_relpos_medium_patch16_224.sw_in1k,87.145,12.855,97.500,2.500,38.75,224,0.900,bicubic,+4.683,+1.418,-54
swin_s3_tiny_224.ms_in1k,87.143,12.857,97.308,2.692,28.33,224,0.900,bicubic,+4.999,+1.354,-12
coatnet_0_rw_224.sw_in1k,87.128,12.872,97.233,2.767,27.44,224,0.950,bicubic,+4.738,+1.397,-50
xcit_small_24_p16_224.fb_in1k,87.119,12.881,97.267,2.733,47.67,224,1.000,bicubic,+4.543,+1.255,-72
repvit_m1_1.dist_450e_in1k,87.104,12.896,97.412,2.588,8.80,224,0.950,bicubic,+5.792,+1.876,+63
swinv2_tiny_window8_256.ms_in1k,87.089,12.911,97.510,2.490,28.35,256,0.900,bicubic,+5.269,+1.516,+12
pit_s_distilled_224.in1k,87.081,12.919,97.363,2.637,24.04,224,0.900,bicubic,+5.267,+1.633,+11
efficientvit_b2.r224_in1k,87.081,12.919,97.203,2.797,24.33,224,0.950,bicubic,+4.933,+1.497,-19
ecaresnet50t.a1_in1k,87.081,12.919,97.122,2.878,25.57,288,1.000,bicubic,+4.953,+1.480,-15
mobilevitv2_200.cvnets_in22k_ft_in1k,87.057,12.943,97.431,2.568,18.45,256,0.888,bicubic,+4.725,+1.490,-46
resnext50_32x4d.a1h_in1k,87.057,12.943,97.331,2.669,25.03,288,1.000,bicubic,+5.043,+1.397,-10
xception41p.ra3_in1k,87.051,12.949,97.201,2.799,26.91,299,0.940,bicubic,+5.079,+1.417,-7
regnetz_b16.ra3_in1k,87.047,12.953,97.412,2.588,9.72,288,1.000,bicubic,+6.319,+1.894,+119
crossvit_15_240.in1k,87.042,12.958,97.423,2.577,27.53,240,0.875,bicubic,+5.506,+1.687,+27
convit_small.fb_in1k,87.042,12.958,97.350,2.650,27.78,224,0.875,bicubic,+5.622,+1.606,+44
tf_efficientnetv2_b3.in1k,87.027,12.973,97.301,2.699,14.36,300,0.904,bicubic,+5.055,+1.499,-10
gcresnet50t.ra2_in1k,87.025,12.975,97.391,2.609,25.90,288,1.000,bicubic,+5.569,+1.673,+37
xcit_small_12_p16_224.fb_in1k,87.010,12.990,97.242,2.759,26.25,224,1.000,bicubic,+5.040,+1.430,-11
nest_tiny_jx.goog_in1k,87.008,12.992,97.378,2.622,17.06,224,0.875,bicubic,+5.582,+1.760,+37
poolformerv2_m36.sail_in1k,87.008,12.992,97.284,2.716,56.08,224,1.000,bicubic,+4.792,+1.360,-42
deit3_small_patch16_224.fb_in1k,87.008,12.992,97.171,2.829,22.06,224,0.900,bicubic,+5.638,+1.715,+45
deit_small_distilled_patch16_224.fb_in1k,87.004,12.996,97.323,2.677,22.44,224,0.900,bicubic,+5.788,+1.699,+56
swinv2_cr_tiny_ns_224.sw_in1k,87.002,12.998,97.280,2.720,28.33,224,0.900,bicubic,+5.200,+1.462,-2
resnet101.a1_in1k,87.000,13.000,96.960,3.040,44.55,288,1.000,bicubic,+4.678,+1.328,-58
resnet152.a2_in1k,86.995,13.005,96.923,3.077,60.19,288,1.000,bicubic,+4.387,+0.795,-102
coatnet_nano_rw_224.sw_in1k,86.993,13.007,97.248,2.752,15.14,224,0.900,bicubic,+5.297,+1.602,0
resmlp_36_224.fb_distilled_in1k,86.987,13.013,97.276,2.724,44.69,224,0.875,bicubic,+5.839,+1.798,+59
poolformer_m36.sail_in1k,86.955,13.045,97.141,2.859,56.17,224,0.950,bicubic,+4.853,+1.443,-34
mobilevitv2_175.cvnets_in22k_ft_in1k,86.951,13.050,97.335,2.665,14.25,256,0.888,bicubic,+5.013,+1.545,-18
regnety_032.tv2_in1k,86.942,13.058,97.410,2.590,19.44,224,0.965,bicubic,+5.186,+1.566,-6
resnet101.a2_in1k,86.942,13.058,96.990,3.010,44.55,288,1.000,bicubic,+4.706,+1.260,-54
xcit_large_24_p16_224.fb_in1k,86.942,13.058,96.921,3.079,189.10,224,1.000,bicubic,+4.040,+1.037,-142
resnet101.tv2_in1k,86.936,13.065,97.248,2.752,44.55,224,0.965,bilinear,+5.047,+1.480,-19
xcit_medium_24_p16_224.fb_in1k,86.933,13.067,97.103,2.897,84.40,224,1.000,bicubic,+4.293,+1.121,-117
resnetv2_50.a1h_in1k,86.929,13.071,97.340,2.660,25.55,288,1.000,bicubic,+5.531,+1.614,+25
poolformerv2_s36.sail_in1k,86.921,13.079,97.353,2.647,30.79,224,1.000,bicubic,+5.355,+1.663,+1
tnt_s_patch16_224,86.914,13.086,97.361,2.639,23.76,224,0.900,bicubic,+5.378,+1.671,+4
vit_relpos_small_patch16_224.sw_in1k,86.891,13.109,97.489,2.511,21.98,224,0.900,bicubic,+5.429,+1.669,+15
vit_small_patch16_224.augreg_in21k_ft_in1k,86.871,13.129,97.609,2.391,22.05,224,0.900,bicubic,+5.486,+1.473,+23
vit_small_r26_s32_224.augreg_in21k_ft_in1k,86.856,13.143,97.530,2.470,36.43,224,0.900,bicubic,+4.992,+1.508,-26
resnet152.a1_in1k,86.856,13.143,96.793,3.207,60.19,288,1.000,bicubic,+4.124,+1.073,-136
ecaresnetlight.miil_in1k,86.852,13.148,97.508,2.492,30.16,288,0.950,bicubic,+5.444,+1.692,+17
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,86.848,13.152,97.521,2.479,194.03,224,0.875,bilinear,+5.010,+1.429,-26
convmixer_1536_20.in1k,86.840,13.161,97.355,2.645,51.63,224,0.960,bicubic,+5.478,+1.741,+21
tf_efficientnet_b3.aa_in1k,86.840,13.161,97.297,2.703,12.23,300,0.904,bicubic,+5.200,+1.575,-15
rexnet_200.nav_in1k,86.840,13.161,97.276,2.724,16.37,224,0.875,bicubic,+5.204,+1.610,-13
resnet50.fb_swsl_ig1b_ft_in1k,86.827,13.173,97.493,2.507,25.56,224,0.875,bilinear,+5.655,+1.507,+33
repvit_m1_1.dist_300e_in1k,86.827,13.173,97.318,2.682,8.80,224,0.950,bicubic,+6.001,+2.148,+75
deit_base_patch16_224.fb_in1k,86.827,13.173,97.052,2.949,86.57,224,0.900,bicubic,+4.835,+1.316,-43
tresnet_m.miil_in1k_448,86.816,13.184,97.216,2.784,31.39,448,0.875,bilinear,+5.106,+1.642,-25
tf_efficientnet_lite4.in1k,86.799,13.201,97.265,2.735,13.01,380,0.920,bilinear,+5.269,+1.601,-9
coat_mini.in1k,86.799,13.201,97.156,2.844,10.34,224,0.900,bicubic,+5.529,+1.774,+22
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,86.795,13.205,97.472,2.528,88.79,224,0.875,bilinear,+5.189,+1.432,-19
eva02_tiny_patch14_336.mim_in22k_ft_in1k,86.784,13.216,97.269,2.731,5.76,336,1.000,bicubic,+6.154,+1.743,+87
seresnet50.ra2_in1k,86.778,13.223,97.361,2.639,28.09,288,0.950,bicubic,+5.493,+1.709,+17
vit_base_patch16_224.orig_in21k_ft_in1k,86.773,13.227,97.442,2.558,86.57,224,0.900,bicubic,+4.983,+1.316,-35
convnextv2_pico.fcmae_ft_in1k,86.773,13.227,97.331,2.669,9.07,288,0.950,bicubic,+5.687,+1.851,+39
regnetx_080.tv2_in1k,86.771,13.229,97.197,2.803,39.57,224,0.965,bicubic,+5.231,+1.655,-18
cs3darknet_l.c2ns_in1k,86.767,13.233,97.459,2.541,21.16,288,0.950,bicubic,+5.871,+1.797,+53
resnetaa50.a1h_in1k,86.765,13.235,97.391,2.609,25.56,288,1.000,bicubic,+5.151,+1.589,-27
tresnet_l.miil_in1k,86.763,13.237,97.273,2.727,55.99,224,0.875,bilinear,+5.283,+1.650,-10
resnet50d.ra2_in1k,86.760,13.239,97.372,2.628,25.58,288,0.950,bicubic,+5.404,+1.634,+4
ese_vovnet39b.ra_in1k,86.756,13.244,97.372,2.628,24.57,288,0.950,bicubic,+6.406,+2.006,+104
twins_svt_small.in1k,86.752,13.248,97.180,2.820,24.06,224,0.900,bicubic,+5.076,+1.522,-37
resnet50_gn.a1h_in1k,86.750,13.250,97.449,2.551,25.56,288,0.950,bicubic,+5.534,+2.065,+12
tiny_vit_5m_224.dist_in22k_ft_in1k,86.748,13.252,97.316,2.684,5.39,224,0.950,bicubic,+5.872,+1.652,+50
seresnet50.a1_in1k,86.746,13.255,96.951,3.049,28.09,288,1.000,bicubic,+5.644,+1.623,+25
mobilevitv2_150.cvnets_in22k_ft_in1k,86.743,13.257,97.214,2.786,10.59,256,0.888,bicubic,+5.255,+1.546,-19
fastvit_s12.apple_dist_in1k,86.741,13.259,97.207,2.793,9.47,256,0.900,bicubic,+5.671,+1.923,+28
crossvit_base_240.in1k,86.733,13.267,97.122,2.878,105.03,240,0.875,bicubic,+4.519,+1.288,-90
levit_256.fb_dist_in1k,86.731,13.269,97.254,2.746,18.89,224,0.900,bicubic,+5.207,+1.760,-27
levit_conv_256.fb_dist_in1k,86.726,13.274,97.256,2.744,18.89,224,0.900,bicubic,+5.204,+1.766,-28
ecaresnet50t.a2_in1k,86.726,13.274,97.081,2.919,25.57,288,1.000,bicubic,+5.068,+1.531,-43
cs3darknet_focus_l.c2ns_in1k,86.722,13.278,97.376,2.624,21.15,288,0.950,bicubic,+5.846,+1.694,+41
convnext_nano_ols.d1h_in1k,86.718,13.282,97.047,2.953,15.65,288,1.000,bicubic,+5.118,+1.411,-39
vit_srelpos_small_patch16_224.sw_in1k,86.707,13.293,97.252,2.748,21.97,224,0.900,bicubic,+5.615,+1.682,+18
resnet50.ram_in1k,86.699,13.301,97.199,2.801,25.56,288,0.950,bicubic,+6.723,+2.147,+118
crossvit_small_240.in1k,86.686,13.314,97.276,2.724,26.86,240,0.875,bicubic,+5.668,+1.820,+22
halo2botnet50ts_256.a1h_in1k,86.686,13.314,97.098,2.902,22.64,256,0.950,bicubic,+4.626,+1.464,-82
pit_b_224.in1k,86.686,13.314,96.894,3.107,73.76,224,0.900,bicubic,+4.248,+1.180,-131
resnet50d.a1_in1k,86.671,13.329,96.693,3.307,25.58,288,1.000,bicubic,+5.221,+1.475,-26
ecaresnet50d_pruned.miil_in1k,86.669,13.331,97.429,2.571,19.94,288,0.950,bicubic,+5.879,+1.859,+44
tf_efficientnet_b1.ns_jft_in1k,86.669,13.331,97.382,2.618,7.79,240,0.882,bicubic,+5.281,+1.644,-22
swin_tiny_patch4_window7_224.ms_in1k,86.658,13.342,97.203,2.797,28.29,224,0.900,bicubic,+5.282,+1.659,-21
poolformer_s36.sail_in1k,86.654,13.346,97.152,2.848,30.86,224,0.900,bicubic,+5.224,+1.708,-29
gernet_l.idstcv_in1k,86.643,13.357,97.188,2.812,31.08,256,0.875,bilinear,+5.289,+1.658,-19
efficientnet_el.ra_in1k,86.630,13.370,97.190,2.810,10.59,300,0.904,bicubic,+5.318,+1.700,-18
twins_pcpvt_small.in1k,86.622,13.378,97.344,2.656,24.11,224,0.900,bicubic,+5.530,+1.696,+5
repvit_m2.dist_in1k,86.615,13.385,97.207,2.793,8.80,224,0.950,bicubic,+6.155,+2.039,+67
resmlp_24_224.fb_distilled_in1k,86.615,13.385,97.137,2.863,30.02,224,0.875,bicubic,+5.859,+1.913,+39
gcresnext50ts.ch_in1k,86.609,13.391,97.188,2.812,15.67,288,1.000,bicubic,+5.379,+1.646,-17
resnet50.c2_in1k,86.605,13.395,97.338,2.662,25.56,288,1.000,bicubic,+5.735,+1.804,+26
nf_resnet50.ra2_in1k,86.592,13.408,97.295,2.705,25.56,288,0.940,bicubic,+5.952,+1.961,+47
resnest50d_4s2x40d.in1k,86.588,13.412,97.267,2.733,30.42,224,0.875,bicubic,+5.468,+1.707,-6
sebotnet33ts_256.a1h_in1k,86.585,13.415,96.793,3.207,13.70,256,0.940,bicubic,+5.417,+1.625,-12
efficientnet_b3_pruned.in1k,86.579,13.421,97.184,2.816,9.86,300,0.904,bicubic,+5.727,+1.940,+24
repvgg_b3.rvgg_in1k,86.579,13.421,97.139,2.861,123.09,224,0.875,bilinear,+6.073,+1.885,+53
wide_resnet50_2.tv2_in1k,86.566,13.434,97.248,2.752,68.88,224,0.965,bilinear,+4.960,+1.488,-64
sehalonet33ts.ra2_in1k,86.566,13.434,97.004,2.995,13.69,256,0.940,bicubic,+5.608,+1.732,+9
fastvit_sa12.apple_in1k,86.564,13.436,97.233,2.767,11.58,256,0.900,bicubic,+5.720,+1.893,+22
resnet50.a1_in1k,86.541,13.459,96.838,3.162,25.56,288,1.000,bicubic,+5.327,+1.736,-22
repvit_m1_0.dist_450e_in1k,86.538,13.462,97.098,2.902,7.30,224,0.950,bicubic,+6.104,+2.180,+59
resnet50.c1_in1k,86.536,13.464,97.235,2.765,25.56,288,1.000,bicubic,+5.624,+1.683,+7
convnext_nano.d1h_in1k,86.536,13.464,97.182,2.818,15.59,288,1.000,bicubic,+5.054,+1.524,-53
xcit_tiny_24_p16_224.fb_dist_in1k,86.534,13.466,97.218,2.782,12.12,224,1.000,bicubic,+6.080,+2.000,+55
seresnet50.a2_in1k,86.519,13.481,96.987,3.013,28.09,288,1.000,bicubic,+5.413,+1.765,-16
vit_small_patch16_384.augreg_in1k,86.496,13.504,97.182,2.818,22.20,384,1.000,bicubic,+5.380,+1.608,-18
halonet50ts.a1h_in1k,86.487,13.513,97.148,2.852,22.73,256,0.940,bicubic,+4.825,+1.538,-80
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,86.483,13.517,97.474,2.526,44.18,224,0.875,bilinear,+5.559,+1.740,+1
maxvit_rmlp_pico_rw_256.sw_in1k,86.481,13.519,97.201,2.799,7.52,256,0.950,bicubic,+5.967,+1.987,+38
haloregnetz_b.ra3_in1k,86.466,13.534,96.947,3.053,11.68,224,0.940,bicubic,+5.420,+1.747,-13
resnet152s.gluon_in1k,86.464,13.536,97.109,2.891,60.32,224,0.875,bicubic,+5.456,+1.693,-11
seresnet33ts.ra2_in1k,86.462,13.538,97.190,2.810,19.78,288,1.000,bicubic,+5.678,+1.828,+14
repvit_m1_0.dist_300e_in1k,86.457,13.543,97.054,2.946,7.30,224,0.950,bicubic,+6.331,+2.310,+75
mobilevitv2_200.cvnets_in1k,86.457,13.543,96.970,3.030,18.45,256,0.888,bicubic,+5.323,+1.608,-27
resnext50d_32x4d.bt_in1k,86.455,13.545,97.165,2.835,25.05,288,0.950,bicubic,+5.791,+1.745,+22
resnet50.d_in1k,86.455,13.545,97.056,2.944,25.56,288,1.000,bicubic,+5.483,+1.626,-12
resnetv2_50x1_bit.goog_in21k_ft_in1k,86.440,13.560,97.605,2.396,25.55,448,1.000,bilinear,+6.098,+1.922,+49
resnet50.tv2_in1k,86.440,13.560,97.145,2.855,25.56,224,0.965,bilinear,+5.592,+1.711,+3
resnet50.b1k_in1k,86.438,13.562,97.235,2.765,25.56,288,1.000,bicubic,+5.732,+1.803,+14
resnest50d_1s4x24d.in1k,86.432,13.568,97.152,2.848,25.68,224,0.875,bicubic,+5.444,+1.826,-19
poolformerv2_s24.sail_in1k,86.389,13.611,97.150,2.850,21.34,224,1.000,bicubic,+5.641,+1.840,+8
regnety_016.tv2_in1k,86.372,13.628,97.188,2.812,11.20,224,0.965,bicubic,+5.706,+1.858,+15
repvgg_b3g4.rvgg_in1k,86.368,13.632,97.047,2.953,83.83,224,0.875,bilinear,+6.152,+1.955,+60
darknetaa53.c2ns_in1k,86.359,13.641,97.165,2.835,36.02,288,1.000,bilinear,+5.853,+1.843,+24
efficientformer_l1.snap_dist_in1k,86.357,13.643,97.019,2.981,12.29,224,0.950,bicubic,+5.859,+2.031,+25
darknet53.c2ns_in1k,86.350,13.649,97.130,2.869,41.61,288,1.000,bicubic,+5.819,+1.698,+20
lamhalobotnet50ts_256.a1h_in1k,86.350,13.649,97.041,2.959,22.57,256,0.950,bicubic,+4.798,+1.549,-89
fastvit_t12.apple_dist_in1k,86.344,13.656,97.098,2.902,7.55,256,0.900,bicubic,+5.992,+2.056,+37
legacy_senet154.in1k,86.344,13.656,96.934,3.066,115.09,224,0.875,bilinear,+5.032,+1.374,-60
resnet50.a1h_in1k,86.340,13.660,97.060,2.940,25.56,224,1.000,bicubic,+5.662,+1.754,+5
cait_xxs36_224.fb_dist_in1k,86.329,13.671,97.118,2.882,17.30,224,1.000,bicubic,+6.583,+2.244,+81
tf_efficientnet_b3.in1k,86.325,13.675,96.964,3.036,12.23,300,0.904,bicubic,+5.451,+1.664,-16
mobilevitv2_175.cvnets_in1k,86.321,13.679,96.985,3.015,14.25,256,0.888,bicubic,+5.461,+1.729,-15
gernet_m.idstcv_in1k,86.319,13.681,97.098,2.902,21.14,224,0.875,bilinear,+5.582,+1.908,-4
resnet50d.a2_in1k,86.314,13.686,96.674,3.326,25.58,288,1.000,bicubic,+5.150,+1.594,-52
vit_small_patch32_384.augreg_in21k_ft_in1k,86.312,13.688,97.419,2.581,22.92,384,1.000,bicubic,+5.826,+1.819,+15
pit_s_224.in1k,86.308,13.692,97.049,2.951,23.46,224,0.900,bicubic,+5.222,+1.719,-42
efficientnet_b2.ra_in1k,86.306,13.694,96.987,3.013,9.11,288,1.000,bicubic,+5.696,+1.673,+3
gcresnet33ts.ra2_in1k,86.301,13.699,97.060,2.940,19.88,288,1.000,bicubic,+5.701,+1.738,+3
resnext50_32x4d.a1_in1k,86.284,13.716,96.710,3.290,25.03,288,1.000,bicubic,+4.818,+1.536,-89
resnext50_32x4d.a2_in1k,86.280,13.720,96.684,3.316,25.03,288,1.000,bicubic,+4.976,+1.588,-71
resnet50.b2k_in1k,86.272,13.729,97.071,2.929,25.56,288,1.000,bicubic,+5.818,+1.753,+16
senet154.gluon_in1k,86.272,13.729,96.957,3.042,115.09,224,0.875,bicubic,+5.046,+1.599,-69
resnext50_32x4d.tv2_in1k,86.267,13.733,97.054,2.946,25.03,224,0.965,bilinear,+5.085,+1.714,-64
eca_resnet33ts.ra2_in1k,86.257,13.743,97.150,2.850,19.68,288,1.000,bicubic,+5.585,+1.786,-9
resnest50d.in1k,86.248,13.752,97.069,2.931,27.48,224,0.875,bilinear,+5.288,+1.687,-41
gcvit_xxtiny.in1k,86.244,13.756,97.107,2.893,12.00,224,0.875,bicubic,+6.518,+2.027,+67
regnetx_032.tv2_in1k,86.244,13.756,97.092,2.908,15.30,224,0.965,bicubic,+5.318,+1.814,-40
vit_base_patch16_384.augreg_in1k,86.231,13.769,96.964,3.036,86.86,384,1.000,bicubic,+5.129,+1.844,-59
convmixer_768_32.in1k,86.220,13.780,97.037,2.963,21.11,224,0.960,bicubic,+6.052,+1.963,+36
efficientnet_el_pruned.in1k,86.192,13.807,97.028,2.972,10.59,300,0.904,bicubic,+5.894,+1.806,+23
tresnet_m.miil_in1k,86.188,13.812,96.667,3.333,31.39,224,0.875,bilinear,+5.390,+1.811,-28
cspdarknet53.ra_in1k,86.178,13.822,97.009,2.991,27.64,256,0.887,bilinear,+6.110,+1.931,+38
rexnet_150.nav_in1k,86.169,13.831,97.060,2.940,9.73,224,0.875,bicubic,+5.845,+2.070,+16
inception_v4.tf_in1k,86.163,13.837,96.919,3.081,42.68,299,0.875,bicubic,+6.007,+1.949,+33
efficientvit_b1.r288_in1k,86.154,13.846,96.932,3.068,9.10,288,1.000,bicubic,+5.830,+1.756,+15
inception_resnet_v2.tf_in1k,86.133,13.867,97.045,2.955,55.84,299,0.897,bicubic,+5.675,+1.855,-1
xcit_tiny_12_p8_224.fb_in1k,86.114,13.886,97.088,2.912,6.71,224,1.000,bicubic,+6.425,+2.034,+61
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,86.101,13.899,97.212,2.788,25.03,224,0.875,bilinear,+5.767,+1.812,+10
res2net101d.in1k,86.099,13.901,96.851,3.149,45.23,224,0.875,bilinear,+4.881,+1.501,-85
resnet50.a2_in1k,86.097,13.903,96.701,3.299,25.56,288,1.000,bicubic,+5.325,+1.713,-34
tf_efficientnet_el.in1k,86.082,13.918,96.955,3.045,10.59,300,0.904,bicubic,+5.834,+1.835,+15
mobilevitv2_150.cvnets_in1k,86.082,13.918,96.857,3.143,10.59,256,0.888,bicubic,+5.712,+1.783,+2
cspresnext50.ra_in1k,86.077,13.923,97.107,2.893,20.57,256,0.887,bilinear,+5.523,+1.781,-20
convnext_pico_ols.d1_in1k,86.069,13.931,97.017,2.983,9.06,288,1.000,bicubic,+5.607,+1.765,-11
resnet101s.gluon_in1k,86.067,13.933,97.030,2.970,44.67,224,0.875,bicubic,+5.763,+1.878,+7
edgenext_small_rw.sw_in1k,86.054,13.946,96.928,3.072,7.83,320,1.000,bicubic,+5.596,+1.620,-10
lambda_resnet50ts.a1h_in1k,86.049,13.950,96.742,3.258,21.54,256,0.950,bicubic,+4.891,+1.644,-84
seresnext101_32x4d.gluon_in1k,86.028,13.972,96.972,3.027,48.96,224,0.875,bicubic,+5.136,+1.676,-56
convnext_pico.d1_in1k,86.015,13.985,96.932,3.068,9.05,288,0.950,bicubic,+5.599,+1.884,-8
poolformer_s24.sail_in1k,86.013,13.987,97.030,2.970,21.39,224,0.900,bicubic,+5.719,+1.956,+5
resnetblur50.bt_in1k,85.992,14.008,96.985,3.015,25.56,288,0.950,bicubic,+5.758,+1.751,+9
resnet152.a3_in1k,85.985,14.015,96.849,3.151,60.19,224,0.950,bicubic,+5.439,+1.849,-28
ecaresnet26t.ra2_in1k,85.981,14.019,97.043,2.957,16.01,320,0.950,bicubic,+6.131,+1.953,+29
tf_efficientnet_b2.ap_in1k,85.981,14.019,96.812,3.188,9.11,260,0.890,bicubic,+5.671,+1.786,-3
seresnext101_64x4d.gluon_in1k,85.973,14.027,96.981,3.019,88.23,224,0.875,bicubic,+5.079,+1.685,-64
efficientformerv2_s1.snap_dist_in1k,85.962,14.038,96.874,3.126,6.19,224,0.950,bicubic,+6.270,+2.158,+41
vit_base_patch32_224.augreg_in21k_ft_in1k,85.956,14.044,97.128,2.872,88.22,224,0.900,bicubic,+5.240,+1.562,-46
resnext50_32x4d.ra_in1k,85.934,14.066,97.032,2.968,25.03,288,0.950,bicubic,+5.236,+1.640,-45
fbnetv3_d.ra2_in1k,85.930,14.070,97.026,2.974,10.31,256,0.950,bilinear,+6.248,+2.082,+40
vit_large_patch32_384.orig_in21k_ft_in1k,85.915,14.085,97.370,2.630,306.63,384,1.000,bicubic,+4.405,+1.280,-137
resnet152d.gluon_in1k,85.915,14.085,96.814,3.186,60.21,224,0.875,bicubic,+5.439,+1.612,-28
tf_efficientnet_b2.aa_in1k,85.896,14.104,96.864,3.136,9.11,260,0.890,bicubic,+5.812,+1.958,+7
tf_efficientnetv2_b2.in1k,85.887,14.113,96.887,3.113,10.10,260,0.890,bicubic,+5.691,+1.845,0
vit_base_patch16_224.sam_in1k,85.872,14.128,96.693,3.307,86.57,224,0.900,bicubic,+5.634,+1.937,-5
repvgg_b2g4.rvgg_in1k,85.862,14.138,96.804,3.196,61.76,224,0.875,bilinear,+6.480,+2.128,+55
resnet101d.gluon_in1k,85.862,14.138,96.674,3.326,44.57,224,0.875,bicubic,+5.436,+1.650,-26
efficientvit_b1.r256_in1k,85.830,14.170,96.782,3.217,9.10,256,1.000,bicubic,+6.096,+2.002,+25
mixnet_xl.ra_in1k,85.804,14.196,96.716,3.284,11.90,224,0.875,bicubic,+5.322,+1.780,-37
inception_resnet_v2.tf_ens_adv_in1k,85.768,14.232,96.759,3.241,55.84,299,0.897,bicubic,+5.790,+1.811,+3
repvit_m0_9.dist_450e_in1k,85.757,14.243,96.810,3.190,5.49,224,0.950,bicubic,+6.691,+2.430,+81
tf_efficientnet_lite3.in1k,85.755,14.245,96.891,3.109,8.20,300,0.904,bilinear,+5.949,+1.977,+18
resnext101_32x4d.gluon_in1k,85.753,14.247,96.637,3.363,44.18,224,0.875,bicubic,+5.413,+1.707,-25
xcit_tiny_24_p16_224.fb_in1k,85.746,14.254,96.932,3.068,12.12,224,1.000,bicubic,+6.298,+2.054,+42
legacy_seresnext101_32x4d.in1k,85.742,14.258,96.772,3.228,48.96,224,0.875,bilinear,+5.510,+1.752,-13
resnet101.a3_in1k,85.736,14.264,96.501,3.499,44.55,224,0.950,bicubic,+5.922,+1.887,+13
res2net50d.in1k,85.729,14.271,96.763,3.237,25.72,224,0.875,bilinear,+5.475,+1.727,-20
regnety_320.pycls_in1k,85.727,14.273,96.725,3.275,145.05,224,0.875,bicubic,+4.917,+1.487,-75
cspresnet50.ra_in1k,85.719,14.281,96.799,3.200,21.62,256,0.887,bilinear,+6.137,+2.090,+28
resmlp_big_24_224.fb_in1k,85.701,14.299,96.426,3.574,129.14,224,0.875,bicubic,+4.665,+1.408,-102
resnext101_64x4d.gluon_in1k,85.693,14.307,96.641,3.358,83.46,224,0.875,bicubic,+5.093,+1.649,-58
xception71.tf_in1k,85.689,14.311,96.774,3.226,42.34,299,0.903,bicubic,+5.815,+1.846,-3
resnet33ts.ra2_in1k,85.689,14.311,96.757,3.243,19.68,288,1.000,bicubic,+5.963,+1.929,+13
deit_small_patch16_224.fb_in1k,85.676,14.324,96.906,3.094,22.05,224,0.900,bicubic,+5.828,+1.862,0
resnet50.ra_in1k,85.674,14.326,96.889,3.111,25.56,288,0.950,bicubic,+5.838,+1.923,+2
efficientnet_em.ra2_in1k,85.672,14.328,96.951,3.049,6.90,240,0.882,bicubic,+6.428,+2.157,+49
dpn107.mx_in1k,85.672,14.328,96.757,3.243,86.92,224,0.875,bicubic,+5.502,+1.815,-21
ecaresnet50t.a3_in1k,85.672,14.328,96.725,3.275,25.57,224,0.950,bicubic,+6.120,+2.031,+22
efficientnet_b2_pruned.in1k,85.642,14.358,96.742,3.258,8.31,260,0.890,bicubic,+5.722,+1.890,-12
resmlp_36_224.fb_in1k,85.623,14.377,96.795,3.205,44.69,224,0.875,bicubic,+5.850,+1.911,+1
tiny_vit_5m_224.in1k,85.605,14.395,96.953,3.047,5.39,224,0.950,bicubic,+6.435,+2.159,+52
mobilevitv2_125.cvnets_in1k,85.578,14.422,96.665,3.335,7.48,256,0.888,bicubic,+5.898,+1.807,+9
resnet50.bt_in1k,85.576,14.425,96.832,3.168,25.56,288,0.950,bicubic,+5.936,+1.940,+11
levit_conv_192.fb_dist_in1k,85.571,14.429,96.742,3.258,10.95,224,0.900,bicubic,+5.733,+1.964,-9
resnet32ts.ra2_in1k,85.569,14.431,96.866,3.134,17.96,288,1.000,bicubic,+6.181,+2.214,+24
levit_192.fb_dist_in1k,85.569,14.431,96.740,3.260,10.95,224,0.900,bicubic,+5.731,+1.956,-9
resnet152c.gluon_in1k,85.569,14.431,96.644,3.356,60.21,224,0.875,bicubic,+5.657,+1.798,-19
pit_xs_distilled_224.in1k,85.561,14.439,96.686,3.314,11.00,224,0.900,bicubic,+6.381,+2.320,+44
tf_efficientnetv2_b1.in1k,85.556,14.444,96.731,3.269,8.14,240,0.882,bicubic,+6.096,+2.009,+16
regnety_120.pycls_in1k,85.554,14.446,96.774,3.226,51.82,224,0.875,bicubic,+5.174,+1.648,-57
fbnetv3_b.ra2_in1k,85.520,14.480,96.862,3.139,8.60,256,0.950,bilinear,+6.374,+2.118,+44
regnetx_320.pycls_in1k,85.520,14.480,96.671,3.329,107.81,224,0.875,bicubic,+5.274,+1.649,-43
nf_regnet_b1.ra2_in1k,85.507,14.493,96.789,3.211,10.22,288,0.900,bicubic,+6.199,+2.049,+25
convnextv2_femto.fcmae_ft_in1k,85.503,14.497,96.806,3.194,5.23,288,0.950,bicubic,+6.165,+2.246,+21
dpn92.mx_in1k,85.501,14.499,96.650,3.350,37.67,224,0.875,bicubic,+5.463,+1.790,-33
fastvit_s12.apple_in1k,85.492,14.508,96.721,3.280,9.47,256,0.900,bicubic,+5.550,+1.927,-31
regnety_160.pycls_in1k,85.486,14.514,96.616,3.384,83.59,224,0.875,bicubic,+5.188,+1.652,-52
resnet152.gluon_in1k,85.482,14.518,96.550,3.450,60.19,224,0.875,bicubic,+5.786,+1.820,-11
resnetrs50.tf_in1k,85.469,14.531,96.733,3.267,35.69,224,0.910,bicubic,+5.575,+1.759,-30
rexnet_130.nav_in1k,85.465,14.536,96.680,3.320,7.56,224,0.875,bicubic,+5.959,+2.002,+1
dpn131.mx_in1k,85.450,14.550,96.620,3.380,79.25,224,0.875,bicubic,+5.636,+1.920,-23
regnetx_160.pycls_in1k,85.396,14.604,96.650,3.350,54.28,224,0.875,bicubic,+5.530,+1.822,-30
tf_efficientnet_b2.in1k,85.396,14.604,96.586,3.414,9.11,260,0.890,bicubic,+5.788,+1.872,-8
convnext_tiny.fb_in22k_ft_in1k,85.381,14.619,96.804,3.196,28.59,288,1.000,bicubic,+6.483,+2.130,+46
dla102x2.in1k,85.375,14.625,96.629,3.371,41.28,224,0.875,bilinear,+5.929,+1.997,+2
repvit_m0_9.dist_300e_in1k,85.373,14.627,96.627,3.373,5.49,224,0.950,bicubic,+6.715,+2.511,+66
dpn98.mx_in1k,85.351,14.649,96.509,3.491,61.57,224,0.875,bicubic,+5.681,+1.855,-15
regnetx_016.tv2_in1k,85.349,14.651,96.817,3.183,9.19,224,0.965,bicubic,+5.913,+2.049,-1
gmlp_s16_224.ra3_in1k,85.349,14.651,96.644,3.356,19.42,224,0.875,bicubic,+5.705,+2.022,-15
botnet26t_256.c1_in1k,85.332,14.668,96.631,3.369,12.49,256,0.950,bicubic,+6.074,+2.099,+15
skresnext50_32x4d.ra_in1k,85.328,14.672,96.390,3.610,27.48,224,0.875,bicubic,+5.164,+1.750,-54
seresnext50_32x4d.gluon_in1k,85.321,14.679,96.674,3.326,27.56,224,0.875,bicubic,+5.397,+1.850,-46
xception65.tf_in1k,85.315,14.685,96.646,3.354,39.92,299,0.903,bicubic,+5.759,+1.988,-15
resnet101c.gluon_in1k,85.311,14.689,96.411,3.589,44.57,224,0.875,bicubic,+5.773,+1.827,-14
lambda_resnet26t.c1_in1k,85.302,14.698,96.721,3.280,10.96,256,0.940,bicubic,+6.214,+2.130,+23
resnext50_32x4d.a3_in1k,85.298,14.702,96.328,3.672,25.03,224,0.950,bicubic,+6.030,+2.022,+7
regnety_064.pycls_in1k,85.283,14.717,96.648,3.352,30.58,224,0.875,bicubic,+5.567,+1.882,-31
resnet34d.ra2_in1k,85.272,14.728,96.701,3.299,21.82,288,0.950,bicubic,+6.836,+2.357,+68
coat_lite_mini.in1k,85.272,14.728,96.686,3.314,11.01,224,0.900,bicubic,+6.170,+2.078,+19
resmlp_24_224.fb_in1k,85.264,14.736,96.490,3.510,30.02,224,0.875,bicubic,+5.890,+1.944,-8
convnext_femto_ols.d1_in1k,85.253,14.747,96.772,3.228,5.23,288,0.950,bicubic,+6.329,+2.246,+28
cait_xxs24_224.fb_dist_in1k,85.238,14.762,96.714,3.286,11.96,224,1.000,bicubic,+6.854,+2.398,+71
regnety_080.pycls_in1k,85.238,14.762,96.635,3.365,39.18,224,0.875,bicubic,+5.370,+1.803,-52
efficientvit_b1.r224_in1k,85.232,14.768,96.462,3.538,9.10,224,0.950,bicubic,+5.980,+2.158,+2
pvt_v2_b1.in1k,85.210,14.790,96.631,3.369,14.01,224,0.900,bicubic,+6.506,+2.129,+43
xcit_tiny_12_p16_224.fb_dist_in1k,85.200,14.800,96.597,3.403,6.72,224,1.000,bicubic,+6.626,+2.399,+47
halonet26t.a1h_in1k,85.191,14.809,96.454,3.546,12.48,256,0.950,bicubic,+6.085,+2.148,+9
resnext101_32x8d.tv_in1k,85.189,14.811,96.456,3.544,88.79,224,0.875,bilinear,+5.879,+1.936,-11
inception_v3.gluon_in1k,85.187,14.813,96.535,3.465,23.83,299,0.875,bicubic,+6.385,+2.159,+29
fastvit_t12.apple_in1k,85.180,14.819,96.607,3.393,7.55,256,0.900,bicubic,+5.916,+2.045,-6
convnext_femto.d1_in1k,85.163,14.837,96.704,3.296,5.22,288,0.950,bicubic,+6.447,+2.273,+34
resnet101.gluon_in1k,85.161,14.839,96.366,3.634,44.55,224,0.875,bicubic,+5.851,+1.844,-16
hrnet_w48.ms_in1k,85.159,14.841,96.490,3.510,77.47,224,0.875,bilinear,+5.853,+1.974,-14
regnetx_120.pycls_in1k,85.138,14.862,96.475,3.525,46.11,224,0.875,bicubic,+5.550,+1.733,-38
eca_halonext26ts.c1_in1k,85.131,14.869,96.580,3.420,10.76,256,0.940,bicubic,+5.645,+1.980,-33
resnet50.am_in1k,85.131,14.869,96.571,3.429,25.56,224,0.875,bicubic,+6.129,+2.173,+8
tf_efficientnet_b1.ap_in1k,85.131,14.869,96.407,3.593,7.79,240,0.882,bicubic,+5.855,+2.095,-16
repvit_m1.dist_in1k,85.121,14.879,96.597,3.403,5.49,224,0.950,bicubic,+6.583,+2.527,+36
eca_botnext26ts_256.c1_in1k,85.121,14.879,96.511,3.489,10.59,256,0.950,bicubic,+5.853,+1.905,-16
legacy_xception.tf_in1k,85.121,14.879,96.469,3.531,22.86,299,0.897,bicubic,+6.081,+2.087,+4
hrnet_w64.ms_in1k,85.114,14.886,96.738,3.262,128.06,224,0.875,bilinear,+5.638,+2.086,-37
lambda_resnet26rpt_256.c1_in1k,85.099,14.901,96.565,3.435,10.99,256,0.940,bicubic,+6.135,+2.129,+4
res2net101_26w_4s.in1k,85.097,14.903,96.381,3.619,45.21,224,0.875,bilinear,+5.897,+1.945,-13
resnet50.fb_ssl_yfcc100m_ft_in1k,85.093,14.907,96.862,3.139,25.56,224,0.875,bilinear,+5.863,+2.036,-16
mobileone_s4.apple_in1k,85.082,14.918,96.434,3.566,14.95,224,0.900,bilinear,+5.656,+1.954,-36
dpn68b.ra_in1k,85.061,14.939,96.449,3.551,12.61,288,1.000,bicubic,+5.701,+2.013,-33
tf_efficientnet_cc_b1_8e.in1k,85.061,14.939,96.430,3.570,39.72,240,0.882,bicubic,+5.759,+2.056,-27
resnest26d.gluon_in1k,85.016,14.984,96.639,3.361,17.07,224,0.875,bilinear,+6.534,+2.345,+34
xcit_nano_12_p8_384.fb_dist_in1k,85.014,14.986,96.629,3.371,3.05,384,1.000,bicubic,+7.194,+2.589,+83
resnext50_32x4d.gluon_in1k,85.005,14.995,96.428,3.572,25.03,224,0.875,bicubic,+5.645,+1.998,-36
tf_efficientnet_b0.ns_jft_in1k,84.999,15.001,96.505,3.495,5.29,224,0.875,bicubic,+6.331,+2.133,+18
coat_tiny.in1k,84.971,15.029,96.422,3.578,5.50,224,0.900,bicubic,+6.545,+2.374,+36
regnety_040.pycls_in1k,84.952,15.048,96.612,3.388,20.65,224,0.875,bicubic,+5.732,+1.956,-24
dla169.in1k,84.931,15.069,96.541,3.459,53.39,224,0.875,bilinear,+6.223,+2.197,+13
tf_efficientnet_b1.aa_in1k,84.918,15.082,96.366,3.634,7.79,240,0.882,bicubic,+6.090,+2.166,0
resnet50d.a3_in1k,84.901,15.099,96.285,3.715,25.58,224,0.950,bicubic,+6.181,+2.053,+8
legacy_seresnext50_32x4d.in1k,84.897,15.103,96.428,3.572,27.56,224,0.875,bilinear,+5.821,+1.996,-17
mobilevitv2_100.cvnets_in1k,84.894,15.106,96.388,3.612,4.90,256,0.888,bicubic,+6.814,+2.218,+55
hrnet_w44.ms_in1k,84.886,15.114,96.430,3.570,67.06,224,0.875,bilinear,+5.992,+2.066,-8
resnet50s.gluon_in1k,84.877,15.123,96.441,3.559,25.68,224,0.875,bicubic,+6.163,+2.199,+6
regnety_008_tv.tv2_in1k,84.875,15.125,96.637,3.363,6.43,224,0.965,bicubic,+6.209,+2.247,+9
regnetx_080.pycls_in1k,84.865,15.136,96.432,3.568,39.57,224,0.875,bicubic,+5.667,+1.878,-31
levit_conv_128.fb_dist_in1k,84.847,15.153,96.353,3.647,9.21,224,0.900,bicubic,+6.353,+2.345,+16
visformer_tiny.in1k,84.845,15.155,96.509,3.491,10.32,224,0.900,bicubic,+6.685,+2.343,+43
levit_128.fb_dist_in1k,84.845,15.155,96.353,3.647,9.21,224,0.900,bicubic,+6.355,+2.341,+17
res2net50_26w_8s.in1k,84.837,15.163,96.355,3.645,48.40,224,0.875,bilinear,+5.895,+2.061,-19
vit_tiny_patch16_384.augreg_in21k_ft_in1k,84.832,15.168,96.714,3.286,5.79,384,1.000,bicubic,+6.408,+2.172,+22
repghostnet_200.in1k,84.830,15.170,96.413,3.587,9.80,224,0.875,bicubic,+6.024,+2.083,-11
resnet50d.gluon_in1k,84.830,15.170,96.398,3.602,25.58,224,0.875,bicubic,+5.752,+1.932,-30
dla60_res2next.in1k,84.826,15.174,96.407,3.593,17.03,224,0.875,bilinear,+6.386,+2.263,+16
resnet152.tv_in1k,84.826,15.174,96.232,3.768,60.19,224,0.875,bilinear,+6.504,+2.186,+26
resnet26t.ra2_in1k,84.824,15.176,96.390,3.610,16.01,320,1.000,bicubic,+6.496,+2.266,+24
dla60_res2net.in1k,84.818,15.182,96.473,3.527,20.85,224,0.875,bilinear,+6.354,+2.275,+11
mixnet_l.ft_in1k,84.815,15.185,96.323,3.677,7.33,224,0.875,bicubic,+5.849,+2.141,-29
dla102x.in1k,84.807,15.193,96.552,3.448,26.31,224,0.875,bilinear,+6.295,+2.316,+2
xception41.tf_in1k,84.785,15.214,96.411,3.589,26.97,299,0.903,bicubic,+6.281,+2.135,+2
pit_xs_224.in1k,84.783,15.217,96.501,3.499,10.62,224,0.900,bicubic,+6.607,+2.339,+28
hrnet_w18.ms_aug_in1k,84.783,15.217,96.466,3.534,21.30,224,0.950,bilinear,+6.661,+2.412,+34
regnetx_064.pycls_in1k,84.773,15.227,96.494,3.506,26.21,224,0.875,bicubic,+5.707,+2.034,-38
resnet34.a1_in1k,84.762,15.238,96.230,3.771,21.80,288,1.000,bicubic,+6.844,+2.466,+46
poolformerv2_s12.sail_in1k,84.756,15.244,96.373,3.627,11.89,224,1.000,bicubic,+6.754,+2.508,+37
gcresnext26ts.ch_in1k,84.756,15.244,96.293,3.707,10.48,288,1.000,bicubic,+6.342,+2.257,+9
hrnet_w40.ms_in1k,84.745,15.255,96.552,3.448,57.56,224,0.875,bilinear,+5.813,+2.088,-35
repvgg_b2.rvgg_in1k,84.726,15.274,96.471,3.529,89.02,224,0.875,bilinear,+5.934,+2.051,-24
res2net50_26w_6s.in1k,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.158,+2.159,-11
resmlp_12_224.fb_distilled_in1k,84.719,15.281,96.225,3.775,15.35,224,0.875,bicubic,+6.765,+2.665,+37
vit_base_patch32_384.augreg_in1k,84.715,15.285,96.323,3.677,88.30,384,1.000,bicubic,+5.959,+2.097,-25
legacy_seresnet152.in1k,84.713,15.287,96.419,3.580,66.82,224,0.875,bilinear,+6.053,+2.049,-17
cs3darknet_m.c2ns_in1k,84.700,15.300,96.488,3.512,9.31,288,0.950,bicubic,+7.066,+2.472,+50
selecsls60b.in1k,84.662,15.338,96.300,3.700,32.77,224,0.875,bicubic,+6.250,+2.132,+1
bat_resnext26ts.ch_in1k,84.653,15.347,96.268,3.732,10.73,256,0.900,bicubic,+6.401,+2.170,+9
hrnet_w32.ms_in1k,84.651,15.349,96.413,3.587,41.23,224,0.875,bilinear,+6.209,+2.223,-7
seresnext26d_32x4d.bt_in1k,84.642,15.357,96.261,3.739,16.81,288,0.950,bicubic,+5.829,+2.022,-37
tf_efficientnetv2_b0.in1k,84.623,15.377,96.274,3.726,7.14,224,0.875,bicubic,+6.265,+2.260,+1
efficientnet_b1.ft_in1k,84.611,15.389,96.334,3.666,7.79,256,1.000,bicubic,+5.811,+1.992,-36
regnetx_040.pycls_in1k,84.606,15.394,96.379,3.621,22.12,224,0.875,bicubic,+6.114,+2.137,-16
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,84.598,15.402,96.014,3.986,119.42,256,0.900,bicubic,+5.114,+1.876,-94
regnety_032.pycls_in1k,84.593,15.407,96.415,3.585,19.44,224,0.875,bicubic,+5.717,+2.007,-46
seresnext26t_32x4d.bt_in1k,84.589,15.411,96.381,3.619,16.81,288,0.950,bicubic,+5.845,+2.069,-36
hrnet_w30.ms_in1k,84.583,15.417,96.381,3.619,37.71,224,0.875,bilinear,+6.387,+2.159,+4
efficientnet_es.ra_in1k,84.581,15.419,96.308,3.692,5.44,224,0.875,bicubic,+6.523,+2.382,+13
tf_mixnet_l.in1k,84.578,15.422,96.244,3.756,7.33,224,0.875,bicubic,+5.802,+2.242,-41
wide_resnet101_2.tv_in1k,84.546,15.454,96.349,3.651,126.89,224,0.875,bilinear,+5.704,+2.067,-49
hrnet_w18_small_v2.gluon_in1k,84.542,15.458,96.285,3.715,15.60,224,0.875,bicubic,+6.352,+2.383,+1
vit_small_patch16_224.augreg_in1k,84.536,15.464,96.272,3.728,22.05,224,0.900,bicubic,+5.688,+1.984,-52
dla60x.in1k,84.531,15.469,96.293,3.707,17.35,224,0.875,bilinear,+6.295,+2.267,-3
legacy_seresnet101.in1k,84.497,15.503,96.326,3.674,49.33,224,0.875,bilinear,+6.111,+2.064,-15
resnet50.a3_in1k,84.484,15.515,96.003,3.997,25.56,224,0.950,bicubic,+6.436,+2.223,+7
cs3darknet_focus_m.c2ns_in1k,84.482,15.518,96.417,3.583,9.30,288,0.950,bicubic,+7.198,+2.451,+51
seresnext26ts.ch_in1k,84.480,15.520,96.321,3.679,10.39,288,1.000,bicubic,+6.210,+2.229,-11
tf_efficientnet_b1.in1k,84.472,15.528,96.074,3.926,7.79,240,0.882,bicubic,+5.910,+1.980,-36
coat_lite_tiny.in1k,84.459,15.541,96.383,3.617,5.72,224,0.900,bicubic,+6.939,+2.461,+32
tf_efficientnet_em.in1k,84.455,15.545,96.185,3.815,6.90,240,0.882,bicubic,+6.329,+2.137,-3
wide_resnet50_2.tv_in1k,84.429,15.571,96.257,3.743,68.88,224,0.875,bilinear,+5.953,+2.169,-31
repvgg_b1.rvgg_in1k,84.420,15.580,96.217,3.783,57.42,224,0.875,bilinear,+6.052,+2.121,-21
efficientnet_b1_pruned.in1k,84.397,15.603,96.140,3.860,6.33,240,0.882,bicubic,+6.157,+2.306,-14
vit_base_patch16_224.augreg_in1k,84.384,15.616,96.042,3.958,86.57,224,0.900,bicubic,+5.230,+1.952,-83
res2net50_26w_4s.in1k,84.359,15.642,96.091,3.909,25.70,224,0.875,bilinear,+6.409,+2.239,+6
hardcorenas_f.miil_green_in1k,84.324,15.676,96.025,3.975,8.20,224,0.875,bilinear,+6.228,+2.222,-7
res2net50_14w_8s.in1k,84.311,15.688,96.076,3.924,25.06,224,0.875,bilinear,+6.153,+2.230,-11
selecsls60.in1k,84.282,15.718,96.103,3.897,30.67,224,0.875,bicubic,+6.294,+2.273,+1
mobilevit_s.cvnets_in1k,84.277,15.723,96.259,3.741,5.58,256,0.900,bicubic,+5.965,+2.111,-24
regnetx_032.pycls_in1k,84.245,15.755,96.242,3.758,15.30,224,0.875,bicubic,+6.077,+2.160,-16
mobileone_s3.apple_in1k,84.233,15.767,96.135,3.865,10.17,224,0.900,bilinear,+6.241,+2.221,-3
convnextv2_atto.fcmae_ft_in1k,84.226,15.774,96.059,3.941,3.71,288,0.950,bicubic,+6.466,+2.333,+9
eca_resnext26ts.ch_in1k,84.224,15.776,96.191,3.809,10.30,288,1.000,bicubic,+6.224,+2.265,-6
ese_vovnet19b_dw.ra_in1k,84.220,15.780,96.259,3.741,6.54,288,0.950,bicubic,+6.476,+2.475,+7
convnext_atto_ols.a2_in1k,84.220,15.780,96.217,3.783,3.70,288,0.950,bicubic,+7.004,+2.541,+36
resnet50c.gluon_in1k,84.213,15.787,96.163,3.837,25.58,224,0.875,bicubic,+6.207,+2.171,-12
res2next50.in1k,84.213,15.787,96.001,3.999,24.67,224,0.875,bilinear,+5.971,+2.109,-28
mobileone_s2.apple_in1k,84.196,15.804,96.063,3.937,7.88,224,0.900,bilinear,+6.680,+2.395,+15
dla102.in1k,84.190,15.810,96.206,3.794,33.27,224,0.875,bilinear,+6.166,+2.272,-15
densenetblur121d.ra_in1k,84.168,15.832,96.240,3.760,8.00,288,0.950,bicubic,+6.846,+2.452,+22
rexnet_100.nav_in1k,84.158,15.842,96.249,3.751,4.80,224,0.875,bicubic,+6.302,+2.609,-5
fastvit_t8.apple_dist_in1k,84.143,15.857,96.078,3.922,4.03,256,0.900,bicubic,+6.967,+2.780,+30
convnext_atto.d2_in1k,84.141,15.859,96.200,3.800,3.70,288,0.950,bicubic,+7.133,+2.498,+39
inception_v3.tf_in1k,84.141,15.859,95.911,4.089,23.83,299,0.875,bicubic,+6.285,+2.045,-7
res2net50_48w_2s.in1k,84.117,15.883,95.960,4.040,25.29,224,0.875,bilinear,+6.603,+2.410,+9
xcit_tiny_12_p16_224.fb_in1k,84.111,15.889,96.236,3.764,6.72,224,1.000,bicubic,+6.971,+2.520,+29
ghostnetv2_160.in1k,84.098,15.902,96.210,3.790,12.39,224,0.875,bicubic,+6.266,+2.270,-9
tf_efficientnet_lite2.in1k,84.087,15.913,96.074,3.926,6.09,260,0.890,bicubic,+6.625,+2.322,+7
poolformer_s12.sail_in1k,84.049,15.951,96.178,3.822,11.92,224,0.900,bicubic,+6.809,+2.646,+20
resnet34.a2_in1k,84.045,15.955,95.922,4.078,21.80,288,1.000,bicubic,+6.887,+2.648,+24
efficientnet_b0.ra_in1k,84.034,15.966,95.965,4.035,5.29,224,0.875,bicubic,+6.340,+2.433,-8
crossvit_9_dagger_240.in1k,84.019,15.981,96.084,3.916,8.78,240,0.875,bicubic,+7.041,+2.466,+31
tf_efficientnet_cc_b0_8e.in1k,83.972,16.028,96.067,3.933,24.01,224,0.875,bicubic,+6.068,+2.405,-19
regnety_016.pycls_in1k,83.966,16.034,96.005,3.995,11.20,224,0.875,bicubic,+6.098,+2.287,-20
gmixer_24_224.ra3_in1k,83.966,16.034,95.854,4.146,24.72,224,0.875,bicubic,+5.940,+2.186,-31
hardcorenas_e.miil_green_in1k,83.963,16.037,95.903,4.097,8.07,224,0.875,bilinear,+6.173,+2.203,-16
resnext50_32x4d.tv_in1k,83.951,16.049,95.969,4.031,25.03,224,0.875,bilinear,+6.329,+2.273,-10
resnet50.gluon_in1k,83.940,16.060,96.020,3.980,25.56,224,0.875,bicubic,+6.358,+2.300,-8
densenet161.tv_in1k,83.910,16.090,96.022,3.978,28.68,224,0.875,bicubic,+6.552,+2.380,+2
mobilenetv2_120d.ra_in1k,83.902,16.098,95.903,4.097,5.83,224,0.875,bicubic,+6.594,+2.401,+3
inception_v3.tf_adv_in1k,83.897,16.103,95.939,4.061,23.83,299,0.875,bicubic,+6.305,+2.209,-13
resnet101.tv_in1k,83.863,16.137,95.888,4.112,44.55,224,0.875,bilinear,+6.483,+2.342,-2
tinynet_a.in1k,83.825,16.175,95.817,4.183,6.19,192,0.875,bicubic,+6.177,+2.277,-19
resnet26d.bt_in1k,83.791,16.209,95.960,4.040,16.01,288,0.950,bicubic,+6.383,+2.322,-5
dpn68b.mx_in1k,83.786,16.214,95.986,4.014,12.61,224,0.875,bicubic,+6.268,+2.134,-13
inception_v3.tv_in1k,83.756,16.244,95.886,4.114,23.83,299,0.875,bicubic,+6.322,+2.412,-9
hardcorenas_d.miil_green_in1k,83.756,16.244,95.738,4.262,7.50,224,0.875,bilinear,+6.322,+2.248,-9
xcit_nano_12_p8_224.fb_dist_in1k,83.733,16.267,95.963,4.037,3.05,224,1.000,bicubic,+7.401,+2.865,+39
dla60.in1k,83.716,16.284,95.926,4.074,22.04,224,0.875,bilinear,+6.670,+2.608,+11
resnext26ts.ra2_in1k,83.701,16.299,95.984,4.016,10.30,288,1.000,bicubic,+6.523,+2.520,+1
repvgg_b1g4.rvgg_in1k,83.699,16.301,96.027,3.973,39.97,224,0.875,bilinear,+6.111,+2.191,-22
convmixer_1024_20_ks9_p14.in1k,83.682,16.318,95.884,4.116,24.38,224,0.960,bicubic,+6.746,+2.534,+13
legacy_seresnet50.in1k,83.669,16.331,95.984,4.016,28.09,224,0.875,bilinear,+6.025,+2.226,-28
regnetx_008.tv2_in1k,83.667,16.333,95.975,4.025,7.26,224,0.965,bicubic,+6.361,+2.311,-10
tf_efficientnet_b0.ap_in1k,83.652,16.348,95.785,4.215,5.29,224,0.875,bicubic,+6.562,+2.523,+2
skresnet34.ra_in1k,83.645,16.355,95.928,4.072,22.28,224,0.875,bicubic,+6.735,+2.784,+11
tf_efficientnet_cc_b0_4e.in1k,83.641,16.359,95.740,4.260,13.31,224,0.875,bicubic,+6.339,+2.404,-12
repghostnet_150.in1k,83.630,16.369,95.920,4.080,6.58,224,0.875,bicubic,+6.171,+2.410,-22
seresnet50.a3_in1k,83.624,16.376,95.709,4.292,28.09,224,0.950,bicubic,+6.598,+2.636,+2
densenet121.ra_in1k,83.596,16.404,96.054,3.946,7.98,288,0.950,bicubic,+7.096,+2.686,+21
resmlp_12_224.fb_in1k,83.569,16.431,95.760,4.240,15.35,224,0.875,bicubic,+6.921,+2.582,+11
densenet201.tv_in1k,83.554,16.446,95.809,4.191,20.01,224,0.875,bicubic,+6.268,+2.329,-16
mobilenetv3_large_100.miil_in21k_ft_in1k,83.554,16.446,95.448,4.552,5.48,224,0.875,bilinear,+5.634,+2.534,-51
mixnet_m.ft_in1k,83.532,16.468,95.687,4.313,5.01,224,0.875,bicubic,+6.272,+2.269,-16
legacy_seresnext26_32x4d.in1k,83.522,16.478,95.709,4.292,16.79,224,0.875,bicubic,+6.414,+2.395,-9
gernet_s.idstcv_in1k,83.513,16.487,95.798,4.202,8.17,224,0.875,bilinear,+6.603,+2.482,+2
tf_efficientnet_b0.aa_in1k,83.502,16.498,95.704,4.296,5.29,224,0.875,bicubic,+6.658,+2.486,+2
hrnet_w18.ms_in1k,83.490,16.511,95.911,4.089,21.30,224,0.875,bilinear,+6.738,+2.467,+3
resnet34.bt_in1k,83.468,16.532,95.965,4.035,21.80,288,0.950,bicubic,+6.988,+2.611,+13
selecsls42b.in1k,83.451,16.549,95.736,4.264,32.46,224,0.875,bicubic,+6.281,+2.344,-17
efficientvit_m5.r224_in1k,83.449,16.551,95.813,4.187,12.47,224,0.875,bicubic,+6.391,+2.629,-12
efficientformerv2_s0.snap_dist_in1k,83.402,16.598,95.817,4.183,3.60,224,0.950,bicubic,+7.288,+2.959,+19
hardcorenas_c.miil_green_in1k,83.353,16.647,95.713,4.287,5.52,224,0.875,bilinear,+6.287,+2.551,-15
ghostnetv2_130.in1k,83.342,16.658,95.843,4.157,8.96,224,0.875,bicubic,+6.586,+2.481,-4
tf_efficientnet_lite1.in1k,83.338,16.662,95.647,4.353,5.42,240,0.882,bicubic,+6.694,+2.423,-2
fastvit_t8.apple_in1k,83.270,16.730,95.832,4.168,4.03,256,0.900,bicubic,+7.096,+2.780,+13
dpn68.mx_in1k,83.195,16.805,95.617,4.383,12.61,224,0.875,bicubic,+6.849,+2.609,+9
tf_mixnet_m.in1k,83.189,16.811,95.469,4.531,5.01,224,0.875,bicubic,+6.235,+2.315,-14
regnetx_016.pycls_in1k,83.180,16.820,95.740,4.260,9.19,224,0.875,bicubic,+6.256,+2.325,-13
tf_efficientnet_es.in1k,83.180,16.820,95.580,4.420,5.44,224,0.875,bicubic,+6.582,+2.378,-5
xcit_nano_12_p16_384.fb_dist_in1k,83.178,16.822,95.755,4.245,3.05,384,1.000,bicubic,+7.720,+3.058,+27
mobilenetv2_140.ra_in1k,83.174,16.826,95.687,4.313,6.11,224,0.875,bicubic,+6.658,+2.699,-2
levit_128s.fb_dist_in1k,83.052,16.948,95.533,4.467,7.78,224,0.900,bicubic,+6.526,+2.661,-5
levit_conv_128s.fb_dist_in1k,83.046,16.954,95.536,4.464,7.78,224,0.900,bicubic,+6.526,+2.670,-5
repvgg_a2.rvgg_in1k,82.996,17.004,95.593,4.407,28.21,224,0.875,bilinear,+6.538,+2.591,-2
resnet50.tv_in1k,82.958,17.042,95.469,4.531,25.56,224,0.875,bilinear,+6.830,+2.611,+4
repghostnet_130.in1k,82.922,17.078,95.459,4.541,5.48,224,0.875,bicubic,+6.546,+2.567,-3
resnet26.bt_in1k,82.917,17.083,95.726,4.274,16.00,288,0.950,bicubic,+6.551,+2.546,-3
hardcorenas_b.miil_green_in1k,82.864,17.136,95.395,4.605,5.18,224,0.875,bilinear,+6.316,+2.633,-13
mobileone_s1.apple_in1k,82.853,17.147,95.540,4.460,4.83,224,0.900,bilinear,+7.067,+2.748,+7
mobilevitv2_075.cvnets_in1k,82.796,17.204,95.570,4.430,2.87,256,0.888,bicubic,+7.188,+2.826,+11
densenet169.tv_in1k,82.689,17.311,95.604,4.396,14.15,224,0.875,bicubic,+6.789,+2.576,+4
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,82.687,17.313,95.847,4.153,6.36,384,1.000,bicubic,+6.727,+2.585,+1
regnety_004.tv2_in1k,82.640,17.360,95.499,4.501,4.34,224,0.965,bicubic,+7.046,+2.799,+9
edgenext_x_small.in1k,82.580,17.420,95.459,4.541,2.34,288,1.000,bicubic,+6.892,+2.693,+4
tf_efficientnet_b0.in1k,82.563,17.437,95.418,4.582,5.29,224,0.875,bicubic,+6.033,+2.410,-19
mixnet_s.ft_in1k,82.520,17.480,95.356,4.644,4.13,224,0.875,bicubic,+6.526,+2.086,-4
vit_small_patch32_224.augreg_in21k_ft_in1k,82.516,17.484,95.664,4.336,22.88,224,0.900,bicubic,+6.522,+2.864,-6
regnety_008.pycls_in1k,82.486,17.514,95.489,4.511,6.26,224,0.875,bicubic,+6.184,+2.427,-11
efficientnet_lite0.ra_in1k,82.371,17.629,95.294,4.706,4.65,224,0.875,bicubic,+6.889,+2.774,+6
resnest14d.gluon_in1k,82.356,17.644,95.335,4.665,10.61,224,0.875,bilinear,+6.848,+2.827,+4
hardcorenas_a.miil_green_in1k,82.322,17.678,95.288,4.712,5.26,224,0.875,bilinear,+6.384,+2.780,-7
efficientnet_es_pruned.in1k,82.294,17.706,95.301,4.699,5.44,224,0.875,bicubic,+7.288,+2.857,+15
mobilenetv3_rw.rmsp_in1k,82.264,17.736,95.234,4.766,5.48,224,0.875,bicubic,+6.644,+2.531,-3
semnasnet_100.rmsp_in1k,82.258,17.742,95.226,4.774,3.89,224,0.875,bicubic,+6.808,+2.628,+4
mobilenetv3_large_100.ra_in1k,82.179,17.821,95.192,4.808,5.48,224,0.875,bicubic,+6.413,+2.654,-8
vit_tiny_patch16_224.augreg_in21k_ft_in1k,82.080,17.920,95.476,4.524,5.72,224,0.900,bicubic,+6.618,+2.632,0
mobilenetv2_110d.ra_in1k,82.068,17.932,95.070,4.930,4.52,224,0.875,bicubic,+7.014,+2.886,+8
tf_mixnet_s.in1k,82.038,17.962,95.126,4.874,4.13,224,0.875,bicubic,+6.386,+2.486,-9
repvgg_b0.rvgg_in1k,81.999,18.001,95.104,4.896,15.82,224,0.875,bilinear,+6.855,+2.688,+2
deit_tiny_distilled_patch16_224.fb_in1k,81.993,18.007,95.134,4.866,5.91,224,0.900,bicubic,+7.489,+3.244,+18
hrnet_w18_small_v2.ms_in1k,81.976,18.024,95.160,4.840,15.60,224,0.875,bilinear,+6.866,+2.744,+2
mixer_b16_224.goog_in21k_ft_in1k,81.976,18.024,94.451,5.549,59.88,224,0.875,bicubic,+5.374,+2.227,-39
tf_efficientnet_lite0.in1k,81.950,18.050,95.160,4.840,4.65,224,0.875,bicubic,+7.118,+2.990,+7
ghostnetv2_100.in1k,81.905,18.095,95.111,4.889,6.16,224,0.875,bicubic,+6.739,+2.757,-4
tinynet_b.in1k,81.880,18.120,94.876,5.124,3.73,188,0.875,bicubic,+6.902,+2.690,+3
tf_mobilenetv3_large_100.in1k,81.848,18.152,95.059,4.941,5.48,224,0.875,bilinear,+6.332,+2.466,-13
pit_ti_distilled_224.in1k,81.779,18.221,95.098,4.902,5.10,224,0.900,bicubic,+7.523,+3.146,+14
repghostnet_111.in1k,81.743,18.257,94.842,5.158,4.54,224,0.875,bicubic,+6.687,+2.650,-4
densenet121.tv_in1k,81.732,18.268,95.036,4.964,7.98,224,0.875,bicubic,+6.968,+2.882,+3
regnety_006.pycls_in1k,81.717,18.283,95.115,4.885,6.06,224,0.875,bicubic,+6.449,+2.589,-11
regnetx_004_tv.tv2_in1k,81.694,18.306,95.057,4.943,5.50,224,0.965,bicubic,+7.094,+2.887,+5
resnet18d.ra2_in1k,81.679,18.321,95.079,4.921,11.71,288,0.950,bicubic,+7.885,+3.241,+19
dla34.in1k,81.664,18.336,94.867,5.133,15.74,224,0.875,bilinear,+7.024,+2.801,+1
xcit_nano_12_p8_224.fb_in1k,81.645,18.355,95.271,4.729,3.05,224,1.000,bicubic,+7.735,+3.103,+15
crossvit_9_240.in1k,81.613,18.387,94.981,5.019,8.55,240,0.875,bicubic,+7.653,+3.019,+11
fbnetc_100.rmsp_in1k,81.559,18.441,94.968,5.032,5.57,224,0.875,bilinear,+6.430,+2.580,-14
mobilevit_xs.cvnets_in1k,81.553,18.447,95.023,4.977,2.32,256,0.900,bicubic,+6.919,+2.675,-2
legacy_seresnet34.in1k,81.538,18.462,94.897,5.103,21.96,224,0.875,bilinear,+6.736,+2.771,-7
efficientvit_m4.r224_in1k,81.498,18.503,95.002,4.998,8.80,224,0.875,bicubic,+7.130,+3.022,+1
regnetx_008.pycls_in1k,81.487,18.513,95.053,4.947,7.26,224,0.875,bicubic,+6.459,+2.715,-14
resnet34.gluon_in1k,81.481,18.520,94.799,5.201,21.80,224,0.875,bicubic,+6.901,+2.817,-4
mnasnet_100.rmsp_in1k,81.446,18.554,94.914,5.086,4.38,224,0.875,bicubic,+6.794,+2.792,-9
vgg19_bn.tv_in1k,81.438,18.562,94.771,5.229,143.68,224,0.875,bilinear,+7.222,+2.927,-1
repvgg_a1.rvgg_in1k,81.256,18.744,94.714,5.286,14.09,224,0.875,bilinear,+6.794,+2.858,-5
vit_base_patch32_224.augreg_in1k,81.143,18.857,94.427,5.572,88.22,224,0.900,bicubic,+6.249,+2.649,-16
convit_tiny.fb_in1k,81.126,18.874,95.036,4.964,5.71,224,0.875,bicubic,+8.014,+3.324,+14
crossvit_tiny_240.in1k,81.098,18.902,94.983,5.017,7.01,240,0.875,bicubic,+7.758,+3.075,+9
resnet18.a1_in1k,81.036,18.964,94.357,5.643,11.69,288,1.000,bicubic,+7.878,+3.331,+11
repghostnet_100.in1k,80.930,19.070,94.543,5.457,4.07,224,0.875,bicubic,+6.724,+3.001,-6
spnasnet_100.rmsp_in1k,80.883,19.117,94.526,5.474,4.42,224,0.875,bilinear,+6.789,+2.706,-6
resnet34.a3_in1k,80.814,19.186,94.353,5.647,21.80,224,0.950,bicubic,+7.844,+3.247,+12
efficientvit_m3.r224_in1k,80.693,19.307,94.556,5.444,6.90,224,0.875,bicubic,+7.319,+3.208,+2
ghostnet_100.in1k,80.678,19.322,94.359,5.641,5.18,224,0.875,bicubic,+6.720,+2.827,-6
regnety_004.pycls_in1k,80.656,19.344,94.682,5.318,4.34,224,0.875,bicubic,+6.630,+2.934,-9
skresnet18.ra_in1k,80.648,19.352,94.378,5.622,11.96,224,0.875,bicubic,+7.614,+3.206,+6
regnetx_006.pycls_in1k,80.639,19.361,94.530,5.470,6.20,224,0.875,bicubic,+6.771,+2.852,-6
pit_ti_224.in1k,80.599,19.401,94.620,5.380,4.85,224,0.900,bicubic,+7.689,+3.216,+8
resnet18.fb_swsl_ig1b_ft_in1k,80.577,19.423,94.741,5.259,11.69,224,0.875,bilinear,+7.289,+3.011,0
vgg16_bn.tv_in1k,80.571,19.429,94.600,5.400,138.37,224,0.875,bilinear,+7.201,+3.086,-4
semnasnet_075.rmsp_in1k,80.481,19.519,94.319,5.681,2.91,224,0.875,bicubic,+7.477,+3.179,+2
hrnet_w18_small.gluon_in1k,80.406,19.593,94.045,5.955,13.19,224,0.875,bicubic,+6.486,+2.851,-13
resnet34.tv_in1k,80.381,19.619,94.430,5.570,21.80,224,0.875,bilinear,+7.075,+3.010,-5
resnet18.a2_in1k,80.310,19.690,94.099,5.901,11.69,288,1.000,bicubic,+7.938,+3.503,+7
mobilenetv2_100.ra_in1k,80.253,19.747,94.188,5.812,3.50,224,0.875,bicubic,+7.285,+3.172,0
xcit_nano_12_p16_224.fb_dist_in1k,80.231,19.769,94.351,5.649,3.05,224,1.000,bicubic,+7.921,+3.491,+7
vit_base_patch32_224.sam_in1k,80.214,19.786,93.823,6.177,88.22,224,0.900,bicubic,+6.520,+2.809,-14
resnet18.fb_ssl_yfcc100m_ft_in1k,80.095,19.905,94.592,5.408,11.69,224,0.875,bilinear,+7.497,+3.176,-1
tf_mobilenetv3_large_075.in1k,80.073,19.927,94.180,5.820,3.99,224,0.875,bilinear,+6.643,+2.828,-15
deit_tiny_patch16_224.fb_in1k,80.011,19.988,94.449,5.551,5.72,224,0.900,bicubic,+7.841,+3.333,+7
hrnet_w18_small.ms_in1k,79.550,20.450,93.906,6.093,13.19,224,0.875,bilinear,+7.214,+3.226,+1
repvgg_a0.rvgg_in1k,79.508,20.492,93.778,6.222,9.11,224,0.875,bilinear,+7.100,+3.286,-4
vgg19.tv_in1k,79.484,20.516,93.868,6.132,143.67,224,0.875,bilinear,+7.106,+2.994,-3
regnetx_004.pycls_in1k,79.420,20.580,93.847,6.153,5.16,224,0.875,bicubic,+7.018,+3.021,-5
tf_mobilenetv3_large_minimal_100.in1k,79.232,20.768,93.702,6.298,3.92,224,0.875,bilinear,+6.968,+3.062,-1
edgenext_xx_small.in1k,79.175,20.825,93.819,6.181,1.33,288,1.000,bicubic,+7.297,+3.267,+4
legacy_seresnet18.in1k,79.164,20.836,93.774,6.226,11.78,224,0.875,bicubic,+7.404,+3.442,+5
resnet14t.c3_in1k,79.143,20.857,93.565,6.435,10.08,224,0.950,bicubic,+6.889,+3.259,-3
repghostnet_080.in1k,79.089,20.911,93.716,6.284,3.28,224,0.875,bicubic,+6.877,+3.233,-3
vgg16.tv_in1k,79.038,20.962,93.644,6.356,138.36,224,0.875,bilinear,+7.446,+3.260,+3
vgg13_bn.tv_in1k,78.995,21.005,93.661,6.339,133.05,224,0.875,bilinear,+7.407,+3.283,+3
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,78.991,21.009,93.898,6.102,6.34,224,0.900,bicubic,+7.193,+3.074,-1
lcnet_100.ra2_in1k,78.899,21.101,93.550,6.450,2.95,224,0.875,bicubic,+6.797,+3.196,-5
pvt_v2_b0.in1k,78.756,21.244,93.836,6.164,3.67,224,0.900,bicubic,+8.096,+3.640,+6
efficientvit_m2.r224_in1k,78.632,21.368,93.552,6.448,4.19,224,0.875,bicubic,+7.818,+3.410,+4
mobileone_s0.apple_in1k,78.496,21.504,93.322,6.678,5.29,224,0.875,bilinear,+7.094,+3.480,-1
tinynet_c.in1k,78.449,21.551,93.125,6.875,2.46,184,0.875,bicubic,+7.207,+3.393,0
efficientvit_b0.r224_in1k,78.425,21.575,92.801,7.199,3.41,224,0.950,bicubic,+7.027,+3.373,-2
resnet18.gluon_in1k,78.376,21.624,93.129,6.871,11.69,224,0.875,bicubic,+7.542,+3.373,-1
mobilevitv2_050.cvnets_in1k,78.124,21.876,93.565,6.435,1.37,256,0.888,bicubic,+7.976,+3.647,+3
vgg11_bn.tv_in1k,77.956,22.044,93.232,6.768,132.87,224,0.875,bilinear,+7.573,+3.424,0
xcit_nano_12_p16_224.fb_in1k,77.904,22.096,93.437,6.563,3.05,224,1.000,bicubic,+7.942,+3.675,+2
regnety_002.pycls_in1k,77.422,22.578,92.905,7.095,3.16,224,0.875,bicubic,+7.142,+3.375,-1
resnet18.tv_in1k,77.285,22.715,92.756,7.244,11.69,224,0.875,bilinear,+7.525,+3.686,+2
mixer_l16_224.goog_in21k_ft_in1k,77.279,22.721,90.584,9.416,208.20,224,0.875,bicubic,+5.225,+2.910,-16
vgg13.tv_in1k,77.227,22.773,92.692,7.308,133.05,224,0.875,bilinear,+7.295,+3.442,-1
mobilevit_xxs.cvnets_in1k,76.604,23.396,92.681,7.319,1.27,256,0.900,bicubic,+7.686,+3.735,+1
resnet18.a3_in1k,76.457,23.543,92.226,7.774,11.69,224,0.950,bicubic,+8.205,+4.054,+6
efficientvit_m1.r224_in1k,76.388,23.612,92.542,7.458,2.98,224,0.875,bicubic,+8.082,+3.872,+4
vgg11.tv_in1k,76.384,23.616,92.156,7.844,132.86,224,0.875,bilinear,+7.362,+3.532,-3
repghostnet_058.in1k,76.224,23.776,92.117,7.883,2.55,224,0.875,bicubic,+7.310,+3.697,-2
resnet10t.c3_in1k,76.171,23.829,92.226,7.774,5.44,224,0.950,bicubic,+7.806,+4.190,0
regnetx_002.pycls_in1k,76.128,23.872,92.198,7.801,2.68,224,0.875,bicubic,+7.376,+3.656,-2
lcnet_075.ra2_in1k,76.036,23.964,92.060,7.940,2.36,224,0.875,bicubic,+7.254,+3.700,-4
dla60x_c.in1k,75.637,24.363,92.171,7.829,1.32,224,0.875,bilinear,+7.725,+3.739,+1
mobilenetv3_small_100.lamb_in1k,74.921,25.078,91.487,8.512,2.54,224,0.875,bicubic,+7.263,+3.852,+1
tf_mobilenetv3_small_100.in1k,74.725,25.275,91.266,8.735,2.54,224,0.875,bilinear,+6.803,+3.594,-2
tinynet_d.in1k,74.292,25.708,90.917,9.083,2.34,152,0.875,bicubic,+7.320,+3.851,0
repghostnet_050.in1k,74.236,25.764,90.808,9.191,2.31,224,0.875,bicubic,+7.270,+3.888,0
mnasnet_small.lamb_in1k,73.801,26.199,90.732,9.268,2.03,224,0.875,bicubic,+7.605,+4.228,0
dla46x_c.in1k,73.655,26.345,91.097,8.903,1.07,224,0.875,bilinear,+7.663,+4.123,0
mobilenetv2_050.lamb_in1k,73.470,26.530,90.317,9.682,1.97,224,0.875,bicubic,+7.522,+4.233,0
tf_mobilenetv3_small_075.in1k,72.814,27.186,90.051,9.949,2.04,224,0.875,bilinear,+7.088,+3.919,0
dla46_c.in1k,72.626,27.374,90.499,9.501,1.30,224,0.875,bilinear,+7.754,+4.201,+1
mobilenetv3_small_075.lamb_in1k,72.317,27.683,89.666,10.334,2.04,224,0.875,bicubic,+7.081,+4.220,-1
efficientvit_m0.r224_in1k,71.091,28.909,89.589,10.411,2.35,224,0.875,bicubic,+7.821,+4.413,0
lcnet_050.ra2_in1k,70.402,29.598,88.825,11.175,1.88,224,0.875,bicubic,+7.264,+4.443,0
tf_mobilenetv3_small_minimal_100.in1k,70.096,29.904,88.516,11.485,2.04,224,0.875,bilinear,+7.202,+4.278,0
tinynet_e.in1k,66.810,33.190,86.280,13.720,2.04,106,0.875,bicubic,+6.944,+4.518,0
mobilenetv3_small_050.lamb_in1k,64.697,35.303,84.858,15.142,1.59,224,0.875,bicubic,+6.781,+4.678,0
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-r.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
convnext_xxlarge.clip_laion2b_soup_ft_in1k,90.623,9.377,97.913,2.087,846.47,256,1.000,bicubic,-7.127,-1.897,+18
eva_giant_patch14_336.clip_ft_in1k,90.550,9.450,97.230,2.770,"1,013.01",336,1.000,bicubic,-7.310,-2.650,+6
eva02_large_patch14_448.mim_m38m_ft_in1k,90.457,9.543,97.267,2.733,305.08,448,1.000,bicubic,-7.373,-2.553,+6
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.293,9.707,97.170,2.830,305.08,448,1.000,bicubic,-7.737,-2.720,-2
eva_giant_patch14_224.clip_ft_in1k,89.843,10.157,97.023,2.977,"1,012.56",224,0.900,bicubic,-7.727,-2.687,+29
eva_giant_patch14_336.m30m_ft_in22k_in1k,88.590,11.410,95.920,4.080,"1,013.01",336,1.000,bicubic,-9.400,-3.980,-2
eva_giant_patch14_560.m30m_ft_in22k_in1k,88.397,11.603,95.610,4.390,"1,014.45",560,1.000,bicubic,-9.603,-4.250,-4
regnety_1280.swag_ft_in1k,88.257,11.743,96.477,3.523,644.81,384,1.000,bicubic,-9.523,-3.383,+6
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,87.980,12.020,95.610,4.390,305.08,448,1.000,bicubic,-10.170,-4.270,-8
eva02_large_patch14_448.mim_in22k_ft_in1k,87.587,12.413,95.730,4.270,305.08,448,1.000,bicubic,-10.273,-4.060,-3
regnety_1280.swag_lc_in1k,86.943,13.057,95.737,4.263,644.81,224,0.965,bicubic,-10.447,-4.003,+36
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,84.437,15.563,94.297,5.703,200.13,384,1.000,bicubic,-12.953,-5.433,+34
vit_large_patch14_clip_336.openai_ft_in12k_in1k,83.923,16.077,93.873,6.127,304.53,336,1.000,bicubic,-13.677,-5.857,+18
vit_large_patch14_clip_336.laion2b_ft_in1k,83.610,16.390,93.510,6.490,304.53,336,1.000,bicubic,-13.620,-6.210,+63
eva_large_patch14_336.in22k_ft_in1k,83.520,16.480,93.103,6.897,304.53,336,1.000,bicubic,-14.290,-6.757,-4
vit_huge_patch14_clip_224.laion2b_ft_in1k,83.263,16.737,93.133,6.867,632.05,224,1.000,bicubic,-13.837,-6.557,+80
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,83.047,16.953,92.837,7.163,632.46,336,1.000,bicubic,-14.563,-6.943,+12
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,82.797,17.203,92.613,7.387,632.05,224,1.000,bicubic,-14.563,-7.187,+36
vit_large_patch14_clip_224.openai_ft_in1k,82.323,17.677,92.907,7.093,304.20,224,1.000,bicubic,-15.117,-6.773,+24
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,82.217,17.783,92.433,7.567,200.13,384,1.000,bicubic,-15.253,-7.327,+19
vit_large_patch14_clip_224.laion2b_ft_in1k,81.700,18.300,92.263,7.737,304.20,224,1.000,bicubic,-15.320,-7.407,+90
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,81.347,18.653,91.963,8.037,200.13,320,1.000,bicubic,-15.913,-7.747,+48
regnety_320.swag_lc_in1k,81.317,18.683,93.153,6.847,145.05,224,0.965,bicubic,-15.463,-6.467,+123
eva_large_patch14_196.in22k_ft_in1k,81.300,18.700,91.533,8.467,304.14,196,1.000,bicubic,-16.220,-8.257,+12
regnety_320.swag_ft_in1k,81.203,18.797,92.707,7.293,145.05,384,1.000,bicubic,-16.177,-7.013,+23
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,80.870,19.130,91.917,8.083,200.13,256,1.000,bicubic,-16.260,-7.863,+65
eva_large_patch14_336.in22k_ft_in22k_in1k,80.087,19.913,89.357,10.643,304.53,336,1.000,bicubic,-17.773,-10.443,-21
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,79.467,20.533,89.197,10.803,468.53,224,0.875,bilinear,-17.303,-10.423,+124
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,78.830,21.170,88.473,11.527,194.03,224,0.875,bilinear,-17.600,-11.157,+183
vit_large_patch14_clip_224.openai_ft_in12k_in1k,78.677,21.323,88.920,11.080,304.20,224,1.000,bicubic,-18.933,-10.810,0
eva_large_patch14_196.in22k_ft_in22k_in1k,78.500,21.500,88.330,11.670,304.14,196,1.000,bicubic,-19.110,-11.480,-3
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.433,21.567,88.500,11.500,304.53,336,1.000,bicubic,-19.027,-11.280,+8
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.267,21.733,88.673,11.327,304.20,224,1.000,bicubic,-19.123,-11.057,+12
regnety_160.swag_lc_in1k,78.187,21.813,91.663,8.337,83.59,224,0.965,bicubic,-18.263,-7.947,+172
regnety_160.swag_ft_in1k,77.683,22.317,90.737,9.263,83.59,384,1.000,bicubic,-19.487,-8.903,+50
eva02_base_patch14_448.mim_in22k_ft_in1k,77.610,22.390,89.307,10.693,87.12,448,1.000,bicubic,-20.110,-10.453,-14
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,77.497,22.503,88.490,11.510,87.12,448,1.000,bicubic,-20.113,-11.330,-10
tf_efficientnet_l2.ns_jft_in1k_475,76.490,23.510,88.640,11.360,480.31,475,0.936,bicubic,-21.260,-11.150,-20
beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.353,23.647,87.090,12.910,304.43,224,0.950,bicubic,-21.397,-12.730,-19
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,76.300,23.700,87.743,12.257,194.03,224,0.875,bilinear,-19.970,-11.757,+206
convnextv2_huge.fcmae_ft_in22k_in1k_384,75.953,24.047,86.637,13.363,660.29,384,1.000,bicubic,-21.917,-13.273,-36
convnextv2_huge.fcmae_ft_in22k_in1k_512,75.823,24.177,86.943,13.057,660.29,512,1.000,bicubic,-21.987,-12.847,-32
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,75.797,24.203,86.197,13.803,88.79,224,0.875,bilinear,-20.143,-13.183,+277
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,75.600,24.400,86.943,13.057,88.79,224,0.875,bilinear,-20.650,-12.597,+203
convnext_base.clip_laiona_augreg_ft_in1k_384,75.210,24.790,88.580,11.420,88.59,384,1.000,bicubic,-21.650,-11.110,+88
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,75.173,24.827,88.460,11.540,88.59,384,1.000,bicubic,-21.867,-11.210,+58
tf_efficientnet_l2.ns_jft_in1k,74.657,25.343,87.557,12.443,480.31,800,0.960,bicubic,-23.123,-12.263,-34
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,73.733,26.267,87.343,12.657,88.59,256,1.000,bicubic,-23.057,-12.337,+97
convnext_base.clip_laion2b_augreg_ft_in1k,73.400,26.600,87.147,12.853,88.59,256,1.000,bicubic,-23.160,-12.503,+134
beit_large_patch16_384.in22k_ft_in22k_in1k,73.277,26.723,85.030,14.970,305.00,384,1.000,bicubic,-24.533,-14.810,-38
beit_large_patch16_512.in22k_ft_in22k_in1k,73.160,26.840,85.090,14.910,305.67,512,1.000,bicubic,-24.620,-14.800,-36
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,72.657,27.343,85.160,14.840,44.18,224,0.875,bilinear,-23.383,-14.250,+242
maxvit_xlarge_tf_512.in21k_ft_in1k,71.873,28.127,82.927,17.073,475.77,512,1.000,bicubic,-25.887,-16.893,-36
maxvit_xlarge_tf_384.in21k_ft_in1k,71.703,28.297,82.713,17.287,475.32,384,1.000,bicubic,-26.037,-17.137,-33
beit_large_patch16_224.in22k_ft_in22k_in1k,71.040,28.960,83.430,16.570,304.43,224,0.900,bicubic,-26.440,-16.260,-17
deit3_huge_patch14_224.fb_in22k_ft_in1k,70.817,29.183,82.200,17.800,632.13,224,1.000,bicubic,-26.433,-17.520,+17
vit_base_patch16_clip_384.laion2b_ft_in1k,70.777,29.223,83.820,16.180,86.86,384,1.000,bicubic,-26.123,-15.850,+69
caformer_b36.sail_in22k_ft_in1k_384,70.750,29.250,82.650,17.350,98.75,384,1.000,bicubic,-26.910,-17.210,-34
deit3_large_patch16_384.fb_in22k_ft_in1k,70.580,29.420,82.437,17.563,304.76,384,1.000,bicubic,-27.000,-17.273,-26
beitv2_large_patch16_224.in1k_ft_in1k,70.403,29.597,83.373,16.627,304.43,224,0.950,bicubic,-26.907,-16.387,0
maxvit_base_tf_512.in21k_ft_in1k,70.383,29.617,81.600,18.400,119.88,512,1.000,bicubic,-27.377,-18.260,-45
maxvit_large_tf_512.in21k_ft_in1k,70.380,29.620,81.650,18.350,212.33,512,1.000,bicubic,-27.290,-18.080,-39
maxvit_large_tf_384.in21k_ft_in1k,70.010,29.990,81.037,18.963,212.03,384,1.000,bicubic,-27.650,-18.783,-38
deit3_large_patch16_224.fb_in22k_ft_in1k,69.710,30.290,81.197,18.803,304.37,224,1.000,bicubic,-27.600,-18.483,-3
maxvit_base_tf_384.in21k_ft_in1k,69.557,30.443,80.730,19.270,119.65,384,1.000,bicubic,-28.003,-19.030,-30
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,69.130,30.870,80.060,19.940,116.14,384,1.000,bicubic,-28.210,-19.630,-11
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,68.970,31.030,82.803,17.197,25.03,224,0.875,bilinear,-26.660,-16.607,+317
vit_base_patch16_clip_224.laion2b_ft_in1k,68.780,31.220,82.503,17.497,86.57,224,1.000,bicubic,-27.540,-17.017,+165
convnextv2_large.fcmae_ft_in22k_in1k_384,68.653,31.347,81.070,18.930,197.96,384,1.000,bicubic,-28.977,-18.730,-43
caformer_b36.sail_in22k_ft_in1k,68.603,31.397,80.857,19.143,98.75,224,1.000,bicubic,-28.757,-18.973,-17
resnet50.fb_swsl_ig1b_ft_in1k,68.287,31.713,83.313,16.687,25.56,224,0.875,bilinear,-26.913,-16.077,+404
convnext_xlarge.fb_in22k_ft_in1k_384,68.157,31.843,80.453,19.547,350.20,384,1.000,bicubic,-29.433,-19.317,-40
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,68.077,31.923,80.740,19.260,116.09,384,1.000,bicubic,-29.373,-19.020,-31
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,67.667,32.333,80.100,19.900,196.74,384,1.000,bicubic,-29.613,-19.680,-7
tf_efficientnet_b7.ns_jft_in1k,67.507,32.493,81.383,18.617,66.35,600,0.949,bicubic,-29.683,-18.317,+8
vit_base_patch16_clip_384.openai_ft_in1k,67.350,32.650,81.687,18.313,86.86,384,1.000,bicubic,-29.460,-18.023,+65
vit_large_patch16_384.augreg_in21k_ft_in1k,67.057,32.943,78.697,21.303,304.72,384,1.000,bicubic,-30.353,-21.083,-33
convnextv2_base.fcmae_ft_in22k_in1k_384,67.030,32.970,79.800,20.200,88.72,384,1.000,bicubic,-30.350,-19.960,-29
convformer_b36.sail_in22k_ft_in1k_384,66.823,33.177,79.443,20.557,99.88,384,1.000,bicubic,-30.667,-20.317,-42
convnext_large.fb_in22k_ft_in1k_384,66.673,33.327,79.797,20.203,197.77,384,1.000,bicubic,-30.627,-19.963,-18
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,66.580,33.420,78.413,21.587,73.88,384,1.000,bicubic,-30.790,-21.287,-30
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,66.530,33.470,78.027,21.973,116.14,224,0.950,bicubic,-30.580,-21.573,+13
swin_large_patch4_window12_384.ms_in22k_ft_in1k,66.287,33.713,79.747,20.253,196.74,384,1.000,bicubic,-30.883,-19.993,+4
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,66.147,33.853,78.883,21.117,86.86,384,1.000,bicubic,-31.073,-20.817,-5
vit_base_patch16_clip_224.openai_ft_in1k,66.027,33.973,80.980,19.020,86.57,224,0.900,bicubic,-30.283,-18.520,+151
beitv2_base_patch16_224.in1k_ft_in22k_in1k,65.757,34.243,78.893,21.107,86.53,224,0.900,bicubic,-31.153,-20.837,+38
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,65.733,34.267,79.317,20.683,87.92,384,1.000,bicubic,-31.537,-20.473,-19
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,65.640,34.360,78.480,21.520,196.74,256,0.900,bicubic,-31.600,-21.230,-13
tf_efficientnet_b6.ns_jft_in1k,65.583,34.417,79.557,20.443,43.04,528,0.942,bicubic,-31.437,-20.153,+20
caformer_m36.sail_in22k_ft_in1k_384,65.580,34.420,78.703,21.297,56.20,384,1.000,bicubic,-31.790,-21.087,-40
convformer_b36.sail_in22k_ft_in1k,65.513,34.487,78.140,21.860,99.88,224,1.000,bicubic,-31.747,-21.610,-22
convnext_xlarge.fb_in22k_ft_in1k,65.373,34.627,78.350,21.650,350.20,288,1.000,bicubic,-32.077,-21.470,-51
vit_base_patch16_clip_384.openai_ft_in12k_in1k,65.353,34.647,78.957,21.043,86.86,384,0.950,bicubic,-31.767,-20.613,-1
convnext_base.fb_in22k_ft_in1k_384,64.903,35.097,78.387,21.613,88.59,384,1.000,bicubic,-32.357,-21.353,-23
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,64.767,35.233,77.770,22.230,86.57,224,0.950,bicubic,-31.853,-21.790,+79
convnextv2_large.fcmae_ft_in22k_in1k,64.710,35.290,78.157,21.843,197.96,288,1.000,bicubic,-32.600,-21.583,-37
vit_large_patch16_224.augreg_in21k_ft_in1k,64.360,35.640,76.180,23.820,304.33,224,0.900,bicubic,-32.340,-23.390,+64
convnext_large.fb_in22k_ft_in1k,64.263,35.737,77.773,22.227,197.77,288,1.000,bicubic,-32.957,-21.957,-20
vit_large_r50_s32_384.augreg_in21k_ft_in1k,64.103,35.897,75.847,24.153,329.09,384,1.000,bicubic,-32.847,-23.783,+18
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,64.010,35.990,77.513,22.487,116.09,224,0.950,bicubic,-33.080,-22.167,-3
swin_large_patch4_window7_224.ms_in22k_ft_in1k,63.887,36.113,78.187,21.813,196.53,224,0.900,bicubic,-33.053,-21.483,+20
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,63.653,36.347,76.623,23.377,149.39,384,1.000,bicubic,-33.637,-23.157,-37
beit_base_patch16_384.in22k_ft_in22k_in1k,63.623,36.377,78.103,21.897,86.74,384,1.000,bicubic,-33.697,-21.617,-46
caformer_m36.sail_in22k_ft_in1k,63.493,36.507,76.920,23.080,56.20,224,1.000,bicubic,-33.527,-22.810,+4
swin_base_patch4_window12_384.ms_in22k_ft_in1k,63.483,36.517,78.050,21.950,87.90,384,1.000,bicubic,-33.647,-21.670,-15
convnextv2_base.fcmae_ft_in22k_in1k,63.307,36.693,77.163,22.837,88.72,288,1.000,bicubic,-33.893,-22.597,-25
caformer_s36.sail_in22k_ft_in1k_384,63.263,36.737,77.460,22.540,39.30,384,1.000,bicubic,-34.027,-22.290,-41
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,63.183,36.817,77.093,22.907,87.92,256,0.900,bicubic,-33.867,-22.497,-6
tf_efficientnet_b5.ns_jft_in1k,63.053,36.947,77.787,22.213,30.39,456,0.934,bicubic,-33.817,-21.853,+23
vit_base_patch16_clip_224.openai_ft_in12k_in1k,62.943,37.057,76.597,23.403,86.57,224,0.950,bicubic,-33.567,-22.953,+87
deit3_base_patch16_384.fb_in22k_ft_in1k,62.637,37.363,75.550,24.450,86.88,384,1.000,bicubic,-34.603,-24.190,-35
convnext_base.fb_in22k_ft_in1k,62.537,37.463,76.550,23.450,88.59,288,1.000,bicubic,-34.663,-23.210,-32
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,62.417,37.583,75.130,24.870,73.88,224,0.950,bicubic,-34.763,-24.520,-29
vit_base_patch8_224.augreg2_in21k_ft_in1k,62.400,37.600,76.600,23.400,86.58,224,0.900,bicubic,-34.550,-23.010,+4
tf_efficientnetv2_l.in21k_ft_in1k,62.363,37.637,76.757,23.243,118.52,480,1.000,bicubic,-34.957,-22.883,-57
vit_base_patch8_224.augreg_in21k_ft_in1k,62.177,37.823,75.627,24.373,86.58,224,0.900,bicubic,-34.913,-23.983,-18
convformer_m36.sail_in22k_ft_in1k_384,62.103,37.897,75.540,24.460,57.05,384,1.000,bicubic,-35.267,-24.140,-65
tf_efficientnetv2_xl.in21k_ft_in1k,62.083,37.917,75.653,24.347,208.12,512,1.000,bicubic,-35.247,-23.947,-62
hrnet_w48_ssld.paddle_in1k,61.923,38.077,75.163,24.837,77.47,288,1.000,bilinear,-34.617,-24.477,+70
deit3_base_patch16_224.fb_in22k_ft_in1k,61.803,38.197,74.717,25.283,86.59,224,1.000,bicubic,-35.057,-24.903,+14
beitv2_base_patch16_224.in1k_ft_in1k,61.427,38.573,75.930,24.070,86.53,224,0.900,bicubic,-35.323,-23.670,+34
coatnet_2_rw_224.sw_in12k_ft_in1k,61.277,38.723,73.967,26.033,73.87,224,0.950,bicubic,-35.713,-25.693,-8
convformer_m36.sail_in22k_ft_in1k,61.257,38.743,74.620,25.380,57.05,224,1.000,bicubic,-35.813,-25.130,-23
tf_efficientnet_b4.ns_jft_in1k,61.233,38.767,76.160,23.840,19.34,380,0.922,bicubic,-35.477,-23.480,+35
convnextv2_huge.fcmae_ft_in1k,61.197,38.803,74.500,25.500,660.29,288,1.000,bicubic,-36.053,-25.220,-53
maxvit_base_tf_512.in1k,61.103,38.897,74.050,25.950,119.88,512,1.000,bicubic,-36.067,-25.630,-38
caformer_s36.sail_in22k_ft_in1k,60.767,39.233,75.167,24.833,39.30,224,1.000,bicubic,-36.053,-24.523,+12
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,60.357,39.643,73.797,26.203,88.30,384,1.000,bicubic,-36.253,-25.683,+47
beit_base_patch16_224.in22k_ft_in22k_in1k,60.317,39.683,75.593,24.407,86.53,224,0.900,bicubic,-36.343,-24.067,+40
tf_efficientnetv2_m.in21k_ft_in1k,60.267,39.733,75.070,24.930,54.14,480,1.000,bicubic,-36.733,-24.560,-18
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,60.230,39.770,73.550,26.450,88.34,448,1.000,bicubic,-36.330,-25.970,+54
vit_base_patch16_384.augreg_in21k_ft_in1k,60.183,39.817,73.843,26.157,86.86,384,1.000,bicubic,-36.837,-25.867,-22
convformer_s36.sail_in22k_ft_in1k_384,60.070,39.930,74.127,25.873,40.01,384,1.000,bicubic,-36.990,-25.583,-32
convnext_small.fb_in22k_ft_in1k_384,59.923,40.077,74.487,25.513,50.22,384,1.000,bicubic,-37.187,-25.153,-40
maxvit_large_tf_512.in1k,59.887,40.113,72.847,27.153,212.33,512,1.000,bicubic,-37.163,-26.813,-32
tiny_vit_21m_512.dist_in22k_ft_in1k,59.580,40.420,74.757,25.243,21.27,512,1.000,bicubic,-37.320,-24.933,-11
swin_base_patch4_window7_224.ms_in22k_ft_in1k,59.527,40.473,74.247,25.753,87.77,224,0.900,bicubic,-37.153,-25.423,+30
vit_base_patch32_clip_224.laion2b_ft_in1k,59.163,40.837,73.883,26.117,88.22,224,0.900,bicubic,-35.587,-25.187,+436
maxvit_base_tf_384.in1k,59.073,40.927,71.687,28.313,119.65,384,1.000,bicubic,-38.047,-27.953,-46
vit_base_patch16_224.augreg2_in21k_ft_in1k,59.060,40.940,73.603,26.397,86.57,224,0.900,bicubic,-37.450,-25.957,+56
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,59.030,40.970,72.837,27.163,93.59,320,1.000,bicubic,-38.260,-26.883,-77
tiny_vit_21m_384.dist_in22k_ft_in1k,59.017,40.983,74.100,25.900,21.23,384,1.000,bicubic,-37.933,-25.610,-22
volo_d5_512.sail_in1k,58.927,41.073,73.210,26.790,296.09,512,1.150,bicubic,-38.373,-26.550,-80
convformer_s36.sail_in22k_ft_in1k,58.913,41.087,72.943,27.057,40.01,224,1.000,bicubic,-37.587,-26.517,+54
convnext_small.in12k_ft_in1k_384,58.807,41.193,72.797,27.203,50.22,384,1.000,bicubic,-38.183,-26.853,-32
volo_d5_448.sail_in1k,58.800,41.200,73.063,26.937,295.91,448,1.150,bicubic,-38.440,-26.607,-72
vit_large_r50_s32_224.augreg_in21k_ft_in1k,58.667,41.333,71.733,28.267,328.99,224,0.900,bicubic,-37.513,-27.777,+117
vit_base_patch32_clip_384.openai_ft_in12k_in1k,58.587,41.413,73.150,26.850,88.30,384,0.950,bicubic,-37.833,-26.350,+66
maxvit_large_tf_384.in1k,58.427,41.573,71.177,28.823,212.03,384,1.000,bicubic,-38.503,-28.393,-26
eva02_small_patch14_336.mim_in22k_ft_in1k,58.363,41.637,72.877,27.123,22.13,336,1.000,bicubic,-38.327,-26.733,+15
deit3_large_patch16_384.fb_in1k,58.357,41.643,72.950,27.050,304.76,384,1.000,bicubic,-38.493,-26.670,-16
deit3_huge_patch14_224.fb_in1k,58.137,41.863,72.143,27.857,632.13,224,0.900,bicubic,-38.443,-27.377,+27
convnextv2_large.fcmae_ft_in1k,58.103,41.897,72.627,27.373,197.96,288,1.000,bicubic,-38.727,-27.133,-16
tf_efficientnet_b8.ap_in1k,57.837,42.163,72.960,27.040,87.41,672,0.954,bicubic,-38.713,-26.600,+34
convnext_small.fb_in22k_ft_in1k,57.743,42.257,72.773,27.227,50.22,288,1.000,bicubic,-39.067,-26.857,-12
seresnextaa101d_32x8d.sw_in12k_ft_in1k,57.643,42.357,71.357,28.643,93.59,288,1.000,bicubic,-39.527,-28.423,-70
mvitv2_large.fb_in1k,57.497,42.503,70.750,29.250,217.99,224,0.900,bicubic,-38.903,-28.790,+62
cait_m48_448.fb_dist_in1k,57.477,42.523,71.857,28.143,356.46,448,1.000,bicubic,-39.403,-27.813,-28
cait_m36_384.fb_dist_in1k,57.473,42.527,72.307,27.693,271.22,384,1.000,bicubic,-39.367,-27.353,-23
tf_efficientnet_b3.ns_jft_in1k,57.417,42.583,72.370,27.630,12.23,300,0.904,bicubic,-38.683,-27.110,+119
volo_d4_448.sail_in1k,57.287,42.713,71.540,28.460,193.41,448,1.150,bicubic,-39.783,-28.090,-62
tiny_vit_21m_224.dist_in22k_ft_in1k,57.143,42.857,72.573,27.427,21.20,224,0.950,bicubic,-39.237,-26.847,+58
maxvit_small_tf_512.in1k,57.077,42.923,70.957,29.043,69.13,512,1.000,bicubic,-40.123,-28.663,-81
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,57.070,42.930,71.267,28.733,88.22,224,0.900,bicubic,-38.160,-27.973,+302
vit_base_patch16_224.augreg_in21k_ft_in1k,56.833,43.167,70.633,29.367,86.57,224,0.900,bicubic,-39.467,-28.897,+75
deit3_medium_patch16_224.fb_in22k_ft_in1k,56.653,43.347,69.740,30.260,38.85,224,1.000,bicubic,-39.487,-29.550,+104
volo_d5_224.sail_in1k,56.507,43.493,70.660,29.340,295.46,224,0.960,bicubic,-40.373,-28.960,-39
deit3_large_patch16_224.fb_in1k,56.453,43.547,70.473,29.527,304.37,224,0.900,bicubic,-39.737,-29.027,+95
xcit_large_24_p8_384.fb_dist_in1k,56.360,43.640,71.307,28.693,188.93,384,1.000,bicubic,-40.400,-28.253,-16
flexivit_large.1200ep_in1k,56.290,43.710,71.560,28.440,304.36,240,0.950,bicubic,-40.490,-28.110,-21
convnextv2_tiny.fcmae_ft_in22k_in1k_384,56.100,43.900,71.897,28.103,28.64,384,1.000,bicubic,-40.520,-27.733,+2
flexivit_large.600ep_in1k,56.077,43.923,71.170,28.830,304.36,240,0.950,bicubic,-40.653,-28.390,-15
xcit_large_24_p8_224.fb_dist_in1k,56.020,43.980,70.670,29.330,188.93,224,1.000,bicubic,-40.610,-28.790,-2
caformer_s18.sail_in22k_ft_in1k_384,56.010,43.990,71.380,28.620,26.34,384,1.000,bicubic,-40.520,-28.200,+16
vit_base_patch32_clip_224.openai_ft_in1k,55.913,44.087,72.157,27.843,88.22,224,0.900,bicubic,-38.527,-26.953,+468
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,55.783,44.217,70.997,29.003,39.03,384,0.950,bicubic,-40.707,-28.623,+24
flexivit_large.300ep_in1k,55.703,44.297,70.703,29.297,304.36,240,0.950,bicubic,-40.997,-28.877,-15
convnext_small.in12k_ft_in1k,55.700,44.300,70.727,29.273,50.22,288,1.000,bicubic,-40.900,-28.833,-1
caformer_b36.sail_in1k_384,55.193,44.807,68.070,31.930,98.75,384,1.000,bicubic,-41.967,-31.540,-90
convformer_s18.sail_in22k_ft_in1k_384,55.123,44.877,70.127,29.873,26.77,384,1.000,bicubic,-41.667,-29.583,-36
caformer_m36.sail_in1k_384,55.083,44.917,68.470,31.530,56.20,384,1.000,bicubic,-41.947,-31.240,-76
swin_small_patch4_window7_224.ms_in22k_ft_in1k,54.977,45.023,71.023,28.977,49.61,224,0.900,bicubic,-41.083,-28.387,+110
xcit_large_24_p16_384.fb_dist_in1k,54.907,45.093,69.850,30.150,189.10,384,1.000,bicubic,-42.033,-29.660,-61
volo_d4_224.sail_in1k,54.757,45.243,68.847,31.153,192.96,224,0.960,bicubic,-42.023,-30.763,-37
maxvit_tiny_tf_512.in1k,54.747,45.253,68.937,31.063,31.05,512,1.000,bicubic,-42.223,-30.733,-69
dm_nfnet_f5.dm_in1k,54.610,45.390,68.673,31.327,377.21,544,0.954,bicubic,-42.420,-31.007,-79
caformer_s36.sail_in1k_384,54.573,45.427,68.740,31.260,39.30,384,1.000,bicubic,-42.307,-30.930,-60
deit3_small_patch16_384.fb_in22k_ft_in1k,54.480,45.520,68.317,31.683,22.21,384,1.000,bicubic,-42.190,-31.323,-20
efficientnet_b5.sw_in12k_ft_in1k,54.413,45.587,69.873,30.127,30.39,448,1.000,bicubic,-42.357,-29.657,-38
vit_base_r50_s16_384.orig_in21k_ft_in1k,54.407,45.593,69.563,30.437,98.95,384,1.000,bicubic,-42.043,-30.047,+17
inception_next_base.sail_in1k_384,54.360,45.640,68.570,31.430,86.67,384,1.000,bicubic,-42.360,-31.040,-33
maxvit_small_tf_384.in1k,54.337,45.663,68.190,31.810,69.02,384,1.000,bicubic,-42.413,-31.350,-38
regnety_160.sw_in12k_ft_in1k,54.330,45.670,69.047,30.953,83.59,288,1.000,bicubic,-42.490,-30.573,-55
resnetv2_152x4_bit.goog_in21k_ft_in1k,54.317,45.683,70.170,29.830,936.53,480,1.000,bilinear,-42.563,-29.490,-65
xcit_large_24_p16_224.fb_dist_in1k,54.250,45.750,68.967,31.033,189.10,224,1.000,bicubic,-42.070,-30.533,+40
regnety_160.lion_in12k_ft_in1k,54.203,45.797,68.990,31.010,83.59,288,1.000,bicubic,-42.607,-30.520,-56
vit_small_r26_s32_384.augreg_in21k_ft_in1k,54.187,45.813,68.760,31.240,36.47,384,1.000,bicubic,-41.873,-30.740,+92
caformer_s18.sail_in22k_ft_in1k,54.113,45.887,69.713,30.287,26.34,224,1.000,bicubic,-41.897,-29.837,+102
volo_d3_448.sail_in1k,53.977,46.023,68.040,31.960,86.63,448,1.000,bicubic,-43.053,-31.630,-94
caformer_b36.sail_in1k,53.977,46.023,66.687,33.313,98.75,224,1.000,bicubic,-42.523,-32.943,0
tf_efficientnet_b5.ap_in1k,53.887,46.113,69.170,30.830,30.39,456,0.934,bicubic,-42.203,-30.370,+80
dm_nfnet_f6.dm_in1k,53.843,46.157,68.413,31.587,438.36,576,0.956,bicubic,-43.127,-31.347,-87
xcit_medium_24_p8_224.fb_dist_in1k,53.650,46.350,68.403,31.597,84.32,224,1.000,bicubic,-42.880,-31.107,-11
tf_efficientnet_b2.ns_jft_in1k,53.600,46.400,70.270,29.730,9.11,260,0.890,bicubic,-41.920,-29.070,+204
cait_s36_384.fb_dist_in1k,53.560,46.440,68.003,31.997,68.37,384,1.000,bicubic,-43.070,-31.607,-35
tf_efficientnet_b6.ap_in1k,53.553,46.447,68.563,31.437,43.04,528,0.942,bicubic,-42.817,-30.987,+16
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,53.537,46.463,69.093,30.907,38.86,256,0.950,bicubic,-42.463,-30.327,+95
dm_nfnet_f3.dm_in1k,53.523,46.477,67.743,32.257,254.92,416,0.940,bicubic,-43.097,-31.837,-36
deit3_base_patch16_384.fb_in1k,53.483,46.517,67.623,32.377,86.88,384,1.000,bicubic,-42.747,-31.817,+47
deit3_base_patch16_224.fb_in1k,53.470,46.530,67.597,32.403,86.59,224,0.900,bicubic,-42.300,-31.673,+145
convformer_s18.sail_in22k_ft_in1k,53.437,46.563,68.680,31.320,26.77,224,1.000,bicubic,-42.663,-30.810,+67
tf_efficientnet_b8.ra_in1k,53.417,46.583,69.093,30.907,87.41,672,0.954,bicubic,-43.283,-30.557,-49
convnextv2_base.fcmae_ft_in1k,53.417,46.583,67.750,32.250,88.72,288,1.000,bicubic,-43.063,-31.800,-9
xcit_medium_24_p8_384.fb_dist_in1k,53.397,46.603,68.130,31.870,84.32,384,1.000,bicubic,-43.373,-31.470,-64
vit_base_patch32_384.augreg_in21k_ft_in1k,53.300,46.700,68.057,31.943,88.30,384,1.000,bicubic,-42.600,-31.283,+110
tf_efficientnet_b7.ap_in1k,53.257,46.743,68.870,31.130,66.35,600,0.949,bicubic,-43.093,-30.650,+9
convnext_large.fb_in1k,53.247,46.753,67.887,32.113,197.77,288,1.000,bicubic,-43.153,-31.563,+1
xcit_medium_24_p16_384.fb_dist_in1k,53.237,46.763,68.043,31.957,84.40,384,1.000,bicubic,-43.453,-31.557,-52
hrnet_w18_ssld.paddle_in1k,53.233,46.767,68.183,31.817,21.30,288,1.000,bilinear,-42.477,-31.107,+150
maxvit_base_tf_224.in1k,53.233,46.767,66.133,33.867,119.47,224,0.950,bicubic,-43.107,-33.447,+11
tf_efficientnetv2_l.in1k,53.160,46.840,67.827,32.173,118.52,480,1.000,bicubic,-43.580,-31.723,-65
tf_efficientnetv2_s.in21k_ft_in1k,53.127,46.873,69.000,31.000,21.46,384,1.000,bicubic,-43.343,-30.570,-18
tf_efficientnet_b4.ap_in1k,53.087,46.913,68.223,31.777,19.34,380,0.922,bicubic,-42.403,-31.197,+188
convnext_tiny.in12k_ft_in1k_384,53.063,46.937,68.503,31.497,28.59,384,1.000,bicubic,-43.497,-31.127,-40
regnetz_e8.ra3_in1k,53.003,46.997,67.147,32.853,57.70,320,1.000,bicubic,-43.597,-32.433,-50
maxvit_large_tf_224.in1k,53.003,46.997,65.337,34.663,211.79,224,0.950,bicubic,-43.327,-34.073,+7
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,52.873,47.127,66.460,33.540,41.72,224,0.950,bicubic,-43.257,-32.880,+46
volo_d3_224.sail_in1k,52.697,47.303,66.307,33.693,86.33,224,0.960,bicubic,-43.733,-33.233,-17
deit3_small_patch16_224.fb_in22k_ft_in1k,52.690,47.310,66.867,33.133,22.06,224,1.000,bicubic,-43.140,-32.473,+110
regnety_120.sw_in12k_ft_in1k,52.637,47.363,67.637,32.363,51.82,288,1.000,bicubic,-43.913,-32.043,-44
dm_nfnet_f4.dm_in1k,52.460,47.540,67.117,32.883,316.07,512,0.951,bicubic,-44.490,-32.523,-112
maxvit_tiny_tf_384.in1k,52.457,47.543,66.777,33.223,30.98,384,1.000,bicubic,-44.143,-32.843,-54
tf_efficientnet_b7.ra_in1k,52.410,47.590,68.220,31.780,66.35,600,0.949,bicubic,-44.160,-31.250,-52
xcit_small_24_p8_384.fb_dist_in1k,52.377,47.623,66.847,33.153,47.63,384,1.000,bicubic,-44.433,-32.813,-92
efficientnetv2_rw_m.agc_in1k,52.343,47.657,67.223,32.777,53.24,416,1.000,bicubic,-43.927,-32.407,+10
resnet18.fb_swsl_ig1b_ft_in1k,52.323,47.677,70.483,29.517,11.69,224,0.875,bilinear,-38.777,-27.717,+762
convformer_b36.sail_in1k_384,52.300,47.700,66.543,33.457,99.88,384,1.000,bicubic,-44.570,-33.107,-106
convformer_m36.sail_in1k_384,52.283,47.717,66.150,33.850,57.05,384,1.000,bicubic,-44.497,-33.580,-90
inception_next_base.sail_in1k,52.273,47.727,65.930,34.070,86.67,224,0.950,bicubic,-43.647,-33.430,+84
deit_base_distilled_patch16_384.fb_in1k,52.260,47.740,67.747,32.253,87.63,384,1.000,bicubic,-44.250,-31.843,-45
xcit_medium_24_p16_224.fb_dist_in1k,52.197,47.803,66.917,33.083,84.40,224,1.000,bicubic,-44.053,-32.493,+10
xcit_small_24_p8_224.fb_dist_in1k,52.183,47.817,66.767,33.233,47.63,224,1.000,bicubic,-44.367,-32.773,-55
convnext_tiny.fb_in22k_ft_in1k_384,52.180,47.820,66.923,33.077,28.59,384,1.000,bicubic,-43.990,-32.577,+23
convformer_s36.sail_in1k_384,51.987,48.013,66.197,33.803,40.01,384,1.000,bicubic,-44.713,-33.333,-81
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,51.937,48.063,68.660,31.340,236.34,384,1.000,bicubic,-44.253,-30.640,+17
fastvit_ma36.apple_dist_in1k,51.923,48.077,67.013,32.987,44.07,256,0.950,bicubic,-44.377,-32.547,-3
convnextv2_tiny.fcmae_ft_in22k_in1k,51.903,48.097,67.800,32.200,28.64,288,1.000,bicubic,-44.437,-31.570,-18
resmlp_big_24_224.fb_in22k_ft_in1k,51.893,48.107,68.463,31.537,129.14,224,0.875,bicubic,-44.457,-31.127,-21
xcit_small_24_p16_384.fb_dist_in1k,51.883,48.117,66.367,33.633,47.67,384,1.000,bicubic,-44.457,-33.183,-21
cait_s24_384.fb_dist_in1k,51.787,48.213,66.307,33.693,47.06,384,1.000,bicubic,-44.783,-33.243,-70
resnetv2_152x2_bit.goog_in21k_ft_in1k,51.763,48.237,69.263,30.737,236.34,448,1.000,bilinear,-44.757,-30.307,-58
caformer_m36.sail_in1k,51.693,48.307,64.497,35.503,56.20,224,1.000,bicubic,-44.717,-35.063,-36
ecaresnet269d.ra2_in1k,51.677,48.323,66.040,33.960,102.09,352,1.000,bicubic,-44.773,-33.620,-44
regnety_2560.seer_ft_in1k,51.650,48.350,68.193,31.807,"1,282.60",384,1.000,bicubic,-44.880,-31.327,-63
rexnetr_300.sw_in12k_ft_in1k,51.627,48.373,68.020,31.980,34.81,288,1.000,bicubic,-44.463,-31.520,+25
caformer_s36.sail_in1k,51.593,48.407,64.907,35.093,39.30,224,1.000,bicubic,-44.487,-34.603,+27
coat_lite_medium_384.in1k,51.570,48.430,65.740,34.260,44.57,384,1.000,bicubic,-45.000,-33.780,-75
mvitv2_base.fb_in1k,51.550,48.450,65.633,34.367,51.47,224,0.900,bicubic,-44.430,-33.957,+50
vit_base_patch16_224_miil.in21k_ft_in1k,51.543,48.457,65.207,34.793,86.54,224,0.875,bilinear,-44.497,-34.143,+38
davit_small.msft_in1k,51.523,48.477,66.440,33.560,49.75,224,0.950,bicubic,-44.507,-32.960,+38
convnext_tiny.in12k_ft_in1k,51.440,48.560,67.063,32.937,28.59,288,1.000,bicubic,-44.800,-32.227,-9
maxvit_rmlp_small_rw_224.sw_in1k,51.430,48.570,65.190,34.810,64.90,224,0.900,bicubic,-44.530,-34.240,+54
tf_efficientnetv2_m.in1k,51.423,48.577,66.623,33.377,54.14,480,1.000,bicubic,-45.057,-32.897,-62
repvit_m2_3.dist_450e_in1k,51.383,48.617,66.737,33.263,23.69,224,0.950,bicubic,-44.607,-32.663,+42
caformer_s18.sail_in1k_384,51.353,48.647,65.657,34.343,26.34,384,1.000,bicubic,-45.057,-33.873,-50
edgenext_base.in21k_ft_in1k,51.283,48.717,65.657,34.343,18.51,320,1.000,bicubic,-44.917,-33.803,-6
davit_base.msft_in1k,51.267,48.733,65.223,34.777,87.95,224,0.950,bicubic,-44.973,-34.417,-14
convformer_b36.sail_in1k,51.203,48.797,64.280,35.720,99.88,224,1.000,bicubic,-45.037,-35.130,-14
maxvit_small_tf_224.in1k,51.190,48.810,65.260,34.740,68.93,224,0.950,bicubic,-45.010,-34.270,-8
xcit_small_12_p8_384.fb_dist_in1k,51.107,48.893,65.840,34.160,26.21,384,1.000,bicubic,-45.363,-33.650,-65
convnext_base.fb_in1k,51.057,48.943,65.880,34.120,88.59,288,1.000,bicubic,-45.253,-33.630,-33
convformer_m36.sail_in1k,51.020,48.980,63.653,36.347,57.05,224,1.000,bicubic,-45.060,-35.737,+13
volo_d2_384.sail_in1k,50.900,49.100,65.623,34.377,58.87,384,1.000,bicubic,-45.810,-33.977,-113
tf_efficientnet_b1.ns_jft_in1k,50.887,49.113,67.910,32.090,7.79,240,0.882,bicubic,-43.973,-31.340,+281
vit_base_patch16_384.orig_in21k_ft_in1k,50.880,49.120,65.277,34.723,86.86,384,1.000,bicubic,-45.320,-34.193,-16
convformer_s36.sail_in1k,50.863,49.137,64.083,35.917,40.01,224,1.000,bicubic,-45.247,-35.377,+1
tiny_vit_11m_224.dist_in22k_ft_in1k,50.830,49.170,66.870,33.130,11.00,224,0.950,bicubic,-44.880,-32.390,+95
xcit_small_24_p16_224.fb_dist_in1k,50.743,49.257,65.047,34.953,47.67,224,1.000,bicubic,-45.047,-34.243,+73
repvit_m2_3.dist_300e_in1k,50.737,49.263,66.743,33.257,23.69,224,0.950,bicubic,-44.853,-32.647,+113
convformer_s18.sail_in1k_384,50.687,49.313,65.637,34.363,26.77,384,1.000,bicubic,-45.563,-33.943,-31
flexivit_base.1200ep_in1k,50.687,49.313,65.133,34.867,86.59,240,0.950,bicubic,-45.433,-34.277,-7
coatnet_rmlp_2_rw_224.sw_in1k,50.603,49.397,63.363,36.637,73.88,224,0.950,bicubic,-45.607,-36.117,-24
xcit_small_12_p16_384.fb_dist_in1k,50.527,49.473,65.300,34.700,26.25,384,1.000,bicubic,-45.813,-34.190,-53
efficientnet_b4.ra2_in1k,50.500,49.500,65.730,34.270,19.34,384,1.000,bicubic,-45.030,-33.670,+122
volo_d1_384.sail_in1k,50.477,49.523,64.913,35.087,26.78,384,1.000,bicubic,-46.003,-34.697,-84
efficientvit_b3.r256_in1k,50.477,49.523,64.180,35.820,48.65,256,1.000,bicubic,-45.353,-35.040,+56
xcit_small_12_p8_224.fb_dist_in1k,50.437,49.563,65.420,34.580,26.21,224,1.000,bicubic,-45.523,-33.950,+25
fastvit_sa36.apple_dist_in1k,50.427,49.573,65.803,34.197,31.53,256,0.900,bicubic,-45.533,-33.577,+27
resnetv2_101x3_bit.goog_in21k_ft_in1k,50.393,49.607,67.783,32.217,387.93,448,1.000,bilinear,-45.857,-31.687,-41
flexivit_base.600ep_in1k,50.353,49.647,64.627,35.373,86.59,240,0.950,bicubic,-45.607,-34.793,+23
efficientvit_b3.r288_in1k,50.340,49.660,64.063,35.937,48.65,288,1.000,bicubic,-45.800,-35.297,-19
regnetz_040_h.ra3_in1k,50.317,49.683,65.623,34.377,28.94,320,1.000,bicubic,-46.003,-33.917,-58
inception_next_small.sail_in1k,50.273,49.727,65.097,34.903,49.37,224,0.875,bicubic,-45.407,-34.153,+84
mvitv2_small.fb_in1k,50.260,49.740,64.893,35.107,34.87,224,0.900,bicubic,-45.630,-34.467,+35
cait_s24_224.fb_dist_in1k,50.250,49.750,65.020,34.980,46.92,224,1.000,bicubic,-45.410,-34.370,+83
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,50.230,49.770,66.033,33.967,194.03,224,0.875,bilinear,-45.170,-33.127,+134
pit_b_distilled_224.in1k,50.220,49.780,64.997,35.003,74.79,224,0.900,bicubic,-45.600,-34.293,+50
eca_nfnet_l2.ra3_in1k,50.203,49.797,65.440,34.560,56.72,384,1.000,bicubic,-46.247,-34.310,-90
resnest269e.in1k,50.187,49.813,64.680,35.320,110.93,416,0.928,bicubic,-45.923,-34.630,-24
vit_small_patch16_384.augreg_in21k_ft_in1k,50.173,49.827,65.790,34.210,22.20,384,1.000,bicubic,-45.807,-33.540,+7
tresnet_v2_l.miil_in21k_ft_in1k,50.163,49.837,65.123,34.877,46.17,224,0.875,bilinear,-45.657,-34.197,+45
pvt_v2_b5.in1k,50.147,49.853,65.027,34.973,81.96,224,0.900,bicubic,-45.793,-34.363,+17
deit3_medium_patch16_224.fb_in1k,50.140,49.860,64.710,35.290,38.85,224,0.900,bicubic,-45.240,-34.640,+133
deit_base_distilled_patch16_224.fb_in1k,50.077,49.923,66.230,33.770,87.34,224,0.900,bicubic,-45.673,-33.200,+55
tf_efficientnet_b3.ap_in1k,50.047,49.953,65.210,34.790,12.23,300,0.904,bicubic,-44.923,-33.900,+228
pvt_v2_b4.in1k,50.023,49.977,65.127,34.873,62.56,224,0.900,bicubic,-45.897,-34.093,+16
flexivit_base.300ep_in1k,50.003,49.997,64.103,35.897,86.59,240,0.950,bicubic,-45.947,-35.367,+11
coat_lite_medium.in1k,49.983,50.017,64.857,35.143,44.57,224,0.900,bicubic,-46.017,-34.643,-3
resnest200e.in1k,49.873,50.127,64.717,35.283,70.20,320,0.909,bicubic,-46.197,-34.763,-23
efficientformer_l7.snap_dist_in1k,49.837,50.163,66.033,33.967,82.23,224,0.950,bicubic,-45.763,-33.357,+79
volo_d2_224.sail_in1k,49.820,50.180,64.580,35.420,58.68,224,0.960,bicubic,-46.600,-34.880,-98
xception65.ra3_in1k,49.780,50.220,63.523,36.477,39.92,299,0.940,bicubic,-45.910,-35.797,+63
seresnextaa101d_32x8d.ah_in1k,49.747,50.253,64.443,35.557,93.59,288,1.000,bicubic,-46.693,-35.067,-103
swinv2_base_window16_256.ms_in1k,49.667,50.333,63.800,36.200,87.92,256,0.900,bicubic,-46.503,-35.590,-47
pvt_v2_b3.in1k,49.613,50.387,64.793,35.207,45.24,224,0.900,bicubic,-45.857,-34.517,+101
convnextv2_nano.fcmae_ft_in22k_in1k_384,49.603,50.397,65.657,34.343,15.62,384,1.000,bicubic,-46.187,-33.733,+36
cait_xs24_384.fb_dist_in1k,49.537,50.463,64.900,35.100,26.67,384,1.000,bicubic,-46.463,-34.530,-13
maxvit_rmlp_tiny_rw_256.sw_in1k,49.530,50.470,63.823,36.177,29.15,256,0.950,bicubic,-46.510,-35.717,-23
tf_efficientnet_b5.ra_in1k,49.523,50.477,65.653,34.347,30.39,456,0.934,bicubic,-46.447,-33.777,-10
fastvit_ma36.apple_in1k,49.500,50.500,63.620,36.380,44.07,256,0.950,bicubic,-46.470,-35.840,-9
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,49.480,50.520,65.623,34.377,236.34,224,0.875,bicubic,-46.270,-33.807,+37
resnet200d.ra2_in1k,49.463,50.537,64.330,35.670,64.69,320,1.000,bicubic,-46.647,-35.190,-46
efficientformerv2_l.snap_dist_in1k,49.457,50.543,64.920,35.080,26.32,224,0.950,bicubic,-46.303,-34.450,+33
xcit_small_12_p16_224.fb_dist_in1k,49.413,50.587,63.847,36.153,26.25,224,1.000,bicubic,-46.317,-35.453,+41
resnest101e.in1k,49.367,50.633,65.583,34.417,48.28,256,0.875,bilinear,-46.203,-33.787,+73
dm_nfnet_f2.dm_in1k,49.350,50.650,63.950,36.050,193.78,352,0.920,bicubic,-47.170,-35.640,-132
regnetz_040.ra3_in1k,49.297,50.703,64.060,35.940,27.12,320,1.000,bicubic,-46.883,-35.470,-62
vit_base_patch32_224.augreg_in21k_ft_in1k,49.267,50.733,64.340,35.660,88.22,224,0.900,bicubic,-45.133,-34.700,+324
tiny_vit_21m_224.in1k,49.267,50.733,64.303,35.697,21.20,224,0.950,bicubic,-46.383,-34.947,+53
resnet152d.ra2_in1k,49.263,50.737,64.413,35.587,60.21,320,1.000,bicubic,-46.607,-35.057,+6
seresnet152d.ra2_in1k,49.237,50.763,64.167,35.833,66.84,320,1.000,bicubic,-47.073,-35.243,-94
xcit_large_24_p8_224.fb_in1k,49.237,50.763,62.840,37.160,188.93,224,1.000,bicubic,-46.853,-36.300,-50
gcvit_base.in1k,49.153,50.847,63.950,36.050,90.32,224,0.875,bicubic,-46.927,-35.300,-49
maxxvit_rmlp_small_rw_256.sw_in1k,49.150,50.850,63.343,36.657,66.01,256,0.950,bicubic,-47.060,-35.937,-77
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,49.093,50.907,65.490,34.510,88.79,224,0.875,bilinear,-46.227,-33.830,+110
resmlp_big_24_224.fb_distilled_in1k,49.093,50.907,65.473,34.527,129.14,224,0.875,bicubic,-46.777,-33.967,-1
convnext_small.fb_in1k,49.067,50.933,64.830,35.170,50.22,288,1.000,bicubic,-46.903,-34.530,-27
resnetaa101d.sw_in12k_ft_in1k,49.013,50.987,64.237,35.763,44.57,288,1.000,bicubic,-47.347,-35.233,-114
resnext101_64x4d.tv_in1k,48.990,51.010,63.510,36.490,83.46,224,0.875,bilinear,-46.830,-35.800,+5
volo_d1_224.sail_in1k,48.973,51.027,63.187,36.813,26.63,224,0.960,bicubic,-47.057,-36.203,-41
repvgg_b3.rvgg_in1k,48.913,51.087,64.880,35.120,123.09,224,0.875,bilinear,-45.657,-33.770,+275
efficientvit_b3.r224_in1k,48.877,51.123,62.947,37.053,48.65,224,0.950,bicubic,-46.663,-36.373,+63
resnetrs420.tf_in1k,48.863,51.137,63.433,36.567,191.89,416,1.000,bicubic,-47.537,-36.097,-126
maxvit_tiny_tf_224.in1k,48.807,51.193,62.933,37.067,30.92,224,0.950,bicubic,-47.003,-36.327,+4
convformer_s18.sail_in1k,48.787,51.213,62.930,37.070,26.77,224,1.000,bicubic,-46.543,-36.370,+99
caformer_s18.sail_in1k,48.753,51.247,62.870,37.130,26.34,224,1.000,bicubic,-46.927,-36.420,+30
deit3_small_patch16_384.fb_in1k,48.667,51.333,62.800,37.200,22.21,384,1.000,bicubic,-46.933,-36.640,+43
seresnext101d_32x8d.ah_in1k,48.603,51.397,62.960,37.040,93.59,288,1.000,bicubic,-47.757,-36.480,-125
swinv2_small_window16_256.ms_in1k,48.593,51.407,62.747,37.253,49.73,256,0.900,bicubic,-47.477,-36.703,-61
regnetz_d32.ra3_in1k,48.583,51.417,65.167,34.833,27.58,320,0.950,bicubic,-47.287,-34.263,-15
efficientnetv2_rw_s.ra2_in1k,48.577,51.423,63.837,36.163,23.94,384,1.000,bicubic,-47.133,-35.503,+17
efficientnet_b3.ra2_in1k,48.567,51.433,64.250,35.750,12.23,320,1.000,bicubic,-46.573,-35.050,+138
edgenext_base.usi_in1k,48.457,51.543,64.317,35.683,18.51,320,1.000,bicubic,-47.333,-34.983,-4
focalnet_base_lrf.ms_in1k,48.440,51.560,63.120,36.880,88.75,224,0.900,bicubic,-47.390,-36.080,-11
focalnet_base_srf.ms_in1k,48.423,51.577,63.103,36.897,88.15,224,0.900,bicubic,-47.477,-36.207,-29
vit_small_r26_s32_224.augreg_in21k_ft_in1k,48.377,51.623,63.800,36.200,36.43,224,0.900,bicubic,-46.743,-35.400,+139
swinv2_base_window8_256.ms_in1k,48.333,51.667,63.610,36.390,87.92,256,0.900,bicubic,-47.727,-35.940,-64
fastvit_sa36.apple_in1k,48.320,51.680,62.793,37.207,31.53,256,0.900,bicubic,-47.290,-36.527,+30
repvgg_b3g4.rvgg_in1k,48.310,51.690,64.780,35.220,83.83,224,0.875,bilinear,-46.190,-34.240,+275
vit_large_patch32_384.orig_in21k_ft_in1k,48.240,51.760,61.827,38.173,306.63,384,1.000,bicubic,-47.000,-37.403,+103
convit_base.fb_in1k,48.210,51.790,63.000,37.000,86.54,224,0.875,bicubic,-46.890,-36.150,+139
swin_s3_base_224.ms_in1k,48.153,51.847,62.243,37.757,71.13,224,0.900,bicubic,-47.887,-37.107,-66
sequencer2d_l.in1k,48.107,51.893,62.353,37.647,54.30,224,0.875,bicubic,-47.763,-37.207,-31
resnext101_32x8d.tv2_in1k,48.093,51.907,62.723,37.277,88.79,224,0.965,bilinear,-47.207,-36.507,+84
resnetrs350.tf_in1k,48.057,51.943,62.667,37.333,163.96,384,1.000,bicubic,-48.193,-36.923,-115
tf_efficientnetv2_b3.in21k_ft_in1k,48.030,51.970,64.747,35.253,14.36,300,0.900,bicubic,-47.570,-34.533,+24
gcvit_small.in1k,48.030,51.970,62.713,37.287,51.09,224,0.875,bicubic,-47.880,-36.567,-42
focalnet_small_lrf.ms_in1k,48.027,51.973,63.130,36.870,50.34,224,0.900,bicubic,-47.713,-36.030,-7
regnetz_d8.ra3_in1k,48.010,51.990,64.423,35.577,23.37,320,1.000,bicubic,-48.000,-35.097,-68
regnety_1280.seer_ft_in1k,47.990,52.010,64.230,35.770,644.81,384,1.000,bicubic,-48.320,-35.320,-131
twins_svt_large.in1k,47.963,52.037,62.907,37.093,99.27,224,0.900,bicubic,-47.737,-36.463,+2
fastvit_sa24.apple_dist_in1k,47.960,52.040,62.797,37.203,21.55,256,0.900,bicubic,-47.590,-36.513,+29
repvit_m1_5.dist_450e_in1k,47.950,52.050,63.643,36.357,14.64,224,0.950,bicubic,-47.330,-35.587,+83
vit_relpos_base_patch16_224.sw_in1k,47.923,52.077,62.840,37.160,86.43,224,0.900,bicubic,-47.207,-36.240,+117
repvgg_b2g4.rvgg_in1k,47.810,52.190,64.397,35.603,61.76,224,0.875,bilinear,-46.020,-34.533,+377
mixer_b16_224.miil_in21k_ft_in1k,47.793,52.207,63.403,36.597,59.88,224,0.875,bilinear,-47.087,-35.677,+175
repvgg_d2se.rvgg_in1k,47.780,52.220,62.770,37.230,133.33,320,1.000,bilinear,-48.170,-36.600,-60
vit_relpos_base_patch16_clsgap_224.sw_in1k,47.760,52.240,62.410,37.590,86.43,224,0.900,bicubic,-47.490,-36.800,+82
tf_efficientnet_b5.aa_in1k,47.737,52.263,63.910,36.090,30.39,456,0.934,bicubic,-48.143,-35.440,-49
repvit_m1_5.dist_300e_in1k,47.697,52.303,63.763,36.237,14.64,224,0.950,bicubic,-47.453,-35.387,+105
mvitv2_tiny.fb_in1k,47.667,52.333,62.830,37.170,24.17,224,0.900,bicubic,-47.733,-36.470,+51
eca_nfnet_l1.ra2_in1k,47.653,52.347,62.767,37.233,41.41,320,1.000,bicubic,-48.277,-36.723,-61
vit_relpos_medium_patch16_cls_224.sw_in1k,47.653,52.347,61.783,38.217,38.76,224,0.900,bicubic,-47.647,-37.317,+67
seresnext101_32x8d.ah_in1k,47.650,52.350,61.477,38.523,93.57,288,1.000,bicubic,-48.490,-38.013,-113
ecaresnet101d.miil_in1k,47.630,52.370,63.540,36.460,44.57,288,0.950,bicubic,-48.000,-35.750,0
regnetz_d8_evos.ch_in1k,47.623,52.377,63.807,36.193,23.46,320,1.000,bicubic,-48.517,-35.673,-117
resnetv2_50x3_bit.goog_in21k_ft_in1k,47.593,52.407,65.603,34.397,217.32,448,1.000,bilinear,-48.677,-33.957,-143
focalnet_small_srf.ms_in1k,47.547,52.453,62.510,37.490,49.89,224,0.900,bicubic,-48.083,-36.930,-2
pit_s_distilled_224.in1k,47.533,52.467,63.173,36.827,24.04,224,0.900,bicubic,-47.157,-35.977,+199
resnest50d_4s2x40d.in1k,47.483,52.517,63.803,36.197,30.42,224,0.875,bicubic,-47.217,-35.327,+194
dm_nfnet_f1.dm_in1k,47.457,52.543,62.083,37.917,132.63,320,0.910,bicubic,-48.843,-37.417,-150
efficientnet_b3_pruned.in1k,47.447,52.553,62.803,37.197,9.86,300,0.904,bicubic,-47.153,-36.287,+217
davit_tiny.msft_in1k,47.410,52.590,63.360,36.640,28.36,224,0.950,bicubic,-47.670,-35.900,+114
crossvit_18_dagger_408.in1k,47.390,52.610,60.927,39.073,44.61,408,1.000,bicubic,-48.760,-38.543,-126
poolformerv2_m48.sail_in1k,47.380,52.620,63.960,36.040,73.35,224,1.000,bicubic,-47.800,-35.160,+87
coatnet_rmlp_1_rw_224.sw_in1k,47.370,52.630,61.437,38.563,41.69,224,0.950,bicubic,-48.120,-37.923,+17
vit_base_patch16_224.orig_in21k_ft_in1k,47.347,52.653,61.617,38.383,86.57,224,0.900,bicubic,-47.853,-37.613,+80
xcit_small_24_p8_224.fb_in1k,47.290,52.710,60.990,39.010,47.63,224,1.000,bicubic,-48.610,-38.190,-70
efficientformer_l3.snap_dist_in1k,47.247,52.753,63.420,36.580,31.41,224,0.950,bicubic,-47.963,-35.890,+72
tresnet_m.miil_in21k_ft_in1k,47.233,52.767,62.003,37.997,31.39,224,0.875,bilinear,-48.147,-37.147,+37
tf_efficientnet_b6.aa_in1k,47.207,52.793,63.110,36.890,43.04,528,0.942,bicubic,-49.093,-36.420,-160
wide_resnet101_2.tv2_in1k,47.207,52.793,61.877,38.123,126.89,224,0.965,bilinear,-48.033,-37.323,+62
efficientvit_b2.r256_in1k,47.207,52.793,61.670,38.330,24.33,256,1.000,bicubic,-48.013,-37.590,+67
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,47.187,52.813,63.387,36.613,44.18,224,0.875,bilinear,-47.953,-35.833,+84
swin_base_patch4_window12_384.ms_in1k,47.183,52.817,62.027,37.973,87.90,384,1.000,bicubic,-49.197,-37.473,-184
repvit_m3.dist_in1k,47.153,52.847,63.347,36.653,10.68,224,0.950,bicubic,-47.567,-35.483,+173
resnetrs270.tf_in1k,47.127,52.873,61.997,38.003,129.86,352,1.000,bicubic,-48.933,-37.483,-116
efficientvit_b2.r288_in1k,47.110,52.890,61.560,38.440,24.33,288,1.000,bicubic,-48.470,-37.710,-12
tf_efficientnet_b4.aa_in1k,47.083,52.917,62.860,37.140,19.34,380,0.922,bicubic,-48.507,-36.470,-16
regnety_320.tv2_in1k,47.073,52.927,62.050,37.950,145.05,224,0.965,bicubic,-48.487,-37.340,-11
vit_base_patch16_rpn_224.sw_in1k,47.063,52.937,62.397,37.603,86.54,224,0.900,bicubic,-47.757,-36.693,+150
rexnetr_200.sw_in12k_ft_in1k,47.053,52.947,63.973,36.027,16.52,288,1.000,bicubic,-48.257,-35.497,+35
convnextv2_tiny.fcmae_ft_in1k,47.043,52.957,62.503,37.497,28.64,288,1.000,bicubic,-48.787,-36.887,-73
swinv2_small_window8_256.ms_in1k,47.020,52.980,62.300,37.700,49.73,256,0.900,bicubic,-48.710,-37.060,-50
inception_next_tiny.sail_in1k,46.983,53.017,62.893,37.107,28.06,224,0.875,bicubic,-48.117,-36.247,+86
xcit_small_12_p8_224.fb_in1k,46.977,53.023,60.523,39.477,26.21,224,1.000,bicubic,-48.443,-38.667,+10
xcit_large_24_p16_224.fb_in1k,46.940,53.060,60.657,39.343,189.10,224,1.000,bicubic,-48.020,-38.173,+118
coat_small.in1k,46.937,53.063,61.307,38.693,21.69,224,0.900,bicubic,-48.253,-37.973,+60
xception65p.ra3_in1k,46.923,53.077,61.087,38.913,39.82,299,0.940,bicubic,-48.737,-38.183,-39
maxvit_tiny_rw_224.sw_in1k,46.907,53.093,60.897,39.103,29.06,224,0.950,bicubic,-48.833,-38.543,-58
resnet101d.ra2_in1k,46.890,53.110,62.340,37.660,44.57,320,1.000,bicubic,-48.850,-36.870,-61
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,46.837,53.163,64.117,35.883,28.29,224,0.900,bicubic,-47.963,-35.173,+145
pvt_v2_b2_li.in1k,46.813,53.187,62.490,37.510,22.55,224,0.900,bicubic,-48.407,-36.640,+44
resnet152.tv2_in1k,46.803,53.197,61.070,38.930,60.19,224,0.965,bilinear,-48.237,-38.100,+95
regnety_640.seer_ft_in1k,46.707,53.293,63.233,36.767,281.38,384,1.000,bicubic,-49.353,-36.247,-135
fastvit_sa24.apple_in1k,46.653,53.347,61.710,38.290,21.55,256,0.900,bicubic,-48.627,-37.580,+27
seresnext101_64x4d.gluon_in1k,46.650,53.350,61.283,38.717,88.23,224,0.875,bicubic,-48.000,-37.687,+173
twins_pcpvt_large.in1k,46.613,53.387,62.263,37.737,60.99,224,0.900,bicubic,-49.107,-37.227,-62
convnextv2_nano.fcmae_ft_in22k_in1k,46.607,53.393,62.957,37.043,15.62,288,1.000,bicubic,-48.813,-36.353,-4
convnext_tiny.fb_in1k,46.587,53.413,63.187,36.813,28.59,288,1.000,bicubic,-48.623,-36.123,+40
swin_base_patch4_window7_224.ms_in1k,46.543,53.457,61.580,38.420,87.77,224,0.900,bicubic,-49.357,-37.860,-104
resnet152.a1h_in1k,46.540,53.460,60.403,39.597,60.19,288,1.000,bicubic,-49.210,-38.877,-75
efficientformerv2_s2.snap_dist_in1k,46.537,53.463,61.717,38.283,12.71,224,0.950,bicubic,-48.573,-37.403,+66
regnetv_064.ra3_in1k,46.480,53.520,62.250,37.750,30.58,288,1.000,bicubic,-49.300,-37.170,-80
crossvit_15_dagger_408.in1k,46.467,53.533,60.490,39.510,28.50,408,1.000,bicubic,-49.353,-38.720,-90
xcit_medium_24_p8_224.fb_in1k,46.467,53.533,59.653,40.347,84.32,224,1.000,bicubic,-49.393,-39.427,-98
resnetrs200.tf_in1k,46.440,53.560,61.067,38.933,93.21,320,1.000,bicubic,-49.910,-38.483,-211
swin_s3_small_224.ms_in1k,46.400,53.600,60.893,39.107,49.74,224,0.900,bicubic,-49.430,-38.287,-96
coatnet_1_rw_224.sw_in1k,46.393,53.607,60.063,39.937,41.72,224,0.950,bicubic,-49.227,-39.157,-52
gcvit_tiny.in1k,46.367,53.633,61.630,38.370,28.22,224,0.875,bicubic,-49.293,-37.700,-61
rexnet_300.nav_in1k,46.360,53.640,62.690,37.310,34.71,224,0.875,bicubic,-49.180,-36.500,-39
fbnetv3_g.ra2_in1k,46.340,53.660,62.387,37.613,16.62,288,0.950,bilinear,-48.780,-36.813,+54
sequencer2d_m.in1k,46.297,53.703,60.920,39.080,38.31,224,0.875,bicubic,-49.283,-38.300,-49
tresnet_xl.miil_in1k,46.290,53.710,61.943,38.057,78.44,224,0.875,bilinear,-48.790,-37.347,+62
xcit_tiny_24_p8_224.fb_dist_in1k,46.283,53.717,60.590,39.410,12.11,224,1.000,bicubic,-49.167,-38.770,-23
xcit_tiny_24_p8_384.fb_dist_in1k,46.260,53.740,60.730,39.270,12.11,384,1.000,bicubic,-49.970,-38.670,-191
deit_small_distilled_patch16_224.fb_in1k,46.173,53.827,62.423,37.577,22.44,224,0.900,bicubic,-48.427,-36.647,+160
regnety_160.deit_in1k,46.170,53.830,61.823,38.177,83.59,288,1.000,bicubic,-49.700,-37.617,-117
gernet_m.idstcv_in1k,46.163,53.837,62.687,37.313,21.14,224,0.875,bilinear,-48.377,-36.233,+174
repvit_m1_1.dist_450e_in1k,46.130,53.870,63.287,36.713,8.80,224,0.950,bicubic,-48.420,-35.863,+172
crossvit_base_240.in1k,46.120,53.880,60.207,39.793,105.03,240,0.875,bicubic,-48.950,-38.773,+59
resnest50d_1s4x24d.in1k,46.100,53.900,62.373,37.627,25.68,224,0.875,bicubic,-48.280,-36.697,+207
swinv2_cr_small_ns_224.sw_in1k,46.100,53.900,60.800,39.200,49.70,224,0.900,bicubic,-49.610,-38.600,-82
poolformerv2_m36.sail_in1k,46.050,53.950,62.247,37.753,56.08,224,1.000,bicubic,-49.000,-36.833,+61
tf_efficientnet_b0.ns_jft_in1k,46.043,53.957,63.277,36.723,5.29,224,0.875,bicubic,-47.707,-35.653,+307
poolformerv2_s36.sail_in1k,46.027,53.973,62.253,37.747,30.79,224,1.000,bicubic,-48.673,-36.977,+126
nest_base_jx.goog_in1k,46.027,53.973,60.100,39.900,67.72,224,0.875,bicubic,-49.513,-39.190,-53
vit_small_patch16_224.augreg_in21k_ft_in1k,46.020,53.980,61.830,38.170,22.05,224,0.900,bicubic,-48.870,-37.240,+89
resnet51q.ra2_in1k,46.020,53.980,60.903,39.097,35.70,288,1.000,bilinear,-49.180,-38.377,+18
vit_relpos_medium_patch16_224.sw_in1k,45.980,54.020,61.033,38.967,38.75,224,0.900,bicubic,-49.210,-38.187,+19
regnety_080.ra3_in1k,45.980,54.020,60.853,39.147,39.18,288,1.000,bicubic,-49.890,-38.577,-127
deit3_small_patch16_224.fb_in1k,45.943,54.057,58.883,41.117,22.06,224,0.900,bicubic,-48.747,-40.297,+127
resnest50d.in1k,45.933,54.067,62.620,37.380,27.48,224,0.875,bilinear,-48.667,-36.530,+147
convnext_nano.in12k_ft_in1k,45.900,54.100,62.693,37.307,15.59,288,1.000,bicubic,-49.450,-36.757,-25
crossvit_18_240.in1k,45.900,54.100,60.350,39.650,43.27,240,0.875,bicubic,-49.170,-38.680,+43
levit_384.fb_dist_in1k,45.877,54.123,61.683,38.317,39.13,224,0.900,bicubic,-49.333,-37.477,+7
regnety_032.ra_in1k,45.877,54.123,61.540,38.460,19.44,288,1.000,bicubic,-49.583,-37.850,-48
levit_conv_384.fb_dist_in1k,45.870,54.130,61.690,38.310,39.13,224,0.900,bicubic,-49.340,-37.590,+6
twins_svt_base.in1k,45.870,54.130,60.973,39.027,56.07,224,0.900,bicubic,-49.690,-38.257,-69
twins_pcpvt_base.in1k,45.857,54.143,61.330,38.670,43.83,224,0.900,bicubic,-49.613,-37.790,-55
convnext_tiny_hnf.a2h_in1k,45.853,54.147,60.180,39.820,28.59,288,1.000,bicubic,-49.397,-39.020,-10
crossvit_18_dagger_240.in1k,45.853,54.147,59.920,40.080,44.27,240,0.875,bicubic,-49.327,-39.240,+11
regnetz_c16.ra3_in1k,45.790,54.210,62.733,37.267,13.46,320,1.000,bicubic,-49.590,-36.687,-38
vit_relpos_medium_patch16_rpn_224.sw_in1k,45.743,54.257,60.983,39.017,38.73,224,0.900,bicubic,-49.317,-38.217,+38
vit_srelpos_medium_patch16_224.sw_in1k,45.723,54.277,61.073,38.927,38.74,224,0.900,bicubic,-49.217,-37.967,+62
crossvit_15_dagger_240.in1k,45.703,54.297,60.073,39.927,28.21,240,0.875,bicubic,-49.287,-39.117,+53
regnetx_320.tv2_in1k,45.673,54.327,60.233,39.767,107.81,224,0.965,bicubic,-49.607,-39.057,-22
convmixer_1536_20.in1k,45.663,54.337,61.743,38.257,51.63,224,0.960,bicubic,-49.307,-37.327,+54
gc_efficientnetv2_rw_t.agc_in1k,45.653,54.347,60.193,39.807,13.68,288,1.000,bicubic,-49.637,-39.187,-27
dm_nfnet_f0.dm_in1k,45.617,54.383,61.277,38.723,71.49,256,0.900,bicubic,-50.073,-38.073,-106
flexivit_small.1200ep_in1k,45.617,54.383,59.887,40.113,22.06,240,0.950,bicubic,-49.573,-39.333,0
efficientnetv2_rw_t.ra2_in1k,45.603,54.397,60.183,39.817,13.65,288,1.000,bicubic,-49.457,-39.037,+29
seresnext101_32x4d.gluon_in1k,45.600,54.400,61.160,38.840,48.96,224,0.875,bicubic,-48.830,-37.870,+164
xcit_tiny_24_p16_384.fb_dist_in1k,45.580,54.420,60.507,39.493,12.12,384,1.000,bicubic,-49.910,-38.883,-72
xcit_medium_24_p16_224.fb_in1k,45.537,54.463,59.007,40.993,84.40,224,1.000,bicubic,-49.593,-39.933,+9
repvit_m1_1.dist_300e_in1k,45.530,54.470,62.647,37.353,8.80,224,0.950,bicubic,-48.650,-36.433,+213
xcit_small_24_p16_224.fb_in1k,45.513,54.487,58.880,41.120,47.67,224,1.000,bicubic,-49.547,-40.190,+26
resnext101_64x4d.c1_in1k,45.450,54.550,59.033,40.967,83.46,288,1.000,bicubic,-50.080,-40.257,-81
resnet152d.gluon_in1k,45.427,54.573,60.080,39.920,60.21,224,0.875,bicubic,-49.003,-39.010,+160
regnety_320.seer_ft_in1k,45.420,54.580,62.227,37.773,145.05,384,1.000,bicubic,-50.370,-37.343,-140
nfnet_l0.ra2_in1k,45.417,54.583,62.087,37.913,35.07,288,1.000,bicubic,-49.963,-37.123,-57
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,45.413,54.587,62.023,37.977,25.03,224,0.875,bilinear,-49.287,-37.057,+90
xcit_small_12_p16_224.fb_in1k,45.413,54.587,59.403,40.597,26.25,224,1.000,bicubic,-49.407,-39.657,+69
resnetv2_50x1_bit.goog_distilled_in1k,45.407,54.593,62.297,37.703,25.55,224,0.875,bicubic,-50.003,-37.023,-67
cs3se_edgenet_x.c2ns_in1k,45.393,54.607,60.447,39.553,50.72,320,1.000,bicubic,-50.607,-38.903,-192
resnet101.tv2_in1k,45.363,54.637,60.060,39.940,44.55,224,0.965,bilinear,-49.477,-38.970,+63
nest_small_jx.goog_in1k,45.350,54.650,59.040,40.960,38.35,224,0.875,bicubic,-50.190,-40.180,-93
tf_efficientnet_b7.aa_in1k,45.307,54.693,61.730,38.270,66.35,600,0.949,bicubic,-50.763,-37.610,-211
resnet61q.ra2_in1k,45.280,54.720,59.407,40.593,36.85,288,1.000,bicubic,-49.850,-39.853,-6
pvt_v2_b2.in1k,45.277,54.723,60.613,39.387,25.36,224,0.900,bicubic,-49.733,-38.437,+24
cs3edgenet_x.c2_in1k,45.257,54.743,60.260,39.740,47.82,288,1.000,bicubic,-50.203,-39.060,-81
nasnetalarge.tf_in1k,45.223,54.777,57.893,42.107,88.75,331,0.911,bicubic,-49.927,-41.377,-15
focalnet_tiny_lrf.ms_in1k,45.217,54.783,61.300,38.700,28.65,224,0.900,bicubic,-49.973,-37.880,-24
tresnet_xl.miil_in1k_448,45.213,54.787,61.447,38.553,78.44,448,0.875,bilinear,-50.297,-37.893,-95
flexivit_small.600ep_in1k,45.203,54.797,59.420,40.580,22.06,240,0.950,bicubic,-50.057,-39.960,-47
convit_small.fb_in1k,45.200,54.800,60.487,39.513,27.78,224,0.875,bicubic,-49.720,-38.693,+36
efficientvit_b2.r224_in1k,45.193,54.807,59.150,40.850,24.33,224,0.950,bicubic,-49.657,-39.970,+51
swin_small_patch4_window7_224.ms_in1k,45.183,54.817,60.357,39.643,49.61,224,0.900,bicubic,-50.527,-38.933,-138
sequencer2d_s.in1k,45.113,54.887,60.067,39.933,27.65,224,0.875,bicubic,-50.347,-39.193,-88
tf_efficientnet_b3.aa_in1k,45.110,54.890,60.637,39.363,12.23,300,0.904,bicubic,-49.800,-38.473,+34
rexnet_200.nav_in1k,45.063,54.937,62.323,37.677,16.37,224,0.875,bicubic,-49.607,-36.767,+87
maxxvit_rmlp_nano_rw_256.sw_in1k,45.050,54.950,59.663,40.337,16.78,256,0.950,bicubic,-50.290,-39.677,-70
resnetrs152.tf_in1k,44.950,55.050,59.690,40.310,86.62,320,1.000,bicubic,-51.010,-39.660,-199
deit_base_patch16_224.fb_in1k,44.867,55.133,59.170,40.830,86.57,224,0.900,bicubic,-50.153,-40.000,+9
flexivit_small.300ep_in1k,44.850,55.150,59.360,40.640,22.06,240,0.950,bicubic,-50.300,-39.770,-29
focalnet_tiny_srf.ms_in1k,44.833,55.167,61.040,38.960,28.43,224,0.900,bicubic,-50.207,-38.240,+2
coatnet_bn_0_rw_224.sw_in1k,44.803,55.197,60.913,39.087,27.44,224,0.950,bicubic,-50.177,-38.317,+13
tiny_vit_11m_224.in1k,44.803,55.197,60.097,39.903,11.00,224,0.950,bicubic,-50.437,-39.223,-54
deit_base_patch16_384.fb_in1k,44.770,55.230,59.600,40.400,86.86,384,1.000,bicubic,-50.870,-39.820,-136
resmlp_36_224.fb_distilled_in1k,44.763,55.237,61.073,38.927,44.69,224,0.875,bicubic,-49.797,-38.087,+99
cait_xxs36_384.fb_dist_in1k,44.760,55.240,59.387,40.613,17.37,384,1.000,bicubic,-50.480,-39.933,-59
resnet101.a1h_in1k,44.727,55.273,59.120,40.880,44.55,288,1.000,bicubic,-50.853,-40.130,-127
gernet_l.idstcv_in1k,44.720,55.280,58.950,41.050,31.08,256,0.875,bilinear,-50.210,-40.250,+16
tf_efficientnet_b2.ap_in1k,44.710,55.290,60.687,39.313,9.11,260,0.890,bicubic,-49.560,-38.353,+158
resmlp_24_224.fb_distilled_in1k,44.707,55.293,61.463,38.537,30.02,224,0.875,bicubic,-49.623,-37.627,+145
xcit_tiny_24_p16_224.fb_dist_in1k,44.707,55.293,59.417,40.583,12.12,224,1.000,bicubic,-49.533,-39.543,+162
repvit_m2.dist_in1k,44.673,55.327,61.730,38.270,8.80,224,0.950,bicubic,-49.727,-37.320,+128
regnety_032.tv2_in1k,44.650,55.350,60.897,39.103,19.44,224,0.965,bicubic,-50.220,-38.243,+24
ecaresnetlight.miil_in1k,44.580,55.420,60.383,39.617,30.16,288,0.950,bicubic,-49.950,-38.797,+95
swinv2_tiny_window16_256.ms_in1k,44.570,55.430,59.573,40.427,28.35,256,0.900,bicubic,-50.760,-39.577,-87
vit_relpos_small_patch16_224.sw_in1k,44.553,55.447,60.210,39.790,21.98,224,0.900,bicubic,-50.127,-38.890,+59
gmlp_s16_224.ra3_in1k,44.470,55.530,58.617,41.383,19.42,224,0.875,bicubic,-49.030,-40.223,+274
tiny_vit_5m_224.dist_in22k_ft_in1k,44.460,55.540,60.940,39.060,5.39,224,0.950,bicubic,-50.170,-38.200,+68
regnety_160.tv2_in1k,44.427,55.573,59.227,40.773,83.59,224,0.965,bicubic,-50.733,-40.023,-50
resnetv2_101.a1h_in1k,44.423,55.577,58.697,41.303,44.54,288,1.000,bicubic,-51.147,-40.573,-138
inception_resnet_v2.tf_ens_adv_in1k,44.393,55.607,58.113,41.887,55.84,299,0.897,bicubic,-49.727,-40.737,+175
tresnet_l.miil_in1k,44.387,55.613,59.947,40.053,55.99,224,0.875,bilinear,-50.513,-39.083,+9
repvit_m1_0.dist_450e_in1k,44.373,55.627,61.417,38.583,7.30,224,0.950,bicubic,-49.897,-37.623,+141
maxxvitv2_nano_rw_256.sw_in1k,44.373,55.627,58.830,41.170,23.70,256,0.950,bicubic,-51.057,-40.360,-114
repvit_m1_0.dist_300e_in1k,44.367,55.633,61.453,38.547,7.30,224,0.950,bicubic,-49.393,-37.467,+220
resnext101_32x4d.gluon_in1k,44.280,55.720,59.057,40.943,44.18,224,0.875,bicubic,-49.850,-39.883,+167
gcvit_xtiny.in1k,44.247,55.753,59.973,40.027,19.98,224,0.875,bicubic,-50.773,-39.187,-20
regnety_080_tv.tv2_in1k,44.183,55.817,58.787,41.213,39.38,224,0.965,bicubic,-51.117,-40.573,-94
maxvit_rmlp_nano_rw_256.sw_in1k,44.180,55.820,58.243,41.757,15.50,256,0.950,bicubic,-51.260,-40.817,-121
poolformer_m48.sail_in1k,44.160,55.840,59.153,40.847,73.47,224,0.950,bicubic,-50.940,-39.947,-43
regnetz_c16_evos.ch_in1k,44.143,55.857,61.067,38.933,13.49,320,0.950,bicubic,-51.497,-38.173,-164
vit_srelpos_small_patch16_224.sw_in1k,44.130,55.870,59.693,40.307,21.97,224,0.900,bicubic,-50.420,-39.397,+74
resnetv2_101x1_bit.goog_in21k_ft_in1k,44.127,55.873,61.950,38.050,44.54,448,1.000,bilinear,-51.193,-37.420,-103
crossvit_15_240.in1k,44.110,55.890,59.143,40.857,27.53,240,0.875,bicubic,-50.590,-40.097,+36
fastvit_sa12.apple_dist_in1k,44.103,55.897,59.110,40.890,11.58,256,0.900,bicubic,-50.577,-39.810,+41
convnextv2_nano.fcmae_ft_in1k,44.100,55.900,60.167,39.833,15.62,288,1.000,bicubic,-51.040,-39.043,-62
pit_b_224.in1k,44.080,55.920,58.023,41.977,73.76,224,0.900,bicubic,-50.730,-41.237,+14
resnet152s.gluon_in1k,44.060,55.940,58.710,41.290,60.32,224,0.875,bicubic,-50.650,-40.350,+28
resnet50.fb_ssl_yfcc100m_ft_in1k,44.023,55.977,61.890,38.110,25.56,224,0.875,bilinear,-50.287,-37.260,+118
poolformer_m36.sail_in1k,44.007,55.993,59.033,40.967,56.17,224,0.950,bicubic,-51.023,-40.067,-36
inception_resnet_v2.tf_in1k,44.007,55.993,57.920,42.080,55.84,299,0.897,bicubic,-50.353,-40.880,+110
pnasnet5large.tf_in1k,43.943,56.057,56.740,43.260,86.06,331,0.911,bicubic,-51.417,-42.390,-119
resnext101_64x4d.gluon_in1k,43.913,56.087,58.707,41.293,83.46,224,0.875,bicubic,-50.437,-40.363,+111
wide_resnet50_2.tv2_in1k,43.907,56.093,59.630,40.370,68.88,224,0.965,bilinear,-50.903,-39.350,+2
coatnext_nano_rw_224.sw_in1k,43.907,56.093,58.663,41.337,14.70,224,0.900,bicubic,-50.943,-40.537,-3
pit_s_224.in1k,43.907,56.093,58.640,41.360,23.46,224,0.900,bicubic,-50.683,-40.500,+52
coat_lite_small.in1k,43.817,56.183,57.127,42.873,19.84,224,0.900,bicubic,-51.253,-41.993,-53
mobilevitv2_200.cvnets_in22k_ft_in1k,43.810,56.190,59.490,40.510,18.45,256,0.888,bicubic,-51.240,-39.670,-46
tnt_s_patch16_224,43.777,56.223,59.220,40.780,23.76,224,0.900,bicubic,-50.783,-39.920,+53
regnetv_040.ra3_in1k,43.777,56.223,58.443,41.557,20.64,288,1.000,bicubic,-51.953,-40.937,-201
swinv2_cr_small_224.sw_in1k,43.767,56.233,57.717,42.283,49.70,224,0.900,bicubic,-51.643,-41.343,-137
cspresnext50.ra_in1k,43.763,56.237,60.147,39.853,20.57,256,0.887,bilinear,-50.477,-38.903,+120
cait_xxs36_224.fb_dist_in1k,43.757,56.243,58.740,41.260,17.30,224,1.000,bicubic,-50.153,-40.350,+172
pit_xs_distilled_224.in1k,43.723,56.277,60.657,39.343,11.00,224,0.900,bicubic,-49.567,-38.133,+271
swin_s3_tiny_224.ms_in1k,43.717,56.283,59.507,40.493,28.33,224,0.900,bicubic,-51.203,-39.593,-29
tf_efficientnetv2_s.in1k,43.710,56.290,58.593,41.407,21.46,384,1.000,bicubic,-52.000,-40.787,-204
rexnet_150.nav_in1k,43.693,56.307,60.923,39.077,9.73,224,0.875,bicubic,-50.587,-38.057,+105
xcit_tiny_12_p8_224.fb_dist_in1k,43.643,56.357,58.477,41.523,6.71,224,1.000,bicubic,-51.047,-40.283,+14
edgenext_small.usi_in1k,43.637,56.363,59.893,40.107,5.59,320,1.000,bicubic,-51.193,-39.517,-14
tf_efficientnet_b5.in1k,43.623,56.377,60.133,39.867,30.39,456,0.934,bicubic,-52.247,-39.257,-238
efficientformer_l1.snap_dist_in1k,43.593,56.407,59.957,40.043,12.29,224,0.950,bicubic,-50.347,-38.973,+158
maxvit_nano_rw_256.sw_in1k,43.523,56.477,57.610,42.390,15.45,256,0.950,bicubic,-51.947,-41.780,-160
wide_resnet50_2.racm_in1k,43.503,56.497,59.053,40.947,68.88,288,0.950,bicubic,-51.627,-40.237,-88
cs3sedarknet_x.c2ns_in1k,43.503,56.497,58.770,41.230,35.40,288,1.000,bicubic,-51.907,-40.660,-151
coatnet_rmlp_nano_rw_224.sw_in1k,43.503,56.497,58.610,41.390,15.15,224,0.900,bicubic,-51.587,-40.560,-75
crossvit_small_240.in1k,43.473,56.527,58.950,41.050,26.86,240,0.875,bicubic,-51.107,-40.100,+32
regnety_016.tv2_in1k,43.433,56.567,59.540,40.460,11.20,224,0.965,bicubic,-50.977,-39.500,+68
resnet101d.gluon_in1k,43.430,56.570,58.627,41.373,44.57,224,0.875,bicubic,-50.750,-40.383,+118
ecaresnet50t.ra2_in1k,43.417,56.583,59.313,40.687,25.57,320,0.950,bicubic,-51.663,-39.827,-79
resnet101s.gluon_in1k,43.370,56.630,58.517,41.483,44.67,224,0.875,bicubic,-50.810,-40.423,+115
efficientvit_b1.r288_in1k,43.367,56.633,57.850,42.150,9.10,288,1.000,bicubic,-50.863,-40.970,+104
cspdarknet53.ra_in1k,43.360,56.640,59.420,40.580,27.64,256,0.887,bilinear,-50.740,-39.560,+125
tf_efficientnet_b4.in1k,43.330,56.670,59.447,40.553,19.34,380,0.922,bicubic,-52.150,-39.823,-174
xcit_tiny_24_p8_224.fb_in1k,43.323,56.677,57.277,42.723,12.11,224,1.000,bicubic,-51.577,-41.743,-44
xcit_tiny_12_p8_384.fb_dist_in1k,43.317,56.683,58.177,41.823,6.71,384,1.000,bicubic,-52.023,-41.133,-149
visformer_small.in1k,43.273,56.727,57.977,42.023,40.22,224,0.900,bicubic,-51.697,-41.193,-60
convmixer_768_32.in1k,43.270,56.730,59.383,40.617,21.11,224,0.960,bicubic,-51.170,-39.497,+52
repvit_m0_9.dist_300e_in1k,43.243,56.757,60.377,39.623,5.49,224,0.950,bicubic,-50.217,-38.573,+222
eca_nfnet_l0.ra2_in1k,43.227,56.773,59.930,40.070,24.14,288,1.000,bicubic,-52.233,-39.350,-176
ecaresnet101d_pruned.miil_in1k,43.220,56.780,58.967,41.033,24.88,288,0.950,bicubic,-51.780,-40.263,-69
regnety_064.ra3_in1k,43.213,56.787,57.253,42.747,30.58,288,1.000,bicubic,-52.577,-42.097,-243
poolformerv2_s24.sail_in1k,43.190,56.810,60.430,39.570,21.34,224,1.000,bicubic,-51.280,-38.580,+40
regnetx_160.tv2_in1k,43.187,56.813,57.480,42.520,54.28,224,0.965,bicubic,-52.023,-41.680,-126
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,43.183,56.817,58.403,41.597,119.42,256,0.900,bicubic,-49.957,-39.907,+258
vit_small_patch32_384.augreg_in21k_ft_in1k,43.150,56.850,59.313,40.687,22.92,384,1.000,bicubic,-51.440,-39.617,+11
resnest26d.gluon_in1k,43.140,56.860,60.637,39.363,17.07,224,0.875,bilinear,-50.080,-38.213,+246
twins_pcpvt_small.in1k,43.120,56.880,58.890,41.110,24.11,224,0.900,bicubic,-51.480,-40.210,+4
regnetx_080.tv2_in1k,43.090,56.910,57.923,42.077,39.57,224,0.965,bicubic,-51.640,-41.107,-25
repvit_m0_9.dist_450e_in1k,43.073,56.927,60.200,39.800,5.49,224,0.950,bicubic,-50.367,-38.450,+215
resmlp_36_224.fb_in1k,43.060,56.940,59.300,40.700,44.69,224,0.875,bicubic,-50.590,-39.350,+174
cspresnet50.ra_in1k,43.050,56.950,59.157,40.843,21.62,256,0.887,bilinear,-50.820,-39.733,+139
coatnet_nano_rw_224.sw_in1k,43.030,56.970,57.927,42.073,15.14,224,0.900,bicubic,-52.020,-41.223,-91
ecaresnet50d.miil_in1k,43.020,56.980,59.417,40.583,25.58,288,0.950,bicubic,-51.650,-39.813,-14
tf_efficientnet_lite4.in1k,42.990,57.010,57.630,42.370,13.01,380,0.920,bilinear,-51.880,-41.470,-55
twins_svt_small.in1k,42.917,57.083,58.460,41.540,24.06,224,0.900,bicubic,-51.843,-40.490,-39
dpn131.mx_in1k,42.917,57.083,57.130,42.870,79.25,224,0.875,bicubic,-50.863,-41.830,+149
mobilevitv2_200.cvnets_in22k_ft_in1k_384,42.913,57.087,58.973,41.027,18.45,384,1.000,bicubic,-52.477,-40.307,-179
resnet152.gluon_in1k,42.900,57.100,57.747,42.253,60.19,224,0.875,bicubic,-51.130,-41.103,+110
fastvit_sa12.apple_in1k,42.880,57.120,58.800,41.200,11.58,256,0.900,bicubic,-51.550,-40.200,+34
fbnetv3_d.ra2_in1k,42.850,57.150,59.677,40.323,10.31,256,0.950,bilinear,-51.020,-39.193,+129
levit_conv_256.fb_dist_in1k,42.823,57.177,57.897,42.103,18.89,224,0.900,bicubic,-51.577,-41.163,+36
resnet50.tv2_in1k,42.820,57.180,58.570,41.430,25.56,224,0.965,bilinear,-51.780,-40.460,-9
resnet152c.gluon_in1k,42.813,57.187,57.720,42.280,60.21,224,0.875,bicubic,-51.057,-41.080,+128
levit_256.fb_dist_in1k,42.810,57.190,57.897,42.103,18.89,224,0.900,bicubic,-51.590,-41.163,+32
tf_efficientnet_b1.ap_in1k,42.800,57.200,58.820,41.180,7.79,240,0.882,bicubic,-50.830,-39.980,+165
resnext50_32x4d.tv2_in1k,42.780,57.220,57.567,42.433,25.03,224,0.965,bilinear,-51.680,-41.333,+18
gcresnet50t.ra2_in1k,42.767,57.233,59.033,40.967,25.90,288,1.000,bicubic,-52.013,-40.087,-53
coatnet_0_rw_224.sw_in1k,42.753,57.247,56.233,43.767,27.44,224,0.950,bicubic,-52.147,-42.957,-76
tresnet_l.miil_in1k_448,42.740,57.260,58.943,41.057,55.99,448,0.875,bilinear,-52.660,-40.457,-193
cs3darknet_x.c2ns_in1k,42.727,57.273,58.190,41.810,35.05,288,1.000,bicubic,-52.553,-41.120,-171
dpn107.mx_in1k,42.710,57.290,57.160,42.840,86.92,224,0.875,bicubic,-51.300,-41.870,+102
seresnext50_32x4d.gluon_in1k,42.683,57.317,58.707,41.293,27.56,224,0.875,bicubic,-51.487,-40.213,+76
convnext_nano.d1h_in1k,42.677,57.323,57.577,42.423,15.59,288,1.000,bicubic,-52.193,-41.653,-75
tresnet_m.miil_in1k,42.670,57.330,58.160,41.840,31.39,224,0.875,bilinear,-51.410,-40.670,+88
fastvit_s12.apple_dist_in1k,42.643,57.357,58.160,41.840,9.47,256,0.900,bicubic,-51.637,-40.700,+48
resnetaa50d.sw_in12k_ft_in1k,42.610,57.390,58.490,41.510,25.58,288,1.000,bicubic,-52.680,-40.730,-180
xcit_tiny_12_p16_384.fb_dist_in1k,42.593,57.407,58.083,41.917,6.72,384,1.000,bicubic,-51.927,-40.887,-6
regnety_040.ra3_in1k,42.577,57.423,57.010,42.990,20.65,288,1.000,bicubic,-52.913,-42.240,-223
resnext101_32x8d.tv_in1k,42.563,57.437,58.303,41.697,88.79,224,0.875,bilinear,-51.217,-40.547,+124
efficientvit_b1.r256_in1k,42.527,57.473,57.427,42.573,9.10,256,1.000,bicubic,-51.183,-41.363,+136
nf_resnet50.ra2_in1k,42.517,57.483,59.530,40.470,25.56,288,0.940,bicubic,-51.863,-39.290,+21
mobilevitv2_175.cvnets_in22k_ft_in1k,42.517,57.483,58.117,41.883,14.25,256,0.888,bicubic,-52.263,-40.973,-66
seresnext50_32x4d.racm_in1k,42.433,57.567,58.093,41.907,27.56,288,0.950,bicubic,-52.567,-41.097,-111
resnetrs101.tf_in1k,42.417,57.583,57.283,42.717,63.62,288,0.940,bicubic,-52.833,-41.697,-180
poolformer_s36.sail_in1k,42.383,57.617,58.827,41.173,30.86,224,0.900,bicubic,-52.237,-40.293,-36
repvit_m1.dist_in1k,42.350,57.650,59.677,40.323,5.49,224,0.950,bicubic,-51.030,-38.973,+191
nest_tiny_jx.goog_in1k,42.320,57.680,57.067,42.933,17.06,224,0.875,bicubic,-52.600,-42.103,-99
mobileone_s4.apple_in1k,42.317,57.683,58.033,41.967,14.95,224,0.900,bilinear,-51.333,-40.917,+137
tf_efficientnetv2_b3.in1k,42.313,57.687,57.943,42.057,14.36,300,0.904,bicubic,-52.807,-41.277,-147
xcit_tiny_24_p16_224.fb_in1k,42.277,57.723,56.837,43.163,12.12,224,1.000,bicubic,-51.573,-41.913,+104
resnet152.a1_in1k,42.273,57.727,55.530,44.470,60.19,288,1.000,bicubic,-52.817,-43.460,-142
convmixer_1024_20_ks9_p14.in1k,42.267,57.733,59.723,40.277,24.38,224,0.960,bicubic,-50.063,-38.707,+292
deit_small_patch16_224.fb_in1k,42.263,57.737,58.030,41.970,22.05,224,0.900,bicubic,-51.727,-41.010,+83
mobileone_s3.apple_in1k,42.257,57.743,59.270,40.730,10.17,224,0.900,bilinear,-50.723,-39.350,+228
tf_efficientnet_cc_b1_8e.in1k,42.247,57.753,58.430,41.570,39.72,240,0.882,bicubic,-51.333,-40.260,+144
legacy_senet154.in1k,42.230,57.770,56.620,43.380,115.09,224,0.875,bilinear,-52.500,-42.480,-74
cait_xxs24_384.fb_dist_in1k,42.183,57.817,57.473,42.527,12.03,384,1.000,bicubic,-52.767,-41.657,-116
dpn98.mx_in1k,42.180,57.820,56.597,43.403,61.57,224,0.875,bicubic,-51.750,-42.313,+83
xception41p.ra3_in1k,42.160,57.840,56.883,43.117,26.91,299,0.940,bicubic,-52.890,-42.257,-140
tf_efficientnet_b2.aa_in1k,42.123,57.877,58.197,41.803,9.11,260,0.890,bicubic,-52.097,-40.733,+38
resnet50.b1k_in1k,42.080,57.920,58.153,41.847,25.56,288,1.000,bicubic,-52.430,-40.917,-24
resnext50_32x4d.gluon_in1k,42.050,57.950,57.673,42.327,25.03,224,0.875,bicubic,-51.620,-41.017,+121
convnext_nano_ols.d1h_in1k,42.023,57.977,56.867,43.133,15.65,288,1.000,bicubic,-52.557,-42.253,-44
pvt_v2_b1.in1k,41.953,58.047,59.593,40.407,14.01,224,0.900,bicubic,-51.537,-39.117,+148
xcit_tiny_12_p16_224.fb_dist_in1k,41.940,58.060,57.247,42.753,6.72,224,1.000,bicubic,-51.410,-41.443,+174
efficientnet_b2.ra_in1k,41.937,58.063,58.297,41.703,9.11,288,1.000,bicubic,-52.423,-40.743,+2
mobilevitv2_150.cvnets_in22k_ft_in1k,41.937,58.063,57.913,42.087,10.59,256,0.888,bicubic,-52.743,-41.197,-69
resnet50.b2k_in1k,41.910,58.090,57.673,42.327,25.56,288,1.000,bicubic,-52.390,-41.257,+12
efficientformerv2_s1.snap_dist_in1k,41.870,58.130,57.973,42.027,6.19,224,0.950,bicubic,-51.970,-40.917,+86
gcvit_xxtiny.in1k,41.837,58.163,58.440,41.560,12.00,224,0.875,bicubic,-52.213,-40.640,+54
fastvit_t12.apple_dist_in1k,41.817,58.183,57.597,42.403,7.55,256,0.900,bicubic,-52.283,-41.353,+47
tf_efficientnet_b3.in1k,41.803,58.197,58.057,41.943,12.23,300,0.904,bicubic,-52.487,-41.043,+9
mobilevitv2_150.cvnets_in22k_ft_in1k_384,41.790,58.210,57.807,42.193,10.59,384,1.000,bicubic,-53.560,-41.313,-228
resnet50d.ra2_in1k,41.787,58.213,58.023,41.977,25.58,288,0.950,bicubic,-53.023,-40.797,-105
resnext50_32x4d.a1h_in1k,41.753,58.247,56.443,43.557,25.03,288,1.000,bicubic,-53.237,-42.717,-142
gcresnext50ts.ch_in1k,41.707,58.293,57.403,42.597,15.67,288,1.000,bicubic,-52.783,-41.607,-36
efficientvit_b1.r224_in1k,41.703,58.297,56.600,43.400,9.10,224,0.950,bicubic,-51.627,-41.970,+166
poolformer_s24.sail_in1k,41.700,58.300,58.470,41.530,21.39,224,0.900,bicubic,-52.690,-40.590,-16
edgenext_small_rw.sw_in1k,41.677,58.323,58.517,41.483,7.83,320,1.000,bicubic,-52.683,-40.533,-10
mobilevitv2_175.cvnets_in22k_ft_in1k_384,41.673,58.327,58.010,41.990,14.25,384,1.000,bicubic,-53.587,-41.150,-218
hrnet_w64.ms_in1k,41.643,58.357,57.113,42.887,128.06,224,0.875,bilinear,-52.187,-41.827,+75
dla102x2.in1k,41.637,58.363,57.970,42.030,41.28,224,0.875,bilinear,-52.353,-40.990,+53
senet154.gluon_in1k,41.627,58.373,56.353,43.647,115.09,224,0.875,bicubic,-53.073,-42.617,-92
seresnet50.ra2_in1k,41.593,58.407,57.987,42.013,28.09,288,0.950,bicubic,-53.147,-41.123,-103
inception_v4.tf_in1k,41.557,58.443,55.380,44.620,42.68,299,0.875,bicubic,-52.823,-43.690,-20
cs3sedarknet_l.c2ns_in1k,41.553,58.447,57.350,42.650,21.91,288,0.950,bicubic,-53.557,-41.860,-183
convnext_tiny.fb_in22k_ft_in1k,41.533,58.467,55.470,44.530,28.59,288,1.000,bicubic,-51.997,-43.130,+121
haloregnetz_b.ra3_in1k,41.527,58.473,57.087,42.913,11.68,224,0.940,bicubic,-52.993,-41.893,-54
swinv2_cr_tiny_ns_224.sw_in1k,41.523,58.477,57.183,42.817,28.33,224,0.900,bicubic,-53.247,-41.927,-113
efficientnet_em.ra2_in1k,41.490,58.510,58.873,41.127,6.90,240,0.882,bicubic,-52.240,-40.057,+79
efficientnet_el.ra_in1k,41.490,58.510,58.293,41.707,10.59,300,0.904,bicubic,-53.180,-40.837,-89
tf_efficientnet_cc_b0_8e.in1k,41.483,58.517,57.370,42.630,24.01,224,0.875,bicubic,-51.387,-41.090,+209
convnextv2_pico.fcmae_ft_in1k,41.477,58.523,58.050,41.950,9.07,288,0.950,bicubic,-53.083,-41.120,-68
halo2botnet50ts_256.a1h_in1k,41.460,58.540,56.190,43.810,22.64,256,0.950,bicubic,-53.550,-42.950,-164
swin_tiny_patch4_window7_224.ms_in1k,41.453,58.547,57.303,42.697,28.29,224,0.900,bicubic,-53.167,-41.847,-87
resnetv2_50d_evos.ah_in1k,41.407,58.593,56.500,43.500,25.59,288,1.000,bicubic,-53.513,-42.600,-151
resnet50.a1h_in1k,41.387,58.613,56.677,43.323,25.56,224,1.000,bicubic,-52.813,-42.243,+6
cait_xxs24_224.fb_dist_in1k,41.370,58.630,57.520,42.480,11.96,224,1.000,bicubic,-52.080,-41.280,+124
swinv2_tiny_window8_256.ms_in1k,41.370,58.630,57.150,42.850,28.35,256,0.900,bicubic,-53.650,-41.820,-173
resnet152.tv_in1k,41.330,58.670,57.513,42.487,60.19,224,0.875,bilinear,-51.910,-41.187,+149
dpn68b.ra_in1k,41.297,58.703,55.077,44.923,12.61,288,1.000,bicubic,-52.493,-43.863,+61
resnet50.ram_in1k,41.297,58.703,55.033,44.967,25.56,288,0.950,bicubic,-52.703,-43.847,+32
cs3darknet_l.c2ns_in1k,41.277,58.723,57.377,42.623,21.16,288,0.950,bicubic,-53.393,-41.883,-103
xception71.tf_in1k,41.273,58.727,55.890,44.110,42.34,299,0.903,bicubic,-52.647,-43.060,+39
gernet_s.idstcv_in1k,41.260,58.740,58.823,41.177,8.17,224,0.875,bilinear,-51.180,-39.677,+232
inception_v3.tf_adv_in1k,41.253,58.747,56.327,43.673,23.83,299,0.875,bicubic,-51.757,-42.493,+171
resnet101.a1_in1k,41.207,58.793,54.273,45.727,44.55,288,1.000,bicubic,-53.733,-44.927,-164
dpn92.mx_in1k,41.203,58.797,56.217,43.783,37.67,224,0.875,bicubic,-52.947,-42.733,+2
resnetv2_50d_gn.ah_in1k,41.113,58.887,56.520,43.480,25.57,288,1.000,bicubic,-54.107,-42.510,-235
resnet50.c1_in1k,41.090,58.910,56.453,43.547,25.56,288,1.000,bicubic,-53.420,-42.547,-74
nf_regnet_b1.ra2_in1k,41.010,58.990,58.133,41.867,10.22,288,0.900,bicubic,-52.880,-40.757,+36
resnet50d.gluon_in1k,40.963,59.037,57.130,42.870,25.58,224,0.875,bicubic,-52.577,-41.580,+94
fbnetv3_b.ra2_in1k,40.960,59.040,58.650,41.350,8.60,256,0.950,bilinear,-52.670,-40.300,+76
resnet152.a3_in1k,40.940,59.060,55.030,44.970,60.19,224,0.950,bicubic,-53.500,-44.150,-63
inception_v3.gluon_in1k,40.910,59.090,55.623,44.377,23.83,299,0.875,bicubic,-52.650,-43.217,+86
ecaresnet50d_pruned.miil_in1k,40.907,59.093,57.633,42.367,19.94,288,0.950,bicubic,-53.393,-41.567,-37
cs3darknet_focus_l.c2ns_in1k,40.893,59.107,56.630,43.370,21.15,288,0.950,bicubic,-53.897,-42.530,-144
resnetv2_50.a1h_in1k,40.890,59.110,56.383,43.617,25.55,288,1.000,bicubic,-53.790,-42.707,-121
levit_conv_192.fb_dist_in1k,40.840,59.160,56.707,43.293,10.95,224,0.900,bicubic,-52.870,-42.113,+59
levit_192.fb_dist_in1k,40.837,59.163,56.710,43.290,10.95,224,0.900,bicubic,-52.873,-42.220,+57
tiny_vit_5m_224.in1k,40.827,59.173,57.257,42.743,5.39,224,0.950,bicubic,-52.963,-41.283,+39
regnety_320.pycls_in1k,40.810,59.190,56.110,43.890,145.05,224,0.875,bicubic,-53.710,-42.850,-92
regnetx_032.tv2_in1k,40.790,59.210,56.627,43.373,15.30,224,0.965,bicubic,-53.730,-42.543,-88
eva02_tiny_patch14_336.mim_in22k_ft_in1k,40.767,59.233,56.067,43.933,5.76,336,1.000,bicubic,-53.683,-43.033,-78
maxvit_rmlp_pico_rw_256.sw_in1k,40.757,59.243,55.203,44.797,7.52,256,0.950,bicubic,-53.453,-43.747,-24
legacy_xception.tf_in1k,40.753,59.247,56.397,43.603,22.86,299,0.897,bicubic,-52.867,-42.373,+65
lamhalobotnet50ts_256.a1h_in1k,40.753,59.247,56.103,43.897,22.57,256,0.950,bicubic,-54.057,-43.127,-156
resnet152.a2_in1k,40.737,59.263,54.280,45.720,60.19,288,1.000,bicubic,-54.233,-44.930,-189
vit_base_patch32_384.augreg_in1k,40.710,59.290,55.197,44.803,88.30,384,1.000,bicubic,-52.450,-43.413,+128
skresnext50_32x4d.ra_in1k,40.703,59.297,56.023,43.977,27.48,224,0.875,bicubic,-53.267,-42.807,+7
resnet101.gluon_in1k,40.693,59.307,56.137,43.863,44.55,224,0.875,bicubic,-53.067,-42.563,+35
hrnet_w40.ms_in1k,40.660,59.340,56.757,43.243,57.56,224,0.875,bilinear,-53.070,-42.043,+39
resmlp_24_224.fb_in1k,40.657,59.343,56.557,43.443,30.02,224,0.875,bicubic,-52.763,-42.273,+98
resnext50_32x4d.ra_in1k,40.627,59.373,56.340,43.660,25.03,288,0.950,bicubic,-53.703,-42.690,-58
resnet50.am_in1k,40.610,59.390,57.377,42.623,25.56,224,0.875,bicubic,-53.030,-41.493,+52
repvgg_b1.rvgg_in1k,40.607,59.393,57.843,42.157,57.42,224,0.875,bilinear,-52.833,-40.947,+90
ese_vovnet39b.ra_in1k,40.590,59.410,56.600,43.400,24.57,288,0.950,bicubic,-53.890,-42.460,-96
halonet50ts.a1h_in1k,40.577,59.423,55.193,44.807,22.73,256,0.940,bicubic,-54.143,-43.867,-153
tf_efficientnet_lite3.in1k,40.560,59.440,56.467,43.533,8.20,300,0.904,bilinear,-53.530,-42.373,-19
mobilevitv2_175.cvnets_in1k,40.537,59.463,56.273,43.727,14.25,256,0.888,bicubic,-53.693,-42.657,-44
xcit_tiny_12_p8_224.fb_in1k,40.537,59.463,55.647,44.353,6.71,224,1.000,bicubic,-53.813,-43.233,-69
dla169.in1k,40.523,59.477,57.240,42.760,53.39,224,0.875,bilinear,-53.277,-41.670,+17
tresnet_m.miil_in1k_448,40.520,59.480,56.687,43.313,31.39,448,0.875,bilinear,-54.140,-42.463,-139
regnetz_b16.ra3_in1k,40.507,59.493,56.003,43.997,9.72,288,1.000,bicubic,-54.163,-43.127,-142
pit_xs_224.in1k,40.493,59.507,56.540,43.460,10.62,224,0.900,bicubic,-52.397,-42.240,+152
resnetaa50.a1h_in1k,40.483,59.517,55.987,44.013,25.56,288,1.000,bicubic,-54.367,-42.983,-184
regnetx_320.pycls_in1k,40.470,59.530,55.653,44.347,107.81,224,0.875,bicubic,-53.760,-43.297,-53
vit_base_patch16_384.augreg_in1k,40.470,59.530,53.250,46.750,86.86,384,1.000,bicubic,-53.970,-45.780,-98
repvgg_b2.rvgg_in1k,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.113,-41.010,+48
coat_mini.in1k,40.420,59.580,55.200,44.800,10.34,224,0.900,bicubic,-54.340,-43.890,-172
eca_resnet33ts.ra2_in1k,40.400,59.600,57.340,42.660,19.68,288,1.000,bicubic,-53.850,-41.690,-60
resnet34d.ra2_in1k,40.400,59.600,56.170,43.830,21.82,288,0.950,bicubic,-53.200,-42.590,+41
skresnet34.ra_in1k,40.393,59.607,56.730,43.270,22.28,224,0.875,bicubic,-52.177,-41.790,+175
efficientnet_el_pruned.in1k,40.383,59.617,56.907,43.093,10.59,300,0.904,bicubic,-53.707,-42.113,-35
efficientnet_b2_pruned.in1k,40.373,59.627,56.520,43.480,8.31,260,0.890,bicubic,-53.427,-42.340,+3
wide_resnet101_2.tv_in1k,40.363,59.637,55.790,44.210,126.89,224,0.875,bilinear,-53.357,-43.010,+16
coat_lite_mini.in1k,40.357,59.643,55.697,44.303,11.01,224,0.900,bicubic,-53.113,-43.073,+60
sebotnet33ts_256.a1h_in1k,40.353,59.647,53.223,46.777,13.70,256,0.940,bicubic,-53.957,-45.377,-80
legacy_seresnext101_32x4d.in1k,40.347,59.653,54.817,45.183,48.96,224,0.875,bilinear,-53.773,-43.973,-45
tf_efficientnet_b0.ap_in1k,40.343,59.657,56.803,43.197,5.29,224,0.875,bicubic,-52.277,-41.567,+161
densenet201.tv_in1k,40.283,59.717,56.713,43.287,20.01,224,0.875,bicubic,-52.417,-41.887,+153
mobileone_s2.apple_in1k,40.263,59.737,57.967,42.033,7.88,224,0.900,bilinear,-52.397,-40.683,+156
regnetx_160.pycls_in1k,40.253,59.747,56.050,43.950,54.28,224,0.875,bicubic,-53.657,-42.840,-19
xception65.tf_in1k,40.253,59.747,55.263,44.737,39.92,299,0.903,bicubic,-53.497,-43.607,+5
resnet101.a2_in1k,40.190,59.810,54.223,45.777,44.55,288,1.000,bicubic,-54.700,-45.047,-210
resnet50.c2_in1k,40.187,59.813,55.247,44.753,25.56,288,1.000,bicubic,-54.083,-43.573,-80
resnet50.ra_in1k,40.180,59.820,56.197,43.803,25.56,288,0.950,bicubic,-53.910,-42.763,-46
resnetblur50.bt_in1k,40.163,59.837,56.190,43.810,25.56,288,0.950,bicubic,-54.007,-42.820,-61
poolformerv2_s12.sail_in1k,40.160,59.840,57.447,42.553,11.89,224,1.000,bicubic,-52.730,-41.083,+129
mobilevitv2_200.cvnets_in1k,40.140,59.860,55.517,44.483,18.45,256,0.888,bicubic,-54.380,-43.653,-137
fastvit_t12.apple_in1k,40.127,59.873,55.243,44.757,7.55,256,0.900,bicubic,-53.363,-43.617,+42
darknetaa53.c2ns_in1k,40.120,59.880,55.787,44.213,36.02,288,1.000,bilinear,-54.090,-43.213,-70
hrnet_w48.ms_in1k,40.110,59.890,56.633,43.367,77.47,224,0.875,bilinear,-53.900,-42.297,-43
vit_base_patch16_224.sam_in1k,40.100,59.900,55.423,44.577,86.57,224,0.900,bicubic,-53.790,-43.317,-28
resnet50_gn.a1h_in1k,40.057,59.943,54.853,45.147,25.56,288,0.950,bicubic,-54.563,-44.197,-168
legacy_seresnet152.in1k,40.050,59.950,55.837,44.163,66.82,224,0.875,bilinear,-53.360,-42.993,+56
resnet50.d_in1k,40.050,59.950,54.713,45.287,25.56,288,1.000,bicubic,-54.420,-44.287,-134
hrnet_w30.ms_in1k,40.043,59.957,57.110,42.890,37.71,224,0.875,bilinear,-53.367,-41.740,+55
seresnet33ts.ra2_in1k,39.990,60.010,56.403,43.597,19.78,288,1.000,bicubic,-54.270,-42.597,-90
regnetx_080.pycls_in1k,39.990,60.010,55.950,44.050,39.57,224,0.875,bicubic,-53.800,-42.960,-18
tf_efficientnet_b1.aa_in1k,39.970,60.030,56.127,43.873,7.79,240,0.882,bicubic,-53.750,-42.683,-8
resnet101c.gluon_in1k,39.953,60.047,55.287,44.713,44.57,224,0.875,bicubic,-53.747,-43.433,-3
fastvit_s12.apple_in1k,39.927,60.073,54.840,45.160,9.47,256,0.900,bicubic,-53.773,-43.920,-3
convnext_pico_ols.d1_in1k,39.887,60.113,55.617,44.383,9.06,288,1.000,bicubic,-54.123,-43.413,-52
resmlp_12_224.fb_distilled_in1k,39.833,60.167,57.430,42.570,15.35,224,0.875,bicubic,-53.037,-41.190,+115
tf_efficientnetv2_b0.in1k,39.800,60.200,56.287,43.713,7.14,224,0.875,bicubic,-53.260,-42.413,+80
res2net50_26w_8s.in1k,39.800,60.200,54.900,45.100,48.40,224,0.875,bilinear,-53.610,-43.850,+49
darknet53.c2ns_in1k,39.737,60.263,55.287,44.713,41.61,288,1.000,bicubic,-54.623,-43.763,-121
res2net101_26w_4s.in1k,39.720,60.280,54.550,45.450,45.21,224,0.875,bilinear,-53.810,-44.020,+17
lambda_resnet50ts.a1h_in1k,39.720,60.280,54.373,45.627,21.54,256,0.950,bicubic,-54.850,-44.537,-167
regnetx_120.pycls_in1k,39.687,60.313,55.643,44.357,46.11,224,0.875,bicubic,-54.573,-43.527,-103
hrnet_w44.ms_in1k,39.687,60.313,55.330,44.670,67.06,224,0.875,bilinear,-53.933,-43.630,0
vit_small_patch32_224.augreg_in21k_ft_in1k,39.677,60.323,55.253,44.747,22.88,224,0.900,bicubic,-52.463,-43.267,+162
resmlp_big_24_224.fb_in1k,39.637,60.363,54.830,45.170,129.14,224,0.875,bicubic,-54.633,-44.120,-106
vit_small_patch16_384.augreg_in1k,39.623,60.377,54.243,45.757,22.20,384,1.000,bicubic,-54.987,-44.897,-185
mixnet_xl.ra_in1k,39.620,60.380,55.870,44.130,11.90,224,0.875,bicubic,-54.610,-43.180,-99
xception41.tf_in1k,39.607,60.393,55.013,44.987,26.97,299,0.903,bicubic,-53.873,-43.737,+19
densenet161.tv_in1k,39.603,60.397,56.130,43.870,28.68,224,0.875,bicubic,-53.287,-42.660,+98
tf_efficientnetv2_b1.in1k,39.573,60.427,55.327,44.673,8.14,240,0.882,bicubic,-54.137,-43.463,-24
dla102x.in1k,39.567,60.433,56.320,43.680,26.31,224,0.875,bilinear,-53.973,-42.530,+5
xcit_tiny_12_p16_224.fb_in1k,39.540,60.460,55.050,44.950,6.72,224,1.000,bicubic,-52.940,-43.380,+133
sehalonet33ts.ra2_in1k,39.540,60.460,54.023,45.977,13.69,256,0.940,bicubic,-54.970,-44.737,-163
convnext_pico.d1_in1k,39.517,60.483,55.317,44.683,9.05,288,0.950,bicubic,-54.513,-43.693,-77
tf_efficientnet_b2.in1k,39.513,60.487,56.107,43.893,9.11,260,0.890,bicubic,-54.197,-42.703,-30
rexnet_130.nav_in1k,39.480,60.520,56.637,43.363,7.56,224,0.875,bicubic,-54.200,-42.063,-23
hrnet_w32.ms_in1k,39.467,60.533,56.120,43.880,41.23,224,0.875,bilinear,-53.483,-42.730,+79
levit_128.fb_dist_in1k,39.447,60.553,55.350,44.650,9.21,224,0.900,bicubic,-53.583,-43.360,+62
levit_conv_128.fb_dist_in1k,39.447,60.553,55.337,44.663,9.21,224,0.900,bicubic,-53.583,-43.363,+62
resnetv2_50x1_bit.goog_in21k_ft_in1k,39.433,60.567,57.853,42.147,25.55,448,1.000,bilinear,-55.307,-41.327,-229
ecaresnet50t.a1_in1k,39.417,60.583,53.680,46.320,25.57,288,1.000,bicubic,-55.473,-45.380,-256
regnety_064.pycls_in1k,39.387,60.613,55.770,44.230,30.58,224,0.875,bicubic,-54.743,-43.260,-100
regnety_120.pycls_in1k,39.343,60.657,55.277,44.723,51.82,224,0.875,bicubic,-54.667,-43.543,-81
gcresnet33ts.ra2_in1k,39.333,60.667,55.880,44.120,19.88,288,1.000,bicubic,-55.017,-43.220,-140
mobilevitv2_150.cvnets_in1k,39.323,60.677,55.230,44.770,10.59,256,0.888,bicubic,-54.727,-43.670,-89
resnet101.tv_in1k,39.290,60.710,55.787,44.213,44.55,224,0.875,bilinear,-53.590,-42.873,+84
tf_efficientnet_el.in1k,39.287,60.713,55.377,44.623,10.59,300,0.904,bicubic,-55.063,-43.583,-145
resnet50s.gluon_in1k,39.260,60.740,55.020,44.980,25.68,224,0.875,bicubic,-54.310,-43.820,-17
resnet101.a3_in1k,39.253,60.747,53.710,46.290,44.55,224,0.950,bicubic,-54.607,-45.050,-65
regnety_160.pycls_in1k,39.250,60.750,55.447,44.553,83.59,224,0.875,bicubic,-54.880,-43.573,-107
repghostnet_200.in1k,39.243,60.757,56.423,43.577,9.80,224,0.875,bicubic,-54.307,-42.397,-16
inception_v3.tf_in1k,39.223,60.777,54.300,45.700,23.83,299,0.875,bicubic,-53.967,-44.360,+34
resnext50_32x4d.a1_in1k,39.190,60.810,53.350,46.650,25.03,288,1.000,bicubic,-55.190,-45.430,-156
densenet169.tv_in1k,39.170,60.830,55.860,44.140,14.15,224,0.875,bicubic,-53.130,-42.750,+123
tf_efficientnetv2_b2.in1k,39.167,60.833,54.563,45.437,10.10,260,0.890,bicubic,-54.893,-44.367,-101
resnext50d_32x4d.bt_in1k,39.100,60.900,54.343,45.657,25.05,288,0.950,bicubic,-55.300,-44.447,-166
legacy_seresnet101.in1k,39.043,60.957,55.003,44.997,49.33,224,0.875,bilinear,-54.227,-43.737,+22
efficientnet_b1_pruned.in1k,39.003,60.997,55.630,44.370,6.33,240,0.882,bicubic,-53.977,-43.100,+57
repvgg_b1g4.rvgg_in1k,38.977,61.023,56.350,43.650,39.97,224,0.875,bilinear,-54.053,-42.250,+40
crossvit_9_dagger_240.in1k,38.970,61.030,54.867,45.133,8.78,240,0.875,bicubic,-53.800,-43.793,+81
resnet50.a1_in1k,38.967,61.033,53.243,46.757,25.56,288,1.000,bicubic,-55.253,-45.507,-131
inception_v3.tv_in1k,38.943,61.057,53.877,46.123,23.83,299,0.875,bicubic,-53.957,-44.853,+64
regnety_080.pycls_in1k,38.910,61.090,55.190,44.810,39.18,224,0.875,bicubic,-54.970,-43.810,-84
res2net101d.in1k,38.903,61.097,53.057,46.943,45.23,224,0.875,bilinear,-55.617,-45.853,-201
legacy_seresnext50_32x4d.in1k,38.890,61.110,54.577,45.423,27.56,224,0.875,bilinear,-54.560,-44.203,-13
dla102.in1k,38.840,61.160,55.317,44.683,33.27,224,0.875,bilinear,-54.430,-43.473,+12
visformer_tiny.in1k,38.830,61.170,55.023,44.977,10.32,224,0.900,bicubic,-54.150,-43.647,+44
regnety_040.pycls_in1k,38.823,61.177,55.580,44.420,20.65,224,0.875,bicubic,-54.807,-43.330,-49
efficientvit_m5.r224_in1k,38.820,61.180,54.980,45.020,12.47,224,0.875,bicubic,-53.330,-43.540,+116
regnetx_040.pycls_in1k,38.710,61.290,55.357,44.643,22.12,224,0.875,bicubic,-54.980,-43.573,-58
res2net50_14w_8s.in1k,38.710,61.290,54.073,45.927,25.06,224,0.875,bilinear,-54.320,-44.747,+31
dpn68.mx_in1k,38.687,61.313,54.687,45.313,12.61,224,0.875,bicubic,-53.613,-43.903,+104
regnetx_032.pycls_in1k,38.680,61.320,55.160,44.840,15.30,224,0.875,bicubic,-54.560,-43.560,+8
res2net50_26w_6s.in1k,38.680,61.320,53.773,46.227,37.05,224,0.875,bilinear,-54.910,-44.857,-49
resnet33ts.ra2_in1k,38.667,61.333,55.173,44.827,19.68,288,1.000,bicubic,-55.263,-43.707,-102
wide_resnet50_2.tv_in1k,38.650,61.350,54.467,45.533,68.88,224,0.875,bilinear,-54.810,-44.353,-26
selecsls60.in1k,38.620,61.380,55.617,44.383,30.67,224,0.875,bicubic,-54.390,-42.873,+28
regnetx_016.tv2_in1k,38.620,61.380,54.733,45.267,9.19,224,0.965,bicubic,-55.400,-44.197,-117
dla60x.in1k,38.610,61.390,55.403,44.597,17.35,224,0.875,bilinear,-54.580,-43.317,+5
tf_efficientnet_b0.aa_in1k,38.597,61.403,55.970,44.030,5.29,224,0.875,bicubic,-53.803,-42.500,+90
densenetblur121d.ra_in1k,38.593,61.407,55.637,44.363,8.00,288,0.950,bicubic,-54.437,-43.063,+22
dla60_res2net.in1k,38.593,61.407,54.563,45.437,20.85,224,0.875,bilinear,-54.777,-44.277,-13
repvgg_a2.rvgg_in1k,38.553,61.447,55.767,44.233,28.21,224,0.875,bilinear,-54.127,-42.513,+64
selecsls60b.in1k,38.550,61.450,55.310,44.690,32.77,224,0.875,bicubic,-54.950,-43.470,-43
seresnet50.a1_in1k,38.537,61.463,53.413,46.587,28.09,288,1.000,bicubic,-55.623,-45.437,-145
dpn68b.mx_in1k,38.530,61.470,55.183,44.817,12.61,224,0.875,bicubic,-54.250,-43.337,+51
resnet32ts.ra2_in1k,38.510,61.490,55.507,44.493,17.96,288,1.000,bicubic,-55.080,-43.233,-61
hardcorenas_f.miil_green_in1k,38.507,61.493,55.653,44.347,8.20,224,0.875,bilinear,-54.473,-42.977,+26
hrnet_w18_small_v2.gluon_in1k,38.460,61.540,56.180,43.820,15.60,224,0.875,bicubic,-54.540,-42.580,+20
resmlp_12_224.fb_in1k,38.440,61.560,56.327,43.673,15.35,224,0.875,bicubic,-53.680,-42.243,+98
tf_efficientnet_cc_b0_4e.in1k,38.430,61.570,55.177,44.823,13.31,224,0.875,bicubic,-54.390,-43.263,+44
dla60_res2next.in1k,38.423,61.577,54.930,45.070,17.03,224,0.875,bilinear,-55.157,-44.140,-63
regnetx_064.pycls_in1k,38.420,61.580,54.993,45.007,26.21,224,0.875,bicubic,-55.220,-44.047,-76
ghostnetv2_160.in1k,38.410,61.590,55.530,44.470,12.39,224,0.875,bicubic,-54.680,-43.210,+2
resnet50.gluon_in1k,38.410,61.590,54.830,45.170,25.56,224,0.875,bicubic,-54.150,-43.720,+65
resnet50d.a1_in1k,38.403,61.597,52.860,47.140,25.58,288,1.000,bicubic,-55.997,-46.200,-204
regnety_008_tv.tv2_in1k,38.337,61.663,54.270,45.730,6.43,224,0.965,bicubic,-54.813,-44.410,-7
hrnet_w18.ms_in1k,38.263,61.737,55.660,44.340,21.30,224,0.875,bilinear,-54.507,-42.960,+41
tinynet_a.in1k,38.230,61.770,55.193,44.807,6.19,192,0.875,bicubic,-54.580,-43.367,+37
ecaresnet50t.a3_in1k,38.227,61.773,53.650,46.350,25.57,224,0.950,bicubic,-55.633,-45.200,-117
resnet34.a1_in1k,38.220,61.780,52.373,47.627,21.80,288,1.000,bicubic,-54.780,-46.257,+9
densenet121.ra_in1k,38.180,61.820,55.137,44.863,7.98,288,0.950,bicubic,-54.520,-43.503,+44
mixnet_l.ft_in1k,38.173,61.827,54.797,45.203,7.33,224,0.875,bicubic,-55.067,-43.953,-21
regnety_032.pycls_in1k,38.167,61.833,54.363,45.637,19.44,224,0.875,bicubic,-55.293,-44.587,-55
poolformer_s12.sail_in1k,38.157,61.843,56.213,43.787,11.92,224,0.900,bicubic,-54.343,-42.177,+58
hardcorenas_e.miil_green_in1k,38.150,61.850,55.177,44.823,8.07,224,0.875,bilinear,-54.790,-43.333,+12
efficientnet_b1.ft_in1k,38.080,61.920,54.000,46.000,7.79,256,1.000,bicubic,-54.940,-44.710,-2
ecaresnet50t.a2_in1k,38.053,61.947,52.967,47.033,25.57,288,1.000,bicubic,-56.517,-46.073,-257
gmixer_24_224.ra3_in1k,38.050,61.950,52.077,47.923,24.72,224,0.875,bicubic,-54.630,-46.453,+39
vit_base_patch16_224.augreg_in1k,38.037,61.963,50.710,49.290,86.57,224,0.900,bicubic,-55.313,-48.050,-37
coat_lite_tiny.in1k,38.033,61.967,53.463,46.537,5.72,224,0.900,bicubic,-54.827,-45.177,+22
resnetrs50.tf_in1k,37.977,62.023,53.303,46.697,35.69,224,0.910,bicubic,-56.053,-45.437,-154
resnext50_32x4d.a2_in1k,37.933,62.067,52.353,47.647,25.03,288,1.000,bicubic,-56.287,-46.687,-182
mobilevitv2_125.cvnets_in1k,37.893,62.107,54.067,45.933,7.48,256,0.888,bicubic,-55.587,-44.773,-68
resnet50c.gluon_in1k,37.873,62.127,54.117,45.883,25.58,224,0.875,bicubic,-55.037,-44.573,+7
hardcorenas_c.miil_green_in1k,37.860,62.140,55.727,44.273,5.52,224,0.875,bilinear,-54.490,-42.613,+58
efficientformerv2_s0.snap_dist_in1k,37.823,62.177,54.053,45.947,3.60,224,0.950,bicubic,-54.037,-44.317,+82
res2net50_26w_4s.in1k,37.810,62.190,53.080,46.920,25.70,224,0.875,bilinear,-55.370,-45.580,-31
efficientnet_es.ra_in1k,37.787,62.213,54.960,45.040,5.44,224,0.875,bicubic,-55.123,-43.720,+4
resnest14d.gluon_in1k,37.777,62.223,56.480,43.520,10.61,224,0.875,bilinear,-53.373,-41.850,+109
convnext_femto.d1_in1k,37.733,62.267,54.100,45.900,5.22,288,0.950,bicubic,-55.707,-44.720,-65
resnext50_32x4d.tv_in1k,37.727,62.273,54.103,45.897,25.03,224,0.875,bilinear,-55.173,-44.217,+3
pit_ti_distilled_224.in1k,37.710,62.290,55.647,44.353,5.10,224,0.900,bicubic,-53.020,-42.603,+121
ecaresnet26t.ra2_in1k,37.667,62.333,54.357,45.643,16.01,320,0.950,bicubic,-56.273,-44.673,-153
seresnet50.a2_in1k,37.667,62.333,52.373,47.627,28.09,288,1.000,bicubic,-56.783,-46.517,-247
vit_base_patch32_224.augreg_in1k,37.563,62.437,51.810,48.190,88.22,224,0.900,bicubic,-53.027,-45.910,+122
fastvit_t8.apple_dist_in1k,37.557,62.443,53.830,46.170,4.03,256,0.900,bicubic,-55.033,-44.710,+30
hardcorenas_d.miil_green_in1k,37.543,62.457,54.720,45.280,7.50,224,0.875,bilinear,-55.067,-43.790,+26
res2next50.in1k,37.483,62.517,52.853,47.147,24.67,224,0.875,bilinear,-55.667,-45.787,-38
convnextv2_femto.fcmae_ft_in1k,37.477,62.523,53.507,46.493,5.23,288,0.950,bicubic,-56.273,-45.463,-131
resnet50.bt_in1k,37.420,62.580,53.860,46.140,25.56,288,0.950,bicubic,-56.540,-45.070,-162
hrnet_w18.ms_aug_in1k,37.337,62.663,54.123,45.877,21.30,224,0.950,bilinear,-56.143,-44.857,-87
lambda_resnet26t.c1_in1k,37.300,62.700,53.563,46.437,10.96,256,0.940,bicubic,-56.130,-45.167,-73
convnext_femto_ols.d1_in1k,37.253,62.747,53.047,46.953,5.23,288,0.950,bicubic,-56.137,-45.863,-67
mobilenetv3_large_100.miil_in21k_ft_in1k,37.243,62.757,53.543,46.457,5.48,224,0.875,bilinear,-55.017,-44.697,+47
hardcorenas_b.miil_green_in1k,37.233,62.767,55.033,44.967,5.18,224,0.875,bilinear,-54.687,-43.377,+60
resnet50d.a2_in1k,37.227,62.773,51.743,48.257,25.58,288,1.000,bicubic,-57.233,-47.287,-261
res2net50d.in1k,37.223,62.777,51.387,48.613,25.72,224,0.875,bilinear,-57.057,-47.703,-223
fastvit_t8.apple_in1k,37.217,62.783,53.133,46.867,4.03,256,0.900,bicubic,-54.713,-45.247,+56
eca_halonext26ts.c1_in1k,37.170,62.830,53.103,46.897,10.76,256,0.940,bicubic,-56.380,-45.497,-106
cs3darknet_focus_m.c2ns_in1k,37.140,62.860,53.910,46.090,9.30,288,0.950,bicubic,-55.960,-44.840,-46
res2net50_48w_2s.in1k,37.120,62.880,53.327,46.673,25.29,224,0.875,bilinear,-55.660,-45.143,-4
lambda_resnet26rpt_256.c1_in1k,37.103,62.897,53.860,46.140,10.99,256,0.940,bicubic,-56.327,-45.020,-84
vit_small_patch16_224.augreg_in1k,37.080,62.920,51.553,48.447,22.05,224,0.900,bicubic,-56.370,-47.227,-90
rexnet_100.nav_in1k,37.057,62.943,54.050,45.950,4.80,224,0.875,bicubic,-55.773,-44.550,-12
bat_resnext26ts.ch_in1k,37.057,62.943,53.743,46.257,10.73,256,0.900,bicubic,-56.063,-44.987,-51
resnet50.a2_in1k,37.050,62.950,51.347,48.653,25.56,288,1.000,bicubic,-57.070,-47.623,-201
dla60.in1k,37.040,62.960,54.220,45.780,22.04,224,0.875,bilinear,-55.610,-44.410,+3
tf_efficientnet_b1.in1k,37.017,62.983,53.417,46.583,7.79,240,0.882,bicubic,-55.913,-45.243,-29
regnety_016.pycls_in1k,37.013,62.987,54.083,45.917,11.20,224,0.875,bicubic,-55.997,-44.587,-43
botnet26t_256.c1_in1k,36.963,63.037,53.053,46.947,12.49,256,0.950,bicubic,-56.477,-45.857,-93
tf_mixnet_l.in1k,36.960,63.040,52.597,47.403,7.33,224,0.875,bicubic,-56.070,-45.933,-48
resnet34.a2_in1k,36.953,63.047,51.433,48.567,21.80,288,1.000,bicubic,-55.617,-47.137,+5
mobileone_s1.apple_in1k,36.930,63.070,54.603,45.397,4.83,224,0.900,bilinear,-54.860,-43.857,+46
ghostnetv2_130.in1k,36.887,63.113,54.137,45.863,8.96,224,0.875,bicubic,-55.353,-44.243,+28
legacy_seresnet50.in1k,36.863,63.137,53.473,46.527,28.09,224,0.875,bilinear,-55.797,-45.207,-6
halonet26t.a1h_in1k,36.843,63.157,52.270,47.730,12.48,256,0.950,bicubic,-56.747,-46.470,-130
densenet121.tv_in1k,36.807,63.193,54.030,45.970,7.98,224,0.875,bicubic,-54.593,-44.220,+60
tf_efficientnet_lite2.in1k,36.800,63.200,53.313,46.687,6.09,260,0.890,bicubic,-55.790,-45.117,-3
mobilenetv2_120d.ra_in1k,36.790,63.210,54.047,45.953,5.83,224,0.875,bicubic,-55.820,-44.383,-7
tf_efficientnet_lite1.in1k,36.730,63.270,53.583,46.417,5.42,240,0.882,bicubic,-55.560,-44.917,+17
regnetx_016.pycls_in1k,36.693,63.307,53.307,46.693,9.19,224,0.875,bicubic,-55.837,-45.243,0
eca_botnext26ts_256.c1_in1k,36.690,63.310,52.493,47.507,10.59,256,0.950,bicubic,-56.660,-46.197,-92
hardcorenas_a.miil_green_in1k,36.687,63.313,54.923,45.077,5.26,224,0.875,bilinear,-54.933,-43.357,+45
levit_conv_128s.fb_dist_in1k,36.623,63.377,53.137,46.863,7.78,224,0.900,bicubic,-54.877,-45.263,+48
levit_128s.fb_dist_in1k,36.623,63.377,53.130,46.870,7.78,224,0.900,bicubic,-54.887,-45.270,+48
repghostnet_150.in1k,36.617,63.383,54.110,45.890,6.58,224,0.875,bicubic,-55.763,-44.420,+4
efficientnet_b0.ra_in1k,36.593,63.407,53.477,46.523,5.29,224,0.875,bicubic,-55.887,-45.203,-3
efficientvit_m4.r224_in1k,36.587,63.413,53.267,46.733,8.80,224,0.875,bicubic,-54.153,-44.773,+74
resnext50_32x4d.a3_in1k,36.553,63.447,51.143,48.857,25.03,224,0.950,bicubic,-56.997,-47.537,-137
vit_base_patch32_224.sam_in1k,36.543,63.457,53.043,46.957,88.22,224,0.900,bicubic,-53.327,-44.557,+94
xcit_nano_12_p8_224.fb_dist_in1k,36.510,63.490,52.873,47.127,3.05,224,1.000,bicubic,-55.920,-45.667,-3
cs3darknet_m.c2ns_in1k,36.480,63.520,53.230,46.770,9.31,288,0.950,bicubic,-56.800,-45.490,-96
repvgg_a1.rvgg_in1k,36.450,63.550,53.770,46.230,14.09,224,0.875,bilinear,-54.670,-44.390,+57
tf_efficientnet_em.in1k,36.393,63.607,52.853,47.147,6.90,240,0.882,bicubic,-56.797,-45.637,-90
mobilevitv2_100.cvnets_in1k,36.387,63.613,53.083,46.917,4.90,256,0.888,bicubic,-56.753,-45.677,-84
resnet50d.a3_in1k,36.330,63.670,51.320,48.680,25.58,224,0.950,bicubic,-57.080,-47.370,-112
skresnet18.ra_in1k,36.313,63.687,54.187,45.813,11.96,224,0.875,bicubic,-53.867,-43.593,+83
repvgg_b0.rvgg_in1k,36.290,63.710,54.057,45.943,15.82,224,0.875,bilinear,-55.390,-44.393,+25
resnet34.bt_in1k,36.257,63.743,52.757,47.243,21.80,288,0.950,bicubic,-56.023,-45.843,-1
resnet50.tv_in1k,36.180,63.820,52.793,47.207,25.56,224,0.875,bilinear,-55.940,-45.617,+8
xcit_nano_12_p16_384.fb_dist_in1k,36.150,63.850,53.260,46.740,3.05,384,1.000,bicubic,-55.980,-45.250,+4
legacy_seresnet34.in1k,36.150,63.850,52.567,47.433,21.96,224,0.875,bilinear,-55.340,-45.633,+33
coat_tiny.in1k,36.117,63.883,51.047,48.953,5.50,224,0.900,bicubic,-57.393,-47.633,-144
efficientvit_m3.r224_in1k,36.090,63.910,52.453,47.547,6.90,224,0.875,bicubic,-53.910,-45.377,+77
resnet34.tv_in1k,36.070,63.930,53.537,46.463,21.80,224,0.875,bilinear,-54.230,-44.433,+70
deit_tiny_distilled_patch16_224.fb_in1k,36.023,63.977,54.243,45.757,5.91,224,0.900,bicubic,-55.057,-44.027,+50
mobilenetv2_140.ra_in1k,35.997,64.003,53.963,46.037,6.11,224,0.875,bicubic,-56.053,-44.287,+4
convnextv2_atto.fcmae_ft_in1k,35.990,64.010,51.187,48.813,3.71,288,0.950,bicubic,-56.930,-47.373,-68
resnet50.a3_in1k,35.957,64.043,50.483,49.517,25.56,224,0.950,bicubic,-56.983,-48.187,-71
tf_efficientnet_lite0.in1k,35.937,64.063,53.477,46.523,4.65,224,0.875,bicubic,-55.343,-44.363,+29
selecsls42b.in1k,35.813,64.187,52.473,47.527,32.46,224,0.875,bicubic,-56.667,-46.157,-25
xcit_nano_12_p8_384.fb_dist_in1k,35.797,64.203,52.300,47.700,3.05,384,1.000,bicubic,-57.503,-46.560,-118
seresnext26ts.ch_in1k,35.780,64.220,53.460,46.540,10.39,288,1.000,bicubic,-57.160,-45.120,-78
resnet34.gluon_in1k,35.780,64.220,52.183,47.817,21.80,224,0.875,bicubic,-55.310,-45.997,+42
convnext_atto.d2_in1k,35.777,64.223,52.323,47.677,3.70,288,0.950,bicubic,-56.993,-46.167,-56
seresnet50.a3_in1k,35.757,64.243,51.163,48.837,28.09,224,0.950,bicubic,-56.603,-47.167,-24
resnet26t.ra2_in1k,35.727,64.273,53.587,46.413,16.01,320,1.000,bicubic,-57.183,-45.113,-74
dla34.in1k,35.657,64.343,52.803,47.197,15.74,224,0.875,bilinear,-55.563,-45.367,+24
efficientnet_lite0.ra_in1k,35.640,64.360,53.663,46.337,4.65,224,0.875,bicubic,-55.610,-44.577,+22
mixnet_m.ft_in1k,35.633,64.367,52.423,47.577,5.01,224,0.875,bicubic,-56.637,-45.937,-20
resnet18.fb_ssl_yfcc100m_ft_in1k,35.613,64.387,53.753,46.247,11.69,224,0.875,bilinear,-55.087,-44.267,+43
mobilenetv3_rw.rmsp_in1k,35.543,64.457,53.707,46.293,5.48,224,0.875,bicubic,-56.007,-44.573,+9
regnetx_008.tv2_in1k,35.527,64.473,51.480,48.520,7.26,224,0.965,bicubic,-56.963,-46.950,-40
convnext_atto_ols.a2_in1k,35.403,64.597,51.390,48.610,3.70,288,0.950,bicubic,-57.577,-47.150,-93
efficientnet_es_pruned.in1k,35.393,64.607,52.840,47.160,5.44,224,0.875,bicubic,-56.307,-45.570,-3
mobilenetv2_110d.ra_in1k,35.313,64.687,52.870,47.130,4.52,224,0.875,bicubic,-56.017,-45.320,+12
repghostnet_130.in1k,35.263,64.737,52.567,47.433,5.48,224,0.875,bicubic,-56.677,-45.823,-14
tf_mixnet_m.in1k,35.193,64.807,50.987,49.013,5.01,224,0.875,bicubic,-57.017,-47.433,-25
hrnet_w18_small_v2.ms_in1k,35.163,64.837,52.420,47.580,15.60,224,0.875,bilinear,-55.997,-45.920,+17
xcit_nano_12_p16_224.fb_dist_in1k,35.120,64.880,52.543,47.457,3.05,224,1.000,bicubic,-55.070,-45.207,+49
resnet34.a3_in1k,35.033,64.967,50.503,49.497,21.80,224,0.950,bicubic,-55.207,-47.377,+45
convit_tiny.fb_in1k,35.027,64.973,51.777,48.223,5.71,224,0.875,bicubic,-55.523,-46.413,+36
gcresnext26ts.ch_in1k,35.017,64.983,51.493,48.507,10.48,288,1.000,bicubic,-57.723,-47.117,-70
eca_resnext26ts.ch_in1k,34.927,65.073,52.370,47.630,10.30,288,1.000,bicubic,-57.823,-46.340,-72
regnety_004.tv2_in1k,34.923,65.077,51.263,48.737,4.34,224,0.965,bicubic,-56.697,-46.907,-8
tinynet_b.in1k,34.867,65.133,51.997,48.003,3.73,188,0.875,bicubic,-56.243,-46.063,+15
resnext26ts.ra2_in1k,34.837,65.163,52.710,47.290,10.30,288,1.000,bicubic,-57.543,-45.680,-46
regnety_008.pycls_in1k,34.780,65.220,51.743,48.257,6.26,224,0.875,bicubic,-57.120,-46.667,-21
pit_ti_224.in1k,34.670,65.330,52.160,47.840,4.85,224,0.900,bicubic,-55.750,-45.850,+33
tf_efficientnet_b0.in1k,34.623,65.377,51.143,48.857,5.29,224,0.875,bicubic,-57.657,-47.407,-41
crossvit_9_240.in1k,34.613,65.387,51.767,48.233,8.55,240,0.875,bicubic,-56.437,-46.553,+16
mobilenetv3_large_100.ra_in1k,34.600,65.400,52.843,47.157,5.48,224,0.875,bicubic,-56.870,-45.477,-7
mixer_b16_224.goog_in21k_ft_in1k,34.427,65.573,48.107,51.893,59.88,224,0.875,bicubic,-56.713,-49.293,+6
pvt_v2_b0.in1k,34.400,65.600,53.103,46.897,3.67,224,0.900,bicubic,-54.570,-44.587,+55
tf_efficientnet_es.in1k,34.260,65.740,51.353,48.647,5.44,224,0.875,bicubic,-57.850,-47.077,-35
fbnetc_100.rmsp_in1k,34.243,65.757,51.190,48.810,5.57,224,0.875,bilinear,-57.037,-46.890,-6
resnet18d.ra2_in1k,34.213,65.787,51.763,48.237,11.71,288,0.950,bicubic,-56.587,-46.397,+12
regnety_006.pycls_in1k,34.153,65.847,51.253,48.747,6.06,224,0.875,bicubic,-57.407,-47.177,-18
repvgg_a0.rvgg_in1k,34.070,65.930,51.967,48.033,9.11,224,0.875,bilinear,-55.610,-45.793,+37
ghostnetv2_100.in1k,34.037,65.963,51.973,48.027,6.16,224,0.875,bicubic,-57.593,-46.317,-24
resnet18.a1_in1k,33.973,66.027,49.410,50.590,11.69,288,1.000,bicubic,-56.227,-48.350,+27
tf_mobilenetv3_large_100.in1k,33.963,66.037,51.470,48.530,5.48,224,0.875,bilinear,-57.457,-46.790,-16
regnetx_008.pycls_in1k,33.803,66.197,50.543,49.457,7.26,224,0.875,bicubic,-57.367,-47.827,-8
repghostnet_111.in1k,33.790,66.210,51.543,48.457,4.54,224,0.875,bicubic,-57.310,-46.687,0
mnasnet_100.rmsp_in1k,33.777,66.223,51.150,48.850,4.38,224,0.875,bicubic,-57.433,-47.070,-11
semnasnet_075.rmsp_in1k,33.773,66.227,52.403,47.597,2.91,224,0.875,bicubic,-56.447,-45.547,+21
lcnet_100.ra2_in1k,33.767,66.233,52.090,47.910,2.95,224,0.875,bicubic,-55.143,-45.290,+45
ese_vovnet19b_dw.ra_in1k,33.753,66.247,50.920,49.080,6.54,288,0.950,bicubic,-59.007,-47.730,-97
regnetx_004_tv.tv2_in1k,33.717,66.283,49.803,50.197,5.50,224,0.965,bicubic,-57.443,-48.297,-12
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,33.633,66.367,50.687,49.313,6.36,384,1.000,bicubic,-58.097,-47.743,-40
mobilevit_s.cvnets_in1k,33.633,66.367,49.287,50.713,5.58,256,0.900,bicubic,-59.517,-49.493,-152
xcit_nano_12_p8_224.fb_in1k,33.580,66.420,50.233,49.767,3.05,224,1.000,bicubic,-57.520,-47.817,-10
vit_tiny_patch16_384.augreg_in21k_ft_in1k,33.550,66.450,51.077,48.923,5.79,384,1.000,bicubic,-59.870,-47.733,-183
semnasnet_100.rmsp_in1k,33.507,66.493,50.803,49.197,3.89,224,0.875,bicubic,-58.163,-47.487,-39
spnasnet_100.rmsp_in1k,33.490,66.510,51.287,48.713,4.42,224,0.875,bilinear,-57.100,-46.663,+1
mixnet_s.ft_in1k,33.467,66.533,51.007,48.993,4.13,224,0.875,bicubic,-58.313,-47.293,-46
crossvit_tiny_240.in1k,33.347,66.653,49.900,50.100,7.01,240,0.875,bicubic,-57.183,-48.050,+3
mobilevitv2_075.cvnets_in1k,33.340,66.660,50.110,49.890,2.87,256,0.888,bicubic,-58.630,-48.190,-56
efficientvit_m2.r224_in1k,33.293,66.707,49.810,50.190,4.19,224,0.875,bicubic,-55.617,-47.580,+32
vgg19_bn.tv_in1k,33.240,66.760,50.777,49.223,143.68,224,0.875,bilinear,-57.750,-47.323,-12
regnetx_006.pycls_in1k,33.147,66.853,50.243,49.757,6.20,224,0.875,bicubic,-57.643,-47.847,-11
repghostnet_100.in1k,33.140,66.860,50.770,49.230,4.07,224,0.875,bicubic,-57.550,-47.350,-7
edgenext_x_small.in1k,33.110,66.890,49.003,50.997,2.34,288,1.000,bicubic,-58.470,-49.187,-44
seresnext26t_32x4d.bt_in1k,33.083,66.917,50.270,49.730,16.81,288,0.950,bicubic,-60.267,-48.390,-183
resnet18.tv_in1k,33.057,66.943,51.173,48.827,11.69,224,0.875,bilinear,-55.093,-45.947,+34
seresnext26d_32x4d.bt_in1k,32.973,67.027,49.823,50.177,16.81,288,0.950,bicubic,-60.087,-48.887,-160
xcit_nano_12_p16_224.fb_in1k,32.953,67.047,49.977,50.023,3.05,224,1.000,bicubic,-56.017,-47.433,+23
mobileone_s0.apple_in1k,32.853,67.147,51.063,48.937,5.29,224,0.875,bilinear,-55.957,-46.157,+25
hrnet_w18_small.gluon_in1k,32.827,67.173,50.380,49.620,13.19,224,0.875,bicubic,-57.483,-47.370,-5
legacy_seresnext26_32x4d.in1k,32.773,67.227,49.217,50.783,16.79,224,0.875,bicubic,-59.827,-49.193,-106
hrnet_w18_small.ms_in1k,32.653,67.347,50.587,49.413,13.19,224,0.875,bilinear,-57.217,-47.303,+1
deit_tiny_patch16_224.fb_in1k,32.650,67.350,50.263,49.737,5.72,224,0.900,bicubic,-56.960,-47.697,+6
legacy_seresnet18.in1k,32.600,67.400,50.317,49.683,11.78,224,0.875,bicubic,-56.660,-47.373,+11
ghostnet_100.in1k,32.550,67.450,50.410,49.590,5.18,224,0.875,bicubic,-57.910,-47.500,-13
mobilenetv2_100.ra_in1k,32.513,67.487,50.790,49.210,3.50,224,0.875,bicubic,-57.357,-47.040,-2
regnetx_004.pycls_in1k,32.493,67.507,49.323,50.677,5.16,224,0.875,bicubic,-56.977,-48.437,+3
resnet26d.bt_in1k,32.420,67.580,49.997,50.003,16.01,288,0.950,bicubic,-60.130,-48.653,-107
resnet18.gluon_in1k,32.417,67.583,49.737,50.263,11.69,224,0.875,bicubic,-56.243,-47.363,+16
regnety_004.pycls_in1k,32.313,67.687,49.463,50.537,4.34,224,0.875,bicubic,-58.457,-48.607,-28
resnet18.a2_in1k,32.223,67.777,47.890,52.110,11.69,288,1.000,bicubic,-57.247,-49.520,0
tf_mixnet_s.in1k,32.200,67.800,48.523,51.477,4.13,224,0.875,bicubic,-59.480,-49.717,-67
resnet26.bt_in1k,32.173,67.827,49.443,50.557,16.00,288,0.950,bicubic,-59.937,-49.107,-83
vit_tiny_patch16_224.augreg_in21k_ft_in1k,32.020,67.980,49.017,50.983,5.72,224,0.900,bicubic,-59.900,-49.323,-77
tf_mobilenetv3_large_075.in1k,31.847,68.153,49.130,50.870,3.99,224,0.875,bilinear,-58.483,-48.740,-21
tf_mobilenetv3_large_minimal_100.in1k,31.600,68.400,49.353,50.647,3.92,224,0.875,bilinear,-57.560,-47.957,+2
efficientvit_m1.r224_in1k,31.297,68.703,48.197,51.803,2.98,224,0.875,bicubic,-55.923,-48.823,+18
repghostnet_080.in1k,30.873,69.127,48.797,51.203,3.28,224,0.875,bicubic,-58.597,-48.833,-6
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,30.790,69.210,47.663,52.337,6.34,224,0.900,bicubic,-58.550,-50.037,-5
tinynet_c.in1k,30.517,69.483,48.477,51.523,2.46,184,0.875,bicubic,-57.883,-48.783,+6
lcnet_075.ra2_in1k,30.373,69.627,48.770,51.230,2.36,224,0.875,bicubic,-56.587,-47.780,+19
vgg16_bn.tv_in1k,30.357,69.643,47.280,52.720,138.37,224,0.875,bilinear,-60.183,-50.710,-32
resnet18.a3_in1k,30.093,69.907,46.333,53.667,11.69,224,0.950,bicubic,-56.977,-50.327,+15
efficientvit_b0.r224_in1k,30.060,69.940,46.603,53.397,3.41,224,0.950,bicubic,-58.240,-50.277,+4
edgenext_xx_small.in1k,29.750,70.250,46.487,53.513,1.33,288,1.000,bicubic,-60.050,-51.013,-19
regnety_002.pycls_in1k,29.707,70.293,46.827,53.173,3.16,224,0.875,bicubic,-58.463,-50.613,+3
mobilevit_xs.cvnets_in1k,29.587,70.413,46.037,53.963,2.32,256,0.900,bicubic,-61.623,-52.013,-63
mobilenetv3_small_100.lamb_in1k,29.050,70.950,47.200,52.800,2.54,224,0.875,bicubic,-57.130,-49.250,+14
mnasnet_small.lamb_in1k,28.953,71.047,47.287,52.713,2.03,224,0.875,bicubic,-56.527,-48.713,+15
vgg13_bn.tv_in1k,28.863,71.137,46.730,53.270,133.05,224,0.875,bilinear,-60.337,-50.800,-13
resnet10t.c3_in1k,28.857,71.143,46.903,53.097,5.44,224,0.950,bicubic,-57.823,-49.827,+10
regnetx_002.pycls_in1k,28.837,71.163,45.453,54.547,2.68,224,0.875,bicubic,-58.533,-51.547,+1
efficientvit_m0.r224_in1k,28.797,71.203,45.777,54.223,2.35,224,0.875,bicubic,-54.423,-49.923,+19
mobilenetv2_050.lamb_in1k,28.657,71.343,46.597,53.403,1.97,224,0.875,bicubic,-56.333,-49.023,+14
vgg19.tv_in1k,28.580,71.420,45.167,54.833,143.67,224,0.875,bilinear,-61.100,-52.383,-27
mobilevitv2_050.cvnets_in1k,28.560,71.440,45.203,54.797,1.37,256,0.888,bicubic,-60.470,-52.397,-17
dla60x_c.in1k,28.443,71.557,46.220,53.780,1.32,224,0.875,bilinear,-58.667,-50.920,0
vgg11_bn.tv_in1k,28.430,71.570,46.460,53.540,132.87,224,0.875,bilinear,-59.970,-50.790,-11
repghostnet_058.in1k,28.400,71.600,46.587,53.413,2.55,224,0.875,bicubic,-58.750,-50.193,-3
tinynet_d.in1k,27.960,72.040,45.877,54.123,2.34,152,0.875,bicubic,-57.480,-50.143,+7
vgg16.tv_in1k,27.877,72.123,44.680,55.320,138.36,224,0.875,bilinear,-61.493,-52.840,-28
resnet14t.c3_in1k,27.553,72.447,44.683,55.317,10.08,224,0.950,bicubic,-61.697,-52.757,-26
tf_mobilenetv3_small_100.in1k,27.293,72.707,44.410,55.590,2.54,224,0.875,bilinear,-58.657,-51.990,0
repghostnet_050.in1k,27.060,72.940,44.977,55.023,2.31,224,0.875,bicubic,-58.390,-51.173,+1
mixer_l16_224.goog_in21k_ft_in1k,26.860,73.140,37.913,62.087,208.20,224,0.875,bicubic,-60.130,-56.157,-6
vgg11.tv_in1k,26.537,73.463,43.470,56.530,132.86,224,0.875,bilinear,-60.813,-53.630,-12
mobilenetv3_small_075.lamb_in1k,26.533,73.467,43.877,56.123,2.04,224,0.875,bicubic,-57.587,-51.643,+4
mobilevit_xxs.cvnets_in1k,26.353,73.647,43.060,56.940,1.27,256,0.900,bicubic,-61.587,-54.130,-17
vgg13.tv_in1k,26.273,73.727,43.353,56.647,133.05,224,0.875,bilinear,-61.297,-53.757,-17
dla46x_c.in1k,26.207,73.793,43.777,56.223,1.07,224,0.875,bilinear,-59.233,-52.643,-4
lcnet_050.ra2_in1k,26.197,73.803,44.577,55.423,1.88,224,0.875,bicubic,-56.843,-50.443,+2
tf_mobilenetv3_small_075.in1k,26.197,73.803,43.617,56.383,2.04,224,0.875,bilinear,-58.303,-52.263,-2
dla46_c.in1k,25.507,74.493,43.777,56.223,1.30,224,0.875,bilinear,-59.193,-52.433,-4
tf_mobilenetv3_small_minimal_100.in1k,25.123,74.877,42.950,57.050,2.04,224,0.875,bilinear,-57.537,-52.080,0
tinynet_e.in1k,23.353,76.647,41.067,58.933,2.04,106,0.875,bicubic,-56.457,-52.903,0
mobilenetv3_small_050.lamb_in1k,21.737,78.263,38.757,61.243,1.59,224,0.875,bicubic,-56.343,-54.253,0
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-a-clean.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.930,1.070,99.910,0.090,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.850,1.150,99.880,0.120,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,98.840,1.160,99.830,0.170,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,98.830,1.170,99.900,0.100,"1,014.45",560,1.000,bicubic
eva_giant_patch14_336.clip_ft_in1k,98.820,1.180,99.810,0.190,"1,013.01",336,1.000,bicubic
eva_giant_patch14_336.m30m_ft_in22k_in1k,98.810,1.190,99.900,0.100,"1,013.01",336,1.000,bicubic
eva_large_patch14_336.in22k_ft_in22k_in1k,98.740,1.260,99.800,0.200,304.53,336,1.000,bicubic
eva_large_patch14_336.in22k_ft_in1k,98.730,1.270,99.870,0.130,304.53,336,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in1k,98.730,1.270,99.790,0.210,305.08,448,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_384,98.670,1.330,99.860,0.140,660.29,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,98.640,1.360,99.800,0.200,87.12,448,1.000,bicubic
maxvit_base_tf_512.in21k_ft_in1k,98.620,1.380,99.800,0.200,119.88,512,1.000,bicubic
maxvit_xlarge_tf_512.in21k_ft_in1k,98.620,1.380,99.800,0.200,475.77,512,1.000,bicubic
maxvit_large_tf_512.in21k_ft_in1k,98.620,1.380,99.790,0.210,212.33,512,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_512,98.600,1.400,99.870,0.130,660.29,512,1.000,bicubic
beit_large_patch16_512.in22k_ft_in22k_in1k,98.560,1.440,99.840,0.160,305.67,512,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k,98.550,1.450,99.820,0.180,480.31,800,0.960,bicubic
beitv2_large_patch16_224.in1k_ft_in22k_in1k,98.540,1.460,99.760,0.240,304.43,224,0.950,bicubic
beit_large_patch16_384.in22k_ft_in22k_in1k,98.520,1.480,99.820,0.180,305.00,384,1.000,bicubic
maxvit_base_tf_384.in21k_ft_in1k,98.520,1.480,99.750,0.250,119.65,384,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k_475,98.500,1.500,99.830,0.170,480.31,475,0.936,bicubic
maxvit_xlarge_tf_384.in21k_ft_in1k,98.500,1.500,99.780,0.220,475.32,384,1.000,bicubic
maxvit_large_tf_384.in21k_ft_in1k,98.490,1.510,99.750,0.250,212.03,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,98.480,1.520,99.780,0.220,200.13,384,1.000,bicubic
deit3_large_patch16_384.fb_in22k_ft_in1k,98.460,1.540,99.760,0.240,304.76,384,1.000,bicubic
eva_giant_patch14_224.clip_ft_in1k,98.460,1.540,99.750,0.250,"1,012.56",224,0.900,bicubic
regnety_1280.swag_ft_in1k,98.450,1.550,99.870,0.130,644.81,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in1k,98.440,1.560,99.820,0.180,87.12,448,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k_384,98.440,1.560,99.800,0.200,98.75,384,1.000,bicubic
convnext_xxlarge.clip_laion2b_soup_ft_in1k,98.440,1.560,99.800,0.200,846.47,256,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k_384,98.420,1.580,99.810,0.190,350.20,384,1.000,bicubic
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,98.420,1.580,99.810,0.190,632.46,336,1.000,bicubic
eva_large_patch14_196.in22k_ft_in22k_in1k,98.420,1.580,99.770,0.230,304.14,196,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k_384,98.400,1.600,99.760,0.240,197.96,384,1.000,bicubic
eva_large_patch14_196.in22k_ft_in1k,98.360,1.640,99.820,0.180,304.14,196,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k_384,98.350,1.650,99.770,0.230,88.72,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,98.340,1.660,99.760,0.240,304.53,336,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,98.300,1.700,99.760,0.240,632.05,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,98.280,1.720,99.770,0.230,200.13,320,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k_384,98.260,1.740,99.830,0.170,99.88,384,1.000,bicubic
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,98.260,1.740,99.780,0.220,116.09,384,1.000,bicubic
vit_large_patch14_clip_336.openai_ft_in12k_in1k,98.260,1.740,99.770,0.230,304.53,336,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,98.250,1.750,99.760,0.240,200.13,384,1.000,bicubic
convnext_large.fb_in22k_ft_in1k_384,98.240,1.760,99.750,0.250,197.77,384,1.000,bicubic
vit_large_patch16_384.augreg_in21k_ft_in1k,98.220,1.780,99.800,0.200,304.72,384,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in12k_in1k,98.220,1.780,99.720,0.280,304.20,224,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in1k,98.220,1.780,99.720,0.280,304.53,336,1.000,bicubic
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,98.210,1.790,99.780,0.220,149.39,384,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in12k_in1k,98.190,1.810,99.660,0.340,86.86,384,0.950,bicubic
beit_large_patch16_224.in22k_ft_in22k_in1k,98.180,1.820,99.760,0.240,304.43,224,0.900,bicubic
deit3_large_patch16_224.fb_in22k_ft_in1k,98.170,1.830,99.760,0.240,304.37,224,1.000,bicubic
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,98.170,1.830,99.760,0.240,116.14,384,1.000,bicubic
deit3_huge_patch14_224.fb_in22k_ft_in1k,98.170,1.830,99.730,0.270,632.13,224,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k,98.160,1.840,99.780,0.220,98.75,224,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in1k,98.160,1.840,99.660,0.340,304.20,224,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k_384,98.150,1.850,99.750,0.250,56.20,384,1.000,bicubic
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,98.130,1.870,99.710,0.290,196.74,384,1.000,bicubic
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,98.120,1.880,99.780,0.220,87.92,384,1.000,bicubic
convnext_large.fb_in22k_ft_in1k,98.120,1.880,99.740,0.260,197.77,288,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k,98.110,1.890,99.780,0.220,350.20,288,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k,98.090,1.910,99.770,0.230,197.96,288,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,98.080,1.920,99.760,0.240,304.20,224,1.000,bicubic
convnext_base.fb_in22k_ft_in1k_384,98.080,1.920,99.650,0.350,88.59,384,1.000,bicubic
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,98.070,1.930,99.720,0.280,73.88,384,1.000,bicubic
regnety_320.swag_ft_in1k,98.060,1.940,99.860,0.140,145.05,384,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k,98.060,1.940,99.760,0.240,88.72,288,1.000,bicubic
swin_large_patch4_window12_384.ms_in22k_ft_in1k,98.050,1.950,99.690,0.310,196.74,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,98.040,1.960,99.750,0.250,88.59,384,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k_384,98.040,1.960,99.690,0.310,57.05,384,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in1k,98.020,1.980,99.720,0.280,632.05,224,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,97.990,2.010,99.660,0.340,86.86,384,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k_384,97.970,2.030,99.720,0.280,39.30,384,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,97.970,2.030,99.700,0.300,93.59,320,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,97.950,2.050,99.710,0.290,200.13,256,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k,97.940,2.060,99.760,0.240,99.88,224,1.000,bicubic
tf_efficientnet_b7.ns_jft_in1k,97.920,2.080,99.720,0.280,66.35,600,0.949,bicubic
beitv2_large_patch16_224.in1k_ft_in1k,97.910,2.090,99.660,0.340,304.43,224,0.950,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k,97.910,2.090,99.660,0.340,93.59,288,1.000,bicubic
swin_base_patch4_window12_384.ms_in22k_ft_in1k,97.900,2.100,99.710,0.290,87.90,384,1.000,bicubic
convnextv2_huge.fcmae_ft_in1k,97.900,2.100,99.670,0.330,660.29,288,1.000,bicubic
tf_efficientnetv2_xl.in21k_ft_in1k,97.900,2.100,99.570,0.430,208.12,512,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in1k,97.890,2.110,99.650,0.350,304.20,224,1.000,bicubic
tiny_vit_21m_512.dist_in22k_ft_in1k,97.870,2.130,99.630,0.370,21.27,512,1.000,bicubic
convnext_base.fb_in22k_ft_in1k,97.860,2.140,99.680,0.320,88.59,288,1.000,bicubic
vit_large_r50_s32_384.augreg_in21k_ft_in1k,97.860,2.140,99.670,0.330,329.09,384,1.000,bicubic
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,97.850,2.150,99.650,0.350,196.74,256,0.900,bicubic
convformer_s36.sail_in22k_ft_in1k_384,97.850,2.150,99.640,0.360,40.01,384,1.000,bicubic
deit3_base_patch16_384.fb_in22k_ft_in1k,97.840,2.160,99.680,0.320,86.88,384,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k,97.840,2.160,99.670,0.330,56.20,224,1.000,bicubic
vit_base_patch16_384.augreg_in21k_ft_in1k,97.840,2.160,99.670,0.330,86.86,384,1.000,bicubic
maxvit_large_tf_512.in1k,97.830,2.170,99.560,0.440,212.33,512,1.000,bicubic
beit_base_patch16_384.in22k_ft_in22k_in1k,97.820,2.180,99.700,0.300,86.74,384,1.000,bicubic
tf_efficientnetv2_m.in21k_ft_in1k,97.820,2.180,99.600,0.400,54.14,480,1.000,bicubic
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,97.810,2.190,99.650,0.350,116.14,224,0.950,bicubic
tf_efficientnetv2_l.in21k_ft_in1k,97.800,2.200,99.770,0.230,118.52,480,1.000,bicubic
convnext_small.in12k_ft_in1k_384,97.800,2.200,99.660,0.340,50.22,384,1.000,bicubic
regnety_160.swag_ft_in1k,97.780,2.220,99.760,0.240,83.59,384,1.000,bicubic
dm_nfnet_f6.dm_in1k,97.780,2.220,99.650,0.350,438.36,576,0.956,bicubic
dm_nfnet_f5.dm_in1k,97.780,2.220,99.600,0.400,377.21,544,0.954,bicubic
volo_d5_512.sail_in1k,97.770,2.230,99.670,0.330,296.09,512,1.150,bicubic
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,97.760,2.240,99.700,0.300,116.09,224,0.950,bicubic
volo_d5_448.sail_in1k,97.750,2.250,99.620,0.380,295.91,448,1.150,bicubic
maxvit_small_tf_512.in1k,97.750,2.250,99.550,0.450,69.13,512,1.000,bicubic
maxvit_base_tf_512.in1k,97.730,2.270,99.610,0.390,119.88,512,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in1k,97.720,2.280,99.630,0.370,86.86,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in22k_in1k,97.690,2.310,99.680,0.320,86.53,224,0.900,bicubic
vit_base_patch8_224.augreg2_in21k_ft_in1k,97.690,2.310,99.650,0.350,86.58,224,0.900,bicubic
volo_d4_448.sail_in1k,97.670,2.330,99.610,0.390,193.41,448,1.150,bicubic
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,97.660,2.340,99.720,0.280,87.92,256,0.900,bicubic
convnextv2_large.fcmae_ft_in1k,97.660,2.340,99.610,0.390,197.96,288,1.000,bicubic
regnety_1280.swag_lc_in1k,97.650,2.350,99.640,0.360,644.81,224,0.965,bicubic
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,97.650,2.350,99.570,0.430,73.88,224,0.950,bicubic
swin_large_patch4_window7_224.ms_in22k_ft_in1k,97.650,2.350,99.570,0.430,196.53,224,0.900,bicubic
dm_nfnet_f4.dm_in1k,97.640,2.360,99.540,0.460,316.07,512,0.951,bicubic
vit_large_patch16_224.augreg_in21k_ft_in1k,97.630,2.370,99.590,0.410,304.33,224,0.900,bicubic
tf_efficientnet_b6.ns_jft_in1k,97.620,2.380,99.580,0.420,43.04,528,0.942,bicubic
convnext_base.clip_laiona_augreg_ft_in1k_384,97.620,2.380,99.550,0.450,88.59,384,1.000,bicubic
convnext_small.fb_in22k_ft_in1k_384,97.610,2.390,99.600,0.400,50.22,384,1.000,bicubic
tiny_vit_21m_384.dist_in22k_ft_in1k,97.610,2.390,99.590,0.410,21.23,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,97.600,2.400,99.720,0.280,88.59,256,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k,97.600,2.400,99.620,0.380,57.05,224,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k,97.600,2.400,99.610,0.390,39.30,224,1.000,bicubic
maxvit_tiny_tf_512.in1k,97.580,2.420,99.560,0.440,31.05,512,1.000,bicubic
vit_base_patch8_224.augreg_in21k_ft_in1k,97.570,2.430,99.670,0.330,86.58,224,0.900,bicubic
maxvit_base_tf_384.in1k,97.570,2.430,99.590,0.410,119.65,384,1.000,bicubic
maxvit_large_tf_384.in1k,97.570,2.430,99.530,0.470,212.03,384,1.000,bicubic
volo_d3_448.sail_in1k,97.550,2.450,99.560,0.440,86.63,448,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in1k,97.540,2.460,99.660,0.340,86.86,384,1.000,bicubic
convformer_b36.sail_in1k_384,97.530,2.470,99.520,0.480,99.88,384,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in12k_in1k,97.530,2.470,99.500,0.500,86.57,224,0.950,bicubic
coatnet_2_rw_224.sw_in12k_ft_in1k,97.520,2.480,99.600,0.400,73.87,224,0.950,bicubic
xcit_large_24_p8_384.fb_dist_in1k,97.520,2.480,99.540,0.460,188.93,384,1.000,bicubic
xcit_large_24_p16_384.fb_dist_in1k,97.520,2.480,99.480,0.520,189.10,384,1.000,bicubic
tf_efficientnet_b5.ns_jft_in1k,97.500,2.500,99.640,0.360,30.39,456,0.934,bicubic
caformer_b36.sail_in1k_384,97.500,2.500,99.580,0.420,98.75,384,1.000,bicubic
resnetv2_152x4_bit.goog_in21k_ft_in1k,97.490,2.510,99.610,0.390,936.53,480,1.000,bilinear
deit3_base_patch16_224.fb_in22k_ft_in1k,97.480,2.520,99.600,0.400,86.59,224,1.000,bicubic
cait_m48_448.fb_dist_in1k,97.480,2.520,99.550,0.450,356.46,448,1.000,bicubic
dm_nfnet_f3.dm_in1k,97.470,2.530,99.560,0.440,254.92,416,0.940,bicubic
tf_efficientnetv2_l.in1k,97.470,2.530,99.530,0.470,118.52,480,1.000,bicubic
regnety_160.lion_in12k_ft_in1k,97.450,2.550,99.600,0.400,83.59,288,1.000,bicubic
regnety_160.sw_in12k_ft_in1k,97.450,2.550,99.590,0.410,83.59,288,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,97.450,2.550,99.540,0.460,86.57,224,0.950,bicubic
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,97.440,2.560,99.640,0.360,39.03,384,0.950,bicubic
caformer_m36.sail_in1k_384,97.440,2.560,99.600,0.400,56.20,384,1.000,bicubic
maxvit_small_tf_384.in1k,97.430,2.570,99.510,0.490,69.02,384,1.000,bicubic
deit3_large_patch16_384.fb_in1k,97.420,2.580,99.620,0.380,304.76,384,1.000,bicubic
caformer_s18.sail_in22k_ft_in1k_384,97.420,2.580,99.570,0.430,26.34,384,1.000,bicubic
flexivit_large.1200ep_in1k,97.410,2.590,99.600,0.400,304.36,240,0.950,bicubic
efficientnet_b5.sw_in12k_ft_in1k,97.410,2.590,99.550,0.450,30.39,448,1.000,bicubic
convformer_m36.sail_in1k_384,97.410,2.590,99.470,0.530,57.05,384,1.000,bicubic
cait_m36_384.fb_dist_in1k,97.400,2.600,99.510,0.490,271.22,384,1.000,bicubic
caformer_s36.sail_in1k_384,97.390,2.610,99.540,0.460,39.30,384,1.000,bicubic
volo_d5_224.sail_in1k,97.380,2.620,99.570,0.430,295.46,224,0.960,bicubic
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,97.370,2.630,99.680,0.320,468.53,224,0.875,bilinear
convnext_small.fb_in22k_ft_in1k,97.360,2.640,99.530,0.470,50.22,288,1.000,bicubic
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,97.360,2.640,99.520,0.480,88.30,384,1.000,bicubic
convnext_small.in12k_ft_in1k,97.350,2.650,99.580,0.420,50.22,288,1.000,bicubic
convnext_tiny.in12k_ft_in1k_384,97.340,2.660,99.600,0.400,28.59,384,1.000,bicubic
cait_s36_384.fb_dist_in1k,97.330,2.670,99.540,0.460,68.37,384,1.000,bicubic
volo_d2_384.sail_in1k,97.320,2.680,99.600,0.400,58.87,384,1.000,bicubic
maxvit_tiny_tf_384.in1k,97.310,2.690,99.500,0.500,30.98,384,1.000,bicubic
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,97.310,2.690,99.480,0.520,88.34,448,1.000,bicubic
flexivit_large.600ep_in1k,97.280,2.720,99.590,0.410,304.36,240,0.950,bicubic
swin_base_patch4_window7_224.ms_in22k_ft_in1k,97.280,2.720,99.540,0.460,87.77,224,0.900,bicubic
regnety_120.sw_in12k_ft_in1k,97.280,2.720,99.530,0.470,51.82,288,1.000,bicubic
volo_d4_224.sail_in1k,97.280,2.720,99.520,0.480,192.96,224,0.960,bicubic
xcit_medium_24_p8_384.fb_dist_in1k,97.280,2.720,99.510,0.490,84.32,384,1.000,bicubic
xcit_medium_24_p16_384.fb_dist_in1k,97.280,2.720,99.470,0.530,84.40,384,1.000,bicubic
convformer_s36.sail_in1k_384,97.280,2.720,99.430,0.570,40.01,384,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k_384,97.270,2.730,99.550,0.450,26.77,384,1.000,bicubic
inception_next_base.sail_in1k_384,97.260,2.740,99.490,0.510,86.67,384,1.000,bicubic
flexivit_large.300ep_in1k,97.250,2.750,99.490,0.510,304.36,240,0.950,bicubic
xcit_small_24_p8_384.fb_dist_in1k,97.240,2.760,99.610,0.390,47.63,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in1k,97.240,2.760,99.550,0.450,88.59,256,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k_384,97.240,2.760,99.520,0.480,28.64,384,1.000,bicubic
xcit_small_12_p8_384.fb_dist_in1k,97.230,2.770,99.480,0.520,26.21,384,1.000,bicubic
convnextv2_base.fcmae_ft_in1k,97.220,2.780,99.540,0.460,88.72,288,1.000,bicubic
regnety_2560.seer_ft_in1k,97.220,2.780,99.520,0.480,"1,282.60",384,1.000,bicubic
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,97.210,2.790,99.570,0.430,88.79,224,0.875,bilinear
tf_efficientnetv2_m.in1k,97.210,2.790,99.530,0.470,54.14,480,1.000,bicubic
tf_efficientnet_b7.ap_in1k,97.200,2.800,99.540,0.460,66.35,600,0.949,bicubic
regnetz_e8.ra3_in1k,97.200,2.800,99.500,0.500,57.70,320,1.000,bicubic
tf_efficientnet_b8.ra_in1k,97.200,2.800,99.500,0.500,87.41,672,0.954,bicubic
tiny_vit_21m_224.dist_in22k_ft_in1k,97.200,2.800,99.490,0.510,21.20,224,0.950,bicubic
vit_base_r50_s16_384.orig_in21k_ft_in1k,97.190,2.810,99.560,0.440,98.95,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in1k,97.170,2.830,99.470,0.530,86.53,224,0.900,bicubic
regnety_320.swag_lc_in1k,97.160,2.840,99.670,0.330,145.05,224,0.965,bicubic
vit_base_patch16_224.augreg2_in21k_ft_in1k,97.150,2.850,99.540,0.460,86.57,224,0.900,bicubic
coat_lite_medium_384.in1k,97.150,2.850,99.450,0.550,44.57,384,1.000,bicubic
eva02_small_patch14_336.mim_in22k_ft_in1k,97.140,2.860,99.470,0.530,22.13,336,1.000,bicubic
deit3_small_patch16_384.fb_in22k_ft_in1k,97.130,2.870,99.510,0.490,22.21,384,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in1k,97.130,2.870,99.460,0.540,86.57,224,1.000,bicubic
xcit_small_24_p16_384.fb_dist_in1k,97.120,2.880,99.450,0.550,47.67,384,1.000,bicubic
tf_efficientnet_b8.ap_in1k,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic
dm_nfnet_f2.dm_in1k,97.110,2.890,99.510,0.490,193.78,352,0.920,bicubic
vit_base_patch32_clip_384.openai_ft_in12k_in1k,97.110,2.890,99.500,0.500,88.30,384,0.950,bicubic
convnext_large.fb_in1k,97.100,2.900,99.450,0.550,197.77,288,1.000,bicubic
ecaresnet269d.ra2_in1k,97.090,2.910,99.470,0.530,102.09,352,1.000,bicubic
volo_d3_224.sail_in1k,97.090,2.910,99.470,0.530,86.33,224,0.960,bicubic
beit_base_patch16_224.in22k_ft_in22k_in1k,97.080,2.920,99.610,0.390,86.53,224,0.900,bicubic
tf_efficientnet_b6.ap_in1k,97.080,2.920,99.610,0.390,43.04,528,0.942,bicubic
convformer_s36.sail_in22k_ft_in1k,97.080,2.920,99.560,0.440,40.01,224,1.000,bicubic
convnext_tiny.fb_in22k_ft_in1k_384,97.080,2.920,99.510,0.490,28.59,384,1.000,bicubic
eca_nfnet_l2.ra3_in1k,97.080,2.920,99.510,0.490,56.72,384,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in1k,97.080,2.920,99.490,0.510,86.57,224,0.900,bicubic
caformer_s18.sail_in1k_384,97.080,2.920,99.420,0.580,26.34,384,1.000,bicubic
cait_s24_384.fb_dist_in1k,97.070,2.930,99.430,0.570,47.06,384,1.000,bicubic
xcit_large_24_p8_224.fb_dist_in1k,97.070,2.930,99.420,0.580,188.93,224,1.000,bicubic
convnext_tiny.in12k_ft_in1k,97.060,2.940,99.550,0.450,28.59,288,1.000,bicubic
deit3_base_patch16_384.fb_in1k,97.040,2.960,99.390,0.610,86.88,384,1.000,bicubic
convformer_s18.sail_in1k_384,97.040,2.960,99.380,0.620,26.77,384,1.000,bicubic
hrnet_w48_ssld.paddle_in1k,97.030,2.970,99.640,0.360,77.47,288,1.000,bilinear
dm_nfnet_f1.dm_in1k,97.030,2.970,99.390,0.610,132.63,320,0.910,bicubic
resnetv2_152x2_bit.goog_in21k_ft_in1k,97.000,3.000,99.590,0.410,236.34,448,1.000,bilinear
tf_efficientnet_b7.ra_in1k,97.000,3.000,99.520,0.480,66.35,600,0.949,bicubic
volo_d2_224.sail_in1k,97.000,3.000,99.390,0.610,58.68,224,0.960,bicubic
efficientnetv2_rw_m.agc_in1k,96.980,3.020,99.530,0.470,53.24,416,1.000,bicubic
resnetv2_101x3_bit.goog_in21k_ft_in1k,96.980,3.020,99.490,0.510,387.93,448,1.000,bilinear
caformer_b36.sail_in1k,96.980,3.020,99.340,0.660,98.75,224,1.000,bicubic
deit3_medium_patch16_224.fb_in22k_ft_in1k,96.970,3.030,99.430,0.570,38.85,224,1.000,bicubic
deit_base_distilled_patch16_384.fb_in1k,96.960,3.040,99.480,0.520,87.63,384,1.000,bicubic
seresnextaa101d_32x8d.ah_in1k,96.960,3.040,99.390,0.610,93.59,288,1.000,bicubic
maxvit_large_tf_224.in1k,96.960,3.040,99.250,0.750,211.79,224,0.950,bicubic
tf_efficientnet_b4.ns_jft_in1k,96.950,3.050,99.580,0.420,19.34,380,0.922,bicubic
maxvit_base_tf_224.in1k,96.950,3.050,99.260,0.740,119.47,224,0.950,bicubic
deit3_large_patch16_224.fb_in1k,96.940,3.060,99.340,0.660,304.37,224,0.900,bicubic
davit_base.msft_in1k,96.940,3.060,99.260,0.740,87.95,224,0.950,bicubic
mvitv2_large.fb_in1k,96.930,3.070,99.400,0.600,217.99,224,0.900,bicubic
xcit_small_12_p16_384.fb_dist_in1k,96.920,3.080,99.400,0.600,26.25,384,1.000,bicubic
xcit_medium_24_p8_224.fb_dist_in1k,96.920,3.080,99.390,0.610,84.32,224,1.000,bicubic
volo_d1_384.sail_in1k,96.910,3.090,99.520,0.480,26.78,384,1.000,bicubic
resnetrs420.tf_in1k,96.910,3.090,99.460,0.540,191.89,416,1.000,bicubic
deit3_huge_patch14_224.fb_in1k,96.900,3.100,99.480,0.520,632.13,224,0.900,bicubic
convformer_b36.sail_in1k,96.900,3.100,99.220,0.780,99.88,224,1.000,bicubic
caformer_m36.sail_in1k,96.890,3.110,99.430,0.570,56.20,224,1.000,bicubic
vit_base_patch16_224.augreg_in21k_ft_in1k,96.880,3.120,99.530,0.470,86.57,224,0.900,bicubic
xcit_small_24_p8_224.fb_dist_in1k,96.870,3.130,99.480,0.520,47.63,224,1.000,bicubic
regnety_1280.seer_ft_in1k,96.860,3.140,99.390,0.610,644.81,384,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k,96.850,3.150,99.460,0.540,28.64,288,1.000,bicubic
regnety_640.seer_ft_in1k,96.850,3.150,99.420,0.580,281.38,384,1.000,bicubic
rexnetr_300.sw_in12k_ft_in1k,96.840,3.160,99.510,0.490,34.81,288,1.000,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,96.830,3.170,99.450,0.550,236.34,384,1.000,bicubic
convnext_base.fb_in1k,96.830,3.170,99.410,0.590,88.59,288,1.000,bicubic
regnety_160.swag_lc_in1k,96.820,3.180,99.650,0.350,83.59,224,0.965,bicubic
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,96.810,3.190,99.600,0.400,194.03,224,0.875,bilinear
maxxvit_rmlp_small_rw_256.sw_in1k,96.800,3.200,99.380,0.620,66.01,256,0.950,bicubic
xcit_large_24_p16_224.fb_dist_in1k,96.800,3.200,99.350,0.650,189.10,224,1.000,bicubic
vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.790,3.210,99.340,0.660,328.99,224,0.900,bicubic
fastvit_ma36.apple_dist_in1k,96.780,3.220,99.330,0.670,44.07,256,0.950,bicubic
seresnet152d.ra2_in1k,96.770,3.230,99.440,0.560,66.84,320,1.000,bicubic
seresnext101_32x8d.ah_in1k,96.770,3.230,99.350,0.650,93.57,288,1.000,bicubic
mvitv2_base.fb_in1k,96.760,3.240,99.260,0.740,51.47,224,0.900,bicubic
resnetrs350.tf_in1k,96.750,3.250,99.370,0.630,163.96,384,1.000,bicubic
swinv2_base_window16_256.ms_in1k,96.750,3.250,99.350,0.650,87.92,256,0.900,bicubic
flexivit_base.1200ep_in1k,96.740,3.260,99.360,0.640,86.59,240,0.950,bicubic
edgenext_base.in21k_ft_in1k,96.730,3.270,99.420,0.580,18.51,320,1.000,bicubic
tf_efficientnetv2_s.in21k_ft_in1k,96.730,3.270,99.420,0.580,21.46,384,1.000,bicubic
resnet200d.ra2_in1k,96.730,3.270,99.330,0.670,64.69,320,1.000,bicubic
vit_base_patch16_384.orig_in21k_ft_in1k,96.720,3.280,99.500,0.500,86.86,384,1.000,bicubic
regnetz_040.ra3_in1k,96.720,3.280,99.480,0.520,27.12,320,1.000,bicubic
resnetv2_50x3_bit.goog_in21k_ft_in1k,96.710,3.290,99.550,0.450,217.32,448,1.000,bilinear
caformer_s18.sail_in22k_ft_in1k,96.710,3.290,99.490,0.510,26.34,224,1.000,bicubic
regnetz_040_h.ra3_in1k,96.700,3.300,99.500,0.500,28.94,320,1.000,bicubic
vit_small_patch16_384.augreg_in21k_ft_in1k,96.700,3.300,99.480,0.520,22.20,384,1.000,bicubic
edgenext_base.usi_in1k,96.700,3.300,99.430,0.570,18.51,320,1.000,bicubic
xcit_small_12_p8_224.fb_dist_in1k,96.700,3.300,99.380,0.620,26.21,224,1.000,bicubic
resnetrs200.tf_in1k,96.700,3.300,99.370,0.630,93.21,320,1.000,bicubic
resnetaa101d.sw_in12k_ft_in1k,96.700,3.300,99.360,0.640,44.57,288,1.000,bicubic
seresnext101d_32x8d.ah_in1k,96.700,3.300,99.360,0.640,93.59,288,1.000,bicubic
eca_nfnet_l1.ra2_in1k,96.700,3.300,99.290,0.710,41.41,320,1.000,bicubic
repvgg_d2se.rvgg_in1k,96.690,3.310,99.370,0.630,133.33,320,1.000,bilinear
caformer_s36.sail_in1k,96.690,3.310,99.360,0.640,39.30,224,1.000,bicubic
maxvit_small_tf_224.in1k,96.690,3.310,99.360,0.640,68.93,224,0.950,bicubic
resnetrs270.tf_in1k,96.690,3.310,99.350,0.650,129.86,352,1.000,bicubic
vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.680,3.320,99.580,0.420,36.47,384,1.000,bicubic
tf_efficientnet_b5.ap_in1k,96.680,3.320,99.460,0.540,30.39,456,0.934,bicubic
convformer_m36.sail_in1k,96.680,3.320,99.080,0.920,57.05,224,1.000,bicubic
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,96.670,3.330,99.490,0.510,38.86,256,0.950,bicubic
tf_efficientnet_b6.aa_in1k,96.670,3.330,99.370,0.630,43.04,528,0.942,bicubic
deit3_small_patch16_224.fb_in22k_ft_in1k,96.660,3.340,99.330,0.670,22.06,224,1.000,bicubic
flexivit_base.600ep_in1k,96.650,3.350,99.330,0.670,86.59,240,0.950,bicubic
davit_small.msft_in1k,96.630,3.370,99.350,0.650,49.75,224,0.950,bicubic
convformer_s18.sail_in22k_ft_in1k,96.630,3.370,99.340,0.660,26.77,224,1.000,bicubic
efficientvit_b3.r288_in1k,96.630,3.370,99.220,0.780,48.65,288,1.000,bicubic
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,96.630,3.370,99.160,0.840,41.72,224,0.950,bicubic
resmlp_big_24_224.fb_in22k_ft_in1k,96.620,3.380,99.510,0.490,129.14,224,0.875,bicubic
regnetz_d8.ra3_in1k,96.620,3.380,99.450,0.550,23.37,320,1.000,bicubic
resnest200e.in1k,96.610,3.390,99.350,0.650,70.20,320,0.909,bicubic
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,96.600,3.400,99.530,0.470,194.03,224,0.875,bilinear
flexivit_base.300ep_in1k,96.600,3.400,99.270,0.730,86.59,240,0.950,bicubic
xcit_medium_24_p16_224.fb_dist_in1k,96.600,3.400,99.270,0.730,84.40,224,1.000,bicubic
regnetz_d32.ra3_in1k,96.590,3.410,99.380,0.620,27.58,320,0.950,bicubic
swin_base_patch4_window12_384.ms_in1k,96.580,3.420,99.250,0.750,87.90,384,1.000,bicubic
resnetrs152.tf_in1k,96.580,3.420,99.240,0.760,86.62,320,1.000,bicubic
convformer_s36.sail_in1k,96.580,3.420,99.170,0.830,40.01,224,1.000,bicubic
maxvit_rmlp_small_rw_224.sw_in1k,96.580,3.420,99.120,0.880,64.90,224,0.900,bicubic
regnetz_d8_evos.ch_in1k,96.570,3.430,99.460,0.540,23.46,320,1.000,bicubic
gcvit_base.in1k,96.560,3.440,99.230,0.770,90.32,224,0.875,bicubic
focalnet_base_srf.ms_in1k,96.560,3.440,99.150,0.850,88.15,224,0.900,bicubic
inception_next_base.sail_in1k,96.560,3.440,99.080,0.920,86.67,224,0.950,bicubic
cait_xs24_384.fb_dist_in1k,96.540,3.460,99.420,0.580,26.67,384,1.000,bicubic
efficientnetv2_rw_s.ra2_in1k,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic
tf_efficientnet_b7.aa_in1k,96.540,3.460,99.300,0.700,66.35,600,0.949,bicubic
crossvit_18_dagger_408.in1k,96.540,3.460,99.260,0.740,44.61,408,1.000,bicubic
coatnet_rmlp_2_rw_224.sw_in1k,96.540,3.460,99.100,0.900,73.88,224,0.950,bicubic
regnety_080.ra3_in1k,96.530,3.470,99.320,0.680,39.18,288,1.000,bicubic
xcit_tiny_24_p8_384.fb_dist_in1k,96.530,3.470,99.320,0.680,12.11,384,1.000,bicubic
swinv2_base_window8_256.ms_in1k,96.530,3.470,99.270,0.730,87.92,256,0.900,bicubic
convnext_small.fb_in1k,96.520,3.480,99.340,0.660,50.22,288,1.000,bicubic
resnest269e.in1k,96.510,3.490,99.350,0.650,110.93,416,0.928,bicubic
vit_base_patch32_384.augreg_in21k_ft_in1k,96.490,3.510,99.410,0.590,88.30,384,1.000,bicubic
swin_small_patch4_window7_224.ms_in22k_ft_in1k,96.480,3.520,99.390,0.610,49.61,224,0.900,bicubic
fastvit_ma36.apple_in1k,96.470,3.530,99.280,0.720,44.07,256,0.950,bicubic
tf_efficientnet_b5.aa_in1k,96.470,3.530,99.240,0.760,30.39,456,0.934,bicubic
swinv2_small_window16_256.ms_in1k,96.470,3.530,99.200,0.800,49.73,256,0.900,bicubic
coat_lite_medium.in1k,96.470,3.530,99.150,0.850,44.57,224,0.900,bicubic
cs3se_edgenet_x.c2ns_in1k,96.450,3.550,99.400,0.600,50.72,320,1.000,bicubic
resmlp_big_24_224.fb_distilled_in1k,96.450,3.550,99.310,0.690,129.14,224,0.875,bicubic
vit_base_patch16_224_miil.in21k_ft_in1k,96.450,3.550,99.300,0.700,86.54,224,0.875,bilinear
focalnet_base_lrf.ms_in1k,96.450,3.550,99.120,0.880,88.75,224,0.900,bicubic
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,96.420,3.580,99.470,0.530,44.18,224,0.875,bilinear
maxvit_rmlp_tiny_rw_256.sw_in1k,96.420,3.580,99.380,0.620,29.15,256,0.950,bicubic
cait_s24_224.fb_dist_in1k,96.420,3.580,99.150,0.850,46.92,224,1.000,bicubic
regnetv_064.ra3_in1k,96.410,3.590,99.360,0.640,30.58,288,1.000,bicubic
xcit_small_24_p8_224.fb_in1k,96.410,3.590,99.150,0.850,47.63,224,1.000,bicubic
xcit_large_24_p8_224.fb_in1k,96.400,3.600,98.990,1.010,188.93,224,1.000,bicubic
resnet152d.ra2_in1k,96.390,3.610,99.390,0.610,60.21,320,1.000,bicubic
tf_efficientnet_b3.ns_jft_in1k,96.390,3.610,99.350,0.650,12.23,300,0.904,bicubic
crossvit_15_dagger_408.in1k,96.390,3.610,99.160,0.840,28.50,408,1.000,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k_384,96.370,3.630,99.400,0.600,15.62,384,1.000,bicubic
mvitv2_small.fb_in1k,96.370,3.630,99.200,0.800,34.87,224,0.900,bicubic
xception65.ra3_in1k,96.360,3.640,99.240,0.760,39.92,299,0.940,bicubic
fastvit_sa36.apple_dist_in1k,96.360,3.640,99.230,0.770,31.53,256,0.900,bicubic
regnety_064.ra3_in1k,96.360,3.640,99.230,0.770,30.58,288,1.000,bicubic
pvt_v2_b5.in1k,96.360,3.640,99.170,0.830,81.96,224,0.900,bicubic
regnety_160.deit_in1k,96.350,3.650,99.330,0.670,83.59,288,1.000,bicubic
pvt_v2_b4.in1k,96.350,3.650,99.180,0.820,62.56,224,0.900,bicubic
regnety_320.seer_ft_in1k,96.340,3.660,99.350,0.650,145.05,384,1.000,bicubic
tf_efficientnet_b5.ra_in1k,96.340,3.660,99.310,0.690,30.39,456,0.934,bicubic
tf_efficientnetv2_s.in1k,96.340,3.660,99.200,0.800,21.46,384,1.000,bicubic
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,96.330,3.670,99.430,0.570,88.79,224,0.875,bilinear
repvit_m2_3.dist_450e_in1k,96.330,3.670,99.400,0.600,23.69,224,0.950,bicubic
volo_d1_224.sail_in1k,96.320,3.680,99.310,0.690,26.63,224,0.960,bicubic
dm_nfnet_f0.dm_in1k,96.310,3.690,99.320,0.680,71.49,256,0.900,bicubic
deit3_base_patch16_224.fb_in1k,96.300,3.700,99.180,0.820,86.59,224,0.900,bicubic
resnet101d.ra2_in1k,96.290,3.710,99.230,0.770,44.57,320,1.000,bicubic
tiny_vit_11m_224.dist_in22k_ft_in1k,96.290,3.710,99.190,0.810,11.00,224,0.950,bicubic
efficientvit_b3.r256_in1k,96.290,3.710,99.120,0.880,48.65,256,1.000,bicubic
gcvit_small.in1k,96.280,3.720,99.140,0.860,51.09,224,0.875,bicubic
swinv2_small_window8_256.ms_in1k,96.270,3.730,99.210,0.790,49.73,256,0.900,bicubic
inception_next_small.sail_in1k,96.240,3.760,99.220,0.780,49.37,224,0.875,bicubic
nest_base_jx.goog_in1k,96.240,3.760,99.200,0.800,67.72,224,0.875,bicubic
fastvit_sa36.apple_in1k,96.240,3.760,99.190,0.810,31.53,256,0.900,bicubic
twins_svt_large.in1k,96.240,3.760,99.170,0.830,99.27,224,0.900,bicubic
swin_s3_base_224.ms_in1k,96.240,3.760,99.150,0.850,71.13,224,0.900,bicubic
maxvit_tiny_rw_224.sw_in1k,96.240,3.760,99.130,0.870,29.06,224,0.950,bicubic
pit_b_distilled_224.in1k,96.230,3.770,99.110,0.890,74.79,224,0.900,bicubic
swin_s3_small_224.ms_in1k,96.230,3.770,99.080,0.920,49.74,224,0.900,bicubic
ecaresnet101d.miil_in1k,96.220,3.780,99.310,0.690,44.57,288,0.950,bicubic
tf_efficientnetv2_b3.in21k_ft_in1k,96.220,3.780,99.230,0.770,14.36,300,0.900,bicubic
xcit_small_24_p16_224.fb_dist_in1k,96.220,3.780,99.210,0.790,47.67,224,1.000,bicubic
xception65p.ra3_in1k,96.210,3.790,99.180,0.820,39.82,299,0.940,bicubic
deit3_small_patch16_384.fb_in1k,96.200,3.800,99.290,0.710,22.21,384,1.000,bicubic
rexnetr_200.sw_in12k_ft_in1k,96.200,3.800,99.260,0.740,16.52,288,1.000,bicubic
resnet152.a1h_in1k,96.200,3.800,99.220,0.780,60.19,288,1.000,bicubic
regnetv_040.ra3_in1k,96.190,3.810,99.330,0.670,20.64,288,1.000,bicubic
convnextv2_tiny.fcmae_ft_in1k,96.190,3.810,99.250,0.750,28.64,288,1.000,bicubic
gcvit_tiny.in1k,96.180,3.820,99.230,0.770,28.22,224,0.875,bicubic
focalnet_small_lrf.ms_in1k,96.180,3.820,99.190,0.810,50.34,224,0.900,bicubic
swinv2_cr_small_ns_224.sw_in1k,96.180,3.820,99.140,0.860,49.70,224,0.900,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k_384,96.180,3.820,99.120,0.880,14.25,384,1.000,bicubic
tf_efficientnet_b4.ap_in1k,96.160,3.840,99.270,0.730,19.34,380,0.922,bicubic
tresnet_v2_l.miil_in21k_ft_in1k,96.160,3.840,99.240,0.760,46.17,224,0.875,bilinear
deit_base_patch16_384.fb_in1k,96.160,3.840,99.140,0.860,86.86,384,1.000,bicubic
twins_svt_base.in1k,96.160,3.840,99.050,0.950,56.07,224,0.900,bicubic
fastvit_sa24.apple_dist_in1k,96.150,3.850,99.210,0.790,21.55,256,0.900,bicubic
efficientnet_b4.ra2_in1k,96.150,3.850,99.190,0.810,19.34,384,1.000,bicubic
sequencer2d_l.in1k,96.150,3.850,99.160,0.840,54.30,224,0.875,bicubic
regnetz_c16_evos.ch_in1k,96.140,3.860,99.360,0.640,13.49,320,0.950,bicubic
twins_pcpvt_large.in1k,96.140,3.860,99.170,0.830,60.99,224,0.900,bicubic
caformer_s18.sail_in1k,96.140,3.860,99.000,1.000,26.34,224,1.000,bicubic
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,96.130,3.870,99.220,0.780,88.22,224,0.900,bicubic
tiny_vit_21m_224.in1k,96.130,3.870,99.160,0.840,21.20,224,0.950,bicubic
repvit_m2_3.dist_300e_in1k,96.120,3.880,99.340,0.660,23.69,224,0.950,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,96.120,3.880,99.270,0.730,236.34,224,0.875,bicubic
nfnet_l0.ra2_in1k,96.120,3.880,99.240,0.760,35.07,288,1.000,bicubic
swin_base_patch4_window7_224.ms_in1k,96.120,3.880,99.060,0.940,87.77,224,0.900,bicubic
resnetv2_50x1_bit.goog_distilled_in1k,96.110,3.890,99.280,0.720,25.55,224,0.875,bicubic
efficientformer_l7.snap_dist_in1k,96.110,3.890,99.270,0.730,82.23,224,0.950,bicubic
xcit_small_12_p8_224.fb_in1k,96.110,3.890,99.160,0.840,26.21,224,1.000,bicubic
resnetv2_101x1_bit.goog_in21k_ft_in1k,96.100,3.900,99.280,0.720,44.54,448,1.000,bilinear
maxvit_tiny_tf_224.in1k,96.100,3.900,99.270,0.730,30.92,224,0.950,bicubic
deit_base_distilled_patch16_224.fb_in1k,96.090,3.910,99.190,0.810,87.34,224,0.900,bicubic
xcit_medium_24_p8_224.fb_in1k,96.090,3.910,98.890,1.110,84.32,224,1.000,bicubic
resnext101_64x4d.c1_in1k,96.080,3.920,99.240,0.760,83.46,288,1.000,bicubic
regnety_320.tv2_in1k,96.080,3.920,99.230,0.770,145.05,224,0.965,bicubic
deit3_medium_patch16_224.fb_in1k,96.080,3.920,99.200,0.800,38.85,224,0.900,bicubic
xcit_tiny_12_p8_384.fb_dist_in1k,96.080,3.920,99.140,0.860,6.71,384,1.000,bicubic
swinv2_cr_small_224.sw_in1k,96.080,3.920,98.860,1.140,49.70,224,0.900,bicubic
tf_efficientnet_b5.in1k,96.070,3.930,99.290,0.710,30.39,456,0.934,bicubic
efficientformerv2_l.snap_dist_in1k,96.070,3.930,99.190,0.810,26.32,224,0.950,bicubic
focalnet_small_srf.ms_in1k,96.070,3.930,99.120,0.880,49.89,224,0.900,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k,96.060,3.940,99.220,0.780,15.62,288,1.000,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k_384,96.060,3.940,99.080,0.920,18.45,384,1.000,bicubic
resnetv2_101.a1h_in1k,96.050,3.950,99.170,0.830,44.54,288,1.000,bicubic
cs3edgenet_x.c2_in1k,96.050,3.950,99.140,0.860,47.82,288,1.000,bicubic
maxxvit_rmlp_nano_rw_256.sw_in1k,96.040,3.960,99.260,0.740,16.78,256,0.950,bicubic
resnext101_64x4d.tv_in1k,96.030,3.970,99.160,0.840,83.46,224,0.875,bilinear
xcit_small_12_p16_224.fb_dist_in1k,96.030,3.970,99.130,0.870,26.25,224,1.000,bicubic
coatnet_1_rw_224.sw_in1k,96.030,3.970,99.060,0.940,41.72,224,0.950,bicubic
efficientvit_b3.r224_in1k,96.030,3.970,98.990,1.010,48.65,224,0.950,bicubic
regnety_040.ra3_in1k,96.020,3.980,99.190,0.810,20.65,288,1.000,bicubic
resnet101.a1h_in1k,96.020,3.980,99.140,0.860,44.55,288,1.000,bicubic
cs3sedarknet_x.c2ns_in1k,96.020,3.980,99.110,0.890,35.40,288,1.000,bicubic
convnext_tiny_hnf.a2h_in1k,96.020,3.980,99.070,0.930,28.59,288,1.000,bicubic
hrnet_w18_ssld.paddle_in1k,95.990,4.010,99.320,0.680,21.30,288,1.000,bilinear
convnext_nano.in12k_ft_in1k,95.990,4.010,99.310,0.690,15.59,288,1.000,bicubic
pvt_v2_b3.in1k,95.990,4.010,99.190,0.810,45.24,224,0.900,bicubic
regnetx_320.tv2_in1k,95.990,4.010,99.100,0.900,107.81,224,0.965,bicubic
tresnet_xl.miil_in1k_448,95.980,4.020,99.130,0.870,78.44,448,0.875,bilinear
sequencer2d_s.in1k,95.980,4.020,99.050,0.950,27.65,224,0.875,bicubic
regnety_160.tv2_in1k,95.970,4.030,99.150,0.850,83.59,224,0.965,bicubic
nest_small_jx.goog_in1k,95.970,4.030,99.030,0.970,38.35,224,0.875,bicubic
maxvit_rmlp_nano_rw_256.sw_in1k,95.970,4.030,98.970,1.030,15.50,256,0.950,bicubic
efficientvit_b2.r288_in1k,95.960,4.040,99.190,0.810,24.33,288,1.000,bicubic
regnety_032.ra_in1k,95.960,4.040,99.190,0.810,19.44,288,1.000,bicubic
coatnet_rmlp_1_rw_224.sw_in1k,95.960,4.040,99.160,0.840,41.69,224,0.950,bicubic
resnext101_32x8d.tv2_in1k,95.950,4.050,99.080,0.920,88.79,224,0.965,bilinear
convformer_s18.sail_in1k,95.950,4.050,98.900,1.100,26.77,224,1.000,bicubic
xcit_tiny_24_p16_384.fb_dist_in1k,95.940,4.060,99.220,0.780,12.12,384,1.000,bicubic
eca_nfnet_l0.ra2_in1k,95.940,4.060,99.210,0.790,24.14,288,1.000,bicubic
regnetz_c16.ra3_in1k,95.940,4.060,99.110,0.890,13.46,320,1.000,bicubic
swinv2_tiny_window16_256.ms_in1k,95.930,4.070,99.150,0.850,28.35,256,0.900,bicubic
swin_small_patch4_window7_224.ms_in1k,95.930,4.070,99.020,0.980,49.61,224,0.900,bicubic
fastvit_sa24.apple_in1k,95.920,4.080,99.160,0.840,21.55,256,0.900,bicubic
maxvit_nano_rw_256.sw_in1k,95.920,4.080,99.010,0.990,15.45,256,0.950,bicubic
coat_small.in1k,95.910,4.090,99.150,0.850,21.69,224,0.900,bicubic
tf_efficientnet_b4.aa_in1k,95.900,4.100,99.170,0.830,19.34,380,0.922,bicubic
repvit_m1_5.dist_450e_in1k,95.900,4.100,99.120,0.880,14.64,224,0.950,bicubic
maxxvitv2_nano_rw_256.sw_in1k,95.900,4.100,99.050,0.950,23.70,256,0.950,bicubic
regnetx_160.tv2_in1k,95.880,4.120,99.090,0.910,54.28,224,0.965,bicubic
resnet51q.ra2_in1k,95.870,4.130,99.130,0.870,35.70,288,1.000,bilinear
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,95.860,4.140,99.250,0.750,25.03,224,0.875,bilinear
resnest101e.in1k,95.860,4.140,99.200,0.800,48.28,256,0.875,bilinear
tresnet_l.miil_in1k_448,95.860,4.140,99.120,0.880,55.99,448,0.875,bilinear
regnety_080_tv.tv2_in1k,95.860,4.140,99.100,0.900,39.38,224,0.965,bicubic
mvitv2_tiny.fb_in1k,95.860,4.140,99.070,0.930,24.17,224,0.900,bicubic
cs3darknet_x.c2ns_in1k,95.850,4.150,99.170,0.830,35.05,288,1.000,bicubic
rexnet_300.nav_in1k,95.840,4.160,99.130,0.870,34.71,224,0.875,bicubic
resnetaa50d.sw_in12k_ft_in1k,95.830,4.170,99.170,0.830,25.58,288,1.000,bicubic
vit_large_patch32_384.orig_in21k_ft_in1k,95.830,4.170,99.150,0.850,306.63,384,1.000,bicubic
cait_xxs36_384.fb_dist_in1k,95.830,4.170,99.090,0.910,17.37,384,1.000,bicubic
tf_efficientnet_b4.in1k,95.820,4.180,99.050,0.950,19.34,380,0.922,bicubic
xcit_tiny_24_p8_224.fb_dist_in1k,95.810,4.190,99.210,0.790,12.11,224,1.000,bicubic
sequencer2d_m.in1k,95.810,4.190,99.110,0.890,38.31,224,0.875,bicubic
convnextv2_nano.fcmae_ft_in1k,95.800,4.200,99.090,0.910,15.62,288,1.000,bicubic
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,95.790,4.210,99.180,0.820,194.03,224,0.875,bilinear
convnext_tiny.fb_in1k,95.790,4.210,99.160,0.840,28.59,288,1.000,bicubic
twins_pcpvt_base.in1k,95.790,4.210,99.130,0.870,43.83,224,0.900,bicubic
resnet61q.ra2_in1k,95.780,4.220,98.990,1.010,36.85,288,1.000,bicubic
tf_efficientnet_b2.ns_jft_in1k,95.770,4.230,99.120,0.880,9.11,260,0.890,bicubic
vit_relpos_base_patch16_clsgap_224.sw_in1k,95.770,4.230,99.040,0.960,86.43,224,0.900,bicubic
poolformerv2_m48.sail_in1k,95.770,4.230,98.980,1.020,73.35,224,1.000,bicubic
ecaresnet101d_pruned.miil_in1k,95.760,4.240,99.180,0.820,24.88,288,0.950,bicubic
seresnext50_32x4d.racm_in1k,95.740,4.260,99.180,0.820,27.56,288,0.950,bicubic
gc_efficientnetv2_rw_t.agc_in1k,95.740,4.260,99.020,0.980,13.68,288,1.000,bicubic
efficientnet_b3.ra2_in1k,95.720,4.280,99.040,0.960,12.23,320,1.000,bicubic
tresnet_m.miil_in21k_ft_in1k,95.710,4.290,99.030,0.970,31.39,224,0.875,bilinear
pnasnet5large.tf_in1k,95.710,4.290,98.920,1.080,86.06,331,0.911,bicubic
coatnet_bn_0_rw_224.sw_in1k,95.700,4.300,99.050,0.950,27.44,224,0.950,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k_384,95.690,4.310,99.140,0.860,10.59,384,1.000,bicubic
nasnetalarge.tf_in1k,95.690,4.310,98.930,1.070,88.75,331,0.911,bicubic
crossvit_15_dagger_240.in1k,95.680,4.320,98.830,1.170,28.21,240,0.875,bicubic
flexivit_small.600ep_in1k,95.670,4.330,99.060,0.940,22.06,240,0.950,bicubic
xcit_tiny_24_p8_224.fb_in1k,95.660,4.340,99.050,0.950,12.11,224,1.000,bicubic
davit_tiny.msft_in1k,95.660,4.340,99.030,0.970,28.36,224,0.950,bicubic
efficientvit_b2.r256_in1k,95.650,4.350,99.060,0.940,24.33,256,1.000,bicubic
repvit_m1_5.dist_300e_in1k,95.640,4.360,98.990,1.010,14.64,224,0.950,bicubic
wide_resnet50_2.racm_in1k,95.630,4.370,99.240,0.760,68.88,288,0.950,bicubic
vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.630,4.370,99.190,0.810,36.43,224,0.900,bicubic
resnetv2_50d_evos.ah_in1k,95.630,4.370,99.110,0.890,25.59,288,1.000,bicubic
poolformer_m48.sail_in1k,95.630,4.370,98.940,1.060,73.47,224,0.950,bicubic
pit_b_224.in1k,95.630,4.370,98.660,1.340,73.76,224,0.900,bicubic
efficientformer_l3.snap_dist_in1k,95.590,4.410,99.160,0.840,31.41,224,0.950,bicubic
efficientnetv2_rw_t.ra2_in1k,95.590,4.410,99.070,0.930,13.65,288,1.000,bicubic
gcvit_xtiny.in1k,95.590,4.410,99.040,0.960,19.98,224,0.875,bicubic
crossvit_18_dagger_240.in1k,95.570,4.430,99.060,0.940,44.27,240,0.875,bicubic
vit_relpos_base_patch16_224.sw_in1k,95.560,4.440,99.030,0.970,86.43,224,0.900,bicubic
pvt_v2_b2_li.in1k,95.560,4.440,98.990,1.010,22.55,224,0.900,bicubic
flexivit_small.1200ep_in1k,95.550,4.450,99.110,0.890,22.06,240,0.950,bicubic
convit_base.fb_in1k,95.550,4.450,98.880,1.120,86.54,224,0.875,bicubic
wide_resnet101_2.tv2_in1k,95.540,4.460,99.080,0.920,126.89,224,0.965,bilinear
coat_lite_small.in1k,95.540,4.460,98.860,1.140,19.84,224,0.900,bicubic
xcit_small_24_p16_224.fb_in1k,95.540,4.460,98.780,1.220,47.67,224,1.000,bicubic
xcit_medium_24_p16_224.fb_in1k,95.540,4.460,98.720,1.280,84.40,224,1.000,bicubic
levit_384.fb_dist_in1k,95.530,4.470,99.060,0.940,39.13,224,0.900,bicubic
levit_conv_384.fb_dist_in1k,95.530,4.470,99.050,0.950,39.13,224,0.900,bicubic
ecaresnet50t.ra2_in1k,95.520,4.480,99.110,0.890,25.57,320,0.950,bicubic
fbnetv3_g.ra2_in1k,95.520,4.480,98.990,1.010,16.62,288,0.950,bilinear
vit_base_patch32_clip_224.laion2b_ft_in1k,95.520,4.480,98.870,1.130,88.22,224,0.900,bicubic
resnet101.a1_in1k,95.520,4.480,98.850,1.150,44.55,288,1.000,bicubic
crossvit_base_240.in1k,95.520,4.480,98.810,1.190,105.03,240,0.875,bicubic
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,95.510,4.490,99.200,0.800,28.29,224,0.900,bicubic
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,95.510,4.490,99.120,0.880,88.79,224,0.875,bilinear
xception41p.ra3_in1k,95.510,4.490,98.910,1.090,26.91,299,0.940,bicubic
focalnet_tiny_srf.ms_in1k,95.500,4.500,99.130,0.870,28.43,224,0.900,bicubic
vit_relpos_medium_patch16_rpn_224.sw_in1k,95.500,4.500,99.080,0.920,38.73,224,0.900,bicubic
resnet152.tv2_in1k,95.500,4.500,98.960,1.040,60.19,224,0.965,bilinear
resnet152.a1_in1k,95.500,4.500,98.780,1.220,60.19,288,1.000,bicubic
swinv2_tiny_window8_256.ms_in1k,95.490,4.510,99.100,0.900,28.35,256,0.900,bicubic
tiny_vit_11m_224.in1k,95.490,4.510,98.990,1.010,11.00,224,0.950,bicubic
flexivit_small.300ep_in1k,95.490,4.510,98.960,1.040,22.06,240,0.950,bicubic
resnet152.a2_in1k,95.490,4.510,98.790,1.210,60.19,288,1.000,bicubic
pvt_v2_b2.in1k,95.480,4.520,99.000,1.000,25.36,224,0.900,bicubic
resnetv2_50d_gn.ah_in1k,95.480,4.520,98.950,1.050,25.57,288,1.000,bicubic
visformer_small.in1k,95.480,4.520,98.900,1.100,40.22,224,0.900,bicubic
vit_relpos_medium_patch16_cls_224.sw_in1k,95.470,4.530,98.950,1.050,38.76,224,0.900,bicubic
inception_next_tiny.sail_in1k,95.460,4.540,99.010,0.990,28.06,224,0.875,bicubic
vit_relpos_medium_patch16_224.sw_in1k,95.460,4.540,98.960,1.040,38.75,224,0.900,bicubic
focalnet_tiny_lrf.ms_in1k,95.460,4.540,98.910,1.090,28.65,224,0.900,bicubic
ecaresnet50d.miil_in1k,95.450,4.550,99.090,0.910,25.58,288,0.950,bicubic
deit_base_patch16_224.fb_in1k,95.450,4.550,98.840,1.160,86.57,224,0.900,bicubic
resnext50_32x4d.a1h_in1k,95.450,4.550,98.840,1.160,25.03,288,1.000,bicubic
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,95.440,4.560,99.120,0.880,44.18,224,0.875,bilinear
tresnet_xl.miil_in1k,95.440,4.560,99.050,0.950,78.44,224,0.875,bilinear
crossvit_18_240.in1k,95.440,4.560,98.790,1.210,43.27,240,0.875,bicubic
coatnet_0_rw_224.sw_in1k,95.440,4.560,98.720,1.280,27.44,224,0.950,bicubic
resnetrs101.tf_in1k,95.430,4.570,99.040,0.960,63.62,288,0.940,bicubic
coatnext_nano_rw_224.sw_in1k,95.430,4.570,99.010,0.990,14.70,224,0.900,bicubic
coatnet_rmlp_nano_rw_224.sw_in1k,95.430,4.570,98.990,1.010,15.15,224,0.900,bicubic
xcit_small_12_p16_224.fb_in1k,95.430,4.570,98.840,1.160,26.25,224,1.000,bicubic
xcit_large_24_p16_224.fb_in1k,95.430,4.570,98.630,1.370,189.10,224,1.000,bicubic
halo2botnet50ts_256.a1h_in1k,95.420,4.580,99.020,0.980,22.64,256,0.950,bicubic
ecaresnet50t.a1_in1k,95.410,4.590,99.010,0.990,25.57,288,1.000,bicubic
resnet101.a2_in1k,95.410,4.590,98.940,1.060,44.55,288,1.000,bicubic
resnet50.fb_swsl_ig1b_ft_in1k,95.400,4.600,99.300,0.700,25.56,224,0.875,bilinear
edgenext_small.usi_in1k,95.400,4.600,99.100,0.900,5.59,320,1.000,bicubic
poolformerv2_m36.sail_in1k,95.400,4.600,98.870,1.130,56.08,224,1.000,bicubic
vit_base_patch16_rpn_224.sw_in1k,95.390,4.610,98.940,1.060,86.54,224,0.900,bicubic
poolformer_m36.sail_in1k,95.390,4.610,98.860,1.140,56.17,224,0.950,bicubic
vit_small_patch16_224.augreg_in21k_ft_in1k,95.370,4.630,99.140,0.860,22.05,224,0.900,bicubic
swinv2_cr_tiny_ns_224.sw_in1k,95.370,4.630,98.940,1.060,28.33,224,0.900,bicubic
efficientformerv2_s2.snap_dist_in1k,95.360,4.640,98.930,1.070,12.71,224,0.950,bicubic
ecaresnet50t.a2_in1k,95.350,4.650,98.920,1.080,25.57,288,1.000,bicubic
convnext_nano.d1h_in1k,95.350,4.650,98.860,1.140,15.59,288,1.000,bicubic
vit_base_patch16_224.orig_in21k_ft_in1k,95.340,4.660,99.000,1.000,86.57,224,0.900,bicubic
seresnet50.ra2_in1k,95.330,4.670,99.010,0.990,28.09,288,0.950,bicubic
tf_efficientnet_b3.ap_in1k,95.320,4.680,98.900,1.100,12.23,300,0.904,bicubic
cs3sedarknet_l.c2ns_in1k,95.310,4.690,99.130,0.870,21.91,288,0.950,bicubic
poolformerv2_s36.sail_in1k,95.310,4.690,98.920,1.080,30.79,224,1.000,bicubic
regnety_032.tv2_in1k,95.310,4.690,98.910,1.090,19.44,224,0.965,bicubic
mixer_b16_224.miil_in21k_ft_in1k,95.300,4.700,98.880,1.120,59.88,224,0.875,bilinear
ecaresnetlight.miil_in1k,95.290,4.710,99.030,0.970,30.16,288,0.950,bicubic
vit_small_patch16_384.augreg_in1k,95.290,4.710,99.000,1.000,22.20,384,1.000,bicubic
tresnet_l.miil_in1k,95.280,4.720,99.010,0.990,55.99,224,0.875,bilinear
cait_xxs24_384.fb_dist_in1k,95.280,4.720,98.960,1.040,12.03,384,1.000,bicubic
resnet101.tv2_in1k,95.280,4.720,98.910,1.090,44.55,224,0.965,bilinear
resnet50_gn.a1h_in1k,95.250,4.750,99.000,1.000,25.56,288,0.950,bicubic
vit_srelpos_medium_patch16_224.sw_in1k,95.240,4.760,98.990,1.010,38.74,224,0.900,bicubic
nest_tiny_jx.goog_in1k,95.240,4.760,98.980,1.020,17.06,224,0.875,bicubic
gcresnet50t.ra2_in1k,95.240,4.760,98.910,1.090,25.90,288,1.000,bicubic
coatnet_nano_rw_224.sw_in1k,95.240,4.760,98.870,1.130,15.14,224,0.900,bicubic
convnextv2_pico.fcmae_ft_in1k,95.230,4.770,98.920,1.080,9.07,288,0.950,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k,95.220,4.780,98.800,1.200,14.25,256,0.888,bicubic
convit_small.fb_in1k,95.210,4.790,98.900,1.100,27.78,224,0.875,bicubic
twins_pcpvt_small.in1k,95.210,4.790,98.880,1.120,24.11,224,0.900,bicubic
repvit_m3.dist_in1k,95.200,4.800,99.090,0.910,10.68,224,0.950,bicubic
resnetaa50.a1h_in1k,95.200,4.800,98.920,1.080,25.56,288,1.000,bicubic
efficientvit_b2.r224_in1k,95.200,4.800,98.820,1.180,24.33,224,0.950,bicubic
twins_svt_small.in1k,95.190,4.810,98.880,1.120,24.06,224,0.900,bicubic
swin_s3_tiny_224.ms_in1k,95.180,4.820,98.950,1.050,28.33,224,0.900,bicubic
regnetz_b16.ra3_in1k,95.170,4.830,99.080,0.920,9.72,288,1.000,bicubic
tf_efficientnet_b1.ns_jft_in1k,95.160,4.840,99.100,0.900,7.79,240,0.882,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k,95.160,4.840,98.950,1.050,18.45,256,0.888,bicubic
tf_efficientnetv2_b3.in1k,95.160,4.840,98.820,1.180,14.36,300,0.904,bicubic
cs3darknet_focus_l.c2ns_in1k,95.150,4.850,98.960,1.040,21.15,288,0.950,bicubic
vit_relpos_small_patch16_224.sw_in1k,95.150,4.850,98.960,1.040,21.98,224,0.900,bicubic
crossvit_15_240.in1k,95.150,4.850,98.930,1.070,27.53,240,0.875,bicubic
lamhalobotnet50ts_256.a1h_in1k,95.150,4.850,98.880,1.120,22.57,256,0.950,bicubic
fastvit_sa12.apple_dist_in1k,95.150,4.850,98.810,1.190,11.58,256,0.900,bicubic
pit_s_distilled_224.in1k,95.140,4.860,98.890,1.110,24.04,224,0.900,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k,95.140,4.860,98.860,1.140,10.59,256,0.888,bicubic
swin_tiny_patch4_window7_224.ms_in1k,95.140,4.860,98.850,1.150,28.29,224,0.900,bicubic
halonet50ts.a1h_in1k,95.140,4.860,98.780,1.220,22.73,256,0.940,bicubic
convnext_nano_ols.d1h_in1k,95.140,4.860,98.730,1.270,15.65,288,1.000,bicubic
xcit_tiny_12_p16_384.fb_dist_in1k,95.130,4.870,99.020,0.980,6.72,384,1.000,bicubic
cs3darknet_l.c2ns_in1k,95.120,4.880,98.980,1.020,21.16,288,0.950,bicubic
efficientnet_el.ra_in1k,95.120,4.880,98.970,1.030,10.59,300,0.904,bicubic
vit_base_patch32_clip_224.openai_ft_in1k,95.110,4.890,98.980,1.020,88.22,224,0.900,bicubic
ecaresnet50d_pruned.miil_in1k,95.110,4.890,98.930,1.070,19.94,288,0.950,bicubic
gernet_l.idstcv_in1k,95.110,4.890,98.900,1.100,31.08,256,0.875,bilinear
regnetx_080.tv2_in1k,95.100,4.900,98.830,1.170,39.57,224,0.965,bicubic
xcit_tiny_12_p8_224.fb_dist_in1k,95.090,4.910,98.910,1.090,6.71,224,1.000,bicubic
convmixer_1536_20.in1k,95.080,4.920,99.030,0.970,51.63,224,0.960,bicubic
poolformer_s36.sail_in1k,95.080,4.920,98.910,1.090,30.86,224,0.900,bicubic
legacy_senet154.in1k,95.070,4.930,98.830,1.170,115.09,224,0.875,bilinear
tiny_vit_5m_224.dist_in22k_ft_in1k,95.050,4.950,98.970,1.030,5.39,224,0.950,bicubic
seresnet33ts.ra2_in1k,95.040,4.960,98.900,1.100,19.78,288,1.000,bicubic
tnt_s_patch16_224,95.040,4.960,98.830,1.170,23.76,224,0.900,bicubic
vit_srelpos_small_patch16_224.sw_in1k,95.030,4.970,98.950,1.050,21.97,224,0.900,bicubic
vit_small_patch32_384.augreg_in21k_ft_in1k,95.020,4.980,98.990,1.010,22.92,384,1.000,bicubic
resnet152s.gluon_in1k,95.020,4.980,98.930,1.070,60.32,224,0.875,bicubic
levit_256.fb_dist_in1k,95.020,4.980,98.880,1.120,18.89,224,0.900,bicubic
levit_conv_256.fb_dist_in1k,95.020,4.980,98.880,1.120,18.89,224,0.900,bicubic
resnetv2_50x1_bit.goog_in21k_ft_in1k,95.010,4.990,99.060,0.940,25.55,448,1.000,bilinear
vit_base_patch32_224.augreg_in21k_ft_in1k,95.010,4.990,99.030,0.970,88.22,224,0.900,bicubic
resnet50d.ra2_in1k,95.010,4.990,98.980,1.020,25.58,288,0.950,bicubic
tf_efficientnet_b3.aa_in1k,95.010,4.990,98.910,1.090,12.23,300,0.904,bicubic
tresnet_m.miil_in1k_448,94.990,5.010,98.980,1.020,31.39,448,0.875,bilinear
deit3_small_patch16_224.fb_in1k,94.990,5.010,98.460,1.540,22.06,224,0.900,bicubic
resnet50.d_in1k,94.980,5.020,98.840,1.160,25.56,288,1.000,bicubic
resnest50d_4s2x40d.in1k,94.970,5.030,99.080,0.920,30.42,224,0.875,bicubic
coat_mini.in1k,94.970,5.030,98.780,1.220,10.34,224,0.900,bicubic
rexnet_200.nav_in1k,94.940,5.060,99.010,0.990,16.37,224,0.875,bicubic
vit_base_patch16_384.augreg_in1k,94.940,5.060,98.890,1.110,86.86,384,1.000,bicubic
seresnext101_64x4d.gluon_in1k,94.930,5.070,98.830,1.170,88.23,224,0.875,bicubic
gcresnet33ts.ra2_in1k,94.920,5.080,98.810,1.190,19.88,288,1.000,bicubic
resnet50.c2_in1k,94.920,5.080,98.810,1.190,25.56,288,1.000,bicubic
senet154.gluon_in1k,94.920,5.080,98.760,1.240,115.09,224,0.875,bicubic
eva02_tiny_patch14_336.mim_in22k_ft_in1k,94.910,5.090,98.880,1.120,5.76,336,1.000,bicubic
repvit_m1_1.dist_450e_in1k,94.900,5.100,98.960,1.040,8.80,224,0.950,bicubic
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,94.890,5.110,98.870,1.130,25.03,224,0.875,bilinear
mobilevitv2_175.cvnets_in1k,94.890,5.110,98.860,1.140,14.25,256,0.888,bicubic
resmlp_36_224.fb_distilled_in1k,94.890,5.110,98.860,1.140,44.69,224,0.875,bicubic
seresnext101_32x4d.gluon_in1k,94.890,5.110,98.820,1.180,48.96,224,0.875,bicubic
tf_efficientnet_lite4.in1k,94.880,5.120,99.020,0.980,13.01,380,0.920,bilinear
fastvit_sa12.apple_in1k,94.880,5.120,98.890,1.110,11.58,256,0.900,bicubic
wide_resnet50_2.tv2_in1k,94.870,5.130,98.940,1.060,68.88,224,0.965,bilinear
ese_vovnet39b.ra_in1k,94.870,5.130,98.910,1.090,24.57,288,0.950,bicubic
gcresnext50ts.ch_in1k,94.860,5.140,98.860,1.140,15.67,288,1.000,bicubic
resnet50.b1k_in1k,94.860,5.140,98.810,1.190,25.56,288,1.000,bicubic
crossvit_small_240.in1k,94.850,5.150,99.020,0.980,26.86,240,0.875,bicubic
resnest50d.in1k,94.850,5.150,98.880,1.120,27.48,224,0.875,bilinear
resnetv2_50.a1h_in1k,94.850,5.150,98.870,1.130,25.55,288,1.000,bicubic
mobilevitv2_200.cvnets_in1k,94.840,5.160,98.710,1.290,18.45,256,0.888,bicubic
convnext_tiny.fb_in22k_ft_in1k,94.840,5.160,98.530,1.470,28.59,288,1.000,bicubic
fastvit_s12.apple_dist_in1k,94.830,5.170,98.810,1.190,9.47,256,0.900,bicubic
cspresnext50.ra_in1k,94.830,5.170,98.770,1.230,20.57,256,0.887,bilinear
resnext50_32x4d.a1_in1k,94.830,5.170,98.590,1.410,25.03,288,1.000,bicubic
res2net101d.in1k,94.820,5.180,98.770,1.230,45.23,224,0.875,bilinear
resnet50.a1h_in1k,94.770,5.230,98.690,1.310,25.56,224,1.000,bicubic
lambda_resnet50ts.a1h_in1k,94.770,5.230,98.470,1.530,21.54,256,0.950,bicubic
repvit_m1_1.dist_300e_in1k,94.760,5.240,98.930,1.070,8.80,224,0.950,bicubic
convnext_pico.d1_in1k,94.760,5.240,98.710,1.290,9.05,288,0.950,bicubic
sehalonet33ts.ra2_in1k,94.760,5.240,98.570,1.430,13.69,256,0.940,bicubic
resnet50.a1_in1k,94.740,5.260,98.710,1.290,25.56,288,1.000,bicubic
repvit_m2.dist_in1k,94.740,5.260,98.680,1.320,8.80,224,0.950,bicubic
resnest50d_1s4x24d.in1k,94.730,5.270,98.980,1.020,25.68,224,0.875,bicubic
resnet50.c1_in1k,94.730,5.270,98.930,1.070,25.56,288,1.000,bicubic
resnet50.b2k_in1k,94.730,5.270,98.820,1.180,25.56,288,1.000,bicubic
resnet152d.gluon_in1k,94.730,5.270,98.750,1.250,60.21,224,0.875,bicubic
resnet152.a3_in1k,94.730,5.270,98.680,1.320,60.19,224,0.950,bicubic
resnet50d.a1_in1k,94.730,5.270,98.490,1.510,25.58,288,1.000,bicubic
resnet101s.gluon_in1k,94.720,5.280,98.820,1.180,44.67,224,0.875,bicubic
deit_small_distilled_patch16_224.fb_in1k,94.710,5.290,99.030,0.970,22.44,224,0.900,bicubic
resnext50_32x4d.ra_in1k,94.700,5.300,98.760,1.240,25.03,288,0.950,bicubic
xcit_tiny_12_p8_224.fb_in1k,94.690,5.310,98.830,1.170,6.71,224,1.000,bicubic
haloregnetz_b.ra3_in1k,94.690,5.310,98.660,1.340,11.68,224,0.940,bicubic
resmlp_big_24_224.fb_in1k,94.680,5.320,98.500,1.500,129.14,224,0.875,bicubic
edgenext_small_rw.sw_in1k,94.670,5.330,98.780,1.220,7.83,320,1.000,bicubic
resnext101_64x4d.gluon_in1k,94.670,5.330,98.660,1.340,83.46,224,0.875,bicubic
regnetx_032.tv2_in1k,94.660,5.340,98.850,1.150,15.30,224,0.965,bicubic
cspdarknet53.ra_in1k,94.660,5.340,98.800,1.200,27.64,256,0.887,bilinear
seresnet50.a2_in1k,94.660,5.340,98.780,1.220,28.09,288,1.000,bicubic
seresnet50.a1_in1k,94.660,5.340,98.720,1.280,28.09,288,1.000,bicubic
poolformerv2_s24.sail_in1k,94.650,5.350,98.840,1.160,21.34,224,1.000,bicubic
maxvit_rmlp_pico_rw_256.sw_in1k,94.640,5.360,98.810,1.190,7.52,256,0.950,bicubic
resnet50.tv2_in1k,94.640,5.360,98.800,1.200,25.56,224,0.965,bilinear
efficientnet_b3_pruned.in1k,94.630,5.370,98.760,1.240,9.86,300,0.904,bicubic
resnet50.a2_in1k,94.630,5.370,98.660,1.340,25.56,288,1.000,bicubic
eca_resnet33ts.ra2_in1k,94.620,5.380,98.910,1.090,19.68,288,1.000,bicubic
darknet53.c2ns_in1k,94.620,5.380,98.900,1.100,41.61,288,1.000,bicubic
gernet_m.idstcv_in1k,94.620,5.380,98.860,1.140,21.14,224,0.875,bilinear
resnext50_32x4d.tv2_in1k,94.620,5.380,98.780,1.220,25.03,224,0.965,bilinear
convnext_pico_ols.d1_in1k,94.620,5.380,98.760,1.240,9.06,288,1.000,bicubic
efficientnet_b2.ra_in1k,94.620,5.380,98.710,1.290,9.11,288,1.000,bicubic
tresnet_m.miil_in1k,94.620,5.380,98.550,1.450,31.39,224,0.875,bilinear
sebotnet33ts_256.a1h_in1k,94.610,5.390,98.510,1.490,13.70,256,0.940,bicubic
fastvit_t12.apple_dist_in1k,94.590,5.410,98.790,1.210,7.55,256,0.900,bicubic
inception_resnet_v2.tf_in1k,94.580,5.420,98.790,1.210,55.84,299,0.897,bicubic
resnet50d.a2_in1k,94.580,5.420,98.690,1.310,25.58,288,1.000,bicubic
resnext50_32x4d.a2_in1k,94.580,5.420,98.650,1.350,25.03,288,1.000,bicubic
pit_s_224.in1k,94.570,5.430,98.700,1.300,23.46,224,0.900,bicubic
poolformer_s24.sail_in1k,94.560,5.440,98.900,1.100,21.39,224,0.900,bicubic
repvgg_b3.rvgg_in1k,94.550,5.450,98.780,1.220,123.09,224,0.875,bilinear
mobilevitv2_150.cvnets_in1k,94.550,5.450,98.710,1.290,10.59,256,0.888,bicubic
resnext50d_32x4d.bt_in1k,94.550,5.450,98.690,1.310,25.05,288,0.950,bicubic
regnety_320.pycls_in1k,94.540,5.460,98.850,1.150,145.05,224,0.875,bicubic
tf_efficientnet_b3.in1k,94.540,5.460,98.800,1.200,12.23,300,0.904,bicubic
nf_resnet50.ra2_in1k,94.540,5.460,98.790,1.210,25.56,288,0.940,bicubic
xcit_tiny_24_p16_224.fb_dist_in1k,94.540,5.460,98.780,1.220,12.12,224,1.000,bicubic
resnext101_32x4d.gluon_in1k,94.540,5.460,98.630,1.370,44.18,224,0.875,bicubic
repvit_m1_0.dist_450e_in1k,94.530,5.470,98.880,1.120,7.30,224,0.950,bicubic
regnety_016.tv2_in1k,94.520,5.480,98.820,1.180,11.20,224,0.965,bicubic
resnet50.ram_in1k,94.520,5.480,98.650,1.350,25.56,288,0.950,bicubic
repvgg_b3g4.rvgg_in1k,94.510,5.490,98.970,1.030,83.83,224,0.875,bilinear
tf_efficientnet_b2.ap_in1k,94.510,5.490,98.620,1.380,9.11,260,0.890,bicubic
convmixer_768_32.in1k,94.500,5.500,98.860,1.140,21.11,224,0.960,bicubic
efficientvit_b1.r288_in1k,94.490,5.510,98.520,1.480,9.10,288,1.000,bicubic
efficientformer_l1.snap_dist_in1k,94.480,5.520,98.830,1.170,12.29,224,0.950,bicubic
rexnet_150.nav_in1k,94.480,5.520,98.800,1.200,9.73,224,0.875,bicubic
regnety_120.pycls_in1k,94.470,5.530,98.820,1.180,51.82,224,0.875,bicubic
darknetaa53.c2ns_in1k,94.470,5.530,98.770,1.230,36.02,288,1.000,bilinear
resnetblur50.bt_in1k,94.460,5.540,98.840,1.160,25.56,288,0.950,bicubic
resnet50.fb_ssl_yfcc100m_ft_in1k,94.450,5.550,98.920,1.080,25.56,224,0.875,bilinear
resmlp_24_224.fb_distilled_in1k,94.450,5.550,98.770,1.230,30.02,224,0.875,bicubic
regnetx_320.pycls_in1k,94.450,5.550,98.740,1.260,107.81,224,0.875,bicubic
gcvit_xxtiny.in1k,94.420,5.580,98.890,1.110,12.00,224,0.875,bicubic
tf_efficientnetv2_b2.in1k,94.420,5.580,98.580,1.420,10.10,260,0.890,bicubic
tf_efficientnet_el.in1k,94.400,5.600,98.710,1.290,10.59,300,0.904,bicubic
efficientnet_el_pruned.in1k,94.390,5.610,98.740,1.260,10.59,300,0.904,bicubic
tf_efficientnet_b2.aa_in1k,94.380,5.620,98.610,1.390,9.11,260,0.890,bicubic
legacy_seresnext101_32x4d.in1k,94.370,5.630,98.630,1.370,48.96,224,0.875,bilinear
inception_v4.tf_in1k,94.370,5.630,98.580,1.420,42.68,299,0.875,bicubic
regnety_160.pycls_in1k,94.360,5.640,98.860,1.140,83.59,224,0.875,bicubic
deit_small_patch16_224.fb_in1k,94.350,5.650,98.690,1.310,22.05,224,0.900,bicubic
ecaresnet50t.a3_in1k,94.350,5.650,98.670,1.330,25.57,224,0.950,bicubic
dpn107.mx_in1k,94.340,5.660,98.500,1.500,86.92,224,0.875,bicubic
seresnext50_32x4d.gluon_in1k,94.330,5.670,98.610,1.390,27.56,224,0.875,bicubic
ecaresnet26t.ra2_in1k,94.320,5.680,98.720,1.280,16.01,320,0.950,bicubic
resnet50.bt_in1k,94.320,5.680,98.640,1.360,25.56,288,0.950,bicubic
resnetrs50.tf_in1k,94.320,5.680,98.640,1.360,35.69,224,0.910,bicubic
res2net50d.in1k,94.320,5.680,98.530,1.470,25.72,224,0.875,bilinear
repvit_m1_0.dist_300e_in1k,94.300,5.700,98.850,1.150,7.30,224,0.950,bicubic
xception71.tf_in1k,94.300,5.700,98.650,1.350,42.34,299,0.903,bicubic
dpn92.mx_in1k,94.290,5.710,98.750,1.250,37.67,224,0.875,bicubic
cait_xxs36_224.fb_dist_in1k,94.270,5.730,98.710,1.290,17.30,224,1.000,bicubic
tiny_vit_5m_224.in1k,94.240,5.760,98.690,1.310,5.39,224,0.950,bicubic
skresnext50_32x4d.ra_in1k,94.240,5.760,98.460,1.540,27.48,224,0.875,bicubic
regnetx_120.pycls_in1k,94.230,5.770,98.670,1.330,46.11,224,0.875,bicubic
resnet101d.gluon_in1k,94.230,5.770,98.540,1.460,44.57,224,0.875,bicubic
resnet50.ra_in1k,94.210,5.790,98.620,1.380,25.56,288,0.950,bicubic
tf_efficientnet_lite3.in1k,94.200,5.800,98.640,1.360,8.20,300,0.904,bilinear
efficientformerv2_s1.snap_dist_in1k,94.200,5.800,98.630,1.370,6.19,224,0.950,bicubic
resmlp_36_224.fb_in1k,94.190,5.810,98.660,1.340,44.69,224,0.875,bicubic
convnextv2_femto.fcmae_ft_in1k,94.190,5.810,98.610,1.390,5.23,288,0.950,bicubic
mixnet_xl.ra_in1k,94.180,5.820,98.320,1.680,11.90,224,0.875,bicubic
regnety_080.pycls_in1k,94.170,5.830,98.680,1.320,39.18,224,0.875,bicubic
inception_resnet_v2.tf_ens_adv_in1k,94.170,5.830,98.600,1.400,55.84,299,0.897,bicubic
levit_192.fb_dist_in1k,94.170,5.830,98.540,1.460,10.95,224,0.900,bicubic
levit_conv_192.fb_dist_in1k,94.170,5.830,98.540,1.460,10.95,224,0.900,bicubic
resnet152c.gluon_in1k,94.160,5.840,98.640,1.360,60.21,224,0.875,bicubic
dpn98.mx_in1k,94.160,5.840,98.590,1.410,61.57,224,0.875,bicubic
vit_base_patch16_224.sam_in1k,94.150,5.850,98.670,1.330,86.57,224,0.900,bicubic
gmlp_s16_224.ra3_in1k,94.150,5.850,98.500,1.500,19.42,224,0.875,bicubic
regnetx_160.pycls_in1k,94.140,5.860,98.750,1.250,54.28,224,0.875,bicubic
regnety_064.pycls_in1k,94.140,5.860,98.710,1.290,30.58,224,0.875,bicubic
efficientnet_b2_pruned.in1k,94.140,5.860,98.520,1.480,8.31,260,0.890,bicubic
regnetx_016.tv2_in1k,94.130,5.870,98.750,1.250,9.19,224,0.965,bicubic
fastvit_s12.apple_in1k,94.130,5.870,98.620,1.380,9.47,256,0.900,bicubic
nf_regnet_b1.ra2_in1k,94.110,5.890,98.620,1.380,10.22,288,0.900,bicubic
tf_efficientnet_b2.in1k,94.110,5.890,98.450,1.550,9.11,260,0.890,bicubic
resnet33ts.ra2_in1k,94.100,5.900,98.650,1.350,19.68,288,1.000,bicubic
efficientvit_b1.r256_in1k,94.090,5.910,98.360,1.640,9.10,256,1.000,bicubic
xcit_tiny_24_p16_224.fb_in1k,94.080,5.920,98.540,1.460,12.12,224,1.000,bicubic
resnet152.gluon_in1k,94.070,5.930,98.460,1.540,60.19,224,0.875,bicubic
dpn131.mx_in1k,94.050,5.950,98.710,1.290,79.25,224,0.875,bicubic
coat_lite_mini.in1k,94.040,5.960,98.550,1.450,11.01,224,0.900,bicubic
eca_halonext26ts.c1_in1k,94.040,5.960,98.490,1.510,10.76,256,0.940,bicubic
resnet101.a3_in1k,94.030,5.970,98.660,1.340,44.55,224,0.950,bicubic
hrnet_w64.ms_in1k,94.030,5.970,98.590,1.410,128.06,224,0.875,bilinear
resmlp_24_224.fb_in1k,94.030,5.970,98.330,1.670,30.02,224,0.875,bicubic
halonet26t.a1h_in1k,94.000,6.000,98.500,1.500,12.48,256,0.950,bicubic
dpn68b.ra_in1k,94.000,6.000,98.340,1.660,12.61,288,1.000,bicubic
fbnetv3_b.ra2_in1k,93.970,6.030,98.630,1.370,8.60,256,0.950,bilinear
resnet50.am_in1k,93.970,6.030,98.520,1.480,25.56,224,0.875,bicubic
dla102x2.in1k,93.970,6.030,98.490,1.510,41.28,224,0.875,bilinear
mobilevitv2_125.cvnets_in1k,93.960,6.040,98.550,1.450,7.48,256,0.888,bicubic
tf_efficientnetv2_b1.in1k,93.950,6.050,98.620,1.380,8.14,240,0.882,bicubic
convnext_femto.d1_in1k,93.930,6.070,98.520,1.480,5.22,288,0.950,bicubic
fbnetv3_d.ra2_in1k,93.920,6.080,98.740,1.260,10.31,256,0.950,bilinear
convnext_femto_ols.d1_in1k,93.920,6.080,98.620,1.380,5.23,288,0.950,bicubic
hrnet_w48.ms_in1k,93.920,6.080,98.610,1.390,77.47,224,0.875,bilinear
fastvit_t12.apple_in1k,93.920,6.080,98.600,1.400,7.55,256,0.900,bicubic
tf_efficientnet_cc_b1_8e.in1k,93.920,6.080,98.260,1.740,39.72,240,0.882,bicubic
regnetx_064.pycls_in1k,93.900,6.100,98.640,1.360,26.21,224,0.875,bicubic
rexnet_130.nav_in1k,93.900,6.100,98.400,1.600,7.56,224,0.875,bicubic
vit_small_patch16_224.augreg_in1k,93.890,6.110,98.440,1.560,22.05,224,0.900,bicubic
regnety_040.pycls_in1k,93.880,6.120,98.660,1.340,20.65,224,0.875,bicubic
repvgg_b2g4.rvgg_in1k,93.880,6.120,98.590,1.410,61.76,224,0.875,bilinear
regnetx_080.pycls_in1k,93.880,6.120,98.520,1.480,39.57,224,0.875,bicubic
efficientnet_em.ra2_in1k,93.830,6.170,98.820,1.180,6.90,240,0.882,bicubic
resnet32ts.ra2_in1k,93.830,6.170,98.650,1.350,17.96,288,1.000,bicubic
lambda_resnet26t.c1_in1k,93.830,6.170,98.640,1.360,10.96,256,0.940,bicubic
resnext101_32x8d.tv_in1k,93.820,6.180,98.580,1.420,88.79,224,0.875,bilinear
resnext50_32x4d.gluon_in1k,93.810,6.190,98.420,1.580,25.03,224,0.875,bicubic
pvt_v2_b1.in1k,93.800,6.200,98.660,1.340,14.01,224,0.900,bicubic
pit_xs_distilled_224.in1k,93.780,6.220,98.620,1.380,11.00,224,0.900,bicubic
eca_botnext26ts_256.c1_in1k,93.780,6.220,98.500,1.500,10.59,256,0.950,bicubic
xception65.tf_in1k,93.780,6.220,98.370,1.630,39.92,299,0.903,bicubic
legacy_seresnext50_32x4d.in1k,93.750,6.250,98.580,1.420,27.56,224,0.875,bilinear
resnet50d.gluon_in1k,93.750,6.250,98.390,1.610,25.58,224,0.875,bicubic
resnet101.gluon_in1k,93.750,6.250,98.380,1.620,44.55,224,0.875,bicubic
cspresnet50.ra_in1k,93.740,6.260,98.640,1.360,21.62,256,0.887,bilinear
wide_resnet101_2.tv_in1k,93.740,6.260,98.540,1.460,126.89,224,0.875,bilinear
mobileone_s4.apple_in1k,93.740,6.260,98.230,1.770,14.95,224,0.900,bilinear
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.740,6.260,98.070,1.930,119.42,256,0.900,bicubic
res2net101_26w_4s.in1k,93.720,6.280,98.310,1.690,45.21,224,0.875,bilinear
lambda_resnet26rpt_256.c1_in1k,93.710,6.290,98.520,1.480,10.99,256,0.940,bicubic
regnety_008_tv.tv2_in1k,93.690,6.310,98.490,1.510,6.43,224,0.965,bicubic
tf_efficientnet_b1.ap_in1k,93.680,6.320,98.360,1.640,7.79,240,0.882,bicubic
resnet101c.gluon_in1k,93.670,6.330,98.420,1.580,44.57,224,0.875,bicubic
resnext50_32x4d.a3_in1k,93.660,6.340,98.520,1.480,25.03,224,0.950,bicubic
vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.650,6.350,98.590,1.410,5.79,384,1.000,bicubic
resnet34d.ra2_in1k,93.640,6.360,98.540,1.460,21.82,288,0.950,bicubic
resnet50s.gluon_in1k,93.640,6.360,98.460,1.540,25.68,224,0.875,bicubic
vit_base_patch32_384.augreg_in1k,93.640,6.360,98.400,1.600,88.30,384,1.000,bicubic
vit_base_patch16_224.augreg_in1k,93.640,6.360,98.240,1.760,86.57,224,0.900,bicubic
tf_efficientnet_b0.ns_jft_in1k,93.620,6.380,98.640,1.360,5.29,224,0.875,bicubic
cait_xxs24_224.fb_dist_in1k,93.610,6.390,98.460,1.540,11.96,224,1.000,bicubic
repvit_m0_9.dist_450e_in1k,93.600,6.400,98.500,1.500,5.49,224,0.950,bicubic
coat_tiny.in1k,93.580,6.420,98.420,1.580,5.50,224,0.900,bicubic
regnetx_040.pycls_in1k,93.560,6.440,98.530,1.470,22.12,224,0.875,bicubic
visformer_tiny.in1k,93.560,6.440,98.490,1.510,10.32,224,0.900,bicubic
seresnext26t_32x4d.bt_in1k,93.560,6.440,98.390,1.610,16.81,288,0.950,bicubic
hrnet_w44.ms_in1k,93.550,6.450,98.700,1.300,67.06,224,0.875,bilinear
hrnet_w18.ms_aug_in1k,93.550,6.450,98.600,1.400,21.30,224,0.950,bilinear
hrnet_w32.ms_in1k,93.530,6.470,98.450,1.550,41.23,224,0.875,bilinear
xcit_nano_12_p8_384.fb_dist_in1k,93.520,6.480,98.530,1.470,3.05,384,1.000,bicubic
efficientvit_b1.r224_in1k,93.510,6.490,98.320,1.680,9.10,224,0.950,bicubic
botnet26t_256.c1_in1k,93.510,6.490,98.300,1.700,12.49,256,0.950,bicubic
repvgg_b2.rvgg_in1k,93.500,6.500,98.730,1.270,89.02,224,0.875,bilinear
hrnet_w40.ms_in1k,93.500,6.500,98.570,1.430,57.56,224,0.875,bilinear
repghostnet_200.in1k,93.500,6.500,98.540,1.460,9.80,224,0.875,bicubic
dla102x.in1k,93.490,6.510,98.500,1.500,26.31,224,0.875,bilinear
tf_efficientnet_b1.aa_in1k,93.490,6.510,98.360,1.640,7.79,240,0.882,bicubic
resnet50d.a3_in1k,93.480,6.520,98.450,1.550,25.58,224,0.950,bicubic
inception_v3.gluon_in1k,93.470,6.530,98.570,1.430,23.83,299,0.875,bicubic
legacy_xception.tf_in1k,93.460,6.540,98.530,1.470,22.86,299,0.897,bicubic
repvit_m0_9.dist_300e_in1k,93.440,6.560,98.710,1.290,5.49,224,0.950,bicubic
seresnext26d_32x4d.bt_in1k,93.440,6.560,98.330,1.670,16.81,288,0.950,bicubic
xception41.tf_in1k,93.430,6.570,98.430,1.570,26.97,299,0.903,bicubic
mixnet_l.ft_in1k,93.430,6.570,98.220,1.780,7.33,224,0.875,bicubic
regnety_032.pycls_in1k,93.410,6.590,98.640,1.360,19.44,224,0.875,bicubic
xcit_tiny_12_p16_224.fb_dist_in1k,93.410,6.590,98.510,1.490,6.72,224,1.000,bicubic
res2net50_26w_6s.in1k,93.400,6.600,98.280,1.720,37.05,224,0.875,bilinear
res2net50_26w_8s.in1k,93.390,6.610,98.170,1.830,48.40,224,0.875,bilinear
legacy_seresnet152.in1k,93.380,6.620,98.340,1.660,66.82,224,0.875,bilinear
dla169.in1k,93.350,6.650,98.610,1.390,53.39,224,0.875,bilinear
cs3darknet_m.c2ns_in1k,93.350,6.650,98.600,1.400,9.31,288,0.950,bicubic
levit_conv_128.fb_dist_in1k,93.350,6.650,98.370,1.630,9.21,224,0.900,bicubic
repvgg_b1.rvgg_in1k,93.330,6.670,98.520,1.480,57.42,224,0.875,bilinear
levit_128.fb_dist_in1k,93.330,6.670,98.370,1.630,9.21,224,0.900,bicubic
resnest26d.gluon_in1k,93.320,6.680,98.620,1.380,17.07,224,0.875,bilinear
resnet152.tv_in1k,93.320,6.680,98.380,1.620,60.19,224,0.875,bilinear
bat_resnext26ts.ch_in1k,93.320,6.680,98.360,1.640,10.73,256,0.900,bicubic
inception_v3.tf_in1k,93.320,6.680,98.040,1.960,23.83,299,0.875,bicubic
tf_mixnet_l.in1k,93.320,6.680,98.030,1.970,7.33,224,0.875,bicubic
legacy_seresnet101.in1k,93.310,6.690,98.520,1.480,49.33,224,0.875,bilinear
selecsls60b.in1k,93.310,6.690,98.290,1.710,32.77,224,0.875,bicubic
mobilevitv2_100.cvnets_in1k,93.300,6.700,98.280,1.720,4.90,256,0.888,bicubic
repvit_m1.dist_in1k,93.290,6.710,98.440,1.560,5.49,224,0.950,bicubic
efficientnet_b1.ft_in1k,93.250,6.750,98.290,1.710,7.79,256,1.000,bicubic
coat_lite_tiny.in1k,93.240,6.760,98.260,1.740,5.72,224,0.900,bicubic
resnet26t.ra2_in1k,93.200,6.800,98.490,1.510,16.01,320,1.000,bicubic
hrnet_w30.ms_in1k,93.200,6.800,98.410,1.590,37.71,224,0.875,bilinear
dla60_res2next.in1k,93.190,6.810,98.400,1.600,17.03,224,0.875,bilinear
dla60_res2net.in1k,93.170,6.830,98.430,1.570,20.85,224,0.875,bilinear
efficientnet_es.ra_in1k,93.170,6.830,98.410,1.590,5.44,224,0.875,bicubic
mobilevit_s.cvnets_in1k,93.160,6.840,98.440,1.560,5.58,256,0.900,bicubic
wide_resnet50_2.tv_in1k,93.160,6.840,98.370,1.630,68.88,224,0.875,bilinear
gcresnext26ts.ch_in1k,93.160,6.840,98.320,1.680,10.48,288,1.000,bicubic
ese_vovnet19b_dw.ra_in1k,93.150,6.850,98.250,1.750,6.54,288,0.950,bicubic
regnetx_032.pycls_in1k,93.110,6.890,98.390,1.610,15.30,224,0.875,bicubic
tf_efficientnetv2_b0.in1k,93.110,6.890,98.390,1.610,7.14,224,0.875,bicubic
resnet34.a1_in1k,93.100,6.900,98.330,1.670,21.80,288,1.000,bicubic
pit_xs_224.in1k,93.100,6.900,98.310,1.690,10.62,224,0.900,bicubic
tf_efficientnet_b1.in1k,93.100,6.900,98.300,1.700,7.79,240,0.882,bicubic
convnext_atto_ols.a2_in1k,93.090,6.910,98.470,1.530,3.70,288,0.950,bicubic
dla60x.in1k,93.080,6.920,98.500,1.500,17.35,224,0.875,bilinear
eca_resnext26ts.ch_in1k,93.060,6.940,98.400,1.600,10.30,288,1.000,bicubic
dla102.in1k,93.050,6.950,98.550,1.450,33.27,224,0.875,bilinear
regnety_016.pycls_in1k,93.040,6.960,98.370,1.630,11.20,224,0.875,bicubic
resnet50c.gluon_in1k,93.030,6.970,98.400,1.600,25.58,224,0.875,bicubic
rexnet_100.nav_in1k,93.020,6.980,98.190,1.810,4.80,224,0.875,bicubic
selecsls60.in1k,93.010,6.990,98.300,1.700,30.67,224,0.875,bicubic
repvgg_b1g4.rvgg_in1k,93.000,7.000,98.430,1.570,39.97,224,0.875,bilinear
ghostnetv2_160.in1k,92.990,7.010,98.230,1.770,12.39,224,0.875,bicubic
cs3darknet_focus_m.c2ns_in1k,92.970,7.030,98.390,1.610,9.30,288,0.950,bicubic
hardcorenas_f.miil_green_in1k,92.970,7.030,98.160,1.840,8.20,224,0.875,bilinear
convnextv2_atto.fcmae_ft_in1k,92.970,7.030,98.060,1.940,3.71,288,0.950,bicubic
seresnext26ts.ch_in1k,92.960,7.040,98.410,1.590,10.39,288,1.000,bicubic
poolformerv2_s12.sail_in1k,92.960,7.040,98.360,1.640,11.89,224,1.000,bicubic
legacy_seresnet50.in1k,92.960,7.040,98.180,1.820,28.09,224,0.875,bilinear
tf_efficientnet_em.in1k,92.940,7.060,98.190,1.810,6.90,240,0.882,bicubic
mobileone_s3.apple_in1k,92.940,7.060,98.180,1.820,10.17,224,0.900,bilinear
inception_v3.tf_adv_in1k,92.900,7.100,98.140,1.860,23.83,299,0.875,bicubic
crossvit_9_dagger_240.in1k,92.890,7.110,98.240,1.760,8.78,240,0.875,bicubic
res2next50.in1k,92.840,7.160,98.180,1.820,24.67,224,0.875,bilinear
tf_efficientnet_cc_b0_8e.in1k,92.840,7.160,98.180,1.820,24.01,224,0.875,bicubic
gmixer_24_224.ra3_in1k,92.830,7.170,97.880,2.120,24.72,224,0.875,bicubic
mobileone_s2.apple_in1k,92.820,7.180,98.270,1.730,7.88,224,0.900,bilinear
resmlp_12_224.fb_distilled_in1k,92.820,7.180,98.140,1.860,15.35,224,0.875,bicubic
resnet101.tv_in1k,92.810,7.190,98.250,1.750,44.55,224,0.875,bilinear
dpn68b.mx_in1k,92.790,7.210,98.150,1.850,12.61,224,0.875,bicubic
convnext_atto.d2_in1k,92.790,7.210,98.080,1.920,3.70,288,0.950,bicubic
efficientnet_b1_pruned.in1k,92.780,7.220,98.040,1.960,6.33,240,0.882,bicubic
hrnet_w18_small_v2.gluon_in1k,92.770,7.230,98.410,1.590,15.60,224,0.875,bicubic
res2net50_14w_8s.in1k,92.770,7.230,98.160,1.840,25.06,224,0.875,bilinear
resnext50_32x4d.tv_in1k,92.750,7.250,98.270,1.730,25.03,224,0.875,bilinear
densenet201.tv_in1k,92.740,7.260,98.230,1.770,20.01,224,0.875,bicubic
inception_v3.tv_in1k,92.730,7.270,97.970,2.030,23.83,299,0.875,bicubic
resnet50.a3_in1k,92.720,7.280,98.170,1.830,25.56,224,0.950,bicubic
resnet34.a2_in1k,92.720,7.280,98.010,1.990,21.80,288,1.000,bicubic
efficientnet_b0.ra_in1k,92.690,7.310,98.070,1.930,5.29,224,0.875,bicubic
tf_efficientnet_lite2.in1k,92.650,7.350,98.230,1.770,6.09,260,0.890,bicubic
legacy_seresnext26_32x4d.in1k,92.640,7.360,98.120,1.880,16.79,224,0.875,bicubic
tf_efficientnet_lite1.in1k,92.630,7.370,98.060,1.940,5.42,240,0.882,bicubic
densenetblur121d.ra_in1k,92.620,7.380,98.260,1.740,8.00,288,0.950,bicubic
poolformer_s12.sail_in1k,92.610,7.390,98.180,1.820,11.92,224,0.900,bicubic
tf_efficientnet_cc_b0_4e.in1k,92.600,7.400,98.080,1.920,13.31,224,0.875,bicubic
hardcorenas_e.miil_green_in1k,92.570,7.430,98.100,1.900,8.07,224,0.875,bilinear
regnetx_008.tv2_in1k,92.550,7.450,98.180,1.820,7.26,224,0.965,bicubic
res2net50_48w_2s.in1k,92.550,7.450,98.080,1.920,25.29,224,0.875,bilinear
resnet50.gluon_in1k,92.540,7.460,98.170,1.830,25.56,224,0.875,bicubic
fastvit_t8.apple_dist_in1k,92.540,7.460,98.040,1.960,4.03,256,0.900,bicubic
densenet121.ra_in1k,92.520,7.480,98.220,1.780,7.98,288,0.950,bicubic
resnet26d.bt_in1k,92.520,7.480,98.210,1.790,16.01,288,0.950,bicubic
xcit_tiny_12_p16_224.fb_in1k,92.510,7.490,98.240,1.760,6.72,224,1.000,bicubic
res2net50_26w_4s.in1k,92.490,7.510,98.030,1.970,25.70,224,0.875,bilinear
densenet161.tv_in1k,92.480,7.520,98.300,1.700,28.68,224,0.875,bicubic
efficientvit_m5.r224_in1k,92.460,7.540,97.990,2.010,12.47,224,0.875,bicubic
tinynet_a.in1k,92.440,7.560,98.080,1.920,6.19,192,0.875,bicubic
resnet34.bt_in1k,92.410,7.590,98.150,1.850,21.80,288,0.950,bicubic
mixnet_m.ft_in1k,92.410,7.590,97.870,2.130,5.01,224,0.875,bicubic
convmixer_1024_20_ks9_p14.in1k,92.400,7.600,98.270,1.730,24.38,224,0.960,bicubic
hardcorenas_d.miil_green_in1k,92.400,7.600,98.080,1.920,7.50,224,0.875,bilinear
mobilenetv2_120d.ra_in1k,92.400,7.600,98.060,1.940,5.83,224,0.875,bicubic
skresnet34.ra_in1k,92.380,7.620,98.150,1.850,22.28,224,0.875,bicubic
repghostnet_150.in1k,92.370,7.630,98.050,1.950,6.58,224,0.875,bicubic
hrnet_w18.ms_in1k,92.320,7.680,98.260,1.740,21.30,224,0.875,bilinear
ghostnetv2_130.in1k,92.320,7.680,98.070,1.930,8.96,224,0.875,bicubic
tf_mixnet_m.in1k,92.300,7.700,97.890,2.110,5.01,224,0.875,bicubic
selecsls42b.in1k,92.290,7.710,98.110,1.890,32.46,224,0.875,bicubic
mobilenetv3_large_100.miil_in21k_ft_in1k,92.260,7.740,97.620,2.380,5.48,224,0.875,bilinear
tf_efficientnet_b0.aa_in1k,92.240,7.760,98.000,2.000,5.29,224,0.875,bicubic
resmlp_12_224.fb_in1k,92.220,7.780,98.150,1.850,15.35,224,0.875,bicubic
dla60.in1k,92.200,7.800,98.100,1.900,22.04,224,0.875,bilinear
tf_efficientnet_b0.ap_in1k,92.200,7.800,98.020,1.980,5.29,224,0.875,bicubic
regnetx_016.pycls_in1k,92.180,7.820,98.200,1.800,9.19,224,0.875,bicubic
gernet_s.idstcv_in1k,92.130,7.870,98.190,1.810,8.17,224,0.875,bilinear
resnext26ts.ra2_in1k,92.130,7.870,98.030,1.970,10.30,288,1.000,bicubic
xcit_nano_12_p8_224.fb_dist_in1k,92.080,7.920,98.160,1.840,3.05,224,1.000,bicubic
tf_efficientnet_b0.in1k,92.080,7.920,97.910,2.090,5.29,224,0.875,bicubic
seresnet50.a3_in1k,92.070,7.930,98.040,1.960,28.09,224,0.950,bicubic
fastvit_t8.apple_in1k,92.060,7.940,97.930,2.070,4.03,256,0.900,bicubic
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,92.040,7.960,98.290,1.710,6.36,384,1.000,bicubic
vit_small_patch32_224.augreg_in21k_ft_in1k,92.040,7.960,98.230,1.770,22.88,224,0.900,bicubic
hardcorenas_c.miil_green_in1k,92.020,7.980,97.840,2.160,5.52,224,0.875,bilinear
resnet26.bt_in1k,91.990,8.010,98.220,1.780,16.00,288,0.950,bicubic
dpn68.mx_in1k,91.990,8.010,98.020,1.980,12.61,224,0.875,bicubic
tf_efficientnet_es.in1k,91.970,8.030,97.880,2.120,5.44,224,0.875,bicubic
levit_128s.fb_dist_in1k,91.960,8.040,98.060,1.940,7.78,224,0.900,bicubic
levit_conv_128s.fb_dist_in1k,91.960,8.040,98.060,1.940,7.78,224,0.900,bicubic
efficientformerv2_s0.snap_dist_in1k,91.960,8.040,97.890,2.110,3.60,224,0.950,bicubic
repvgg_a2.rvgg_in1k,91.940,8.060,98.140,1.860,28.21,224,0.875,bilinear
densenet169.tv_in1k,91.940,8.060,98.100,1.900,14.15,224,0.875,bicubic
repghostnet_130.in1k,91.890,8.110,97.930,2.070,5.48,224,0.875,bicubic
resnet50.tv_in1k,91.880,8.120,98.040,1.960,25.56,224,0.875,bilinear
mixer_b16_224.goog_in21k_ft_in1k,91.880,8.120,97.260,2.740,59.88,224,0.875,bicubic
mobilenetv2_140.ra_in1k,91.840,8.160,97.860,2.140,6.11,224,0.875,bicubic
xcit_nano_12_p16_384.fb_dist_in1k,91.830,8.170,98.020,1.980,3.05,384,1.000,bicubic
mixnet_s.ft_in1k,91.820,8.180,97.700,2.300,4.13,224,0.875,bicubic
vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.770,8.230,98.040,1.960,5.72,224,0.900,bicubic
mobilevitv2_075.cvnets_in1k,91.760,8.240,97.860,2.140,2.87,256,0.888,bicubic
hardcorenas_b.miil_green_in1k,91.760,8.240,97.780,2.220,5.18,224,0.875,bilinear
regnety_008.pycls_in1k,91.730,8.270,98.180,1.820,6.26,224,0.875,bicubic
resnest14d.gluon_in1k,91.720,8.280,97.870,2.130,10.61,224,0.875,bilinear
edgenext_x_small.in1k,91.710,8.290,97.600,2.400,2.34,288,1.000,bicubic
regnety_004.tv2_in1k,91.580,8.420,97.890,2.110,4.34,224,0.965,bicubic
tf_mixnet_s.in1k,91.520,8.480,97.620,2.380,4.13,224,0.875,bicubic
repvgg_b0.rvgg_in1k,91.390,8.610,98.000,2.000,15.82,224,0.875,bilinear
regnety_006.pycls_in1k,91.390,8.610,97.700,2.300,6.06,224,0.875,bicubic
hardcorenas_a.miil_green_in1k,91.350,8.650,97.850,2.150,5.26,224,0.875,bilinear
mobilenetv3_large_100.ra_in1k,91.350,8.650,97.710,2.290,5.48,224,0.875,bicubic
semnasnet_100.rmsp_in1k,91.310,8.690,97.560,2.440,3.89,224,0.875,bicubic
mobileone_s1.apple_in1k,91.280,8.720,97.820,2.180,4.83,224,0.900,bilinear
tf_mobilenetv3_large_100.in1k,91.230,8.770,97.660,2.340,5.48,224,0.875,bilinear
mobilenetv3_rw.rmsp_in1k,91.210,8.790,97.660,2.340,5.48,224,0.875,bicubic
hrnet_w18_small_v2.ms_in1k,91.200,8.800,97.900,2.100,15.60,224,0.875,bilinear
vit_base_patch32_224.augreg_in1k,91.190,8.810,97.390,2.610,88.22,224,0.900,bicubic
efficientnet_es_pruned.in1k,91.170,8.830,97.740,2.260,5.44,224,0.875,bicubic
efficientnet_lite0.ra_in1k,91.120,8.880,97.640,2.360,4.65,224,0.875,bicubic
regnetx_008.pycls_in1k,91.050,8.950,97.720,2.280,7.26,224,0.875,bicubic
tf_efficientnet_lite0.in1k,91.050,8.950,97.580,2.420,4.65,224,0.875,bicubic
xcit_nano_12_p8_224.fb_in1k,91.010,8.990,97.770,2.230,3.05,224,1.000,bicubic
resnet34.gluon_in1k,90.980,9.020,97.630,2.370,21.80,224,0.875,bicubic
mobilenetv2_110d.ra_in1k,90.960,9.040,97.540,2.460,4.52,224,0.875,bicubic
tinynet_b.in1k,90.920,9.080,97.660,2.340,3.73,188,0.875,bicubic
ghostnetv2_100.in1k,90.900,9.100,97.700,2.300,6.16,224,0.875,bicubic
legacy_seresnet34.in1k,90.900,9.100,97.580,2.420,21.96,224,0.875,bilinear
densenet121.tv_in1k,90.890,9.110,97.710,2.290,7.98,224,0.875,bicubic
mobilevit_xs.cvnets_in1k,90.820,9.180,97.930,2.070,2.32,256,0.900,bicubic
pit_ti_distilled_224.in1k,90.770,9.230,97.610,2.390,5.10,224,0.900,bicubic
dla34.in1k,90.760,9.240,97.650,2.350,15.74,224,0.875,bilinear
fbnetc_100.rmsp_in1k,90.730,9.270,97.210,2.790,5.57,224,0.875,bilinear
deit_tiny_distilled_patch16_224.fb_in1k,90.710,9.290,97.560,2.440,5.91,224,0.900,bicubic
repghostnet_111.in1k,90.710,9.290,97.470,2.530,4.54,224,0.875,bicubic
resnet18.fb_swsl_ig1b_ft_in1k,90.700,9.300,97.700,2.300,11.69,224,0.875,bilinear
convit_tiny.fb_in1k,90.660,9.340,97.730,2.270,5.71,224,0.875,bicubic
regnetx_004_tv.tv2_in1k,90.640,9.360,97.600,2.400,5.50,224,0.965,bicubic
crossvit_9_240.in1k,90.630,9.370,97.730,2.270,8.55,240,0.875,bicubic
repvgg_a1.rvgg_in1k,90.600,9.400,97.650,2.350,14.09,224,0.875,bilinear
efficientvit_m4.r224_in1k,90.580,9.420,97.530,2.470,8.80,224,0.875,bicubic
mnasnet_100.rmsp_in1k,90.500,9.500,97.470,2.530,4.38,224,0.875,bicubic
regnety_004.pycls_in1k,90.490,9.510,97.530,2.470,4.34,224,0.875,bicubic
regnetx_006.pycls_in1k,90.350,9.650,97.430,2.570,6.20,224,0.875,bicubic
spnasnet_100.rmsp_in1k,90.330,9.670,97.190,2.810,4.42,224,0.875,bilinear
repghostnet_100.in1k,90.290,9.710,97.480,2.520,4.07,224,0.875,bicubic
resnet18d.ra2_in1k,90.280,9.720,97.560,2.440,11.71,288,0.950,bicubic
crossvit_tiny_240.in1k,90.230,9.770,97.590,2.410,7.01,240,0.875,bicubic
resnet18.fb_ssl_yfcc100m_ft_in1k,90.210,9.790,97.550,2.450,11.69,224,0.875,bilinear
ghostnet_100.in1k,90.180,9.820,97.290,2.710,5.18,224,0.875,bicubic
vgg16_bn.tv_in1k,90.090,9.910,97.370,2.630,138.37,224,0.875,bilinear
vgg19_bn.tv_in1k,90.080,9.920,97.580,2.420,143.68,224,0.875,bilinear
semnasnet_075.rmsp_in1k,90.070,9.930,97.440,2.560,2.91,224,0.875,bicubic
resnet34.tv_in1k,89.950,10.050,97.340,2.660,21.80,224,0.875,bilinear
pit_ti_224.in1k,89.940,10.060,97.450,2.550,4.85,224,0.900,bicubic
resnet34.a3_in1k,89.940,10.060,97.180,2.820,21.80,224,0.950,bicubic
efficientvit_m3.r224_in1k,89.860,10.140,97.540,2.460,6.90,224,0.875,bicubic
vit_base_patch32_224.sam_in1k,89.740,10.260,97.000,3.000,88.22,224,0.900,bicubic
xcit_nano_12_p16_224.fb_dist_in1k,89.700,10.300,97.100,2.900,3.05,224,1.000,bicubic
resnet18.a1_in1k,89.680,10.320,97.100,2.900,11.69,288,1.000,bicubic
deit_tiny_patch16_224.fb_in1k,89.660,10.340,97.450,2.550,5.72,224,0.900,bicubic
skresnet18.ra_in1k,89.660,10.340,97.230,2.770,11.96,224,0.875,bicubic
tf_mobilenetv3_large_075.in1k,89.640,10.360,97.190,2.810,3.99,224,0.875,bilinear
mobilenetv2_100.ra_in1k,89.600,10.400,97.150,2.850,3.50,224,0.875,bicubic
resnet18.a2_in1k,89.570,10.430,96.960,3.040,11.69,288,1.000,bicubic
hrnet_w18_small.gluon_in1k,89.470,10.530,97.060,2.940,13.19,224,0.875,bicubic
repvgg_a0.rvgg_in1k,89.280,10.720,96.890,3.110,9.11,224,0.875,bilinear
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.180,10.820,97.220,2.780,6.34,224,0.900,bicubic
hrnet_w18_small.ms_in1k,89.050,10.950,97.120,2.880,13.19,224,0.875,bilinear
vgg19.tv_in1k,89.050,10.950,96.870,3.130,143.67,224,0.875,bilinear
resnet14t.c3_in1k,88.990,11.010,96.730,3.270,10.08,224,0.950,bicubic
tf_mobilenetv3_large_minimal_100.in1k,88.950,11.050,96.860,3.140,3.92,224,0.875,bilinear
regnetx_004.pycls_in1k,88.930,11.070,97.120,2.880,5.16,224,0.875,bicubic
legacy_seresnet18.in1k,88.890,11.110,96.980,3.020,11.78,224,0.875,bicubic
edgenext_xx_small.in1k,88.890,11.110,96.700,3.300,1.33,288,1.000,bicubic
repghostnet_080.in1k,88.840,11.160,96.700,3.300,3.28,224,0.875,bicubic
pvt_v2_b0.in1k,88.780,11.220,96.860,3.140,3.67,224,0.900,bicubic
vgg13_bn.tv_in1k,88.750,11.250,96.980,3.020,133.05,224,0.875,bilinear
lcnet_100.ra2_in1k,88.750,11.250,96.720,3.280,2.95,224,0.875,bicubic
xcit_nano_12_p16_224.fb_in1k,88.620,11.380,96.790,3.210,3.05,224,1.000,bicubic
vgg16.tv_in1k,88.560,11.440,96.800,3.200,138.36,224,0.875,bilinear
efficientvit_m2.r224_in1k,88.470,11.530,96.900,3.100,4.19,224,0.875,bicubic
resnet18.gluon_in1k,88.370,11.630,96.670,3.330,11.69,224,0.875,bicubic
mobileone_s0.apple_in1k,88.230,11.770,96.400,3.600,5.29,224,0.875,bilinear
mobilevitv2_050.cvnets_in1k,88.180,11.820,96.990,3.010,1.37,256,0.888,bicubic
efficientvit_b0.r224_in1k,87.940,12.060,96.130,3.870,3.41,224,0.950,bicubic
tinynet_c.in1k,87.780,12.220,96.370,3.630,2.46,184,0.875,bicubic
vgg11_bn.tv_in1k,87.500,12.500,96.820,3.180,132.87,224,0.875,bilinear
resnet18.tv_in1k,87.380,12.620,96.290,3.710,11.69,224,0.875,bilinear
regnety_002.pycls_in1k,87.370,12.630,96.610,3.390,3.16,224,0.875,bicubic
mobilevit_xxs.cvnets_in1k,87.160,12.840,96.100,3.900,1.27,256,0.900,bicubic
mixer_l16_224.goog_in21k_ft_in1k,87.150,12.850,93.520,6.480,208.20,224,0.875,bicubic
vgg13.tv_in1k,87.040,12.960,96.330,3.670,133.05,224,0.875,bilinear
efficientvit_m1.r224_in1k,86.790,13.210,96.030,3.970,2.98,224,0.875,bicubic
vgg11.tv_in1k,86.580,13.420,96.280,3.720,132.86,224,0.875,bilinear
repghostnet_058.in1k,86.540,13.460,95.900,4.100,2.55,224,0.875,bicubic
resnet18.a3_in1k,86.450,13.550,95.880,4.120,11.69,224,0.950,bicubic
dla60x_c.in1k,86.290,13.710,96.160,3.840,1.32,224,0.875,bilinear
resnet10t.c3_in1k,86.220,13.780,95.740,4.260,5.44,224,0.950,bicubic
regnetx_002.pycls_in1k,86.140,13.860,95.970,4.030,2.68,224,0.875,bicubic
lcnet_075.ra2_in1k,85.970,14.030,95.680,4.320,2.36,224,0.875,bicubic
mobilenetv3_small_100.lamb_in1k,85.220,14.780,95.650,4.350,2.54,224,0.875,bicubic
tf_mobilenetv3_small_100.in1k,85.190,14.810,95.770,4.230,2.54,224,0.875,bilinear
repghostnet_050.in1k,85.060,14.940,95.200,4.800,2.31,224,0.875,bicubic
tinynet_d.in1k,84.720,15.280,95.170,4.830,2.34,152,0.875,bicubic
mnasnet_small.lamb_in1k,84.420,15.580,95.190,4.810,2.03,224,0.875,bicubic
dla46x_c.in1k,84.230,15.770,95.270,4.730,1.07,224,0.875,bilinear
mobilenetv2_050.lamb_in1k,83.910,16.090,94.720,5.280,1.97,224,0.875,bicubic
dla46_c.in1k,83.610,16.390,94.950,5.050,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075.in1k,83.500,16.500,94.840,5.160,2.04,224,0.875,bilinear
mobilenetv3_small_075.lamb_in1k,83.030,16.970,94.100,5.900,2.04,224,0.875,bicubic
efficientvit_m0.r224_in1k,82.350,17.650,94.430,5.570,2.35,224,0.875,bicubic
lcnet_050.ra2_in1k,81.800,18.200,93.710,6.290,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100.in1k,81.400,18.600,93.680,6.320,2.04,224,0.875,bilinear
tinynet_e.in1k,78.920,21.080,92.540,7.460,2.04,106,0.875,bicubic
mobilenetv3_small_050.lamb_in1k,77.020,22.980,91.300,8.700,1.59,224,0.875,bicubic
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt113-cu117-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,72737.62,14.068,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,54822.3,18.668,1024,224,0.03,0.92,1.59
lcnet_035,53629.35,19.084,1024,224,0.03,1.04,1.64
lcnet_050,45492.41,22.499,1024,224,0.05,1.26,1.88
mobilenetv3_small_075,39215.51,26.102,1024,224,0.05,1.3,2.04
tinynet_d,37346.61,27.409,1024,152,0.05,1.42,2.34
mobilenetv3_small_100,36280.34,28.214,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,31726.33,32.265,1024,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,31503.43,32.494,1024,224,0.05,1.3,2.04
lcnet_075,29817.69,34.332,1024,224,0.1,1.99,2.36
tf_mobilenetv3_small_100,29444.91,34.767,1024,224,0.06,1.42,2.54
mnasnet_small,25354.86,40.376,1024,224,0.07,2.16,2.03
lcnet_100,24134.76,42.417,1024,224,0.16,2.52,2.95
regnetx_002,23983.4,42.686,1024,224,0.2,2.16,2.68
levit_128s,22675.73,45.148,1024,224,0.31,1.88,7.78
regnety_002,21709.37,47.158,1024,224,0.2,2.17,3.16
mobilenetv2_035,21673.44,47.236,1024,224,0.07,2.86,1.68
mnasnet_050,20010.27,51.163,1024,224,0.11,3.07,2.22
ghostnet_050,18932.82,54.075,1024,224,0.05,1.77,2.59
tinynet_c,18428.42,55.556,1024,184,0.11,2.87,2.46
semnasnet_050,17215.18,59.471,1024,224,0.11,3.44,2.08
mobilenetv2_050,17194.94,59.542,1024,224,0.1,3.64,1.97
cs3darknet_focus_s,16189.76,63.24,1024,256,0.69,2.7,3.27
lcnet_150,15557.15,65.811,1024,224,0.34,3.79,4.5
cs3darknet_s,15369.47,66.615,1024,256,0.72,2.97,3.28
levit_128,15337.67,66.754,1024,224,0.41,2.71,9.21
gernet_s,15288.68,66.966,1024,224,0.75,2.65,8.17
mobilenetv3_large_075,14216.3,72.019,1024,224,0.16,4.0,3.99
mixer_s32_224,14182.92,72.188,1024,224,1.0,2.28,19.1
vit_tiny_r_s16_p8_224,14125.39,72.482,1024,224,0.44,2.06,6.34
resnet10t,14112.07,72.551,1024,224,1.1,2.43,5.44
vit_small_patch32_224,13799.47,74.195,1024,224,1.15,2.5,22.88
regnetx_004,13610.2,75.225,1024,224,0.4,3.14,5.16
levit_192,13524.14,75.706,1024,224,0.66,3.2,10.95
mobilenetv3_rw,12956.58,79.021,1024,224,0.23,4.41,5.48
hardcorenas_a,12803.61,79.966,1024,224,0.23,4.38,5.26
mobilenetv3_large_100,12749.93,80.304,1024,224,0.23,4.41,5.48
mnasnet_075,12532.36,81.697,1024,224,0.23,4.77,3.17
tf_mobilenetv3_large_075,12186.51,84.017,1024,224,0.16,4.0,3.99
tinynet_b,12083.18,84.735,1024,188,0.21,4.44,3.73
regnety_004,11918.36,85.906,1024,224,0.41,3.89,4.34
tf_mobilenetv3_large_minimal_100,11715.94,87.392,1024,224,0.22,4.4,3.92
hardcorenas_c,11548.05,88.662,1024,224,0.28,5.01,5.52
hardcorenas_b,11510.71,88.949,1024,224,0.26,5.09,5.18
ese_vovnet19b_slim_dw,11501.95,89.018,1024,224,0.4,5.28,1.9
ghostnet_100,11332.61,90.348,1024,224,0.15,3.55,5.18
mnasnet_100,11138.43,91.923,1024,224,0.33,5.46,4.38
gluon_resnet18_v1b,11098.78,92.252,1024,224,1.82,2.48,11.69
resnet18,11083.1,92.383,1024,224,1.82,2.48,11.69
swsl_resnet18,11062.48,92.555,1024,224,1.82,2.48,11.69
ssl_resnet18,11061.11,92.565,1024,224,1.82,2.48,11.69
tf_mobilenetv3_large_100,11018.56,92.922,1024,224,0.23,4.41,5.48
mnasnet_b1,10993.58,93.135,1024,224,0.33,5.46,4.38
hardcorenas_d,10910.47,93.843,1024,224,0.3,4.93,7.5
semnasnet_075,10898.09,93.951,1024,224,0.23,5.54,2.91
mobilenetv2_075,10893.76,93.988,1024,224,0.22,5.86,2.64
seresnet18,10385.56,98.588,1024,224,1.82,2.49,11.78
legacy_seresnet18,10064.41,101.734,1024,224,1.82,2.49,11.78
spnasnet_100,10009.21,102.296,1024,224,0.35,6.03,4.42
tf_efficientnetv2_b0,9930.95,103.1,1024,224,0.73,4.77,7.14
levit_256,9858.1,103.863,1024,224,1.13,4.23,18.89
tinynet_a,9720.11,105.337,1024,192,0.35,5.41,6.19
hardcorenas_f,9714.91,105.393,1024,224,0.35,5.57,8.2
semnasnet_100,9623.78,106.393,1024,224,0.32,6.23,3.89
mnasnet_a1,9623.77,106.393,1024,224,0.32,6.23,3.89
mobilenetv2_100,9598.91,106.667,1024,224,0.31,6.68,3.5
hardcorenas_e,9571.87,106.966,1024,224,0.35,5.65,8.07
dla46_c,9568.4,107.007,1024,224,0.58,4.5,1.3
efficientnet_lite0,9361.14,109.377,1024,224,0.4,6.74,4.65
fbnetc_100,9352.03,109.484,1024,224,0.4,6.51,5.57
resnet18d,9334.83,109.687,1024,224,2.06,3.29,11.71
ese_vovnet19b_slim,9109.47,112.4,1024,224,1.69,3.52,3.17
regnety_006,9097.63,112.542,1024,224,0.61,4.33,6.06
regnetz_005,8607.49,118.955,1024,224,0.52,5.86,7.12
xcit_nano_12_p16_224_dist,8577.2,119.375,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,8554.61,119.689,1024,224,0.56,4.17,3.05
levit_256d,8382.88,122.143,1024,224,1.4,4.93,26.21
regnetx_006,8379.52,122.192,1024,224,0.61,3.98,6.2
ghostnet_130,8278.59,123.681,1024,224,0.24,4.6,7.36
tf_efficientnet_lite0,8080.51,126.714,1024,224,0.4,6.74,4.65
efficientnet_b0,7965.17,128.548,1024,224,0.4,6.75,5.29
mnasnet_140,7779.42,131.618,1024,224,0.6,7.71,7.12
deit_tiny_distilled_patch16_224,7467.68,137.113,1024,224,1.27,6.01,5.91
rexnetr_100,7464.12,137.179,1024,224,0.43,7.72,4.88
deit_tiny_patch16_224,7430.15,137.806,1024,224,1.26,5.97,5.72
resnet14t,7429.68,137.815,1024,224,1.69,5.8,10.08
vit_tiny_patch16_224,7424.93,137.902,1024,224,1.26,5.97,5.72
regnetx_008,7394.88,138.463,1024,224,0.81,5.15,7.26
mobilenetv2_110d,7247.12,141.287,1024,224,0.45,8.71,4.52
hrnet_w18_small,7232.93,141.561,1024,224,1.61,5.72,13.19
tf_efficientnet_b0,7016.18,145.938,1024,224,0.4,6.75,5.29
regnety_008,6938.46,147.571,1024,224,0.81,5.25,6.26
mobilevitv2_050,6848.87,149.503,1024,256,0.48,8.04,1.37
pit_ti_distilled_224,6811.68,150.317,1024,224,0.71,6.23,5.1
pit_ti_224,6784.24,150.927,1024,224,0.7,6.19,4.85
gernet_m,6679.85,153.286,1024,224,3.02,5.24,21.14
efficientnet_b1_pruned,6642.37,154.15,1024,240,0.4,6.21,6.33
resnet34,6496.42,157.614,1024,224,3.67,3.74,21.8
gluon_resnet34_v1b,6494.61,157.658,1024,224,3.67,3.74,21.8
tv_resnet34,6481.01,157.989,1024,224,3.67,3.74,21.8
tf_efficientnetv2_b1,6476.52,158.098,1024,240,1.21,7.34,8.14
semnasnet_140,6454.5,158.637,1024,224,0.6,8.87,6.11
nf_regnet_b0,6452.24,158.693,1024,256,0.64,5.58,8.76
ese_vovnet19b_dw,6335.13,161.627,1024,224,1.34,8.25,6.54
mobilenetv2_140,6271.56,163.266,1024,224,0.6,9.57,6.11
rexnet_100,6226.48,164.447,1024,224,0.41,7.44,4.8
efficientnet_lite1,6187.91,165.472,1024,240,0.62,10.14,5.42
efficientnet_es_pruned,6115.4,167.434,1024,224,1.81,8.73,5.44
efficientnet_es,6115.12,167.443,1024,224,1.81,8.73,5.44
visformer_tiny,6103.09,167.772,1024,224,1.27,5.72,10.32
seresnet34,6058.13,169.019,1024,224,3.67,3.74,21.96
fbnetv3_b,6018.76,170.124,1024,256,0.55,9.1,8.6
selecsls42,5953.76,171.98,1024,224,2.94,4.62,30.35
selecsls42b,5921.2,172.924,1024,224,2.98,4.62,32.46
resnet26,5895.21,173.69,1024,224,2.36,7.35,16.0
edgenext_xx_small,5893.72,173.732,1024,288,0.33,4.21,1.33
levit_384,5880.4,174.126,1024,224,2.36,6.26,39.13
resnet34d,5865.98,174.555,1024,224,3.91,4.54,21.82
legacy_seresnet34,5850.24,175.025,1024,224,3.67,3.74,21.96
dla34,5827.3,175.712,1024,224,3.07,5.02,15.74
tf_efficientnet_es,5781.29,177.112,1024,224,1.81,8.73,5.44
cs3darknet_focus_m,5721.39,178.967,1024,288,2.51,6.19,9.3
resnetblur18,5636.65,181.657,1024,224,2.34,3.39,11.69
rexnetr_130,5590.0,183.173,1024,224,0.68,9.81,7.61
mobilevit_xxs,5524.87,185.333,1024,256,0.42,8.34,1.27
tf_efficientnet_lite1,5524.68,185.339,1024,240,0.62,10.14,5.42
cs3darknet_m,5478.07,186.916,1024,288,2.63,6.69,9.31
convnext_atto,5460.54,187.516,1024,288,0.91,6.3,3.7
xcit_tiny_12_p16_224_dist,5457.72,187.611,1024,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,5456.63,187.649,1024,224,1.24,6.29,6.72
skresnet18,5413.1,189.159,1024,224,1.82,3.24,11.96
darknet17,5401.37,189.571,1024,256,3.26,7.18,14.3
mixnet_s,5392.58,189.878,1024,224,0.25,6.25,4.13
resmlp_12_224,5366.15,190.814,1024,224,3.01,5.5,15.35
resmlp_12_distilled_224,5364.91,190.857,1024,224,3.01,5.5,15.35
convnext_atto_ols,5288.94,193.6,1024,288,0.96,6.8,3.7
vit_base_patch32_clip_224,5280.68,193.903,1024,224,4.41,5.01,88.22
vit_base_patch32_224,5280.52,193.908,1024,224,4.41,5.01,88.22
pit_xs_distilled_224,5272.13,194.218,1024,224,1.41,7.76,11.0
pit_xs_224,5271.0,194.259,1024,224,1.4,7.71,10.62
repvgg_b0,5252.66,194.939,1024,224,3.41,6.15,15.82
mixer_b32_224,5221.71,196.094,1024,224,3.24,6.29,60.29
pvt_v2_b0,5210.31,196.521,1024,224,0.57,7.99,3.67
resnetaa34d,5171.78,197.986,1024,224,4.43,5.07,21.82
selecsls60,5160.83,198.407,1024,224,3.59,5.52,30.67
selecsls60b,5119.51,200.008,1024,224,3.63,5.52,32.77
mobilenetv2_120d,5111.95,200.304,1024,224,0.69,11.97,5.83
resnet26d,5108.26,200.449,1024,224,2.6,8.15,16.01
gmixer_12_224,5064.97,202.162,1024,224,2.67,7.26,12.7
gmlp_ti16_224,5007.93,204.464,1024,224,1.34,7.55,5.87
mixer_s16_224,4998.69,204.842,1024,224,3.79,5.97,18.53
tf_mixnet_s,4989.18,205.231,1024,224,0.25,6.25,4.13
efficientnet_b0_g16_evos,4930.67,207.667,1024,224,1.01,7.42,8.11
rexnetr_150,4900.22,208.959,1024,224,0.89,11.13,9.78
fbnetv3_d,4881.14,209.776,1024,256,0.68,11.1,10.31
darknet21,4850.41,211.105,1024,256,3.93,7.47,20.86
nf_resnet26,4816.48,212.591,1024,224,2.41,7.35,16.0
efficientnet_lite2,4781.65,214.14,1024,260,0.89,12.9,6.09
convnext_femto,4749.12,215.607,1024,288,1.3,7.56,5.22
tf_efficientnetv2_b2,4718.26,217.018,1024,260,1.72,9.84,10.1
sedarknet21,4656.51,219.895,1024,256,3.93,7.47,20.95
dla46x_c,4636.77,220.831,1024,224,0.54,5.66,1.07
convnext_femto_ols,4618.33,221.714,1024,288,1.35,8.06,5.23
resnext26ts,4603.25,222.441,1024,256,2.43,10.52,10.3
efficientformer_l1,4566.14,224.248,1024,224,1.3,5.53,12.29
dpn48b,4506.78,227.201,1024,224,1.69,8.92,9.13
crossvit_tiny_240,4481.69,228.473,1024,240,1.57,9.08,7.01
dla60x_c,4459.27,229.622,1024,224,0.59,6.01,1.32
eca_resnext26ts,4456.63,229.759,1024,256,2.43,10.52,10.3
seresnext26ts,4453.99,229.896,1024,256,2.43,10.52,10.39
legacy_seresnext26_32x4d,4441.15,230.558,1024,224,2.49,9.39,16.79
gernet_l,4396.56,232.898,1024,256,4.57,8.0,31.08
mobilevitv2_075,4393.87,233.041,1024,256,1.05,12.06,2.87
gcresnext26ts,4384.92,233.516,1024,256,2.43,10.53,10.48
tf_efficientnet_b1,4370.6,234.282,1024,240,0.71,10.88,7.79
tf_efficientnet_lite2,4293.9,238.467,1024,260,0.89,12.9,6.09
rexnet_130,4262.16,240.243,1024,224,0.68,9.71,7.56
efficientnet_b1,4239.44,241.53,1024,256,0.77,12.22,7.79
vit_small_patch32_384,4239.1,241.55,1024,384,3.45,8.25,22.92
crossvit_9_240,4212.37,243.082,1024,240,1.85,9.52,8.55
crossvit_9_dagger_240,4095.03,250.049,1024,240,1.99,9.97,8.78
nf_ecaresnet26,4091.86,250.24,1024,224,2.41,7.36,16.0
nf_seresnet26,4088.47,250.449,1024,224,2.41,7.36,17.4
efficientnet_cc_b0_8e,4076.51,251.183,1024,224,0.42,9.42,24.01
efficientnet_cc_b0_4e,4073.3,251.382,1024,224,0.41,9.42,13.31
ecaresnet50d_pruned,4055.39,252.492,1024,224,2.53,6.43,19.94
efficientnet_b2_pruned,4030.92,254.025,1024,260,0.73,9.13,8.31
ecaresnext50t_32x4d,4018.73,254.796,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,4017.09,254.9,1024,224,2.7,10.09,15.41
seresnext26t_32x4d,4014.43,255.069,1024,224,2.7,10.09,16.81
seresnext26tn_32x4d,4014.36,255.074,1024,224,2.7,10.09,16.81
repvgg_a2,3987.84,256.77,1024,224,5.7,6.26,28.21
poolformer_s12,3982.67,257.103,1024,224,1.82,5.53,11.92
seresnext26d_32x4d,3979.57,257.303,1024,224,2.73,10.19,16.81
vit_tiny_r_s16_p8_384,3963.05,258.374,1024,384,1.34,6.49,6.36
resnet26t,3939.46,259.923,1024,256,3.35,10.52,16.01
nf_regnet_b1,3911.64,261.772,1024,288,1.02,9.2,10.22
rexnet_150,3881.93,263.775,1024,224,0.9,11.21,9.73
nf_regnet_b2,3879.78,263.921,1024,272,1.22,9.27,14.31
resnetv2_50,3865.49,264.896,1024,224,4.11,11.11,25.55
regnetx_016,3852.41,265.794,1024,224,1.62,7.93,9.19
tf_efficientnet_cc_b0_4e,3812.08,268.608,1024,224,0.41,9.42,13.31
tf_efficientnet_cc_b0_8e,3803.67,269.202,1024,224,0.42,9.42,24.01
convnext_pico,3747.49,273.239,1024,288,2.27,10.08,9.05
ecaresnetlight,3744.45,273.459,1024,224,4.11,8.42,30.16
dpn68,3724.59,274.917,1024,224,2.35,10.47,12.61
edgenext_x_small,3714.71,275.646,1024,288,0.68,7.5,2.34
gluon_resnet50_v1b,3672.76,278.798,1024,224,4.11,11.11,25.56
ssl_resnet50,3671.85,278.866,1024,224,4.11,11.11,25.56
efficientnet_em,3671.25,278.913,1024,240,3.04,14.34,6.9
resnet50,3668.58,279.116,1024,224,4.11,11.11,25.56
swsl_resnet50,3668.32,279.136,1024,224,4.11,11.11,25.56
tv_resnet50,3667.14,279.225,1024,224,4.11,11.11,25.56
dpn68b,3667.07,279.229,1024,224,2.35,10.47,12.61
rexnetr_200,3659.45,279.811,1024,224,1.59,15.11,16.52
convnext_pico_ols,3651.34,280.434,1024,288,2.37,10.74,9.06
botnet26t_256,3594.28,284.883,1024,256,3.32,11.98,12.49
bat_resnext26ts,3569.91,286.828,1024,256,2.53,12.51,10.73
resnetv2_50t,3547.32,288.657,1024,224,4.32,11.82,25.57
mixnet_m,3537.26,289.477,1024,224,0.36,8.19,5.01
regnety_016,3531.88,289.919,1024,224,1.63,8.04,11.2
tf_efficientnet_em,3529.62,290.106,1024,240,3.04,14.34,6.9
resnetv2_50d,3525.02,290.482,1024,224,4.35,11.92,25.57
halonet26t,3515.15,291.299,1024,256,3.19,11.69,12.48
resnet32ts,3492.62,293.179,1024,256,4.63,11.58,17.96
hrnet_w18_small_v2,3482.81,294.001,1024,224,2.62,9.65,15.6
gluon_resnet50_v1c,3481.59,294.107,1024,224,4.35,11.92,25.58
dla60,3466.91,295.351,1024,224,4.26,10.16,22.04
resnet33ts,3460.78,295.875,1024,256,4.76,11.66,19.68
tf_efficientnet_b2,3402.3,300.962,1024,260,1.02,13.83,9.11
convit_tiny,3399.61,301.199,1024,224,1.26,7.94,5.71
resnet50t,3373.72,303.51,1024,224,4.32,11.82,25.57
tf_mixnet_m,3366.38,304.167,1024,224,0.36,8.19,5.01
efficientnet_b3_pruned,3360.1,304.74,1024,300,1.04,11.86,9.86
seresnet33ts,3354.27,305.27,1024,256,4.76,11.66,19.78
resnet50d,3351.47,305.527,1024,224,4.35,11.92,25.58
eca_resnet33ts,3350.95,305.574,1024,256,4.76,11.66,19.68
vit_small_resnet26d_224,3346.77,305.954,1024,224,5.07,11.12,63.61
cs3darknet_focus_l,3335.18,307.018,1024,288,5.9,10.16,21.15
gluon_resnet50_v1d,3334.65,307.068,1024,224,4.35,11.92,25.58
mobilevitv2_100,3324.63,307.994,1024,256,1.84,16.08,4.9
vovnet39a,3320.12,308.408,1024,224,7.09,6.73,22.6
legacy_seresnet50,3312.33,309.135,1024,224,3.88,10.6,28.09
efficientnet_b0_gn,3307.86,309.554,1024,224,0.42,6.75,5.29
gcresnet33ts,3307.01,309.633,1024,256,4.76,11.68,19.88
pit_s_distilled_224,3301.25,310.173,1024,224,2.9,11.64,24.04
pit_s_224,3299.97,310.295,1024,224,2.88,11.56,23.46
mobilevit_xs,3252.28,314.844,1024,256,1.05,16.33,2.32
deit_small_distilled_patch16_224,3233.6,316.663,1024,224,4.63,12.02,22.44
efficientnet_b2a,3223.97,317.608,1024,288,1.12,16.2,9.11
efficientnet_b2,3223.9,317.615,1024,288,1.12,16.2,9.11
deit_small_patch16_224,3218.99,318.1,1024,224,4.61,11.95,22.05
vit_small_patch16_224,3218.38,318.16,1024,224,4.61,11.95,22.05
cs3darknet_l,3210.26,318.965,1024,288,6.16,10.83,21.16
ese_vovnet39b,3206.21,319.369,1024,224,7.09,6.74,24.57
eca_vovnet39b,3203.77,319.612,1024,224,7.09,6.74,22.6
convnextv2_atto,3196.73,320.315,1024,288,0.91,6.3,3.71
coatnet_pico_rw_224,3189.82,321.008,1024,224,2.05,14.62,10.85
seresnet50,3181.57,321.841,1024,224,4.11,11.13,28.09
pvt_v2_b1,3147.37,325.339,1024,224,2.12,15.39,14.01
coat_lite_tiny,3146.41,325.439,1024,224,1.6,11.65,5.72
res2net50_48w_2s,3127.52,327.404,1024,224,4.18,11.72,25.29
eca_botnext26ts_256,3112.32,329.003,1024,256,2.46,11.6,10.59
ecaresnet101d_pruned,3103.16,329.973,1024,224,3.48,7.69,24.88
efficientnet_b0_g8_gn,3073.2,333.192,1024,224,0.66,6.75,6.56
ssl_resnext50_32x4d,3071.68,333.356,1024,224,4.26,14.4,25.03
dla60x,3071.64,333.359,1024,224,3.54,13.8,17.35
swsl_resnext50_32x4d,3070.7,333.464,1024,224,4.26,14.4,25.03
tv_resnext50_32x4d,3069.81,333.56,1024,224,4.26,14.4,25.03
resnext50_32x4d,3069.72,333.57,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,3068.47,333.704,1024,224,4.26,14.4,25.03
vit_small_r26_s32_224,3061.92,334.417,1024,224,3.56,9.85,36.43
skresnet34,3055.95,335.073,1024,224,3.67,5.13,22.28
deit3_small_patch16_224_in21ft1k,3048.82,335.855,1024,224,4.61,11.95,22.06
deit3_small_patch16_224,3047.23,336.031,1024,224,4.61,11.95,22.06
eca_halonext26ts,3035.71,337.305,1024,256,2.44,11.46,10.76
haloregnetz_b,3032.47,337.665,1024,224,1.97,11.94,11.68
vit_relpos_base_patch32_plus_rpn_256,3026.45,338.338,1024,256,7.68,8.01,119.42
vit_relpos_small_patch16_rpn_224,3019.95,339.067,1024,224,4.59,13.05,21.97
vit_relpos_small_patch16_224,3008.26,340.383,1024,224,4.59,13.05,21.98
vit_srelpos_small_patch16_224,3000.96,341.213,1024,224,4.59,12.16,21.97
xcit_nano_12_p16_384_dist,3000.48,341.266,1024,384,1.64,12.15,3.05
cs3sedarknet_l,2995.41,341.845,1024,288,6.16,10.83,21.91
resnetaa50d,2993.03,342.116,1024,224,5.39,12.44,25.58
vgg11,2983.47,85.796,256,224,7.61,7.44,132.86
selecsls84,2973.16,344.402,1024,224,5.9,7.57,50.95
resnetrs50,2963.42,345.535,1024,224,4.48,12.14,35.69
seresnet50t,2957.12,346.271,1024,224,4.32,11.83,28.1
resnest14d,2954.69,346.556,1024,224,2.76,7.33,10.61
gluon_resnet50_v1s,2953.65,346.677,1024,224,5.47,13.52,25.68
coat_lite_mini,2952.61,346.799,1024,224,2.0,12.25,11.01
ecaresnet50d,2945.96,347.583,1024,224,4.35,11.93,25.58
densenet121,2933.45,349.064,1024,224,2.87,6.9,7.98
tv_densenet121,2929.69,349.514,1024,224,2.87,6.9,7.98
vit_base_patch32_plus_256,2929.65,349.519,1024,256,7.79,7.76,119.48
rexnet_200,2927.94,349.723,1024,224,1.56,14.91,16.37
xcit_tiny_24_p16_224_dist,2927.0,349.834,1024,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,2921.97,350.436,1024,224,2.34,11.82,12.12
coatnet_nano_cc_224,2867.38,357.108,1024,224,2.24,15.02,13.76
gcresnext50ts,2857.34,358.363,1024,256,3.75,15.46,15.67
lambda_resnet26rpt_256,2853.55,358.839,1024,256,3.16,11.87,10.99
resnext50d_32x4d,2845.08,359.908,1024,224,4.5,15.2,25.05
mixnet_l,2828.6,361.996,1024,224,0.58,10.84,7.33
densenet121d,2824.08,362.584,1024,224,3.11,7.7,8.0
efficientnet_lite3,2821.84,362.87,1024,300,1.65,21.85,8.2
cspresnet50,2793.65,366.534,1024,256,4.54,11.5,21.62
coatnet_nano_rw_224,2781.93,368.077,1024,224,2.41,15.41,15.14
vgg11_bn,2760.38,370.949,1024,224,7.62,7.44,132.87
vovnet57a,2755.77,371.572,1024,224,8.95,7.52,36.64
resmlp_24_224,2750.33,372.306,1024,224,5.96,10.91,30.02
resmlp_24_distilled_224,2740.33,373.665,1024,224,5.96,10.91,30.02
convnextv2_femto,2735.91,374.269,1024,288,1.3,7.56,5.23
flexivit_small,2735.78,374.287,1024,240,5.35,14.18,22.06
gcresnet50t,2732.04,374.8,1024,256,5.42,14.67,25.9
legacy_seresnext50_32x4d,2722.84,376.065,1024,224,4.26,14.42,27.56
seresnext50_32x4d,2721.47,376.256,1024,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,2720.58,376.379,1024,224,4.26,14.42,27.56
visformer_small,2719.93,376.468,1024,224,4.88,11.43,40.22
twins_svt_small,2713.39,377.374,1024,224,2.94,13.75,24.06
resnetv2_50x1_bit_distilled,2708.81,378.014,1024,224,4.23,11.11,25.55
res2net50_14w_8s,2692.9,380.248,1024,224,4.21,13.28,25.06
resnetblur50,2685.97,381.228,1024,224,5.16,12.02,25.56
vit_base_resnet26d_224,2684.6,381.421,1024,224,6.97,13.16,101.4
tf_mixnet_l,2680.8,381.958,1024,224,0.58,10.84,7.33
seresnetaa50d,2658.93,385.106,1024,224,5.4,12.46,28.11
dla60_res2net,2656.16,385.506,1024,224,4.15,12.34,20.85
cspresnet50d,2655.05,385.668,1024,256,4.86,12.55,21.64
coatnext_nano_rw_224,2655.0,385.674,1024,224,2.47,12.8,14.7
ese_vovnet57b,2654.33,385.773,1024,224,8.95,7.52,38.61
tf_efficientnetv2_b3,2654.14,385.8,1024,300,3.04,15.74,14.36
cspresnet50w,2641.68,387.621,1024,256,5.04,12.19,28.12
res2net50_26w_4s,2629.64,389.395,1024,224,4.28,12.61,25.7
regnetz_b16,2626.71,389.828,1024,288,2.39,16.43,9.72
convnext_nano,2611.78,392.059,1024,288,4.06,13.84,15.59
efficientnetv2_rw_t,2601.49,393.609,1024,288,3.19,16.42,13.65
fbnetv3_g,2595.29,394.549,1024,288,1.77,21.09,16.62
gmixer_24_224,2595.15,394.571,1024,224,5.28,14.45,24.72
mobilevit_s,2586.09,395.952,1024,256,2.03,19.94,5.58
coatnet_rmlp_nano_rw_224,2569.7,398.478,1024,224,2.62,20.34,15.15
gcvit_xxtiny,2561.41,399.768,1024,224,2.14,15.36,12.0
tf_efficientnet_lite3,2530.94,404.582,1024,300,1.65,21.85,8.2
efficientnet_cc_b1_8e,2530.65,404.628,1024,240,0.75,15.44,39.72
densenetblur121d,2522.66,405.908,1024,224,3.11,7.9,8.0
resnetblur50d,2509.45,408.045,1024,224,5.4,12.82,25.58
nf_ecaresnet50,2490.39,411.168,1024,224,4.21,11.13,25.56
inception_v3,2485.21,412.025,1024,299,5.73,8.97,23.83
nf_seresnet50,2482.66,412.449,1024,224,4.21,11.13,28.09
tf_inception_v3,2481.38,412.658,1024,299,5.73,8.97,23.83
gc_efficientnetv2_rw_t,2480.59,412.793,1024,288,3.2,16.45,13.68
adv_inception_v3,2479.41,412.983,1024,299,5.73,8.97,23.83
repvgg_b1g4,2473.34,414.003,1024,224,8.15,10.64,39.97
mobilevitv2_125,2472.28,414.18,1024,256,2.86,20.1,7.48
gluon_inception_v3,2468.42,414.827,1024,299,5.73,8.97,23.83
nf_regnet_b3,2461.52,415.991,1024,320,2.05,14.61,18.59
xcit_small_12_p16_224_dist,2446.89,418.478,1024,224,4.82,12.58,26.25
xcit_small_12_p16_224,2446.42,418.558,1024,224,4.82,12.58,26.25
cspresnext50,2438.96,419.836,1024,256,4.05,15.86,20.57
convnext_nano_ols,2435.0,420.521,1024,288,4.38,15.5,15.65
regnetx_032,2429.42,421.489,1024,224,3.2,11.37,15.3
densenet169,2426.29,422.031,1024,224,3.4,7.3,14.15
sehalonet33ts,2419.4,423.234,1024,256,3.55,14.7,13.69
tf_efficientnet_cc_b1_8e,2406.19,425.557,1024,240,0.75,15.44,39.72
semobilevit_s,2402.02,426.294,1024,256,2.03,19.95,5.74
resnetv2_101,2330.6,439.36,1024,224,7.83,16.23,44.54
twins_pcpvt_small,2312.72,442.754,1024,224,3.83,18.08,24.11
xcit_nano_12_p8_224_dist,2295.5,446.077,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,2292.87,446.587,1024,224,2.16,15.71,3.05
gmlp_s16_224,2290.73,447.007,1024,224,4.42,15.1,19.42
cs3darknet_focus_x,2287.2,447.697,1024,256,8.03,10.69,35.02
vit_base_r26_s32_224,2275.25,450.047,1024,224,6.81,12.36,101.38
gluon_resnet101_v1b,2260.37,453.01,1024,224,7.83,16.23,44.55
tv_resnet101,2258.59,453.368,1024,224,7.83,16.23,44.55
resnet101,2258.28,453.43,1024,224,7.83,16.23,44.55
skresnet50,2234.62,458.23,1024,224,4.11,12.5,25.8
ecaresnet26t,2232.29,458.709,1024,320,5.24,16.44,16.01
edgenext_small,2226.69,459.86,1024,320,1.97,14.16,5.59
dla102,2219.96,461.255,1024,224,7.19,14.18,33.27
res2next50,2214.71,462.347,1024,224,4.2,13.71,24.67
dla60_res2next,2210.67,463.194,1024,224,3.49,13.17,17.03
resnetv2_101d,2203.82,464.633,1024,224,8.07,17.04,44.56
gluon_resnet101_v1c,2194.65,466.578,1024,224,8.08,17.04,44.57
resnest26d,2170.04,471.869,1024,224,3.64,9.97,17.07
vgg13,2149.71,476.331,1024,224,11.31,12.25,133.05
gluon_resnet101_v1d,2137.49,479.053,1024,224,8.08,17.04,44.57
skresnet50d,2115.22,484.098,1024,224,4.36,13.31,25.82
convnextv2_pico,2108.5,485.64,1024,288,2.27,10.08,9.07
vit_base_resnet50d_224,2101.17,487.333,1024,224,8.73,16.92,110.97
coatnet_0_rw_224,2082.49,491.706,1024,224,4.43,18.73,27.44
crossvit_small_240,2081.5,491.94,1024,240,5.63,18.17,26.86
deit3_medium_patch16_224_in21ft1k,2076.53,493.118,1024,224,8.0,15.93,38.85
deit3_medium_patch16_224,2072.34,494.116,1024,224,8.0,15.93,38.85
mobilevitv2_150,2071.36,494.349,1024,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,2070.3,494.603,1024,256,4.09,24.11,10.59
sebotnet33ts_256,2067.91,247.581,512,256,3.89,17.46,13.7
wide_resnet50_2,2057.08,497.78,1024,224,11.43,14.4,68.88
vit_relpos_medium_patch16_rpn_224,2044.85,500.757,1024,224,7.97,17.02,38.73
efficientformer_l3,2041.79,501.507,1024,224,3.93,12.01,31.41
poolformer_s24,2040.35,501.863,1024,224,3.41,10.68,21.39
vit_relpos_medium_patch16_224,2037.47,502.572,1024,224,7.97,17.02,38.75
cspdarknet53,2035.94,502.949,1024,256,6.57,16.81,27.64
resnet51q,2034.41,503.329,1024,288,8.07,20.94,35.7
vit_srelpos_medium_patch16_224,2033.15,503.638,1024,224,7.96,16.21,38.74
maxvit_rmlp_pico_rw_256,2008.78,509.748,1024,256,1.85,24.86,7.52
vit_relpos_medium_patch16_cls_224,2007.24,510.141,1024,224,8.03,18.24,38.76
dla102x,2006.55,510.315,1024,224,5.89,19.42,26.31
legacy_seresnet101,2003.12,511.188,1024,224,7.61,15.74,49.33
swin_tiny_patch4_window7_224,1995.14,513.235,1024,224,4.51,17.06,28.29
repvgg_b1,1985.42,515.747,1024,224,13.16,10.64,57.42
resnetaa101d,1982.98,516.381,1024,224,9.12,17.56,44.57
coatnet_rmlp_0_rw_224,1981.75,516.703,1024,224,4.72,24.89,27.45
tf_efficientnet_b3,1975.92,518.226,1024,300,1.87,23.83,12.23
gcvit_xtiny,1969.68,519.869,1024,224,2.93,20.26,19.98
hrnet_w18,1967.17,520.531,1024,224,4.32,16.31,21.3
gluon_resnet101_v1s,1965.68,520.926,1024,224,9.19,18.64,44.67
maxvit_pico_rw_256,1965.38,521.006,1024,256,1.83,22.3,7.46
resnetaa50,1958.15,522.93,1024,288,8.52,19.24,25.56
seresnet101,1954.63,523.871,1024,224,7.84,16.27,49.33
efficientnet_b3,1949.54,525.239,1024,320,2.01,26.52,12.23
efficientnet_b3a,1949.11,525.356,1024,320,2.01,26.52,12.23
edgenext_small_rw,1932.68,529.816,1024,320,2.46,14.85,7.83
regnetx_040,1932.62,529.839,1024,224,3.99,12.2,22.12
cs3sedarknet_xdw,1925.4,531.825,1024,256,5.97,17.18,21.6
coatnet_bn_0_rw_224,1920.71,533.123,1024,224,4.67,22.04,27.44
xcit_tiny_12_p16_384_dist,1911.65,535.652,1024,384,3.64,18.26,6.72
ssl_resnext101_32x4d,1910.73,535.909,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1910.43,535.993,1024,224,8.01,21.23,44.18
resnext101_32x4d,1909.99,536.115,1024,224,8.01,21.23,44.18
gluon_resnext101_32x4d,1909.34,536.298,1024,224,8.01,21.23,44.18
darknet53,1903.77,537.866,1024,288,11.78,15.68,41.61
darknetaa53,1898.12,539.468,1024,288,10.08,15.68,36.02
crossvit_15_240,1892.46,541.083,1024,240,5.81,19.77,27.53
halonet50ts,1881.53,544.226,1024,256,5.3,19.2,22.73
vgg13_bn,1879.72,544.749,1024,224,11.33,12.25,133.05
mixnet_xl,1872.46,546.86,1024,224,0.93,14.57,11.9
res2net50_26w_6s,1870.88,547.321,1024,224,6.33,15.28,37.05
ecaresnet101d,1869.88,547.616,1024,224,8.08,17.07,44.57
densenet201,1869.57,547.706,1024,224,4.34,7.85,20.01
nf_resnet101,1858.48,550.976,1024,224,8.01,16.23,44.55
coatnet_0_224,1857.28,275.661,512,224,4.58,24.01,25.04
pvt_v2_b2,1854.85,552.053,1024,224,4.05,27.53,25.36
crossvit_15_dagger_240,1850.69,553.295,1024,240,6.13,20.43,28.21
resmlp_36_224,1846.41,554.574,1024,224,8.91,16.33,44.69
resmlp_36_distilled_224,1845.04,554.99,1024,224,8.91,16.33,44.69
resnet61q,1841.84,555.954,1024,288,9.87,21.52,36.85
swin_s3_tiny_224,1817.5,563.398,1024,224,4.64,19.13,28.33
cait_xxs24_224,1796.55,569.968,1024,224,2.53,20.29,11.96
cs3darknet_x,1789.33,572.268,1024,288,10.6,14.36,35.05
vit_medium_patch16_gap_240,1785.54,573.481,1024,240,9.22,18.81,44.4
nf_resnet50,1784.84,573.708,1024,288,6.88,18.37,25.56
resnet50_gn,1764.31,580.385,1024,224,4.14,11.11,25.56
mixer_b16_224_miil,1761.45,581.327,1024,224,12.62,14.53,59.88
mixer_b16_224,1759.76,581.885,1024,224,12.62,14.53,59.88
resnetblur101d,1757.96,582.482,1024,224,9.12,17.94,44.57
eca_nfnet_l0,1726.58,593.068,1024,288,7.12,17.29,24.14
nfnet_l0,1721.83,594.705,1024,288,7.13,17.29,35.07
vit_large_patch32_224,1717.59,596.169,1024,224,15.41,13.32,327.9
vgg16,1717.44,596.224,1024,224,15.47,13.56,138.36
regnetz_c16,1710.89,598.505,1024,320,3.92,25.88,13.46
pvt_v2_b2_li,1709.89,598.855,1024,224,3.91,27.6,22.55
resnest50d_1s4x24d,1705.52,600.391,1024,224,4.43,13.57,25.68
coat_lite_small,1704.55,600.733,1024,224,3.96,22.09,19.84
resnetv2_50d_frn,1697.1,603.368,1024,224,4.33,11.92,25.59
cs3sedarknet_x,1689.8,605.975,1024,288,10.6,14.37,35.4
seresnext101_32x4d,1687.65,606.747,1024,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1687.1,606.945,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1684.69,607.813,1024,224,8.02,21.26,48.96
regnetv_040,1682.92,608.454,1024,288,6.6,20.3,20.64
mobilevitv2_175,1677.66,457.769,768,256,5.54,28.13,14.25
regnety_040,1677.03,610.59,1024,288,6.61,20.3,20.65
mobilevitv2_175_in22ft1k,1677.0,457.949,768,256,5.54,28.13,14.25
convnext_tiny_hnf,1676.16,610.908,1024,288,7.39,22.21,28.59
res2net101_26w_4s,1675.37,611.195,1024,224,8.1,18.45,45.21
vit_tiny_patch16_384,1665.76,614.72,1024,384,4.7,25.39,5.79
sequencer2d_s,1661.32,616.362,1024,224,4.96,11.31,27.65
ese_vovnet39b_evos,1661.21,616.404,1024,224,7.07,6.74,24.58
vit_base_patch32_384,1649.27,620.868,1024,384,13.06,16.5,88.3
vit_base_patch32_clip_384,1648.64,621.105,1024,384,13.06,16.5,88.3
mixer_l32_224,1645.23,622.393,1024,224,11.27,19.86,206.94
convnext_tiny,1642.14,623.562,1024,288,7.39,22.21,28.59
botnet50ts_256,1639.64,312.25,512,256,5.54,22.23,22.74
swinv2_cr_tiny_224,1630.02,628.199,1024,224,4.66,28.45,28.33
resnetv2_50d_evob,1627.44,629.196,1024,224,4.33,11.92,25.59
twins_pcpvt_base,1615.12,633.996,1024,224,6.68,25.25,43.83
resnetv2_152,1614.43,634.268,1024,224,11.55,22.56,60.19
hrnet_w32,1605.06,637.96,1024,224,8.97,22.02,41.23
swinv2_cr_tiny_ns_224,1600.43,639.811,1024,224,4.66,28.45,28.33
xception41p,1598.79,480.351,768,299,9.25,39.86,26.91
tv_resnet152,1582.54,647.049,1024,224,11.56,22.56,60.19
gluon_resnet152_v1b,1581.57,647.444,1024,224,11.56,22.56,60.19
resnet152,1581.02,647.671,1024,224,11.56,22.56,60.19
xception,1579.88,648.138,1024,299,8.4,35.83,22.86
halo2botnet50ts_256,1572.75,651.076,1024,256,5.02,21.78,22.64
res2net50_26w_8s,1568.85,652.695,1024,224,8.37,17.95,48.4
vit_medium_patch16_gap_256,1564.22,654.626,1024,256,10.59,22.15,38.86
resnetv2_152d,1557.03,657.648,1024,224,11.8,23.36,60.2
efficientnet_el_pruned,1555.14,658.449,1024,300,8.0,30.7,10.59
maxvit_rmlp_nano_rw_256,1551.85,659.845,1024,256,4.47,31.92,15.5
regnetx_064,1550.52,660.413,1024,224,6.49,16.37,26.21
efficientnet_el,1549.97,660.646,1024,300,8.0,30.7,10.59
gluon_resnet152_v1c,1548.96,661.078,1024,224,11.8,23.36,60.21
nf_ecaresnet101,1546.58,662.091,1024,224,8.01,16.27,44.55
nf_seresnet101,1539.38,665.191,1024,224,8.02,16.27,49.33
mvitv2_tiny,1537.54,665.985,1024,224,4.7,21.16,24.17
nfnet_f0,1525.01,671.456,1024,256,12.62,18.05,71.49
vgg16_bn,1523.86,671.963,1024,224,15.5,13.56,138.37
cs3edgenet_x,1521.21,673.136,1024,288,14.59,16.36,47.82
gluon_resnet152_v1d,1520.11,673.621,1024,224,11.8,23.36,60.21
maxvit_nano_rw_256,1517.43,674.812,1024,256,4.46,30.28,15.45
tf_efficientnet_el,1506.16,679.862,1024,300,8.0,30.7,10.59
convnextv2_nano,1500.71,511.746,768,288,4.06,13.84,15.62
resnest50d,1492.63,686.022,1024,224,5.4,14.36,27.48
ese_vovnet99b,1489.17,687.617,1024,224,16.51,11.27,63.2
dla169,1471.11,696.059,1024,224,11.6,20.2,53.39
regnety_032,1467.85,697.604,1024,288,5.29,18.61,19.44
skresnext50_32x4d,1463.28,699.785,1024,224,4.5,17.18,27.48
xcit_tiny_12_p8_224_dist,1458.7,701.981,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,1458.23,702.211,1024,224,4.81,23.6,6.71
convit_small,1457.54,702.541,1024,224,5.76,17.87,27.78
mobilevitv2_200_in22ft1k,1456.59,527.247,768,256,7.22,32.15,18.45
mobilevitv2_200,1456.02,527.451,768,256,7.22,32.15,18.45
ecaresnet50t,1438.32,711.929,1024,320,8.82,24.13,25.57
gluon_resnet152_v1s,1432.22,714.961,1024,224,12.92,24.96,60.32
nest_tiny,1415.33,542.618,768,224,5.83,25.48,17.06
regnety_040s_gn,1412.65,724.867,1024,224,4.03,12.29,20.65
vgg19,1393.71,183.67,256,224,19.63,14.86,143.67
jx_nest_tiny,1389.62,552.657,768,224,5.83,25.48,17.06
legacy_seresnet152,1383.83,739.96,1024,224,11.33,22.08,66.82
densenet161,1376.52,743.891,1024,224,7.79,11.06,28.68
poolformer_s36,1370.67,747.069,1024,224,5.0,15.82,30.86
vit_small_resnet50d_s16_224,1367.59,748.748,1024,224,13.48,24.82,57.53
twins_svt_base,1362.65,751.463,1024,224,8.59,26.33,56.07
seresnet152,1361.7,751.99,1024,224,11.57,22.61,66.82
xception41,1356.44,566.173,768,299,9.28,39.86,26.97
maxvit_tiny_rw_224,1350.45,758.254,1024,224,5.11,33.11,29.06
crossvit_18_240,1348.85,759.154,1024,240,9.05,26.26,43.27
maxxvit_rmlp_nano_rw_256,1347.73,759.767,1024,256,4.37,26.05,16.78
efficientnet_lite4,1343.74,571.528,768,380,4.04,45.66,13.01
gcvit_tiny,1339.65,764.364,1024,224,4.79,29.82,28.22
pvt_v2_b3,1325.92,772.282,1024,224,6.92,37.7,45.24
crossvit_18_dagger_240,1313.78,779.419,1024,240,9.5,27.03,44.27
volo_d1_224,1312.37,780.255,1024,224,6.94,24.43,26.63
xcit_small_24_p16_224_dist,1307.3,783.278,1024,224,9.1,23.64,47.67
tresnet_m,1305.71,784.234,1024,224,5.74,7.31,31.39
inception_v4,1305.41,784.412,1024,299,12.28,15.09,42.68
repvgg_b2,1305.22,784.529,1024,224,20.45,12.9,89.02
xcit_small_24_p16_224,1303.71,785.433,1024,224,9.1,23.64,47.67
sequencer2d_m,1295.72,790.281,1024,224,6.55,14.26,38.31
edgenext_base,1283.77,797.633,1024,320,6.01,24.32,18.51
hrnet_w30,1280.53,799.653,1024,224,8.15,21.21,37.71
dm_nfnet_f0,1275.46,802.834,1024,256,12.62,18.05,71.49
coatnet_rmlp_1_rw_224,1268.37,807.322,1024,224,7.85,35.47,41.69
maxxvitv2_nano_rw_256,1259.7,812.877,1024,256,6.26,23.05,23.7
efficientnetv2_s,1254.49,816.255,1024,384,8.44,35.77,21.46
vgg19_bn,1246.52,205.36,256,224,19.66,14.86,143.68
nf_regnet_b4,1235.79,828.604,1024,384,4.7,28.61,30.21
swin_small_patch4_window7_224,1235.74,828.641,1024,224,8.77,27.47,49.61
tf_efficientnet_lite4,1232.22,623.25,768,380,4.04,45.66,13.01
regnetz_d32,1223.51,836.919,1024,320,9.33,37.08,27.58
mixnet_xxl,1219.27,629.871,768,224,2.04,23.43,23.96
tf_efficientnetv2_s,1219.16,839.906,1024,384,8.44,35.77,21.46
deit_base_patch16_224,1213.08,844.121,1024,224,17.58,23.9,86.57
deit_base_distilled_patch16_224,1212.98,844.19,1024,224,17.68,24.05,87.34
vit_base_patch16_clip_224,1211.82,844.996,1024,224,17.58,23.9,86.57
vit_base_patch16_224_miil,1211.26,845.389,1024,224,17.59,23.91,94.4
dpn92,1210.45,845.948,1024,224,6.54,18.21,37.67
vit_base_patch16_224,1210.28,846.074,1024,224,17.58,23.9,86.57
coatnet_rmlp_1_rw2_224,1208.65,847.215,1024,224,8.11,40.13,41.72
cait_xxs36_224,1205.51,849.419,1024,224,3.77,30.34,17.3
maxvit_tiny_tf_224,1200.3,639.828,768,224,5.6,35.78,30.92
swinv2_tiny_window8_256,1200.06,853.274,1024,256,5.96,24.57,28.35
efficientnetv2_rw_s,1199.87,853.413,1024,384,8.72,38.03,23.94
dla102x2,1198.52,854.374,1024,224,9.34,29.91,41.28
regnetx_160,1195.08,856.833,1024,224,15.99,25.52,54.28
dpn98,1183.92,864.908,1024,224,11.73,25.2,61.57
vit_base_patch16_rpn_224,1180.39,867.498,1024,224,17.49,23.75,86.54
twins_pcpvt_large,1168.64,876.22,1024,224,9.84,35.82,60.99
deit3_base_patch16_224,1164.77,879.134,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1164.5,879.334,1024,224,17.58,23.9,86.59
regnetz_d8,1163.64,879.982,1024,320,6.19,37.08,23.37
swsl_resnext101_32x8d,1158.15,884.156,1024,224,16.48,31.21,88.79
resnext101_32x8d,1158.05,884.232,1024,224,16.48,31.21,88.79
ssl_resnext101_32x8d,1158.02,884.255,1024,224,16.48,31.21,88.79
wide_resnet101_2,1157.66,884.531,1024,224,22.8,21.23,126.89
ig_resnext101_32x8d,1157.3,884.8,1024,224,16.48,31.21,88.79
coatnet_1_rw_224,1155.72,886.014,1024,224,8.04,34.6,41.72
vit_base_patch16_gap_224,1154.73,886.777,1024,224,17.49,25.59,86.57
vit_base_patch32_clip_448,1154.21,887.173,1024,448,17.93,23.9,88.34
resnet200,1149.71,890.646,1024,224,15.07,32.19,64.67
mvitv2_small,1146.92,892.812,1024,224,7.0,28.08,34.87
xception65p,1145.07,670.686,768,299,13.91,52.48,39.82
cs3se_edgenet_x,1143.17,895.738,1024,320,18.01,20.21,50.72
vit_relpos_base_patch16_rpn_224,1143.15,895.76,1024,224,17.51,24.97,86.41
vit_relpos_base_patch16_224,1141.31,897.204,1024,224,17.51,24.97,86.43
tnt_s_patch16_224,1135.32,901.935,1024,224,5.24,24.37,23.76
resnetrs101,1134.67,902.454,1024,288,13.56,28.53,63.62
vit_relpos_base_patch16_clsgap_224,1128.94,907.03,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1126.78,908.771,1024,224,17.6,25.12,86.43
inception_resnet_v2,1126.73,908.809,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,1125.41,909.877,1024,299,13.18,25.06,55.84
beit_base_patch16_224,1112.26,920.631,1024,224,17.58,23.9,86.53
coat_tiny,1108.72,923.572,1024,224,4.35,27.2,5.5
beitv2_base_patch16_224,1108.55,923.711,1024,224,17.58,23.9,86.53
mvitv2_small_cls,1101.66,929.491,1024,224,7.04,28.17,34.87
resnetv2_50d_gn,1092.35,937.413,1024,288,7.24,19.7,25.57
pit_b_distilled_224,1078.48,474.731,512,224,12.5,33.07,74.79
pit_b_224,1075.34,476.117,512,224,12.42,32.94,73.76
hrnet_w40,1059.78,966.217,1024,224,12.75,25.29,57.56
coatnet_1_224,1045.17,489.859,512,224,8.7,39.0,42.23
resnet101d,1039.88,984.712,1024,320,16.48,34.77,44.57
flexivit_base,1037.21,987.248,1024,240,20.29,28.36,86.59
gluon_resnext101_64x4d,1034.86,989.491,1024,224,15.52,31.21,83.46
vit_small_patch16_36x1_224,1033.13,991.146,1024,224,13.71,35.69,64.67
vit_large_r50_s32_224,1030.67,993.517,1024,224,19.58,24.41,328.99
maxvit_rmlp_tiny_rw_256,1029.25,746.162,768,256,6.77,46.92,29.15
xcit_tiny_24_p16_384_dist,1027.64,996.444,1024,384,6.87,34.29,12.12
efficientnet_b4,1014.08,504.879,512,384,4.51,50.04,19.34
maxvit_tiny_rw_256,1008.0,1015.861,1024,256,6.74,44.35,29.07
vit_small_patch16_18x2_224,1006.7,1017.169,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,1005.28,1018.603,1024,224,9.07,50.27,49.7
regnetx_080,1004.51,1019.384,1024,224,8.02,14.06,39.57
repvgg_b3,994.23,1029.925,1024,224,29.16,15.1,123.09
swinv2_cr_small_ns_224,993.75,1030.424,1024,224,9.08,50.27,49.7
repvgg_b2g4,988.97,1035.405,1024,224,12.63,12.9,61.76
convnext_small,988.3,1036.113,1024,288,14.39,35.65,50.22
gluon_xception65,987.82,777.458,768,299,13.96,52.48,39.92
vit_small_r26_s32_384,982.68,1042.031,1024,384,10.43,29.85,36.47
xception65,978.83,784.597,768,299,13.96,52.48,39.92
regnetz_040,975.77,787.056,768,320,6.35,37.78,27.12
regnetz_040h,971.51,790.512,768,320,6.43,37.94,28.94
gluon_seresnext101_64x4d,965.3,1060.794,1024,224,15.53,31.25,88.23
maxvit_tiny_pm_256,964.03,1062.189,1024,256,6.61,47.9,30.09
efficientformer_l7,962.55,1063.825,1024,224,10.17,24.45,82.23
twins_svt_large,962.19,1064.229,1024,224,15.15,35.1,99.27
tf_efficientnet_b4,957.62,534.646,512,380,4.49,49.49,19.34
pvt_v2_b4,957.38,1069.569,1024,224,10.14,53.74,62.56
poolformer_m36,954.91,1072.334,1024,224,8.8,22.02,56.17
cait_s24_224,954.44,1072.866,1024,224,9.35,40.58,46.92
regnetz_b16_evos,950.47,808.013,768,288,2.36,16.43,9.74
resnest50d_4s2x40d,938.07,1091.586,1024,224,4.4,17.94,30.42
hrnet_w48,936.07,1093.917,1024,224,17.34,28.56,77.47
gmlp_b16_224,930.95,1099.935,1024,224,15.78,30.21,73.08
convnextv2_tiny,930.82,550.041,512,288,7.39,22.21,28.64
convnextv2_small,928.68,1102.629,1024,224,8.71,21.56,50.32
maxxvit_rmlp_tiny_rw_256,918.72,1114.583,1024,256,6.66,39.76,29.64
mobilevitv2_150_384_in22ft1k,915.49,419.435,384,384,9.2,54.25,10.59
pvt_v2_b5,909.79,1125.516,1024,224,11.76,50.92,81.96
nest_small,903.21,850.284,768,224,10.35,40.04,38.35
swin_s3_small_224,899.98,853.339,768,224,9.43,37.84,49.74
xcit_medium_24_p16_224_dist,898.61,1139.525,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,898.6,1139.542,1024,224,16.13,31.71,84.4
jx_nest_small,892.03,860.939,768,224,10.35,40.04,38.35
coat_mini,880.8,1162.569,1024,224,6.82,33.68,10.34
swin_base_patch4_window7_224,875.38,1169.764,1024,224,15.47,36.63,87.77
dpn131,865.2,1183.527,1024,224,16.09,32.97,79.25
resnetv2_50d_evos,854.82,1197.895,1024,288,7.15,19.7,25.59
xcit_small_12_p16_384_dist,853.54,1199.694,1024,384,14.14,36.51,26.25
sequencer2d_l,839.78,1219.347,1024,224,9.74,22.12,54.3
crossvit_base_240,839.43,914.892,768,240,21.22,36.33,105.03
hrnet_w44,821.37,1246.671,1024,224,14.94,26.92,67.06
eca_nfnet_l1,818.87,1250.489,1024,320,14.92,34.42,41.41
vit_base_r50_s16_224,817.55,1252.502,1024,224,21.67,35.31,114.69
maxvit_rmlp_small_rw_224,816.34,1254.368,1024,224,10.75,49.3,64.9
gcvit_small,815.24,1256.055,1024,224,8.57,41.61,51.09
regnety_080,811.28,1262.191,1024,288,13.22,29.69,39.18
densenet264,804.85,1272.268,1024,224,12.95,12.8,72.69
mvitv2_base,804.14,1273.395,1024,224,10.16,40.5,51.47
repvgg_b3g4,802.85,1275.443,1024,224,17.89,15.1,83.83
vit_base_patch16_plus_240,782.25,1309.022,1024,240,27.41,33.08,117.56
swinv2_tiny_window16_256,781.61,655.045,512,256,6.68,39.02,28.35
maxvit_small_tf_224,777.04,658.899,512,224,11.66,53.17,68.93
xcit_tiny_24_p8_224,771.1,1327.958,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,770.21,1329.496,1024,224,9.21,45.39,12.11
coatnet_2_rw_224,763.52,670.562,512,224,15.09,49.22,73.87
vit_relpos_base_patch16_plus_240,763.4,1341.361,1024,240,27.3,34.33,117.38
efficientnet_b3_gn,763.0,671.023,512,320,2.14,28.83,11.73
coatnet_rmlp_2_rw_224,759.73,673.906,512,224,15.18,54.78,73.88
vit_small_patch16_384,753.82,1018.79,768,384,15.52,50.78,22.2
hrnet_w64,750.36,1364.663,1024,224,28.97,35.09,128.06
xception71,749.7,1024.396,768,299,18.09,69.92,42.34
resnet152d,742.37,1379.356,1024,320,24.08,47.67,60.21
swinv2_small_window8_256,741.95,1380.134,1024,256,11.58,40.14,49.73
mobilevitv2_175_384_in22ft1k,739.09,519.544,384,384,12.47,63.29,14.25
ecaresnet200d,736.17,1390.959,1024,256,20.0,43.15,64.69
seresnet200d,733.28,1396.444,1024,256,20.01,43.15,71.86
swin_s3_base_224,733.27,1396.459,1024,224,13.69,48.26,71.13
convit_base,731.09,1400.636,1024,224,17.52,31.77,86.54
resnest101e,726.65,1409.184,1024,256,13.38,28.66,48.28
deit3_small_patch16_384,726.49,1057.125,768,384,15.52,50.78,22.21
deit3_small_patch16_384_in21ft1k,726.32,1057.368,768,384,15.52,50.78,22.21
volo_d2_224,722.61,1417.079,1024,224,14.34,41.34,58.68
tnt_b_patch16_224,721.24,1419.762,1024,224,14.09,39.01,65.41
xcit_nano_12_p8_384_dist,720.41,1421.4,1024,384,6.34,46.08,3.05
swinv2_cr_base_224,719.23,1423.721,1024,224,15.86,59.66,87.88
poolformer_m48,719.07,1424.046,1024,224,11.59,29.17,73.47
coatnet_2_224,715.36,715.711,512,224,16.5,52.67,74.68
swinv2_cr_base_ns_224,712.96,1436.239,1024,224,15.86,59.66,87.88
dpn107,691.0,1481.897,1024,224,18.38,33.46,86.92
convnext_base,687.14,1490.219,1024,288,25.43,47.53,88.59
resnetv2_50x1_bitm,684.31,374.087,256,448,16.62,44.46,25.55
efficientnet_b3_g8_gn,664.63,770.341,512,320,3.2,28.83,14.25
regnety_064,657.71,1556.911,1024,288,10.56,27.11,30.58
regnetv_064,652.6,1569.096,1024,288,10.55,27.11,30.58
xcit_small_12_p8_224,651.3,1572.214,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,651.08,1572.755,1024,224,18.69,47.21,26.21
resnetrs152,649.95,1575.501,1024,320,24.34,48.14,86.62
mobilevitv2_200_384_in22ft1k,647.42,395.4,256,384,16.24,72.34,18.45
seresnet152d,645.69,1585.88,1024,320,24.09,47.72,66.84
tresnet_l,644.38,1589.105,1024,224,10.88,11.9,55.99
tresnet_v2_l,642.3,1594.246,1024,224,8.81,16.34,46.17
nest_base,640.98,798.76,512,224,17.96,53.39,67.72
regnetx_120,640.37,1599.07,1024,224,12.13,21.37,46.11
seresnext101_32x8d,639.53,1601.159,1024,288,27.24,51.63,93.57
regnetz_e8,639.43,1601.423,1024,320,15.46,63.94,57.7
ese_vovnet99b_iabn,636.1,1609.798,1024,224,16.49,11.27,63.2
jx_nest_base,634.61,806.787,512,224,17.96,53.39,67.72
regnety_120,625.75,1636.422,1024,224,12.14,21.38,51.82
efficientnetv2_m,624.53,1639.618,1024,416,18.6,67.5,54.14
seresnext101d_32x8d,621.55,1647.466,1024,288,27.64,52.95,93.59
resnext101_64x4d,619.77,1652.21,1024,288,25.66,51.59,83.46
swsl_resnext101_32x16d,612.21,1672.624,1024,224,36.27,51.18,194.03
ig_resnext101_32x16d,611.98,1673.243,1024,224,36.27,51.18,194.03
maxvit_rmlp_small_rw_256,611.67,1255.571,768,256,14.15,66.09,64.9
ssl_resnext101_32x16d,611.31,1675.063,1024,224,36.27,51.18,194.03
regnety_320,605.31,1691.684,1024,224,32.34,30.26,145.05
gcvit_base,602.42,1699.782,1024,224,14.87,55.48,90.32
regnetz_c16_evos,596.93,857.706,512,320,3.86,25.88,13.49
maxxvit_rmlp_small_rw_256,590.18,1735.046,1024,256,14.67,58.38,66.01
legacy_senet154,585.86,1747.854,1024,224,20.77,38.69,115.09
senet154,585.53,1748.836,1024,224,20.77,38.69,115.09
seresnextaa101d_32x8d,585.08,1750.175,1024,288,28.51,56.44,93.59
gluon_senet154,584.86,1750.843,1024,224,20.77,38.69,115.09
convmixer_768_32,581.95,1759.577,1024,224,19.55,25.95,21.11
seresnet269d,574.5,1782.4,1024,256,26.59,53.6,113.67
nf_regnet_b5,565.36,905.602,512,456,11.7,61.95,49.74
mixer_l16_224,553.66,1849.49,1024,224,44.6,41.69,208.2
resnet200d,545.14,1878.401,1024,320,31.25,67.33,64.69
nfnet_f1,544.28,1881.353,1024,320,35.97,46.77,132.63
vit_large_patch32_384,543.45,1884.237,1024,384,45.31,43.86,306.63
efficientnetv2_rw_m,543.37,1884.512,1024,416,21.49,79.62,53.24
vit_medium_patch16_gap_384,539.24,949.475,512,384,26.08,67.54,39.03
efficientnet_b5,533.21,960.212,512,448,9.59,93.56,30.39
swinv2_base_window8_256,531.81,1925.495,1024,256,20.37,52.59,87.92
maxxvitv2_rmlp_base_rw_224,525.72,1947.791,1024,224,24.2,62.77,116.09
xcit_large_24_p16_224_dist,509.19,2011.039,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,509.15,2011.169,1024,224,35.86,47.27,189.1
swin_large_patch4_window7_224,504.4,1522.593,768,224,34.53,54.94,196.53
halonet_h1,503.39,508.543,256,256,3.0,51.17,8.1
volo_d3_224,502.58,2037.467,1024,224,20.78,60.09,86.33
swinv2_small_window16_256,488.97,1047.084,512,256,12.82,66.29,49.73
tresnet_xl,481.58,2126.301,1024,224,15.17,15.34,78.44
vit_small_patch8_224,479.11,1068.641,512,224,22.44,80.84,21.67
tf_efficientnet_b5,476.47,805.919,384,456,10.46,98.86,30.39
maxvit_rmlp_base_rw_224,472.06,2169.196,1024,224,23.15,92.64,116.14
resnetrs200,471.68,2170.964,1024,320,31.51,67.81,93.21
xcit_tiny_12_p8_384_dist,471.45,2172.002,1024,384,14.13,69.14,6.71
dm_nfnet_f1,461.24,2220.087,1024,320,35.97,46.77,132.63
tf_efficientnetv2_m,458.93,1673.426,768,480,24.76,89.84,54.14
xcit_small_24_p16_384_dist,457.16,2239.891,1024,384,26.72,68.58,47.67
coatnet_rmlp_3_rw_224,439.5,582.463,256,224,33.56,79.47,165.15
maxvit_base_tf_224,430.05,1190.542,512,224,24.04,95.01,119.47
swinv2_cr_large_224,423.86,1811.887,768,224,35.1,78.42,196.68
resnetv2_152x2_bit_teacher,423.36,2418.743,1024,224,46.95,45.11,236.34
swinv2_cr_tiny_384,423.1,907.565,384,384,15.34,161.01,28.33
coatnet_3_rw_224,421.95,606.701,256,224,33.44,73.83,181.81
resnetv2_101x1_bitm,419.35,610.453,256,448,31.65,64.93,44.54
coatnet_3_224,405.07,631.982,256,224,36.56,79.01,166.97
convnextv2_base,403.59,1268.593,512,288,25.43,47.53,88.72
eca_nfnet_l2,401.73,2548.946,1024,384,30.05,68.28,56.72
regnetz_d8_evos,394.39,1947.294,768,320,7.03,38.92,23.46
convmixer_1024_20_ks9_p14,393.5,2602.254,1024,224,5.55,5.51,24.38
eva_large_patch14_196,392.3,2610.234,1024,196,61.57,63.52,304.14
crossvit_15_dagger_408,390.72,655.182,256,408,21.45,95.05,28.5
vit_large_patch16_224,390.66,2621.182,1024,224,61.6,63.52,304.33
vit_base_patch16_18x2_224,384.38,2663.987,1024,224,52.51,71.38,256.73
deit3_large_patch16_224_in21ft1k,377.58,2711.976,1024,224,61.6,63.52,304.37
deit3_large_patch16_224,377.53,2712.348,1024,224,61.6,63.52,304.37
convnext_large,373.02,2058.836,768,288,56.87,71.29,197.77
beit_large_patch16_224,360.62,2839.572,1024,224,61.6,63.52,304.43
beitv2_large_patch16_224,360.58,2839.86,1024,224,61.6,63.52,304.43
swinv2_base_window12to16_192to256_22kft1k,360.56,1065.006,384,256,22.02,84.71,87.92
swinv2_base_window16_256,360.23,1065.959,384,256,22.02,84.71,87.92
regnety_160,353.5,2172.566,768,288,26.37,38.07,83.59
nasnetalarge,345.63,1111.004,384,331,23.89,90.56,88.75
maxvit_tiny_tf_384,344.01,744.157,256,384,17.53,123.42,30.98
xcit_small_24_p8_224,342.37,2990.915,1024,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,342.26,2991.817,1024,224,35.81,90.78,47.63
flexivit_large,335.35,3053.52,1024,240,70.99,75.39,304.36
maxxvitv2_rmlp_large_rw_224,332.33,3081.271,1024,224,44.14,87.15,215.42
vit_large_r50_s32_384,329.8,3104.921,1024,384,57.43,76.52,329.09
pnasnet5large,328.89,1167.534,384,331,25.04,92.89,86.06
tresnet_m_448,325.8,3143.01,1024,448,22.94,29.21,31.39
volo_d1_384,323.04,1584.906,512,384,22.75,108.55,26.78
volo_d4_224,318.96,3210.439,1024,224,44.34,80.22,192.96
xcit_medium_24_p16_384_dist,312.74,3274.268,1024,384,47.39,91.64,84.4
nfnet_f2,310.6,3296.869,1024,352,63.22,79.06,193.78
vit_base_patch16_384,307.09,1250.42,384,384,55.54,101.56,86.86
deit_base_patch16_384,306.8,1251.599,384,384,55.54,101.56,86.86
vit_base_patch16_clip_384,306.29,1253.685,384,384,55.54,101.56,86.86
deit_base_distilled_patch16_384,305.48,1257.017,384,384,55.65,101.82,87.63
ecaresnet269d,305.06,3356.684,1024,352,50.25,101.25,102.09
maxvit_large_tf_224,301.43,1273.908,384,224,43.68,127.35,211.79
deit3_base_patch16_384_in21ft1k,298.01,1288.526,384,384,55.54,101.56,86.88
deit3_base_patch16_384,297.88,1289.093,384,384,55.54,101.56,86.88
resnetrs270,296.97,3448.186,1024,352,51.13,105.48,129.86
regnetx_320,289.44,2653.413,768,224,31.81,36.3,107.81
efficientnet_b6,287.31,890.997,256,528,19.4,167.39,43.04
vit_large_patch14_224,286.23,3577.501,1024,224,81.08,88.79,304.2
vit_large_patch14_clip_224,285.99,3580.5,1024,224,81.08,88.79,304.2
crossvit_18_dagger_408,285.18,673.248,192,408,32.47,124.87,44.61
cait_xxs24_384,281.48,3637.936,1024,384,9.63,122.66,12.03
ig_resnext101_32x32d,275.12,1860.956,512,224,87.29,91.12,468.53
tf_efficientnet_b6,274.07,700.545,192,528,19.4,167.39,43.04
dm_nfnet_f2,264.79,2900.408,768,352,63.22,79.06,193.78
beit_base_patch16_384,261.27,1469.733,384,384,55.54,101.56,86.74
efficientnetv2_l,260.33,1966.694,512,480,56.4,157.99,118.52
swinv2_cr_small_384,259.75,985.56,256,384,29.7,298.03,49.7
tf_efficientnetv2_l,257.29,1989.923,512,480,56.4,157.99,118.52
resnest200e,254.36,1006.453,256,320,35.69,82.78,70.2
mvitv2_large,249.99,2048.061,512,224,43.87,112.02,217.99
xcit_tiny_24_p8_384_dist,248.25,4124.916,1024,384,27.05,132.95,12.11
convnext_xlarge,242.63,2110.182,512,288,100.8,95.05,350.2
resmlp_big_24_224_in22ft1k,241.9,4233.056,1024,224,100.23,87.31,129.14
resmlp_big_24_224,241.74,4235.988,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,241.44,4241.249,1024,224,100.23,87.31,129.14
convnextv2_large,239.52,1068.782,256,288,56.87,71.29,197.96
coatnet_4_224,238.62,1072.827,256,224,62.48,129.26,275.43
swin_base_patch4_window12_384,236.12,813.144,192,384,47.19,134.78,87.9
xcit_medium_24_p8_224_dist,233.5,3289.007,768,224,63.53,121.23,84.32
xcit_medium_24_p8_224,233.5,3289.104,768,224,63.53,121.23,84.32
eca_nfnet_l3,229.87,2227.284,512,448,52.55,118.4,72.04
vit_base_r50_s16_384,226.32,1696.687,384,384,67.43,135.03,98.95
maxvit_small_tf_384,224.01,857.105,192,384,35.87,183.65,69.02
xcit_small_12_p8_384_dist,221.54,1733.28,384,384,54.92,138.29,26.21
swinv2_large_window12to16_192to256_22kft1k,220.1,1163.101,256,256,47.81,121.53,196.74
volo_d5_224,210.88,4855.76,1024,224,72.4,118.11,295.46
vit_base_patch8_224,199.67,1282.079,256,224,78.22,161.69,86.58
cait_xs24_384,197.64,3885.811,768,384,19.28,183.98,26.67
resnetrs350,196.19,5219.377,1024,384,77.59,154.74,163.96
cait_xxs36_384,188.27,5439.03,1024,384,14.35,183.7,17.37
swinv2_cr_base_384,185.68,1378.725,256,384,50.57,333.68,87.88
coatnet_rmlp_2_rw_384,184.84,1038.746,192,384,47.69,209.43,73.88
swinv2_cr_huge_224,184.09,2085.934,384,224,115.97,121.08,657.83
convnext_xxlarge,183.68,2787.486,512,224,151.66,95.29,846.47
volo_d2_384,180.56,2126.753,384,384,46.17,184.51,58.87
xcit_large_24_p16_384_dist,176.39,5805.281,1024,384,105.35,137.17,189.1
regnety_640,174.81,4393.396,768,224,64.16,42.5,281.38
maxvit_xlarge_tf_224,171.63,1491.6,256,224,97.49,191.02,474.95
nfnet_f3,170.11,4514.791,768,416,115.58,141.78,254.92
densenet264d_iabn,167.13,6126.84,1024,224,13.47,14.0,72.74
efficientnet_b7,166.38,1153.975,192,600,38.33,289.94,66.35
maxvit_tiny_tf_512,163.72,781.809,128,512,33.49,257.59,31.05
efficientnetv2_xl,162.7,3146.865,512,512,93.85,247.32,208.12
tf_efficientnetv2_xl,161.32,3173.821,512,512,93.85,247.32,208.12
tf_efficientnet_b7,160.43,1196.798,192,600,38.33,289.94,66.35
resnetv2_152x2_bit_teacher_384,159.54,1604.579,256,384,136.16,132.56,236.34
tresnet_l_448,154.66,6620.743,1024,448,43.5,47.56,55.99
vit_huge_patch14_224,154.27,6637.58,1024,224,167.43,139.43,658.75
vit_huge_patch14_clip_224,154.17,6642.017,1024,224,167.4,139.41,632.05
maxxvitv2_rmlp_base_rw_384,153.9,1663.429,256,384,72.98,213.74,116.09
cait_s24_384,152.41,3359.254,512,384,32.17,245.31,47.06
deit3_huge_patch14_224_in21ft1k,150.05,6824.53,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224,149.59,6845.356,1024,224,167.4,139.41,632.13
dm_nfnet_f3,145.48,3519.403,512,416,115.58,141.78,254.92
resnetrs420,142.37,5394.528,768,416,108.45,213.79,191.89
swin_large_patch4_window12_384,138.37,925.016,128,384,104.08,202.16,196.74
resnetv2_50x3_bitm,133.5,1438.189,192,448,145.7,133.37,217.32
maxvit_rmlp_base_rw_384,131.6,1945.285,256,384,70.97,318.95,116.14
xcit_large_24_p8_224_dist,131.32,3898.808,512,224,141.23,181.56,188.93
xcit_large_24_p8_224,131.27,3900.391,512,224,141.23,181.56,188.93
coatnet_5_224,130.48,1471.508,192,224,145.49,194.24,687.47
maxvit_base_tf_384,122.48,1567.652,192,384,73.8,332.9,119.65
resnest269e,119.17,2148.198,256,416,77.69,171.98,110.93
resnetv2_152x2_bitm,117.29,2182.534,256,448,184.99,180.43,236.34
xcit_small_24_p8_384_dist,116.59,3293.649,384,384,105.24,265.91,47.63
tresnet_xl_448,115.63,8855.938,1024,448,60.65,61.31,78.44
swinv2_cr_large_384,113.43,1128.479,128,384,108.95,404.96,196.68
maxvit_small_tf_512,106.82,1198.298,128,512,67.26,383.77,69.13
efficientnet_b8,106.21,1205.18,128,672,63.48,442.89,87.41
tf_efficientnet_b8,102.86,1244.358,128,672,63.48,442.89,87.41
eva_large_patch14_336,102.71,2492.371,256,336,191.1,270.24,304.53
vit_large_patch14_clip_336,102.52,2496.99,256,336,191.11,270.24,304.53
vit_large_patch16_384,102.5,2497.593,256,384,191.21,270.24,304.72
cait_s36_384,101.88,5025.316,512,384,47.99,367.4,68.37
eva_giant_patch14_224,101.84,10055.112,1024,224,267.18,192.64,1012.56
vit_giant_patch14_224,100.71,7625.752,768,224,267.18,192.64,1012.61
vit_giant_patch14_clip_224,100.43,7646.856,768,224,267.18,192.64,1012.65
deit3_large_patch16_384_in21ft1k,99.81,2564.809,256,384,191.21,270.24,304.76
deit3_large_patch16_384,99.8,2564.994,256,384,191.21,270.24,304.76
swinv2_base_window12to24_192to384_22kft1k,96.12,665.832,64,384,55.25,280.36,87.92
nfnet_f4,89.33,5731.574,512,512,216.26,262.26,316.07
beit_large_patch16_384,88.56,2890.58,256,384,191.21,270.24,305.0
maxvit_large_tf_384,86.44,1480.84,128,384,132.55,445.84,212.03
regnety_1280,82.49,4654.845,384,224,127.66,71.58,644.81
xcit_medium_24_p8_384_dist,79.96,3201.705,256,384,186.67,354.73,84.32
resnetv2_101x3_bitm,79.41,2417.67,192,448,280.33,194.78,387.93
volo_d3_448,77.64,2473.021,192,448,96.33,446.83,86.63
dm_nfnet_f4,77.54,4952.036,384,512,216.26,262.26,316.07
nfnet_f5,67.46,5691.915,384,544,290.97,349.71,377.21
tf_efficientnet_l2,63.66,1507.989,96,475,172.11,609.89,480.31
swinv2_large_window12to24_192to384_22kft1k,60.94,787.651,48,384,116.15,407.83,196.74
vit_gigantic_patch14_224,60.18,8507.121,512,224,483.95,275.37,1844.44
vit_gigantic_patch14_clip_224,60.11,8517.85,512,224,483.96,275.37,1844.91
volo_d4_448,57.87,3317.675,192,448,197.13,527.35,193.41
maxvit_base_tf_512,57.86,2212.256,128,512,138.02,703.99,119.88
dm_nfnet_f5,57.78,6645.368,384,544,290.97,349.71,377.21
vit_huge_patch14_clip_336,57.4,4460.085,256,336,390.97,407.54,632.46
ig_resnext101_32x48d,56.43,6804.709,384,224,153.57,131.06,828.41
convnextv2_huge,56.31,1704.92,96,384,337.96,232.35,660.29
convmixer_1536_20,55.47,18461.426,1024,224,48.68,33.03,51.63
swinv2_cr_giant_224,52.39,3665.046,192,224,483.85,309.15,2598.76
nfnet_f6,51.81,7411.574,384,576,378.69,452.2,438.36
maxvit_xlarge_tf_384,50.76,1891.335,96,384,292.78,668.76,475.32
swinv2_cr_huge_384,49.01,1305.73,64,384,352.04,583.18,657.94
regnety_2560,47.69,8051.463,384,224,257.07,87.48,826.14
xcit_large_24_p8_384_dist,44.91,4275.004,192,384,415.0,531.82,188.93
dm_nfnet_f6,44.62,5737.462,256,576,378.69,452.2,438.36
nfnet_f7,41.13,6224.782,256,608,480.39,570.85,499.5
maxvit_large_tf_512,41.04,1559.597,64,512,244.75,942.15,212.33
eva_giant_patch14_336,39.89,6418.269,256,336,620.64,550.67,1013.01
volo_d5_448,39.88,3209.812,128,448,315.06,737.92,295.91
beit_large_patch16_512,35.33,2716.953,96,512,362.24,656.39,305.67
cait_m36_384,32.89,7783.487,256,384,173.11,734.81,271.22
resnetv2_152x4_bitm,30.46,3151.929,96,480,844.84,414.26,936.53
volo_d5_512,27.89,4590.0,128,512,425.09,1105.37,296.09
maxvit_xlarge_tf_512,24.38,1968.424,48,512,534.14,1413.22,475.77
efficientnet_l2,23.13,1383.428,32,800,479.12,1707.39,480.31
swinv2_cr_giant_384,15.06,2124.735,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.86,9235.876,128,448,329.41,1708.23,356.46
eva_giant_patch14_560,10.52,3043.009,32,560,1906.76,2577.17,1014.45
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-a.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,88.227,11.773,97.093,2.907,305.08,448,1.000,bicubic,-10.623,-2.787,+1
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,87.893,12.107,96.920,3.080,305.08,448,1.000,bicubic,-11.037,-2.990,-1
eva_giant_patch14_560.m30m_ft_in22k_in1k,87.573,12.427,96.893,3.107,"1,014.45",560,1.000,bicubic,-11.257,-3.007,+1
eva02_large_patch14_448.mim_m38m_ft_in1k,87.107,12.893,96.280,3.720,305.08,448,1.000,bicubic,-11.623,-3.590,+5
eva02_large_patch14_448.mim_in22k_ft_in1k,86.227,13.773,95.787,4.213,305.08,448,1.000,bicubic,-12.613,-4.043,-2
eva_giant_patch14_336.clip_ft_in1k,85.307,14.693,95.720,4.280,"1,013.01",336,1.000,bicubic,-13.513,-4.090,-1
eva_giant_patch14_336.m30m_ft_in22k_in1k,85.147,14.853,96.360,3.640,"1,013.01",336,1.000,bicubic,-13.663,-3.540,-1
tf_efficientnet_l2.ns_jft_in1k,84.747,15.253,96.147,3.853,480.31,800,0.960,bicubic,-13.803,-3.673,+9
regnety_1280.swag_ft_in1k,83.907,16.093,96.200,3.800,644.81,384,1.000,bicubic,-14.543,-3.670,+18
eva_large_patch14_336.in22k_ft_in22k_in1k,83.853,16.147,95.347,4.653,304.53,336,1.000,bicubic,-14.887,-4.453,-3
convnextv2_huge.fcmae_ft_in22k_in1k_512,83.827,16.173,96.173,3.827,660.29,512,1.000,bicubic,-14.773,-3.697,+4
maxvit_xlarge_tf_512.in21k_ft_in1k,83.400,16.600,95.520,4.480,475.77,512,1.000,bicubic,-15.220,-4.270,+1
tf_efficientnet_l2.ns_jft_in1k_475,83.400,16.600,95.453,4.547,480.31,475,0.936,bicubic,-15.100,-4.327,+8
eva_large_patch14_336.in22k_ft_in1k,82.760,17.240,95.507,4.493,304.53,336,1.000,bicubic,-15.970,-4.283,-6
maxvit_large_tf_512.in21k_ft_in1k,81.733,18.267,95.027,4.973,212.33,512,1.000,bicubic,-16.887,-4.773,-1
beit_large_patch16_512.in22k_ft_in22k_in1k,81.600,18.400,94.880,5.120,305.67,512,1.000,bicubic,-16.960,-4.960,0
maxvit_base_tf_512.in21k_ft_in1k,81.360,18.640,94.467,5.533,119.88,512,1.000,bicubic,-17.260,-5.333,-5
eva_giant_patch14_224.clip_ft_in1k,81.213,18.787,94.333,5.667,"1,012.56",224,0.900,bicubic,-17.247,-5.417,+8
maxvit_xlarge_tf_384.in21k_ft_in1k,81.067,18.933,94.640,5.360,475.32,384,1.000,bicubic,-17.433,-5.190,+3
convnextv2_huge.fcmae_ft_in22k_in1k_384,79.893,20.107,94.640,5.360,660.29,384,1.000,bicubic,-18.777,-5.220,-10
deit3_large_patch16_384.fb_in22k_ft_in1k,79.187,20.813,93.613,6.387,304.76,384,1.000,bicubic,-19.273,-6.147,+4
beit_large_patch16_384.in22k_ft_in22k_in1k,79.107,20.893,94.267,5.733,305.00,384,1.000,bicubic,-19.413,-5.553,-3
caformer_b36.sail_in22k_ft_in1k_384,78.360,21.640,93.467,6.533,98.75,384,1.000,bicubic,-20.080,-6.333,+6
maxvit_large_tf_384.in21k_ft_in1k,78.013,21.987,93.267,6.733,212.03,384,1.000,bicubic,-20.477,-6.483,-1
eva02_base_patch14_448.mim_in22k_ft_in1k,77.547,22.453,93.120,6.880,87.12,448,1.000,bicubic,-20.893,-6.680,+3
vit_large_patch14_clip_336.openai_ft_in12k_in1k,77.333,22.667,93.627,6.373,304.53,336,1.000,bicubic,-20.927,-6.143,+16
convnext_xxlarge.clip_laion2b_soup_ft_in1k,77.120,22.880,94.320,5.680,846.47,256,1.000,bicubic,-21.320,-5.500,+3
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,76.893,23.107,92.693,7.307,87.12,448,1.000,bicubic,-21.747,-7.107,-17
maxvit_base_tf_384.in21k_ft_in1k,76.853,23.147,92.600,7.400,119.65,384,1.000,bicubic,-21.667,-7.150,-9
beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.773,23.227,93.173,6.827,304.43,224,0.950,bicubic,-21.767,-6.587,-12
eva_large_patch14_196.in22k_ft_in22k_in1k,75.507,24.493,91.760,8.240,304.14,196,1.000,bicubic,-22.913,-8.050,+2
regnety_1280.swag_lc_in1k,74.587,25.413,91.680,8.320,644.81,224,0.965,bicubic,-23.063,-7.890,+79
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,74.253,25.747,90.827,9.173,116.14,384,1.000,bicubic,-23.917,-8.933,+19
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,74.240,25.760,92.253,7.747,632.46,336,1.000,bicubic,-24.180,-7.517,-2
regnety_320.swag_ft_in1k,74.200,25.800,92.960,7.040,145.05,384,1.000,bicubic,-23.860,-6.800,+30
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,73.933,26.067,91.733,8.267,196.74,384,1.000,bicubic,-24.197,-7.977,+21
eva_large_patch14_196.in22k_ft_in1k,73.160,26.840,91.413,8.587,304.14,196,1.000,bicubic,-25.200,-8.407,-2
caformer_m36.sail_in22k_ft_in1k_384,72.987,27.013,90.600,9.400,56.20,384,1.000,bicubic,-25.163,-9.150,+18
vit_large_patch14_clip_224.openai_ft_in12k_in1k,72.293,27.707,90.880,9.120,304.20,224,1.000,bicubic,-25.927,-8.840,+7
convnextv2_large.fcmae_ft_in22k_in1k_384,72.067,27.933,91.013,8.987,197.96,384,1.000,bicubic,-26.333,-8.747,-6
vit_large_patch14_clip_224.openai_ft_in1k,71.813,28.187,91.453,8.547,304.20,224,1.000,bicubic,-26.347,-8.207,+14
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,71.800,28.200,90.227,9.773,304.53,336,1.000,bicubic,-26.540,-9.533,-5
convformer_b36.sail_in22k_ft_in1k_384,71.547,28.453,90.240,9.760,99.88,384,1.000,bicubic,-26.713,-9.590,-3
vit_large_patch16_384.augreg_in21k_ft_in1k,71.227,28.773,89.773,10.227,304.72,384,1.000,bicubic,-26.993,-9.947,+1
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,71.200,28.800,91.320,8.680,87.92,384,1.000,bicubic,-26.920,-8.420,+12
deit3_base_patch16_384.fb_in22k_ft_in1k,71.200,28.800,89.933,10.067,86.88,384,1.000,bicubic,-26.640,-9.737,+43
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,70.720,29.280,90.547,9.453,200.13,384,1.000,bicubic,-27.760,-9.233,-23
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,70.680,29.320,90.400,9.600,632.05,224,1.000,bicubic,-27.620,-9.360,-10
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,70.600,29.400,89.227,10.773,73.88,384,1.000,bicubic,-27.470,-10.493,+15
caformer_s36.sail_in22k_ft_in1k_384,70.347,29.653,90.067,9.933,39.30,384,1.000,bicubic,-27.623,-9.653,+22
deit3_huge_patch14_224.fb_in22k_ft_in1k,70.253,29.747,90.707,9.293,632.13,224,1.000,bicubic,-27.917,-9.053,+2
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,69.880,30.120,90.493,9.507,200.13,320,1.000,bicubic,-28.400,-9.277,-13
swin_large_patch4_window12_384.ms_in22k_ft_in1k,69.640,30.360,89.547,10.453,196.74,384,1.000,bicubic,-28.410,-10.143,+14
volo_d5_512.sail_in1k,69.587,30.413,90.427,9.573,296.09,512,1.150,bicubic,-28.183,-9.243,+46
convnext_xlarge.fb_in22k_ft_in1k_384,69.320,30.680,89.307,10.693,350.20,384,1.000,bicubic,-29.100,-10.503,-24
caformer_b36.sail_in22k_ft_in1k,69.133,30.867,89.600,10.400,98.75,224,1.000,bicubic,-29.027,-10.180,-2
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,69.067,30.933,89.880,10.120,116.09,384,1.000,bicubic,-29.193,-9.900,-16
deit3_large_patch16_224.fb_in22k_ft_in1k,68.693,31.307,89.973,10.027,304.37,224,1.000,bicubic,-29.477,-9.757,-7
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,68.560,31.440,88.613,11.387,149.39,384,1.000,bicubic,-29.650,-11.167,-11
beit_large_patch16_224.in22k_ft_in22k_in1k,68.467,31.533,89.573,10.427,304.43,224,0.900,bicubic,-29.713,-10.187,-10
regnety_160.swag_ft_in1k,68.093,31.907,90.707,9.293,83.59,384,1.000,bicubic,-29.687,-8.893,+35
convnextv2_large.fcmae_ft_in22k_in1k,68.093,31.907,89.720,10.280,197.96,288,1.000,bicubic,-29.997,-10.050,0
volo_d5_448.sail_in1k,68.080,31.920,89.720,10.280,295.91,448,1.150,bicubic,-29.670,-9.830,+39
maxvit_base_tf_512.in1k,67.933,32.067,88.493,11.507,119.88,512,1.000,bicubic,-29.797,-11.117,+40
maxvit_large_tf_512.in1k,67.880,32.120,87.653,12.347,212.33,512,1.000,bicubic,-29.950,-11.907,+26
tf_efficientnetv2_xl.in21k_ft_in1k,67.787,32.213,87.347,12.653,208.12,512,1.000,bicubic,-30.113,-12.223,+15
beitv2_large_patch16_224.in1k_ft_in1k,67.640,32.360,88.667,11.333,304.43,224,0.950,bicubic,-30.270,-10.993,+10
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,67.320,32.680,88.000,12.000,196.74,256,0.900,bicubic,-30.530,-11.640,+18
vit_large_patch14_clip_336.laion2b_ft_in1k,67.080,32.920,89.493,10.507,304.53,336,1.000,bicubic,-31.140,-10.307,-22
tf_efficientnet_b7.ns_jft_in1k,67.027,32.973,88.667,11.333,66.35,600,0.949,bicubic,-30.893,-11.053,+6
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,67.000,33.000,87.960,12.040,304.20,224,1.000,bicubic,-31.080,-11.690,-9
convnext_xlarge.fb_in22k_ft_in1k,66.960,33.040,88.947,11.053,350.20,288,1.000,bicubic,-31.150,-10.833,-12
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,66.880,33.120,89.280,10.720,200.13,384,1.000,bicubic,-31.370,-10.480,-30
convformer_m36.sail_in22k_ft_in1k_384,66.853,33.147,87.800,12.200,57.05,384,1.000,bicubic,-31.187,-11.950,-5
convnextv2_base.fcmae_ft_in22k_in1k_384,66.787,33.213,88.893,11.107,88.72,384,1.000,bicubic,-31.563,-10.877,-39
volo_d4_448.sail_in1k,66.667,33.333,88.987,11.013,193.41,448,1.150,bicubic,-31.003,-10.623,+32
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,65.987,34.013,87.880,12.120,93.59,320,1.000,bicubic,-31.983,-11.820,-4
beit_base_patch16_384.in22k_ft_in22k_in1k,65.920,34.080,88.507,11.493,86.74,384,1.000,bicubic,-31.900,-11.193,+14
regnety_320.swag_lc_in1k,65.573,34.427,88.080,11.920,145.05,224,0.965,bicubic,-31.587,-11.590,+109
convnext_large.fb_in22k_ft_in1k_384,65.533,34.467,87.467,12.533,197.77,384,1.000,bicubic,-32.707,-12.283,-36
vit_huge_patch14_clip_224.laion2b_ft_in1k,65.493,34.507,87.720,12.280,632.05,224,1.000,bicubic,-32.527,-12.000,-11
volo_d3_448.sail_in1k,65.400,34.600,87.573,12.427,86.63,448,1.000,bicubic,-32.150,-11.987,+45
convnext_large.fb_in22k_ft_in1k,65.000,35.000,87.947,12.053,197.77,288,1.000,bicubic,-33.120,-11.833,-24
tf_efficientnetv2_l.in21k_ft_in1k,64.947,35.053,87.840,12.160,118.52,480,1.000,bicubic,-32.853,-11.820,+11
swin_base_patch4_window12_384.ms_in22k_ft_in1k,64.467,35.533,87.520,12.480,87.90,384,1.000,bicubic,-33.433,-12.150,-6
convnextv2_huge.fcmae_ft_in1k,64.440,35.560,87.080,12.920,660.29,288,1.000,bicubic,-33.460,-12.630,-6
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,64.187,35.813,85.520,14.480,116.14,224,0.950,bicubic,-33.623,-14.130,+7
vit_base_patch16_384.augreg_in21k_ft_in1k,63.693,36.307,86.693,13.307,86.86,384,1.000,bicubic,-34.147,-12.977,+2
maxvit_large_tf_384.in1k,63.507,36.493,85.093,14.907,212.03,384,1.000,bicubic,-34.063,-14.577,+37
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,63.307,36.693,87.507,12.493,87.92,256,0.900,bicubic,-34.353,-12.103,+19
convnextv2_base.fcmae_ft_in22k_in1k,62.893,37.107,87.667,12.333,88.72,288,1.000,bicubic,-35.167,-12.193,-25
maxvit_small_tf_512.in1k,62.867,37.133,86.307,13.693,69.13,512,1.000,bicubic,-34.883,-13.313,+11
maxvit_base_tf_384.in1k,62.613,37.387,85.187,14.813,119.65,384,1.000,bicubic,-34.957,-14.343,+32
convformer_b36.sail_in22k_ft_in1k,62.600,37.400,85.467,14.533,99.88,224,1.000,bicubic,-35.340,-14.293,-19
cait_m48_448.fb_dist_in1k,62.373,37.627,86.453,13.547,356.46,448,1.000,bicubic,-35.107,-13.147,+43
convnext_base.fb_in22k_ft_in1k_384,62.360,37.640,86.240,13.760,88.59,384,1.000,bicubic,-35.720,-13.520,-33
tf_efficientnet_b6.ns_jft_in1k,62.227,37.773,85.160,14.840,43.04,528,0.942,bicubic,-35.393,-14.390,+19
caformer_b36.sail_in1k_384,62.160,37.840,84.493,15.507,98.75,384,1.000,bicubic,-35.340,-15.147,+37
vit_base_patch8_224.augreg2_in21k_ft_in1k,62.027,37.973,85.840,14.160,86.58,224,0.900,bicubic,-35.663,-13.810,+8
convformer_s36.sail_in22k_ft_in1k_384,61.920,38.080,85.960,14.040,40.01,384,1.000,bicubic,-35.930,-13.690,-13
beitv2_base_patch16_224.in1k_ft_in22k_in1k,61.667,38.333,85.480,14.520,86.53,224,0.900,bicubic,-36.023,-14.200,+5
vit_large_r50_s32_384.augreg_in21k_ft_in1k,61.493,38.507,84.027,15.973,329.09,384,1.000,bicubic,-36.367,-15.643,-17
seresnextaa101d_32x8d.sw_in12k_ft_in1k,61.080,38.920,85.920,14.080,93.59,288,1.000,bicubic,-36.830,-13.740,-25
caformer_m36.sail_in22k_ft_in1k,61.067,38.933,84.893,15.107,56.20,224,1.000,bicubic,-36.773,-14.787,-15
swin_large_patch4_window7_224.ms_in22k_ft_in1k,61.000,39.000,85.867,14.133,196.53,224,0.900,bicubic,-36.650,-13.703,+8
convnext_base.fb_in22k_ft_in1k,60.893,39.107,86.147,13.853,88.59,288,1.000,bicubic,-36.967,-13.533,-22
resnetv2_152x4_bit.goog_in21k_ft_in1k,60.773,39.227,83.560,16.440,936.53,480,1.000,bilinear,-36.717,-16.050,+29
convnext_small.in12k_ft_in1k_384,60.733,39.267,84.960,15.040,50.22,384,1.000,bicubic,-37.067,-14.810,-12
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,60.547,39.453,86.293,13.707,200.13,256,1.000,bicubic,-37.403,-13.417,-35
deit3_large_patch16_384.fb_in1k,60.533,39.467,85.733,14.267,304.76,384,1.000,bicubic,-36.887,-13.837,+36
caformer_m36.sail_in1k_384,60.533,39.467,84.760,15.240,56.20,384,1.000,bicubic,-36.907,-14.880,+35
tf_efficientnet_b5.ns_jft_in1k,60.293,39.707,84.453,15.547,30.39,456,0.934,bicubic,-37.207,-15.127,+22
vit_base_patch16_clip_384.openai_ft_in12k_in1k,60.227,39.773,84.613,15.387,86.86,384,0.950,bicubic,-37.963,-15.047,-64
regnety_160.swag_lc_in1k,60.147,39.853,85.760,14.240,83.59,224,0.965,bicubic,-36.673,-13.890,+131
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,59.973,40.027,83.760,16.240,73.88,224,0.950,bicubic,-37.677,-15.880,-3
vit_large_patch14_clip_224.laion2b_ft_in1k,59.947,40.053,85.667,14.333,304.20,224,1.000,bicubic,-37.943,-13.983,-34
xcit_large_24_p8_384.fb_dist_in1k,59.947,40.053,85.467,14.533,188.93,384,1.000,bicubic,-37.573,-14.073,+15
coatnet_2_rw_224.sw_in12k_ft_in1k,59.533,40.467,84.213,15.787,73.87,224,0.950,bicubic,-37.987,-15.387,+13
tf_efficientnetv2_m.in21k_ft_in1k,59.413,40.587,84.573,15.427,54.14,480,1.000,bicubic,-38.407,-15.027,-26
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,59.160,40.840,84.480,15.520,116.09,224,0.950,bicubic,-38.600,-15.220,-19
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,59.147,40.853,83.280,16.720,86.86,384,1.000,bicubic,-38.843,-16.380,-50
vit_base_patch8_224.augreg_in21k_ft_in1k,58.960,41.040,82.733,17.267,86.58,224,0.900,bicubic,-38.610,-16.857,+2
maxvit_tiny_tf_512.in1k,58.800,41.200,84.573,15.427,31.05,512,1.000,bicubic,-38.780,-14.987,0
tiny_vit_21m_512.dist_in22k_ft_in1k,58.733,41.267,83.693,16.307,21.27,512,1.000,bicubic,-39.137,-15.937,-41
caformer_s18.sail_in22k_ft_in1k_384,58.640,41.360,85.347,14.653,26.34,384,1.000,bicubic,-38.780,-14.273,+23
volo_d2_384.sail_in1k,58.600,41.400,84.280,15.720,58.87,384,1.000,bicubic,-38.720,-15.320,+35
tiny_vit_21m_384.dist_in22k_ft_in1k,58.320,41.680,83.653,16.347,21.23,384,1.000,bicubic,-39.290,-15.937,-8
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,58.253,41.747,85.480,14.520,88.59,384,1.000,bicubic,-39.787,-14.210,-60
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,58.040,41.960,80.640,19.360,468.53,224,0.875,bilinear,-39.330,-19.040,+26
cait_m36_384.fb_dist_in1k,57.840,42.160,84.840,15.160,271.22,384,1.000,bicubic,-39.560,-14.670,+22
dm_nfnet_f5.dm_in1k,57.640,42.360,82.267,17.733,377.21,544,0.954,bicubic,-40.140,-17.493,-32
dm_nfnet_f6.dm_in1k,57.560,42.440,82.360,17.640,438.36,576,0.956,bicubic,-40.220,-17.290,-34
caformer_s36.sail_in1k_384,57.347,42.653,82.787,17.213,39.30,384,1.000,bicubic,-40.043,-16.753,+20
deit3_base_patch16_224.fb_in22k_ft_in1k,57.267,42.733,83.520,16.480,86.59,224,1.000,bicubic,-40.213,-16.030,+3
volo_d5_224.sail_in1k,57.107,42.893,82.733,17.267,295.46,224,0.960,bicubic,-40.273,-16.837,+19
convnext_base.clip_laiona_augreg_ft_in1k_384,57.080,42.920,84.773,15.227,88.59,384,1.000,bicubic,-40.540,-14.807,-19
deit3_small_patch16_384.fb_in22k_ft_in1k,57.080,42.920,83.067,16.933,22.21,384,1.000,bicubic,-40.050,-16.443,+55
convnextv2_large.fcmae_ft_in1k,56.880,43.120,83.467,16.533,197.96,288,1.000,bicubic,-40.780,-16.253,-28
regnety_160.lion_in12k_ft_in1k,56.747,43.253,83.453,16.547,83.59,288,1.000,bicubic,-40.703,-16.147,+2
dm_nfnet_f4.dm_in1k,56.707,43.293,81.760,18.240,316.07,512,0.951,bicubic,-40.933,-17.780,-26
xcit_medium_24_p8_384.fb_dist_in1k,56.693,43.307,83.453,16.547,84.32,384,1.000,bicubic,-40.587,-16.067,+27
maxvit_small_tf_384.in1k,56.600,43.400,82.293,17.707,69.02,384,1.000,bicubic,-40.830,-17.217,+4
regnety_160.sw_in12k_ft_in1k,56.253,43.747,82.840,17.160,83.59,288,1.000,bicubic,-41.197,-16.750,-1
convformer_m36.sail_in22k_ft_in1k,55.880,44.120,81.813,18.187,57.05,224,1.000,bicubic,-41.720,-17.807,-23
caformer_s36.sail_in22k_ft_in1k,55.813,44.187,82.133,17.867,39.30,224,1.000,bicubic,-41.787,-17.587,-23
vit_large_patch16_224.augreg_in21k_ft_in1k,55.573,44.427,80.107,19.893,304.33,224,0.900,bicubic,-42.057,-19.483,-31
convformer_b36.sail_in1k_384,55.453,44.547,81.293,18.707,99.88,384,1.000,bicubic,-42.077,-18.227,-18
vit_base_patch16_clip_384.openai_ft_in1k,55.000,45.000,82.613,17.387,86.86,384,1.000,bicubic,-42.540,-17.047,-20
vit_base_r50_s16_384.orig_in21k_ft_in1k,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.563,-18.347,+37
cait_s36_384.fb_dist_in1k,54.360,45.640,81.373,18.627,68.37,384,1.000,bicubic,-42.970,-18.167,+10
volo_d1_384.sail_in1k,54.333,45.667,81.000,19.000,26.78,384,1.000,bicubic,-42.577,-18.460,+81
deit3_huge_patch14_224.fb_in1k,54.320,45.680,82.093,17.907,632.13,224,0.900,bicubic,-42.580,-17.127,+82
vit_base_patch16_clip_384.laion2b_ft_in1k,54.267,45.733,80.880,19.120,86.86,384,1.000,bicubic,-43.453,-18.750,-48
xcit_small_24_p8_384.fb_dist_in1k,54.253,45.747,81.547,18.453,47.63,384,1.000,bicubic,-42.987,-18.003,+20
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,54.173,45.827,81.640,18.360,39.03,384,0.950,bicubic,-43.267,-17.960,-11
resnetv2_101x3_bit.goog_in21k_ft_in1k,54.027,45.973,81.027,18.973,387.93,448,1.000,bilinear,-42.953,-18.503,+63
resnetv2_152x2_bit.goog_in21k_ft_in1k,54.013,45.987,82.000,18.000,236.34,448,1.000,bilinear,-42.987,-17.590,+58
beitv2_base_patch16_224.in1k_ft_in1k,53.813,46.187,81.853,18.147,86.53,224,0.900,bicubic,-43.357,-17.617,+29
dm_nfnet_f3.dm_in1k,53.773,46.227,79.813,20.187,254.92,416,0.940,bicubic,-43.697,-19.747,-20
convformer_m36.sail_in1k_384,53.547,46.453,80.733,19.267,57.05,384,1.000,bicubic,-43.863,-18.867,-9
deit3_base_patch16_384.fb_in1k,53.427,46.573,80.547,19.453,86.88,384,1.000,bicubic,-43.613,-18.833,+50
convnext_small.in12k_ft_in1k,53.240,46.760,81.400,18.600,50.22,288,1.000,bicubic,-44.110,-18.180,-4
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,53.067,46.933,76.907,23.093,194.03,224,0.875,bilinear,-43.743,-22.693,+83
volo_d4_224.sail_in1k,52.987,47.013,80.427,19.573,192.96,224,0.960,bicubic,-44.293,-19.113,+3
xcit_large_24_p16_384.fb_dist_in1k,52.853,47.147,81.827,18.173,189.10,384,1.000,bicubic,-44.667,-17.653,-32
convnext_small.fb_in22k_ft_in1k_384,52.427,47.573,80.813,19.187,50.22,384,1.000,bicubic,-45.183,-18.787,-48
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,52.320,47.680,82.480,17.520,88.59,256,1.000,bicubic,-45.280,-17.130,-47
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,52.320,47.680,79.747,20.253,88.34,448,1.000,bicubic,-44.990,-19.733,-5
maxvit_tiny_tf_384.in1k,52.080,47.920,79.813,20.187,30.98,384,1.000,bicubic,-45.230,-19.687,-7
regnety_120.sw_in12k_ft_in1k,51.813,48.187,80.787,19.213,51.82,288,1.000,bicubic,-45.467,-18.743,-4
swin_base_patch4_window7_224.ms_in22k_ft_in1k,51.440,48.560,80.080,19.920,87.77,224,0.900,bicubic,-45.840,-19.510,-6
tf_efficientnet_b4.ns_jft_in1k,51.253,48.747,79.173,20.827,19.34,380,0.922,bicubic,-45.697,-20.087,+53
efficientnet_b5.sw_in12k_ft_in1k,51.213,48.787,78.840,21.160,30.39,448,1.000,bicubic,-46.197,-20.710,-23
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,51.213,48.787,78.240,21.760,88.79,224,0.875,bilinear,-45.997,-21.330,+6
flexivit_large.1200ep_in1k,51.200,48.800,80.667,19.333,304.36,240,0.950,bicubic,-46.210,-18.803,-27
eva02_small_patch14_336.mim_in22k_ft_in1k,51.200,48.800,79.120,20.880,22.13,336,1.000,bicubic,-45.940,-20.350,+16
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,51.120,48.880,78.533,21.467,236.34,384,1.000,bicubic,-45.710,-20.877,+66
convnext_small.fb_in22k_ft_in1k,51.067,48.933,80.920,19.080,50.22,288,1.000,bicubic,-46.293,-18.610,-22
mvitv2_large.fb_in1k,50.867,49.133,78.493,21.507,217.99,224,0.900,bicubic,-46.063,-20.907,+50
beit_base_patch16_224.in22k_ft_in22k_in1k,50.720,49.280,79.733,20.267,86.53,224,0.900,bicubic,-46.360,-19.877,+21
tf_efficientnetv2_l.in1k,50.693,49.307,77.613,22.387,118.52,480,1.000,bicubic,-46.777,-21.917,-41
vit_base_patch16_384.orig_in21k_ft_in1k,50.653,49.347,78.200,21.800,86.86,384,1.000,bicubic,-46.067,-21.280,+78
xcit_small_12_p8_384.fb_dist_in1k,50.587,49.413,79.573,20.427,26.21,384,1.000,bicubic,-46.643,-19.907,-6
convformer_s36.sail_in1k_384,50.333,49.667,78.893,21.107,40.01,384,1.000,bicubic,-46.947,-20.577,-14
convnext_tiny.in12k_ft_in1k_384,50.320,49.680,79.800,20.200,28.59,384,1.000,bicubic,-47.020,-19.800,-26
volo_d3_224.sail_in1k,50.320,49.680,78.213,21.787,86.33,224,0.960,bicubic,-46.770,-21.257,+14
flexivit_large.600ep_in1k,50.253,49.747,80.013,19.987,304.36,240,0.950,bicubic,-47.027,-19.417,-23
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,50.120,49.880,78.040,21.960,86.57,224,0.950,bicubic,-47.330,-21.500,-45
convformer_s18.sail_in22k_ft_in1k_384,50.067,49.933,80.973,19.027,26.77,384,1.000,bicubic,-47.203,-18.577,-18
vit_base_patch16_224.augreg2_in21k_ft_in1k,49.827,50.173,78.960,21.040,86.57,224,0.900,bicubic,-47.323,-20.490,-1
vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.800,50.200,77.120,22.880,86.57,224,0.950,bicubic,-47.730,-22.380,-61
cait_s24_384.fb_dist_in1k,49.733,50.267,78.760,21.240,47.06,384,1.000,bicubic,-47.337,-20.670,+16
inception_next_base.sail_in1k_384,49.693,50.307,79.133,20.867,86.67,384,1.000,bicubic,-47.567,-20.357,-21
xcit_medium_24_p16_384.fb_dist_in1k,49.333,50.667,79.867,20.133,84.40,384,1.000,bicubic,-47.947,-19.643,-26
deit_base_distilled_patch16_384.fb_in1k,49.333,50.667,79.227,20.773,87.63,384,1.000,bicubic,-47.627,-20.253,+28
caformer_s18.sail_in1k_384,49.147,50.853,78.693,21.307,26.34,384,1.000,bicubic,-47.933,-20.797,+11
coat_lite_medium_384.in1k,49.093,50.907,78.600,21.400,44.57,384,1.000,bicubic,-48.057,-20.940,-7
tf_efficientnet_b8.ra_in1k,48.933,51.067,77.227,22.773,87.41,672,0.954,bicubic,-48.267,-22.273,-14
convnextv2_base.fcmae_ft_in1k,48.693,51.307,78.827,21.173,88.72,288,1.000,bicubic,-48.527,-20.713,-21
deit3_large_patch16_224.fb_in1k,48.627,51.373,78.107,21.893,304.37,224,0.900,bicubic,-48.313,-21.153,+27
caformer_b36.sail_in1k,48.533,51.467,75.667,24.333,98.75,224,1.000,bicubic,-48.447,-23.823,+19
flexivit_large.300ep_in1k,48.520,51.480,78.653,21.347,304.36,240,0.950,bicubic,-48.730,-20.837,-29
convnext_base.clip_laion2b_augreg_ft_in1k,48.453,51.547,79.827,20.173,88.59,256,1.000,bicubic,-48.787,-19.693,-28
tf_efficientnetv2_s.in21k_ft_in1k,48.453,51.547,77.867,22.133,21.46,384,1.000,bicubic,-48.277,-21.463,+54
resnest269e.in1k,48.213,51.787,74.333,25.667,110.93,416,0.928,bicubic,-48.297,-25.017,+106
xcit_large_24_p8_224.fb_dist_in1k,48.160,51.840,79.093,20.907,188.93,224,1.000,bicubic,-48.910,-20.327,+2
deit3_medium_patch16_224.fb_in22k_ft_in1k,48.160,51.840,77.027,22.973,38.85,224,1.000,bicubic,-48.810,-22.403,+15
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.960,52.040,76.853,23.147,88.30,384,1.000,bicubic,-49.400,-22.667,-51
regnety_2560.seer_ft_in1k,47.907,52.093,76.800,23.200,"1,282.60",384,1.000,bicubic,-49.313,-22.720,-30
regnetz_e8.ra3_in1k,47.800,52.200,76.200,23.800,57.70,320,1.000,bicubic,-49.400,-23.340,-27
convnext_tiny.in12k_ft_in1k,47.507,52.493,78.747,21.253,28.59,288,1.000,bicubic,-49.553,-20.803,-1
convformer_s36.sail_in22k_ft_in1k,47.440,52.560,77.107,22.893,40.01,224,1.000,bicubic,-49.640,-22.453,-9
resnetv2_50x3_bit.goog_in21k_ft_in1k,47.307,52.693,77.333,22.667,217.32,448,1.000,bilinear,-49.403,-22.157,+49
vit_base_patch16_clip_224.openai_ft_in1k,47.200,52.800,77.533,22.467,86.57,224,0.900,bicubic,-49.880,-22.077,-8
xcit_large_24_p8_224.fb_in1k,47.160,52.840,74.480,25.520,188.93,224,1.000,bicubic,-49.240,-24.510,+112
xcit_small_24_p16_384.fb_dist_in1k,46.987,53.013,77.160,22.840,47.67,384,1.000,bicubic,-50.133,-22.290,-22
tf_efficientnet_b8.ap_in1k,46.893,53.107,76.533,23.467,87.41,672,0.954,bicubic,-50.217,-22.977,-22
convnext_large.fb_in1k,46.813,53.187,76.627,23.373,197.77,288,1.000,bicubic,-50.287,-22.823,-20
dm_nfnet_f2.dm_in1k,46.640,53.360,74.813,25.187,193.78,352,0.920,bicubic,-50.470,-24.847,-23
efficientnetv2_rw_m.agc_in1k,46.293,53.707,75.720,24.280,53.24,416,1.000,bicubic,-50.687,-23.620,-2
swinv2_base_window16_256.ms_in1k,46.213,53.787,75.173,24.827,87.92,256,0.900,bicubic,-50.537,-24.177,+34
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,46.120,53.880,72.240,27.760,194.03,224,0.875,bilinear,-50.480,-27.030,+68
caformer_m36.sail_in1k,46.080,53.920,74.533,25.467,56.20,224,1.000,bicubic,-50.810,-24.897,+13
volo_d2_224.sail_in1k,46.067,53.933,75.253,24.747,58.68,224,0.960,bicubic,-50.933,-24.137,-7
vit_small_patch16_384.augreg_in21k_ft_in1k,45.893,54.107,76.720,23.280,22.20,384,1.000,bicubic,-50.807,-22.710,+40
ecaresnet269d.ra2_in1k,45.853,54.147,75.067,24.933,102.09,352,1.000,bicubic,-51.237,-24.403,-27
convnextv2_tiny.fcmae_ft_in22k_in1k_384,45.760,54.240,76.973,23.027,28.64,384,1.000,bicubic,-51.480,-22.637,-51
vit_small_r26_s32_384.augreg_in21k_ft_in1k,45.720,54.280,76.040,23.960,36.47,384,1.000,bicubic,-50.960,-23.040,+48
tf_efficientnet_b7.ap_in1k,45.373,54.627,74.200,25.800,66.35,600,0.949,bicubic,-51.827,-25.300,-47
swin_base_patch4_window12_384.ms_in1k,45.333,54.667,74.360,25.640,87.90,384,1.000,bicubic,-51.247,-24.810,+64
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,45.320,54.680,70.907,29.093,88.79,224,0.875,bilinear,-51.010,-28.493,+111
xcit_medium_24_p8_224.fb_dist_in1k,45.227,54.773,76.760,23.240,84.32,224,1.000,bicubic,-51.693,-22.640,-1
tiny_vit_21m_224.dist_in22k_ft_in1k,45.027,54.973,75.547,24.453,21.20,224,0.950,bicubic,-52.173,-23.943,-48
eca_nfnet_l2.ra3_in1k,44.987,55.013,75.880,24.120,56.72,384,1.000,bicubic,-52.093,-23.630,-29
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,44.787,55.213,73.747,26.253,41.72,224,0.950,bicubic,-51.843,-25.473,+51
maxxvit_rmlp_small_rw_256.sw_in1k,44.600,55.400,75.040,24.960,66.01,256,0.950,bicubic,-52.200,-24.340,+11
crossvit_18_dagger_408.in1k,44.253,55.747,73.840,26.160,44.61,408,1.000,bicubic,-52.287,-25.520,+68
resnest200e.in1k,44.133,55.867,73.493,26.507,70.20,320,0.909,bicubic,-52.477,-25.857,+51
seresnextaa101d_32x8d.ah_in1k,43.973,56.027,73.347,26.653,93.59,288,1.000,bicubic,-52.987,-25.903,-16
cait_xs24_384.fb_dist_in1k,43.947,56.053,75.160,24.840,26.67,384,1.000,bicubic,-52.593,-24.260,+62
mvitv2_base.fb_in1k,43.747,56.253,74.507,25.493,51.47,224,0.900,bicubic,-53.013,-24.753,+12
resnetrs200.tf_in1k,43.733,56.267,72.827,27.173,93.21,320,1.000,bicubic,-52.967,-26.543,+26
rexnetr_300.sw_in12k_ft_in1k,43.653,56.347,76.507,23.493,34.81,288,1.000,bicubic,-53.187,-23.003,-1
tresnet_xl.miil_in1k_448,43.493,56.507,72.427,27.573,78.44,448,0.875,bilinear,-52.487,-26.623,+177
caformer_s18.sail_in22k_ft_in1k,43.333,56.667,74.960,25.040,26.34,224,1.000,bicubic,-53.377,-24.590,+18
xcit_small_12_p16_384.fb_dist_in1k,43.307,56.693,73.933,26.067,26.25,384,1.000,bicubic,-53.613,-25.457,-16
vit_base_patch16_224.augreg_in21k_ft_in1k,43.267,56.733,72.907,27.093,86.57,224,0.900,bicubic,-53.613,-26.623,-10
swin_small_patch4_window7_224.ms_in22k_ft_in1k,43.227,56.773,76.187,23.813,49.61,224,0.900,bicubic,-53.253,-23.203,+65
resnetrs420.tf_in1k,43.147,56.853,70.440,29.560,191.89,416,1.000,bicubic,-53.763,-29.080,-16
vit_base_patch32_clip_384.openai_ft_in12k_in1k,43.107,56.893,73.253,26.747,88.30,384,0.950,bicubic,-54.003,-26.247,-53
edgenext_base.in21k_ft_in1k,43.040,56.960,75.440,24.560,18.51,320,1.000,bicubic,-53.690,-23.980,+5
coatnet_rmlp_2_rw_224.sw_in1k,43.040,56.960,71.693,28.307,73.88,224,0.950,bicubic,-53.500,-27.607,+55
xcit_medium_24_p8_224.fb_in1k,43.040,56.960,70.320,29.680,84.32,224,1.000,bicubic,-53.050,-28.570,+142
tf_efficientnet_b7.ra_in1k,42.973,57.027,73.147,26.853,66.35,600,0.949,bicubic,-54.027,-26.373,-38
tf_efficientnetv2_m.in1k,42.813,57.187,72.600,27.400,54.14,480,1.000,bicubic,-54.397,-26.930,-74
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,42.707,57.293,74.307,25.693,38.86,256,0.950,bicubic,-53.963,-25.063,+23
hrnet_w48_ssld.paddle_in1k,42.600,57.400,72.147,27.853,77.47,288,1.000,bilinear,-54.430,-27.243,-44
dm_nfnet_f1.dm_in1k,42.547,57.453,71.560,28.440,132.63,320,0.910,bicubic,-54.483,-28.080,-44
xcit_tiny_24_p8_384.fb_dist_in1k,42.453,57.547,72.853,27.147,12.11,384,1.000,bicubic,-54.077,-26.417,+49
gcvit_base.in1k,42.440,57.560,73.773,26.227,90.32,224,0.875,bicubic,-54.120,-25.377,+39
swinv2_small_window16_256.ms_in1k,42.360,57.640,72.867,27.133,49.73,256,0.900,bicubic,-54.110,-26.333,+55
maxvit_rmlp_small_rw_224.sw_in1k,42.227,57.773,72.387,27.613,64.90,224,0.900,bicubic,-54.353,-26.863,+35
convformer_s18.sail_in1k_384,42.120,57.880,74.453,25.547,26.77,384,1.000,bicubic,-54.920,-24.937,-51
convnextv2_tiny.fcmae_ft_in22k_in1k,42.093,57.907,75.747,24.253,28.64,288,1.000,bicubic,-54.757,-23.713,-24
maxvit_base_tf_224.in1k,41.987,58.013,70.080,29.920,119.47,224,0.950,bicubic,-54.963,-29.500,-39
convnext_base.fb_in1k,41.947,58.053,73.987,26.013,88.59,288,1.000,bicubic,-54.883,-25.463,-22
xcit_small_24_p8_224.fb_dist_in1k,41.907,58.093,73.653,26.347,47.63,224,1.000,bicubic,-54.963,-25.827,-30
crossvit_15_dagger_408.in1k,41.907,58.093,72.040,27.960,28.50,408,1.000,bicubic,-54.483,-27.310,+63
resnetaa101d.sw_in12k_ft_in1k,41.893,58.107,72.387,27.613,44.57,288,1.000,bicubic,-54.807,-26.973,0
maxvit_large_tf_224.in1k,41.880,58.120,68.653,31.347,211.79,224,0.950,bicubic,-55.080,-30.737,-46
xcit_small_24_p8_224.fb_in1k,41.800,58.200,71.080,28.920,47.63,224,1.000,bicubic,-54.610,-28.070,+55
vit_large_r50_s32_224.augreg_in21k_ft_in1k,41.640,58.360,70.227,29.773,328.99,224,0.900,bicubic,-55.150,-29.113,-23
vit_base_patch16_clip_224.laion2b_ft_in1k,41.613,58.387,73.627,26.373,86.57,224,1.000,bicubic,-55.517,-25.833,-80
swinv2_base_window8_256.ms_in1k,41.547,58.453,72.427,27.573,87.92,256,0.900,bicubic,-54.983,-26.893,+34
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,41.547,58.453,71.720,28.280,44.18,224,0.875,bilinear,-54.873,-27.430,+48
deit3_small_patch16_224.fb_in22k_ft_in1k,41.200,58.800,71.893,28.107,22.06,224,1.000,bicubic,-55.460,-27.437,+5
maxvit_rmlp_tiny_rw_256.sw_in1k,41.173,58.827,71.187,28.813,29.15,256,0.950,bicubic,-55.247,-28.193,+46
regnety_1280.seer_ft_in1k,41.147,58.853,71.200,28.800,644.81,384,1.000,bicubic,-55.713,-28.190,-39
seresnext101d_32x8d.ah_in1k,41.147,58.853,70.920,29.080,93.59,288,1.000,bicubic,-55.553,-28.560,-9
convnext_tiny.fb_in22k_ft_in1k_384,41.053,58.947,72.533,27.467,28.59,384,1.000,bicubic,-56.027,-26.977,-76
caformer_s36.sail_in1k,41.040,58.960,70.893,29.107,39.30,224,1.000,bicubic,-55.650,-28.467,-8
davit_base.msft_in1k,40.880,59.120,72.747,27.253,87.95,224,0.950,bicubic,-56.060,-26.593,-54
tf_efficientnet_b6.ap_in1k,40.827,59.173,71.627,28.373,43.04,528,0.942,bicubic,-56.253,-27.793,-81
flexivit_base.1200ep_in1k,40.640,59.360,72.307,27.693,86.59,240,0.950,bicubic,-56.100,-27.053,-28
convformer_b36.sail_in1k,40.453,59.547,69.440,30.560,99.88,224,1.000,bicubic,-56.447,-30.040,-50
resmlp_big_24_224.fb_in22k_ft_in1k,40.373,59.627,74.800,25.200,129.14,224,0.875,bicubic,-56.247,-24.650,+1
deit3_small_patch16_384.fb_in1k,40.333,59.667,70.333,29.667,22.21,384,1.000,bicubic,-55.867,-28.957,+77
tresnet_l.miil_in1k_448,40.213,59.787,69.920,30.080,55.99,448,0.875,bilinear,-55.647,-29.280,+158
deit_base_patch16_384.fb_in1k,40.187,59.813,70.813,29.187,86.86,384,1.000,bicubic,-55.973,-28.427,+86
regnetz_040_h.ra3_in1k,40.000,60.000,71.320,28.680,28.94,320,1.000,bicubic,-56.700,-27.970,-26
resnetrs350.tf_in1k,39.960,60.040,68.907,31.093,163.96,384,1.000,bicubic,-56.790,-30.463,-37
flexivit_base.600ep_in1k,39.920,60.080,71.893,28.107,86.59,240,0.950,bicubic,-56.730,-27.437,-10
regnetz_d8.ra3_in1k,39.907,60.093,71.707,28.293,23.37,320,1.000,bicubic,-56.713,-27.803,-5
swin_s3_base_224.ms_in1k,39.853,60.147,70.467,29.533,71.13,224,0.900,bicubic,-56.387,-28.683,+62
flexivit_base.300ep_in1k,39.533,60.467,71.000,29.000,86.59,240,0.950,bicubic,-57.067,-28.530,-4
seresnext101_32x8d.ah_in1k,39.520,60.480,69.480,30.520,93.57,288,1.000,bicubic,-57.250,-29.870,-44
gcvit_small.in1k,39.400,60.600,70.480,29.520,51.09,224,0.875,bicubic,-56.880,-28.660,+53
regnetz_d8_evos.ch_in1k,39.360,60.640,71.427,28.573,23.46,320,1.000,bicubic,-57.210,-28.033,0
deit3_base_patch16_224.fb_in1k,39.173,60.827,70.933,29.067,86.59,224,0.900,bicubic,-57.127,-28.247,+47
volo_d1_224.sail_in1k,39.013,60.987,70.267,29.733,26.63,224,0.960,bicubic,-57.307,-29.043,+44
vit_large_patch32_384.orig_in21k_ft_in1k,38.920,61.080,68.933,31.067,306.63,384,1.000,bicubic,-56.910,-30.237,+151
resnetv2_101x1_bit.goog_in21k_ft_in1k,38.907,61.093,71.027,28.973,44.54,448,1.000,bilinear,-57.193,-28.243,+90
mvitv2_small.fb_in1k,38.773,61.227,70.413,29.587,34.87,224,0.900,bicubic,-57.597,-28.787,+29
regnetz_040.ra3_in1k,38.747,61.253,70.440,29.560,27.12,320,1.000,bicubic,-57.973,-29.060,-43
coat_lite_medium.in1k,38.600,61.400,71.093,28.907,44.57,224,0.900,bicubic,-57.870,-28.147,+12
xcit_small_12_p8_224.fb_dist_in1k,38.280,61.720,71.373,28.627,26.21,224,1.000,bicubic,-58.420,-27.987,-39
resnet200d.ra2_in1k,38.133,61.867,68.573,31.427,64.69,320,1.000,bicubic,-58.597,-30.847,-48
davit_small.msft_in1k,38.120,61.880,70.747,29.253,49.75,224,0.950,bicubic,-58.510,-28.413,-25
tf_efficientnet_b7.aa_in1k,38.120,61.880,69.320,30.680,66.35,600,0.949,bicubic,-58.420,-29.940,-5
focalnet_base_srf.ms_in1k,37.827,62.173,69.747,30.253,88.15,224,0.900,bicubic,-58.733,-29.483,-10
focalnet_base_lrf.ms_in1k,37.787,62.213,68.573,31.427,88.75,224,0.900,bicubic,-58.663,-30.727,+10
convformer_m36.sail_in1k,37.760,62.240,67.280,32.720,57.05,224,1.000,bicubic,-58.920,-32.300,-34
swinv2_small_window8_256.ms_in1k,37.733,62.267,69.853,30.147,49.73,256,0.900,bicubic,-58.537,-29.357,+38
xcit_large_24_p16_224.fb_dist_in1k,37.653,62.347,71.613,28.387,189.10,224,1.000,bicubic,-59.147,-27.737,-67
seresnet152d.ra2_in1k,37.653,62.347,69.493,30.507,66.84,320,1.000,bicubic,-59.117,-29.947,-63
maxvit_small_tf_224.in1k,37.560,62.440,67.947,32.053,68.93,224,0.950,bicubic,-59.130,-31.423,-42
xcit_small_12_p8_224.fb_in1k,37.547,62.453,68.160,31.840,26.21,224,1.000,bicubic,-58.563,-31.000,+74
eca_nfnet_l1.ra2_in1k,37.533,62.467,70.933,29.067,41.41,320,1.000,bicubic,-59.167,-28.447,-47
fastvit_ma36.apple_dist_in1k,37.493,62.507,71.053,28.947,44.07,256,0.950,bicubic,-59.287,-28.277,-69
efficientvit_b3.r288_in1k,37.427,62.573,69.893,30.107,48.65,288,1.000,bicubic,-59.203,-29.457,-35
twins_svt_large.in1k,37.213,62.787,69.187,30.813,99.27,224,0.900,bicubic,-59.027,-30.013,+34
regnetz_d32.ra3_in1k,37.160,62.840,70.480,29.520,27.58,320,0.950,bicubic,-59.430,-28.900,-29
vit_base_patch32_384.augreg_in21k_ft_in1k,37.107,62.893,69.787,30.213,88.30,384,1.000,bicubic,-59.383,-29.623,-11
regnety_064.ra3_in1k,37.013,62.987,68.107,31.893,30.58,288,1.000,bicubic,-59.347,-31.123,+11
swin_s3_small_224.ms_in1k,36.933,63.067,68.253,31.747,49.74,224,0.900,bicubic,-59.297,-30.827,+34
resnext101_64x4d.c1_in1k,36.840,63.160,66.627,33.373,83.46,288,1.000,bicubic,-59.240,-32.573,+70
regnety_160.deit_in1k,36.827,63.173,69.120,30.880,83.59,288,1.000,bicubic,-59.523,-30.060,+9
efficientnetv2_rw_s.ra2_in1k,36.827,63.173,68.320,31.680,23.94,384,1.000,bicubic,-59.713,-30.780,-24
convnext_small.fb_in1k,36.707,63.293,71.067,28.933,50.22,288,1.000,bicubic,-59.813,-28.273,-19
pit_b_distilled_224.in1k,36.707,63.293,68.093,31.907,74.79,224,0.900,bicubic,-59.523,-31.017,+28
pvt_v2_b4.in1k,36.627,63.373,68.653,31.347,62.56,224,0.900,bicubic,-59.723,-30.677,+7
pvt_v2_b5.in1k,36.293,63.707,68.427,31.573,81.96,224,0.900,bicubic,-60.067,-30.813,+4
cait_xxs36_384.fb_dist_in1k,36.267,63.733,67.733,32.267,17.37,384,1.000,bicubic,-59.563,-31.417,+120
fastvit_sa36.apple_dist_in1k,36.187,63.813,69.267,30.733,31.53,256,0.900,bicubic,-60.173,-29.903,0
regnety_640.seer_ft_in1k,36.120,63.880,68.307,31.693,281.38,384,1.000,bicubic,-60.730,-31.113,-94
nest_base_jx.goog_in1k,36.053,63.947,66.747,33.253,67.72,224,0.875,bicubic,-60.187,-32.473,+17
coatnet_1_rw_224.sw_in1k,35.973,64.027,67.133,32.867,41.72,224,0.950,bicubic,-60.057,-32.027,+74
maxvit_tiny_rw_224.sw_in1k,35.960,64.040,65.573,34.427,29.06,224,0.950,bicubic,-60.280,-33.597,+19
repvgg_d2se.rvgg_in1k,35.827,64.173,66.720,33.280,133.33,320,1.000,bilinear,-60.863,-32.640,-67
cs3se_edgenet_x.c2ns_in1k,35.667,64.333,67.827,32.173,50.72,320,1.000,bicubic,-60.783,-31.573,-22
sequencer2d_l.in1k,35.600,64.400,67.360,32.640,54.30,224,0.875,bicubic,-60.550,-31.800,+38
swin_base_patch4_window7_224.ms_in1k,35.560,64.440,68.160,31.840,87.77,224,0.900,bicubic,-60.560,-30.900,+45
regnety_080.ra3_in1k,35.560,64.440,67.213,32.787,39.18,288,1.000,bicubic,-60.970,-32.107,-35
tf_efficientnet_b3.ns_jft_in1k,35.520,64.480,67.773,32.227,12.23,300,0.904,bicubic,-60.870,-31.617,-15
inception_next_base.sail_in1k,35.187,64.813,66.533,33.467,86.67,224,0.950,bicubic,-61.373,-32.547,-44
tf_efficientnet_b6.aa_in1k,35.147,64.853,67.733,32.267,43.04,528,0.942,bicubic,-61.523,-31.757,-66
resnetrs270.tf_in1k,34.973,65.027,65.453,34.547,129.86,352,1.000,bicubic,-61.717,-33.897,-72
gcvit_tiny.in1k,34.947,65.053,66.893,33.107,28.22,224,0.875,bicubic,-61.233,-32.297,+21
tf_efficientnet_b5.ap_in1k,34.813,65.187,67.467,32.533,30.39,456,0.934,bicubic,-61.867,-31.993,-72
xcit_tiny_12_p8_384.fb_dist_in1k,34.653,65.347,66.293,33.707,6.71,384,1.000,bicubic,-61.427,-32.567,+49
fastvit_ma36.apple_in1k,34.627,65.373,66.987,33.013,44.07,256,0.950,bicubic,-61.843,-32.163,-37
vit_base_patch16_224_miil.in21k_ft_in1k,34.533,65.467,64.973,35.027,86.54,224,0.875,bilinear,-61.917,-34.337,-32
xcit_medium_24_p16_224.fb_dist_in1k,34.333,65.667,67.880,32.120,84.40,224,1.000,bicubic,-62.267,-31.390,-61
resnet152d.ra2_in1k,34.280,65.720,65.947,34.053,60.21,320,1.000,bicubic,-62.110,-33.213,-26
deit3_medium_patch16_224.fb_in1k,34.253,65.747,66.013,33.987,38.85,224,0.900,bicubic,-61.827,-33.227,+43
coat_small.in1k,34.187,65.813,66.133,33.867,21.69,224,0.900,bicubic,-61.723,-33.017,+82
tresnet_m.miil_in1k_448,34.107,65.893,64.533,35.467,31.39,448,0.875,bilinear,-60.883,-33.927,+253
resmlp_big_24_224.fb_distilled_in1k,34.053,65.947,69.573,30.427,129.14,224,0.875,bicubic,-62.397,-29.547,-39
regnetv_064.ra3_in1k,34.000,66.000,67.880,32.120,30.58,288,1.000,bicubic,-62.410,-31.480,-34
tiny_vit_11m_224.dist_in22k_ft_in1k,33.867,66.133,65.827,34.173,11.00,224,0.950,bicubic,-62.423,-33.403,-12
xcit_tiny_24_p16_384.fb_dist_in1k,33.827,66.173,65.400,34.600,12.12,384,1.000,bicubic,-62.113,-33.810,+70
inception_next_small.sail_in1k,33.800,66.200,65.773,34.227,49.37,224,0.875,bicubic,-62.440,-33.417,-10
caformer_s18.sail_in1k,33.707,66.293,65.400,34.600,26.34,224,1.000,bicubic,-62.433,-33.770,+19
pvt_v2_b3.in1k,33.640,66.360,67.680,32.320,45.24,224,0.900,bicubic,-62.350,-31.510,+55
focalnet_small_srf.ms_in1k,33.520,66.480,65.907,34.093,49.89,224,0.900,bicubic,-62.550,-33.383,+38
coatnet_rmlp_1_rw_224.sw_in1k,33.507,66.493,65.573,34.427,41.69,224,0.950,bicubic,-62.453,-33.617,+62
convformer_s18.sail_in22k_ft_in1k,33.453,66.547,68.280,31.720,26.77,224,1.000,bicubic,-63.177,-31.060,-83
resnet152.a1h_in1k,33.427,66.573,63.453,36.547,60.19,288,1.000,bicubic,-62.773,-35.807,-2
twins_pcpvt_large.in1k,33.387,66.613,67.907,32.093,60.99,224,0.900,bicubic,-62.753,-31.453,+12
convformer_s36.sail_in1k,33.320,66.680,63.827,36.173,40.01,224,1.000,bicubic,-63.260,-35.413,-74
focalnet_small_lrf.ms_in1k,33.227,66.773,67.067,32.933,50.34,224,0.900,bicubic,-62.953,-32.163,-1
fastvit_sa36.apple_in1k,33.200,66.800,66.000,34.000,31.53,256,0.900,bicubic,-63.040,-33.130,-18
twins_svt_base.in1k,33.200,66.800,65.720,34.280,56.07,224,0.900,bicubic,-62.960,-33.330,+3
pit_b_224.in1k,33.160,66.840,62.347,37.653,73.76,224,0.900,bicubic,-62.470,-36.893,+110
tiny_vit_21m_224.in1k,33.133,66.867,67.453,32.547,21.20,224,0.950,bicubic,-62.997,-31.767,+9
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,33.040,66.960,65.120,34.880,25.03,224,0.875,bilinear,-62.820,-33.950,+68
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,33.027,66.973,64.240,35.760,236.34,224,0.875,bicubic,-63.093,-35.100,+9
mobilevitv2_200.cvnets_in22k_ft_in1k_384,32.987,67.013,65.493,34.507,18.45,384,1.000,bicubic,-63.073,-33.587,+27
convnextv2_nano.fcmae_ft_in22k_in1k_384,32.947,67.053,67.173,32.827,15.62,384,1.000,bicubic,-63.423,-32.227,-48
regnety_320.seer_ft_in1k,32.933,67.067,66.333,33.667,145.05,384,1.000,bicubic,-63.407,-33.017,-41
swinv2_cr_small_ns_224.sw_in1k,32.893,67.107,65.947,34.053,49.70,224,0.900,bicubic,-63.287,-33.173,-10
xception65.ra3_in1k,32.747,67.253,62.973,37.027,39.92,299,0.940,bicubic,-63.613,-36.257,-49
xcit_large_24_p16_224.fb_in1k,32.733,67.267,62.067,37.933,189.10,224,1.000,bicubic,-62.697,-36.773,+150
ecaresnet101d.miil_in1k,32.707,67.293,65.973,34.027,44.57,288,0.950,bicubic,-63.513,-33.337,-24
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,32.640,67.360,63.987,36.013,194.03,224,0.875,bilinear,-63.150,-35.173,+73
swin_small_patch4_window7_224.ms_in1k,32.573,67.427,65.347,34.653,49.61,224,0.900,bicubic,-63.357,-33.803,+49
mobilevitv2_175.cvnets_in22k_ft_in1k_384,32.480,67.520,64.747,35.253,14.25,384,1.000,bicubic,-63.700,-34.393,-15
efficientvit_b3.r256_in1k,32.440,67.560,65.960,34.040,48.65,256,1.000,bicubic,-63.850,-33.230,-39
tf_efficientnetv2_b3.in21k_ft_in1k,32.333,67.667,66.133,33.867,14.36,300,0.900,bicubic,-63.887,-33.097,-28
nest_small_jx.goog_in1k,32.280,67.720,63.747,36.253,38.35,224,0.875,bicubic,-63.690,-35.283,+34
convnext_tiny_hnf.a2h_in1k,32.227,67.773,62.893,37.107,28.59,288,1.000,bicubic,-63.793,-36.247,+25
resnext101_64x4d.tv_in1k,32.160,67.840,64.227,35.773,83.46,224,0.875,bilinear,-63.870,-34.833,+17
efficientvit_b3.r224_in1k,32.080,67.920,64.867,35.133,48.65,224,0.950,bicubic,-63.950,-34.263,+19
vit_base_patch16_224.orig_in21k_ft_in1k,32.053,67.947,61.613,38.387,86.57,224,0.900,bicubic,-63.287,-37.387,+153
convnextv2_tiny.fcmae_ft_in1k,32.027,67.973,67.067,32.933,28.64,288,1.000,bicubic,-64.163,-32.263,-27
maxvit_nano_rw_256.sw_in1k,31.933,68.067,64.187,35.813,15.45,256,0.950,bicubic,-63.987,-34.823,+41
tf_efficientnet_b5.ra_in1k,31.787,68.213,65.280,34.720,30.39,456,0.934,bicubic,-64.553,-34.030,-57
rexnetr_200.sw_in12k_ft_in1k,31.733,68.267,67.653,32.347,16.52,288,1.000,bicubic,-64.467,-31.567,-33
swinv2_cr_small_224.sw_in1k,31.733,68.267,62.547,37.453,49.70,224,0.900,bicubic,-64.347,-36.593,+1
swinv2_tiny_window16_256.ms_in1k,31.707,68.293,65.667,34.333,28.35,256,0.900,bicubic,-64.223,-33.353,+34
regnetz_c16_evos.ch_in1k,31.493,68.507,66.347,33.653,13.49,320,0.950,bicubic,-64.647,-32.653,-21
fastvit_sa24.apple_dist_in1k,31.427,68.573,64.760,35.240,21.55,256,0.900,bicubic,-64.723,-34.430,-25
resnest101e.in1k,31.413,68.587,64.320,35.680,48.28,256,0.875,bilinear,-64.447,-34.780,+42
maxvit_rmlp_nano_rw_256.sw_in1k,31.400,68.600,63.413,36.587,15.50,256,0.950,bicubic,-64.570,-35.737,+21
regnety_320.tv2_in1k,31.360,68.640,64.840,35.160,145.05,224,0.965,bicubic,-64.720,-34.390,-8
maxxvit_rmlp_nano_rw_256.sw_in1k,31.333,68.667,64.360,35.640,16.78,256,0.950,bicubic,-64.707,-34.900,+2
regnetv_040.ra3_in1k,31.307,68.693,64.680,35.320,20.64,288,1.000,bicubic,-64.883,-34.570,-40
crossvit_base_240.in1k,31.267,68.733,61.307,38.693,105.03,240,0.875,bicubic,-64.253,-37.563,+95
cait_s24_224.fb_dist_in1k,31.187,68.813,64.573,35.427,46.92,224,1.000,bicubic,-65.233,-34.897,-85
convnext_nano.in12k_ft_in1k,31.107,68.893,67.333,32.667,15.59,288,1.000,bicubic,-64.883,-31.987,+8
efficientnet_b4.ra2_in1k,30.880,69.120,64.667,35.333,19.34,384,1.000,bicubic,-65.270,-34.543,-33
repvit_m2_3.dist_450e_in1k,30.787,69.213,63.840,36.160,23.69,224,0.950,bicubic,-65.543,-35.590,-69
regnety_040.ra3_in1k,30.613,69.387,63.827,36.173,20.65,288,1.000,bicubic,-65.407,-35.243,0
maxvit_tiny_tf_224.in1k,30.587,69.413,62.773,37.227,30.92,224,0.950,bicubic,-65.513,-36.507,-22
crossvit_18_240.in1k,30.587,69.413,61.920,38.080,43.27,240,0.875,bicubic,-64.853,-37.200,+113
sequencer2d_m.in1k,30.560,69.440,62.987,37.013,38.31,224,0.875,bicubic,-65.250,-36.223,+40
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,30.560,69.440,62.053,37.947,88.22,224,0.900,bicubic,-65.570,-37.107,-34
xcit_small_24_p16_224.fb_dist_in1k,30.547,69.453,64.733,35.267,47.67,224,1.000,bicubic,-65.673,-34.477,-56
crossvit_18_dagger_240.in1k,30.520,69.480,61.787,38.213,44.27,240,0.875,bicubic,-65.050,-37.273,+69
maxxvitv2_nano_rw_256.sw_in1k,30.440,69.560,63.707,36.293,23.70,256,0.950,bicubic,-65.460,-35.463,+21
mvitv2_tiny.fb_in1k,30.173,69.827,64.320,35.680,24.17,224,0.900,bicubic,-65.687,-34.800,+27
xcit_medium_24_p16_224.fb_in1k,30.173,69.827,59.307,40.693,84.40,224,1.000,bicubic,-65.367,-39.473,+74
rexnet_300.nav_in1k,30.027,69.973,63.920,36.080,34.71,224,0.875,bicubic,-65.813,-35.210,+27
cait_xxs24_384.fb_dist_in1k,30.013,69.987,63.907,36.093,12.03,384,1.000,bicubic,-65.267,-35.003,+133
tf_efficientnet_b5.aa_in1k,29.973,70.027,63.080,36.920,30.39,456,0.934,bicubic,-66.497,-36.200,-110
convnext_tiny.fb_in1k,29.960,70.040,65.120,34.880,28.59,288,1.000,bicubic,-65.830,-34.060,+33
twins_pcpvt_base.in1k,29.947,70.053,64.627,35.373,43.83,224,0.900,bicubic,-65.843,-34.503,+33
cs3sedarknet_x.c2ns_in1k,29.933,70.067,62.000,38.000,35.40,288,1.000,bicubic,-66.087,-37.190,-13
resnet50.fb_swsl_ig1b_ft_in1k,29.813,70.187,63.827,36.173,25.56,224,0.875,bilinear,-65.587,-35.273,+107
mobilevitv2_150.cvnets_in22k_ft_in1k_384,29.813,70.187,62.133,37.867,10.59,384,1.000,bicubic,-65.877,-37.007,+43
vit_relpos_base_patch16_clsgap_224.sw_in1k,29.733,70.267,62.880,37.120,86.43,224,0.900,bicubic,-66.037,-36.240,+32
resnet152.a1_in1k,29.720,70.280,57.280,42.720,60.19,288,1.000,bicubic,-65.780,-41.800,+78
convnextv2_nano.fcmae_ft_in22k_in1k,29.693,70.307,65.920,34.080,15.62,288,1.000,bicubic,-66.367,-33.300,-29
deit_base_distilled_patch16_224.fb_in1k,29.587,70.413,64.387,35.613,87.34,224,0.900,bicubic,-66.503,-34.803,-40
convit_base.fb_in1k,29.520,70.480,61.747,38.253,86.54,224,0.875,bicubic,-66.030,-37.363,+57
vit_relpos_medium_patch16_cls_224.sw_in1k,29.307,70.693,60.587,39.413,38.76,224,0.900,bicubic,-66.163,-38.363,+82
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,29.293,70.707,66.107,33.893,28.29,224,0.900,bicubic,-66.217,-33.013,+67
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,29.120,70.880,61.000,39.000,88.79,224,0.875,bilinear,-66.390,-38.200,+67
davit_tiny.msft_in1k,29.107,70.893,63.573,36.427,28.36,224,0.950,bicubic,-66.553,-35.477,+38
tf_efficientnetv2_s.in1k,29.040,70.960,61.227,38.773,21.46,384,1.000,bicubic,-67.300,-37.973,-99
edgenext_base.usi_in1k,29.027,70.973,64.907,35.093,18.51,320,1.000,bicubic,-67.673,-34.593,-175
xception65p.ra3_in1k,28.987,71.013,59.907,40.093,39.82,299,0.940,bicubic,-67.223,-39.273,-80
convnext_tiny.fb_in22k_ft_in1k,28.987,71.013,55.600,44.400,28.59,288,1.000,bicubic,-65.853,-43.110,+195
resnet101d.ra2_in1k,28.973,71.027,62.080,37.920,44.57,320,1.000,bicubic,-67.317,-37.040,-97
fastvit_sa24.apple_in1k,28.960,71.040,62.413,37.587,21.55,256,0.900,bicubic,-66.960,-36.747,-9
resnetv2_101.a1h_in1k,28.933,71.067,59.867,40.133,44.54,288,1.000,bicubic,-67.117,-39.273,-40
resnetrs152.tf_in1k,28.893,71.107,60.507,39.493,86.62,320,1.000,bicubic,-67.687,-38.613,-152
regnety_160.tv2_in1k,28.867,71.133,61.627,38.373,83.59,224,0.965,bicubic,-67.103,-37.343,-25
vit_relpos_medium_patch16_224.sw_in1k,28.853,71.147,61.973,38.027,38.75,224,0.900,bicubic,-66.607,-37.037,+71
xcit_tiny_24_p8_224.fb_dist_in1k,28.707,71.293,61.373,38.627,12.11,224,1.000,bicubic,-67.103,-37.737,+5
xcit_tiny_24_p8_224.fb_in1k,28.653,71.347,60.440,39.560,12.11,224,1.000,bicubic,-67.007,-38.590,+25
efficientvit_b2.r288_in1k,28.627,71.373,64.227,35.773,24.33,288,1.000,bicubic,-67.333,-34.933,-26
repvit_m2_3.dist_300e_in1k,28.627,71.373,61.907,38.093,23.69,224,0.950,bicubic,-67.493,-37.333,-68
crossvit_15_dagger_240.in1k,28.547,71.453,60.293,39.707,28.21,240,0.875,bicubic,-67.133,-38.537,+20
cs3edgenet_x.c2_in1k,28.507,71.493,61.133,38.867,47.82,288,1.000,bicubic,-67.543,-38.037,-48
pvt_v2_b2_li.in1k,28.480,71.520,62.000,38.000,22.55,224,0.900,bicubic,-67.080,-37.030,+34
xcit_small_24_p16_224.fb_in1k,28.320,71.680,58.707,41.293,47.67,224,1.000,bicubic,-67.220,-40.013,+38
efficientformerv2_l.snap_dist_in1k,28.253,71.747,61.920,38.080,26.32,224,0.950,bicubic,-67.817,-37.200,-56
resnet101.a1h_in1k,28.160,71.840,59.387,40.613,44.55,288,1.000,bicubic,-67.860,-39.723,-45
efficientformer_l7.snap_dist_in1k,28.000,72.000,63.013,36.987,82.23,224,0.950,bicubic,-68.110,-36.267,-70
flexivit_small.1200ep_in1k,27.853,72.147,58.653,41.347,22.06,240,0.950,bicubic,-67.697,-40.227,+30
resnetaa50d.sw_in12k_ft_in1k,27.813,72.187,62.253,37.747,25.58,288,1.000,bicubic,-68.017,-36.837,-11
pvt_v2_b2.in1k,27.600,72.400,60.733,39.267,25.36,224,0.900,bicubic,-67.880,-38.267,+52
regnetz_c16.ra3_in1k,27.587,72.413,62.720,37.280,13.46,320,1.000,bicubic,-68.353,-36.500,-31
resnext101_32x8d.tv2_in1k,27.573,72.427,59.827,40.173,88.79,224,0.965,bilinear,-68.377,-39.073,-36
vit_base_patch16_384.augreg_in1k,27.547,72.453,57.253,42.747,86.86,384,1.000,bicubic,-67.393,-41.637,+150
coat_lite_small.in1k,27.507,72.493,58.533,41.467,19.84,224,0.900,bicubic,-68.033,-40.547,+27
deit_base_patch16_224.fb_in1k,27.413,72.587,58.880,41.120,86.57,224,0.900,bicubic,-68.037,-40.210,+55
vit_relpos_base_patch16_224.sw_in1k,27.320,72.680,61.147,38.853,86.43,224,0.900,bicubic,-68.240,-37.843,+20
resnetv2_50x1_bit.goog_in21k_ft_in1k,27.307,72.693,62.867,37.133,25.55,448,1.000,bilinear,-67.703,-36.113,+136
regnety_080_tv.tv2_in1k,27.280,72.720,61.520,38.480,39.38,224,0.965,bicubic,-68.580,-37.730,-24
coatnet_bn_0_rw_224.sw_in1k,27.213,72.787,61.253,38.747,27.44,224,0.950,bicubic,-68.487,-37.797,-1
xcit_small_12_p16_224.fb_dist_in1k,27.147,72.853,59.800,40.200,26.25,224,1.000,bicubic,-68.883,-39.190,-63
dm_nfnet_f0.dm_in1k,27.067,72.933,58.320,41.680,71.49,256,0.900,bicubic,-69.243,-41.000,-129
vit_small_patch16_224.augreg_in21k_ft_in1k,27.040,72.960,59.253,40.747,22.05,224,0.900,bicubic,-68.330,-39.687,+67
coatnet_0_rw_224.sw_in1k,27.027,72.973,59.387,40.613,27.44,224,0.950,bicubic,-68.413,-39.663,+52
sequencer2d_s.in1k,26.867,73.133,60.640,39.360,27.65,224,0.875,bicubic,-69.113,-38.490,-55
flexivit_small.600ep_in1k,26.853,73.147,57.253,42.747,22.06,240,0.950,bicubic,-68.817,-41.807,-3
tresnet_v2_l.miil_in21k_ft_in1k,26.707,73.293,59.827,40.173,46.17,224,0.875,bilinear,-69.453,-39.443,-105
gcvit_xtiny.in1k,26.693,73.307,60.907,39.093,19.98,224,0.875,bicubic,-68.897,-38.133,+7
mobilevitv2_200.cvnets_in22k_ft_in1k,26.653,73.347,59.400,40.600,18.45,256,0.888,bicubic,-68.507,-39.700,+94
swin_s3_tiny_224.ms_in1k,26.520,73.480,60.347,39.653,28.33,224,0.900,bicubic,-68.660,-38.603,+90
coatnet_rmlp_nano_rw_224.sw_in1k,26.467,73.533,60.547,39.453,15.15,224,0.900,bicubic,-68.963,-38.493,+48
swinv2_tiny_window8_256.ms_in1k,26.387,73.613,60.480,39.520,28.35,256,0.900,bicubic,-69.103,-38.480,+27
tf_efficientnet_b4.aa_in1k,26.293,73.707,60.080,39.920,19.34,380,0.922,bicubic,-69.607,-38.970,-46
regnetx_320.tv2_in1k,26.267,73.733,58.133,41.867,107.81,224,0.965,bicubic,-69.723,-40.967,-66
tf_efficientnet_b4.ap_in1k,26.227,73.773,60.240,39.760,19.34,380,0.922,bicubic,-69.933,-38.900,-116
coatnext_nano_rw_224.sw_in1k,26.227,73.773,59.613,40.387,14.70,224,0.900,bicubic,-69.203,-39.397,+43
deit3_small_patch16_224.fb_in1k,26.227,73.773,54.480,45.520,22.06,224,0.900,bicubic,-68.763,-44.500,+123
nfnet_l0.ra2_in1k,26.213,73.787,61.747,38.253,35.07,288,1.000,bicubic,-69.907,-37.523,-103
regnety_032.ra_in1k,26.200,73.800,61.013,38.987,19.44,288,1.000,bicubic,-69.760,-38.177,-64
ecaresnet50t.ra2_in1k,26.120,73.880,60.013,39.987,25.57,320,0.950,bicubic,-69.400,-38.797,+7
fbnetv3_g.ra2_in1k,26.107,73.893,61.080,38.920,16.62,288,0.950,bilinear,-69.413,-38.030,+7
mobilevitv2_175.cvnets_in22k_ft_in1k,26.013,73.987,58.520,41.480,14.25,256,0.888,bicubic,-69.207,-40.280,+71
flexivit_small.300ep_in1k,25.907,74.093,57.080,42.920,22.06,240,0.950,bicubic,-69.583,-42.020,+18
visformer_small.in1k,25.853,74.147,58.933,41.067,40.22,224,0.900,bicubic,-69.627,-39.967,+21
inception_next_tiny.sail_in1k,25.800,74.200,59.813,40.187,28.06,224,0.875,bicubic,-69.660,-39.097,+22
vit_small_patch16_384.augreg_in1k,25.773,74.227,57.587,42.413,22.20,384,1.000,bicubic,-69.517,-41.413,+57
convformer_s18.sail_in1k,25.680,74.320,57.960,42.040,26.77,224,1.000,bicubic,-70.270,-41.120,-69
halo2botnet50ts_256.a1h_in1k,25.573,74.427,56.827,43.173,22.64,256,0.950,bicubic,-69.847,-42.193,+34
vit_relpos_medium_patch16_rpn_224.sw_in1k,25.520,74.480,58.667,41.333,38.73,224,0.900,bicubic,-69.980,-40.113,+7
coat_mini.in1k,25.507,74.493,57.707,42.293,10.34,224,0.900,bicubic,-69.463,-41.373,+112
crossvit_15_240.in1k,25.453,74.547,57.587,42.413,27.53,240,0.875,bicubic,-69.697,-41.223,+76
vit_srelpos_medium_patch16_224.sw_in1k,25.373,74.627,58.427,41.573,38.74,224,0.900,bicubic,-69.867,-40.443,+56
resnet101.a1_in1k,25.253,74.747,55.120,44.880,44.55,288,1.000,bicubic,-70.267,-43.730,-3
resnetv2_50x1_bit.goog_distilled_in1k,25.200,74.800,59.667,40.333,25.55,224,0.875,bicubic,-70.910,-39.603,-117
convit_small.fb_in1k,25.147,74.853,57.293,42.707,27.78,224,0.875,bicubic,-70.063,-41.607,+59
xcit_small_12_p16_224.fb_in1k,25.107,74.893,56.067,43.933,26.25,224,1.000,bicubic,-70.323,-42.563,+24
vit_base_patch16_rpn_224.sw_in1k,25.080,74.920,58.680,41.320,86.54,224,0.900,bicubic,-70.310,-40.180,+31
gc_efficientnetv2_rw_t.agc_in1k,25.067,74.933,57.707,42.293,13.68,288,1.000,bicubic,-70.673,-41.473,-43
resnet152.a2_in1k,25.053,74.947,54.320,45.680,60.19,288,1.000,bicubic,-70.437,-44.670,+3
eca_nfnet_l0.ra2_in1k,24.800,75.200,60.040,39.960,24.14,288,1.000,bicubic,-71.140,-39.070,-80
xception41p.ra3_in1k,24.787,75.213,55.253,44.747,26.91,299,0.940,bicubic,-70.723,-43.657,-7
efficientvit_b2.r256_in1k,24.773,75.227,59.720,40.280,24.33,256,1.000,bicubic,-70.877,-39.340,-36
tnt_s_patch16_224,24.707,75.293,58.173,41.827,23.76,224,0.900,bicubic,-70.333,-40.657,+85
convnext_nano_ols.d1h_in1k,24.547,75.453,57.027,42.973,15.65,288,1.000,bicubic,-70.593,-41.823,+70
xcit_tiny_12_p16_384.fb_dist_in1k,24.453,75.547,57.080,42.920,6.72,384,1.000,bicubic,-70.677,-41.940,+70
cs3darknet_x.c2ns_in1k,24.387,75.613,57.760,42.240,35.05,288,1.000,bicubic,-71.463,-41.410,-69
efficientnetv2_rw_t.ra2_in1k,24.280,75.720,57.400,42.600,13.65,288,1.000,bicubic,-71.310,-41.670,-33
swinv2_cr_tiny_ns_224.sw_in1k,24.147,75.853,58.187,41.813,28.33,224,0.900,bicubic,-71.223,-40.953,+23
eva02_tiny_patch14_336.mim_in22k_ft_in1k,24.133,75.867,55.400,44.600,5.76,336,1.000,bicubic,-70.777,-43.480,+100
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,24.120,75.880,57.373,42.627,44.18,224,0.875,bilinear,-71.320,-41.347,+3
twins_svt_small.in1k,24.107,75.893,57.160,42.840,24.06,224,0.900,bicubic,-71.083,-41.720,+48
coatnet_nano_rw_224.sw_in1k,24.093,75.907,57.147,42.853,15.14,224,0.900,bicubic,-71.147,-41.843,+39
vit_small_r26_s32_224.augreg_in21k_ft_in1k,24.093,75.907,56.213,43.787,36.43,224,0.900,bicubic,-71.537,-42.727,-44
tf_efficientnet_b5.in1k,24.080,75.920,58.347,41.653,30.39,456,0.934,bicubic,-71.990,-40.843,-125
tf_efficientnet_b2.ns_jft_in1k,24.040,75.960,57.320,42.680,9.11,260,0.890,bicubic,-71.730,-41.660,-65
mobilevitv2_150.cvnets_in22k_ft_in1k,24.013,75.987,55.920,44.080,10.59,256,0.888,bicubic,-71.127,-42.860,+55
vit_relpos_small_patch16_224.sw_in1k,24.000,76.000,58.160,41.840,21.98,224,0.900,bicubic,-71.150,-40.800,+48
convnext_nano.d1h_in1k,24.000,76.000,56.200,43.800,15.59,288,1.000,bicubic,-71.350,-42.720,+17
cs3sedarknet_l.c2ns_in1k,23.960,76.040,58.760,41.240,21.91,288,0.950,bicubic,-71.350,-40.370,+19
ecaresnet50d.miil_in1k,23.933,76.067,58.760,41.240,25.58,288,0.950,bicubic,-71.517,-40.080,-10
poolformer_m48.sail_in1k,23.867,76.133,57.160,42.840,73.47,224,0.950,bicubic,-71.763,-42.030,-50
vit_small_patch32_384.augreg_in21k_ft_in1k,23.760,76.240,57.280,42.720,22.92,384,1.000,bicubic,-71.260,-41.600,+68
tiny_vit_11m_224.in1k,23.627,76.373,58.973,41.027,11.00,224,0.950,bicubic,-71.863,-39.817,-23
lamhalobotnet50ts_256.a1h_in1k,23.613,76.387,55.320,44.680,22.57,256,0.950,bicubic,-71.537,-43.560,+44
nasnetalarge.tf_in1k,23.493,76.507,55.000,45.000,88.75,331,0.911,bicubic,-72.197,-43.930,-64
crossvit_small_240.in1k,23.427,76.573,56.840,43.160,26.86,240,0.875,bicubic,-71.423,-42.180,+95
levit_384.fb_dist_in1k,23.400,76.600,56.400,43.600,39.13,224,0.900,bicubic,-72.130,-42.660,-42
levit_conv_384.fb_dist_in1k,23.387,76.613,56.413,43.587,39.13,224,0.900,bicubic,-72.143,-42.637,-42
seresnext50_32x4d.racm_in1k,23.360,76.640,57.453,42.547,27.56,288,0.950,bicubic,-72.380,-41.567,-75
pnasnet5large.tf_in1k,23.333,76.667,53.627,46.373,86.06,331,0.911,bicubic,-72.377,-45.403,-72
focalnet_tiny_srf.ms_in1k,23.267,76.733,58.333,41.667,28.43,224,0.900,bicubic,-72.233,-40.797,-36
efficientnet_b3.ra2_in1k,23.200,76.800,55.947,44.053,12.23,320,1.000,bicubic,-72.520,-43.093,-76
wide_resnet50_2.racm_in1k,23.187,76.813,56.000,44.000,68.88,288,0.950,bicubic,-72.443,-42.660,-65
pit_s_distilled_224.in1k,23.160,76.840,57.093,42.907,24.04,224,0.900,bicubic,-71.980,-41.637,+36
hrnet_w18_ssld.paddle_in1k,23.147,76.853,55.160,44.840,21.30,288,1.000,bilinear,-72.843,-44.150,-130
efficientformer_l3.snap_dist_in1k,23.120,76.880,57.147,42.853,31.41,224,0.950,bicubic,-72.470,-42.013,-63
nest_tiny_jx.goog_in1k,23.107,76.893,56.200,43.800,17.06,224,0.875,bicubic,-72.133,-42.710,+12
focalnet_tiny_lrf.ms_in1k,23.080,76.920,58.560,41.440,28.65,224,0.900,bicubic,-72.380,-40.400,-29
tiny_vit_5m_224.dist_in22k_ft_in1k,23.067,76.933,56.480,43.520,5.39,224,0.950,bicubic,-71.983,-42.490,+47
resnet61q.ra2_in1k,23.013,76.987,55.760,44.240,36.85,288,1.000,bicubic,-72.767,-43.230,-91
regnetx_160.tv2_in1k,22.987,77.013,56.333,43.667,54.28,224,0.965,bicubic,-72.893,-42.757,-111
resmlp_big_24_224.fb_in1k,22.893,77.107,54.280,45.720,129.14,224,0.875,bicubic,-71.787,-44.220,+106
vit_srelpos_small_patch16_224.sw_in1k,22.880,77.120,55.747,44.253,21.97,224,0.900,bicubic,-72.150,-43.203,+46
resnetv2_50d_evos.ah_in1k,22.867,77.133,55.173,44.827,25.59,288,1.000,bicubic,-72.763,-43.937,-75
halonet50ts.a1h_in1k,22.867,77.133,54.013,45.987,22.73,256,0.940,bicubic,-72.273,-44.877,+29
vit_base_patch32_clip_224.laion2b_ft_in1k,22.853,77.147,55.013,44.987,88.22,224,0.900,bicubic,-72.667,-43.977,-57
tf_efficientnet_b4.in1k,22.773,77.227,57.093,42.907,19.34,380,0.922,bicubic,-73.047,-41.957,-105
poolformerv2_m48.sail_in1k,22.760,77.240,55.747,44.253,73.35,224,1.000,bicubic,-73.010,-43.293,-96
twins_pcpvt_small.in1k,22.693,77.307,56.813,43.187,24.11,224,0.900,bicubic,-72.517,-42.067,+6
repvit_m1_5.dist_450e_in1k,22.680,77.320,56.107,43.893,14.64,224,0.950,bicubic,-73.220,-43.013,-122
convnextv2_nano.fcmae_ft_in1k,22.653,77.347,58.307,41.693,15.62,288,1.000,bicubic,-73.147,-40.783,-106
poolformer_m36.sail_in1k,22.587,77.413,55.373,44.627,56.17,224,0.950,bicubic,-72.803,-43.567,-23
vit_base_patch32_clip_224.openai_ft_in1k,22.560,77.440,55.333,44.667,88.22,224,0.900,bicubic,-72.550,-43.597,+25
vit_base_patch32_224.augreg_in21k_ft_in1k,22.427,77.573,54.027,45.973,88.22,224,0.900,bicubic,-72.583,-45.033,+41
resnetv2_50d_gn.ah_in1k,22.307,77.693,55.000,45.000,25.57,288,1.000,bicubic,-73.173,-43.950,-51
ecaresnet101d_pruned.miil_in1k,22.267,77.733,56.227,43.773,24.88,288,0.950,bicubic,-73.493,-42.953,-103
wide_resnet101_2.tv2_in1k,22.160,77.840,54.973,45.027,126.89,224,0.965,bilinear,-73.380,-43.887,-76
ecaresnet50t.a1_in1k,22.093,77.907,53.680,46.320,25.57,288,1.000,bicubic,-73.317,-45.330,-35
xcit_tiny_12_p8_224.fb_dist_in1k,22.067,77.933,54.267,45.733,6.71,224,1.000,bicubic,-73.023,-44.643,+23
resnext50_32x4d.a1h_in1k,22.013,77.987,54.080,45.920,25.03,288,1.000,bicubic,-73.437,-44.760,-48
efficientvit_b2.r224_in1k,22.000,78.000,55.533,44.467,24.33,224,0.950,bicubic,-73.200,-43.387,-3
res2net101d.in1k,21.853,78.147,51.680,48.320,45.23,224,0.875,bilinear,-72.967,-47.090,+67
tresnet_m.miil_in21k_ft_in1k,21.680,78.320,53.853,46.147,31.39,224,0.875,bilinear,-74.030,-45.067,-106
repvit_m1_5.dist_300e_in1k,21.613,78.387,54.707,45.293,14.64,224,0.950,bicubic,-74.027,-44.283,-97
efficientformerv2_s2.snap_dist_in1k,21.547,78.453,54.240,45.760,12.71,224,0.950,bicubic,-73.813,-44.690,-33
fastvit_sa12.apple_dist_in1k,21.493,78.507,54.613,45.387,11.58,256,0.900,bicubic,-73.657,-44.347,+3
resnet50_gn.a1h_in1k,21.387,78.613,54.067,45.933,25.56,288,0.950,bicubic,-73.863,-44.933,-20
maxvit_rmlp_pico_rw_256.sw_in1k,21.240,78.760,51.920,48.080,7.52,256,0.950,bicubic,-73.400,-46.890,+88
convmixer_1536_20.in1k,21.213,78.787,55.507,44.493,51.63,224,0.960,bicubic,-73.867,-43.523,+14
swin_tiny_patch4_window7_224.ms_in1k,21.173,78.827,55.933,44.067,28.29,224,0.900,bicubic,-73.967,-42.927,+1
resnet101.a2_in1k,21.173,78.827,51.920,48.080,44.55,288,1.000,bicubic,-74.237,-47.020,-46
pit_s_224.in1k,21.080,78.920,53.613,46.387,23.46,224,0.900,bicubic,-73.490,-45.087,+100
xcit_tiny_12_p8_224.fb_in1k,20.960,79.040,52.467,47.533,6.71,224,1.000,bicubic,-73.730,-46.193,+73
resnet51q.ra2_in1k,20.920,79.080,55.680,44.320,35.70,288,1.000,bilinear,-74.950,-43.450,-144
poolformerv2_m36.sail_in1k,20.920,79.080,53.120,46.880,56.08,224,1.000,bicubic,-74.480,-46.180,-47
resnetrs101.tf_in1k,20.867,79.133,52.827,47.173,63.62,288,0.940,bicubic,-74.563,-46.163,-59
deit_small_distilled_patch16_224.fb_in1k,20.720,79.280,55.133,44.867,22.44,224,0.900,bicubic,-73.990,-43.897,+67
sebotnet33ts_256.a1h_in1k,20.720,79.280,48.760,51.240,13.70,256,0.940,bicubic,-73.890,-49.750,+89
regnety_032.tv2_in1k,20.547,79.453,54.347,45.653,19.44,224,0.965,bicubic,-74.763,-44.563,-39
resnet152.tv2_in1k,20.493,79.507,52.347,47.653,60.19,224,0.965,bilinear,-75.007,-46.613,-83
ecaresnetlight.miil_in1k,20.480,79.520,53.413,46.587,30.16,288,0.950,bicubic,-74.810,-45.617,-39
resnest50d_4s2x40d.in1k,20.387,79.613,52.773,47.227,30.42,224,0.875,bicubic,-74.583,-46.007,+19
resnetaa50.a1h_in1k,20.067,79.933,51.947,48.053,25.56,288,1.000,bicubic,-75.133,-47.143,-26
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,20.040,79.960,53.560,46.440,25.03,224,0.875,bilinear,-74.850,-45.300,+27
haloregnetz_b.ra3_in1k,20.013,79.987,49.987,50.013,11.68,224,0.940,bicubic,-74.677,-48.843,+62
regnetz_b16.ra3_in1k,19.813,80.187,52.853,47.147,9.72,288,1.000,bicubic,-75.357,-46.227,-25
xcit_nano_12_p8_384.fb_dist_in1k,19.787,80.213,50.587,49.413,3.05,384,1.000,bicubic,-73.733,-47.943,+221
resnext50_32x4d.a1_in1k,19.733,80.267,50.173,49.827,25.03,288,1.000,bicubic,-75.097,-48.417,+40
tresnet_xl.miil_in1k,19.640,80.360,53.160,46.840,78.44,224,0.875,bilinear,-75.800,-45.630,-75
senet154.gluon_in1k,19.320,80.680,47.600,52.400,115.09,224,0.875,bicubic,-75.600,-51.160,+18
rexnet_200.nav_in1k,19.227,80.773,52.640,47.360,16.37,224,0.875,bicubic,-75.713,-46.370,+12
levit_conv_256.fb_dist_in1k,19.187,80.813,50.067,49.933,18.89,224,0.900,bicubic,-75.833,-48.923,+1
levit_256.fb_dist_in1k,19.173,80.827,50.107,49.893,18.89,224,0.900,bicubic,-75.847,-48.823,-1
lambda_resnet50ts.a1h_in1k,19.147,80.853,49.240,50.760,21.54,256,0.950,bicubic,-75.623,-49.450,+37
repvgg_b3.rvgg_in1k,19.067,80.933,50.293,49.707,123.09,224,0.875,bilinear,-75.483,-48.417,+79
mixer_b16_224.miil_in21k_ft_in1k,19.040,80.960,51.213,48.787,59.88,224,0.875,bilinear,-76.260,-47.667,-56
legacy_senet154.in1k,19.040,80.960,47.947,52.053,115.09,224,0.875,bilinear,-76.030,-50.883,-11
resnet50d.a1_in1k,18.960,81.040,48.813,51.187,25.58,288,1.000,bicubic,-75.770,-49.937,+44
seresnext101_64x4d.gluon_in1k,18.933,81.067,49.213,50.787,88.23,224,0.875,bicubic,-75.997,-49.617,+6
deit_small_patch16_224.fb_in1k,18.893,81.107,51.400,48.600,22.05,224,0.900,bicubic,-75.457,-47.290,+105
mobilevitv2_200.cvnets_in1k,18.893,81.107,50.520,49.480,18.45,256,0.888,bicubic,-75.947,-48.010,+23
gcvit_xxtiny.in1k,18.773,81.227,53.347,46.653,12.00,224,0.875,bicubic,-75.647,-45.543,+95
edgenext_small.usi_in1k,18.680,81.320,53.600,46.400,5.59,320,1.000,bicubic,-76.720,-45.270,-77
tf_efficientnet_b1.ns_jft_in1k,18.653,81.347,51.733,48.267,7.79,240,0.882,bicubic,-76.507,-47.217,-42
regnetx_080.tv2_in1k,18.573,81.427,50.333,49.667,39.57,224,0.965,bicubic,-76.527,-48.497,-24
ecaresnet50t.a2_in1k,18.533,81.467,48.773,51.227,25.57,288,1.000,bicubic,-76.817,-50.087,-73
poolformer_s36.sail_in1k,18.493,81.507,52.187,47.813,30.86,224,0.900,bicubic,-76.587,-46.723,-23
repvit_m3.dist_in1k,18.480,81.520,51.867,48.133,10.68,224,0.950,bicubic,-76.720,-46.953,-52
wide_resnet50_2.tv2_in1k,18.467,81.533,52.467,47.533,68.88,224,0.965,bilinear,-76.403,-46.443,+8
cait_xxs36_224.fb_dist_in1k,18.307,81.693,49.480,50.520,17.30,224,1.000,bicubic,-75.963,-49.230,+106
cs3darknet_l.c2ns_in1k,18.293,81.707,51.840,48.160,21.16,288,0.950,bicubic,-76.827,-47.140,-35
seresnet50.ra2_in1k,18.293,81.707,51.253,48.747,28.09,288,0.950,bicubic,-77.037,-47.757,-76
sehalonet33ts.ra2_in1k,18.227,81.773,47.733,52.267,13.69,256,0.940,bicubic,-76.533,-50.837,+21
ese_vovnet39b.ra_in1k,18.200,81.800,49.880,50.120,24.57,288,0.950,bicubic,-76.670,-49.060,+4
tf_efficientnet_lite4.in1k,18.120,81.880,50.707,49.293,13.01,380,0.920,bilinear,-76.760,-48.183,0
vit_tiny_patch16_384.augreg_in21k_ft_in1k,18.027,81.973,50.307,49.693,5.79,384,1.000,bicubic,-75.623,-48.283,+177
tiny_vit_5m_224.in1k,18.000,82.000,50.173,49.827,5.39,224,0.950,bicubic,-76.240,-48.287,+100
gcresnet50t.ra2_in1k,17.880,82.120,49.413,50.587,25.90,288,1.000,bicubic,-77.360,-49.567,-68
resnet50d.ra2_in1k,17.853,82.147,50.240,49.760,25.58,288,0.950,bicubic,-77.157,-48.670,-23
mobilevitv2_175.cvnets_in1k,17.787,82.213,49.747,50.253,14.25,256,0.888,bicubic,-77.103,-49.113,-8
resnest50d_1s4x24d.in1k,17.640,82.360,49.760,50.240,25.68,224,0.875,bicubic,-77.090,-49.220,+16
resnetv2_50.a1h_in1k,17.587,82.413,49.813,50.187,25.55,288,1.000,bicubic,-77.263,-49.057,+1
resnet50.c2_in1k,17.533,82.467,49.760,50.240,25.56,288,1.000,bicubic,-77.387,-49.050,-16
resnest50d.in1k,17.360,82.640,50.733,49.267,27.48,224,0.875,bilinear,-77.490,-48.147,-2
convnext_pico.d1_in1k,17.347,82.653,50.213,49.787,9.05,288,0.950,bicubic,-77.413,-48.717,+8
seresnext101_32x4d.gluon_in1k,17.333,82.667,46.373,53.627,48.96,224,0.875,bicubic,-77.557,-52.447,-12
efficientnet_el.ra_in1k,17.320,82.680,50.027,49.973,10.59,300,0.904,bicubic,-77.800,-48.943,-50
inception_v4.tf_in1k,17.293,82.707,45.867,54.133,42.68,299,0.875,bicubic,-77.077,-52.763,+75
tf_efficientnet_b3.ap_in1k,17.227,82.773,49.680,50.320,12.23,300,0.904,bicubic,-78.093,-49.220,-92
gcresnext50ts.ch_in1k,17.173,82.827,48.107,51.893,15.67,288,1.000,bicubic,-77.687,-50.753,-11
xcit_tiny_24_p16_224.fb_dist_in1k,17.160,82.840,47.507,52.493,12.12,224,1.000,bicubic,-77.380,-51.293,+49
fastvit_s12.apple_dist_in1k,17.120,82.880,49.400,50.600,9.47,256,0.900,bicubic,-77.710,-49.370,-6
regnetx_032.tv2_in1k,17.067,82.933,48.107,51.893,15.30,224,0.965,bicubic,-77.593,-50.693,+18
xception71.tf_in1k,17.027,82.973,45.547,54.453,42.34,299,0.903,bicubic,-77.273,-53.103,+80
regnety_016.tv2_in1k,16.973,83.027,49.853,50.147,11.20,224,0.965,bicubic,-77.547,-48.967,+48
tf_efficientnet_b3.aa_in1k,16.973,83.027,49.280,50.720,12.23,300,0.904,bicubic,-78.037,-49.750,-39
cs3darknet_focus_l.c2ns_in1k,16.960,83.040,50.493,49.507,21.15,288,0.950,bicubic,-78.190,-48.437,-72
convnextv2_pico.fcmae_ft_in1k,16.907,83.093,50.307,49.693,9.07,288,0.950,bicubic,-78.323,-48.613,-86
resmlp_36_224.fb_distilled_in1k,16.880,83.120,51.493,48.507,44.69,224,0.875,bicubic,-78.010,-47.377,-26
resnext101_64x4d.gluon_in1k,16.867,83.133,44.147,55.853,83.46,224,0.875,bicubic,-77.803,-54.513,+10
poolformerv2_s36.sail_in1k,16.680,83.320,49.600,50.400,30.79,224,1.000,bicubic,-78.630,-49.320,-102
resnet152d.gluon_in1k,16.627,83.373,44.213,55.787,60.21,224,0.875,bicubic,-78.103,-54.277,-2
tf_efficientnetv2_b3.in1k,16.600,83.400,48.680,51.320,14.36,300,0.904,bicubic,-78.560,-50.140,-79
gmlp_s16_224.ra3_in1k,16.547,83.453,45.080,54.920,19.42,224,0.875,bicubic,-77.603,-53.590,+90
resnet152s.gluon_in1k,16.547,83.453,44.507,55.493,60.32,224,0.875,bicubic,-78.473,-54.373,-54
tresnet_l.miil_in1k,16.533,83.467,49.893,50.107,55.99,224,0.875,bilinear,-78.747,-49.067,-102
inception_resnet_v2.tf_in1k,16.520,83.480,44.973,55.027,55.84,299,0.897,bicubic,-78.060,-53.817,+22
convnext_pico_ols.d1_in1k,16.507,83.493,49.707,50.293,9.06,288,1.000,bicubic,-78.113,-49.153,+16
seresnet50.a1_in1k,16.493,83.507,46.760,53.240,28.09,288,1.000,bicubic,-78.167,-52.020,+5
resmlp_24_224.fb_distilled_in1k,16.453,83.547,50.347,49.653,30.02,224,0.875,bicubic,-77.997,-48.423,+44
mobilevitv2_150.cvnets_in1k,16.453,83.547,48.453,51.547,10.59,256,0.888,bicubic,-78.097,-50.327,+25
resnet101.tv2_in1k,16.400,83.600,48.707,51.293,44.55,224,0.965,bilinear,-78.880,-50.303,-106
gernet_l.idstcv_in1k,16.320,83.680,47.227,52.773,31.08,256,0.875,bilinear,-78.790,-51.753,-73
repvit_m1_1.dist_450e_in1k,16.280,83.720,49.680,50.320,8.80,224,0.950,bicubic,-78.620,-49.280,-46
fastvit_sa12.apple_in1k,16.280,83.720,49.653,50.347,11.58,256,0.900,bicubic,-78.600,-49.367,-38
inception_resnet_v2.tf_ens_adv_in1k,16.280,83.720,43.640,56.360,55.84,299,0.897,bicubic,-77.890,-54.900,+73
repvgg_b3g4.rvgg_in1k,16.227,83.773,47.653,52.347,83.83,224,0.875,bilinear,-78.283,-51.317,+28
xcit_tiny_24_p16_224.fb_in1k,16.227,83.773,46.000,54.000,12.12,224,1.000,bicubic,-77.853,-52.540,+86
resnet50.a1_in1k,16.040,83.960,45.773,54.227,25.56,288,1.000,bicubic,-78.700,-52.907,-24
xception65.tf_in1k,16.040,83.960,43.760,56.240,39.92,299,0.903,bicubic,-77.740,-54.610,+119
resnet50.c1_in1k,16.000,84.000,47.387,52.613,25.56,288,1.000,bicubic,-78.730,-51.433,-23
ecaresnet50d_pruned.miil_in1k,15.987,84.013,49.707,50.293,19.94,288,0.950,bicubic,-79.123,-49.193,-83
edgenext_small_rw.sw_in1k,15.987,84.013,49.667,50.333,7.83,320,1.000,bicubic,-78.683,-49.113,-14
resnet50.fb_ssl_yfcc100m_ft_in1k,15.920,84.080,49.413,50.587,25.56,224,0.875,bilinear,-78.530,-49.327,+30
resnext50_32x4d.ra_in1k,15.867,84.133,47.227,52.773,25.03,288,0.950,bicubic,-78.833,-51.533,-20
fastvit_t12.apple_dist_in1k,15.640,84.360,47.680,52.320,7.55,256,0.900,bicubic,-78.950,-51.110,+2
resnet50.d_in1k,15.640,84.360,45.160,54.840,25.56,288,1.000,bicubic,-79.340,-53.680,-67
regnety_320.pycls_in1k,15.627,84.373,44.760,55.240,145.05,224,0.875,bicubic,-78.913,-54.030,+9
vit_base_patch32_384.augreg_in1k,15.600,84.400,44.147,55.853,88.30,384,1.000,bicubic,-78.040,-54.093,+127
convmixer_768_32.in1k,15.507,84.493,47.907,52.093,21.11,224,0.960,bicubic,-78.993,-50.953,+17
ecaresnet26t.ra2_in1k,15.453,84.547,47.960,52.040,16.01,320,0.950,bicubic,-78.867,-50.760,+38
resnext50d_32x4d.bt_in1k,15.400,84.600,46.187,53.813,25.05,288,0.950,bicubic,-79.150,-52.503,+3
coat_tiny.in1k,15.400,84.600,45.587,54.413,5.50,224,0.900,bicubic,-78.180,-52.833,+129
resnet50d.a2_in1k,15.387,84.613,44.813,55.187,25.58,288,1.000,bicubic,-79.193,-53.877,-4
skresnext50_32x4d.ra_in1k,15.373,84.627,44.547,55.453,27.48,224,0.875,bicubic,-78.867,-54.143,+43
resnext50_32x4d.a2_in1k,15.293,84.707,45.253,54.747,25.03,288,1.000,bicubic,-79.287,-53.397,-5
efficientvit_b1.r288_in1k,15.280,84.720,46.600,53.400,9.10,288,1.000,bicubic,-79.210,-51.920,+11
seresnet33ts.ra2_in1k,15.267,84.733,47.373,52.627,19.78,288,1.000,bicubic,-79.773,-51.527,-91
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,15.240,84.760,42.640,57.360,119.42,256,0.900,bicubic,-78.500,-55.900,+107
cait_xxs24_224.fb_dist_in1k,15.160,84.840,44.893,55.107,11.96,224,1.000,bicubic,-78.450,-53.567,+119
eca_resnet33ts.ra2_in1k,15.053,84.947,49.013,50.987,19.68,288,1.000,bicubic,-79.567,-49.747,-21
vit_base_patch16_224.augreg_in1k,15.013,84.987,42.027,57.973,86.57,224,0.900,bicubic,-78.627,-56.373,+115
fastvit_s12.apple_in1k,14.920,85.080,45.320,54.680,9.47,256,0.900,bicubic,-79.210,-53.430,+56
repvit_m1_0.dist_450e_in1k,14.907,85.093,46.827,53.173,7.30,224,0.950,bicubic,-79.623,-52.053,-3
levit_conv_192.fb_dist_in1k,14.907,85.093,44.933,55.067,10.95,224,0.900,bicubic,-79.263,-53.747,+46
levit_192.fb_dist_in1k,14.880,85.120,44.933,55.067,10.95,224,0.900,bicubic,-79.290,-53.607,+43
seresnet50.a2_in1k,14.840,85.160,44.400,55.600,28.09,288,1.000,bicubic,-79.820,-54.320,-34
poolformerv2_s24.sail_in1k,14.800,85.200,45.920,54.080,21.34,224,1.000,bicubic,-79.850,-52.920,-33
repvit_m1_1.dist_300e_in1k,14.760,85.240,47.227,52.773,8.80,224,0.950,bicubic,-80.000,-51.483,-57
rexnet_150.nav_in1k,14.733,85.267,46.880,53.120,9.73,224,0.875,bicubic,-79.747,-51.920,0
darknet53.c2ns_in1k,14.680,85.320,47.107,52.893,41.61,288,1.000,bicubic,-79.940,-51.793,-30
gcresnet33ts.ra2_in1k,14.667,85.333,46.320,53.680,19.88,288,1.000,bicubic,-80.253,-52.490,-86
resnet50.tv2_in1k,14.640,85.360,46.973,53.027,25.56,224,0.965,bilinear,-80.000,-51.827,-36
res2net50d.in1k,14.627,85.373,44.480,55.520,25.72,224,0.875,bilinear,-79.693,-54.160,+18
darknetaa53.c2ns_in1k,14.560,85.440,45.427,54.573,36.02,288,1.000,bilinear,-79.910,-53.393,-4
coat_lite_mini.in1k,14.560,85.440,44.493,55.507,11.01,224,0.900,bicubic,-79.480,-54.057,+52
efficientnet_el_pruned.in1k,14.480,85.520,46.107,53.893,10.59,300,0.904,bicubic,-79.910,-52.633,+3
efficientnet_b2.ra_in1k,14.440,85.560,46.053,53.947,9.11,288,1.000,bicubic,-80.180,-52.727,-33
poolformer_s24.sail_in1k,14.267,85.733,47.360,52.640,21.39,224,0.900,bicubic,-80.293,-51.540,-26
legacy_seresnext101_32x4d.in1k,14.173,85.827,43.000,57.000,48.96,224,0.875,bilinear,-80.197,-55.580,+2
fbnetv3_d.ra2_in1k,14.093,85.907,46.467,53.533,10.31,256,0.950,bilinear,-79.827,-52.153,+59
gernet_m.idstcv_in1k,14.080,85.920,46.080,53.920,21.14,224,0.875,bilinear,-80.540,-52.830,-40
pvt_v2_b1.in1k,14.027,85.973,47.733,52.267,14.01,224,0.900,bicubic,-79.773,-50.927,+73
repvit_m2.dist_in1k,14.013,85.987,46.373,53.627,8.80,224,0.950,bicubic,-80.727,-52.337,-69
mobilevitv2_125.cvnets_in1k,14.013,85.987,45.027,54.973,7.48,256,0.888,bicubic,-79.947,-53.523,+53
dpn68b.ra_in1k,13.893,86.107,40.307,59.693,12.61,288,1.000,bicubic,-80.107,-58.193,+47
resnext101_32x4d.gluon_in1k,13.853,86.147,41.640,58.360,44.18,224,0.875,bicubic,-80.687,-57.140,-26
seresnext50_32x4d.gluon_in1k,13.627,86.373,43.773,56.227,27.56,224,0.875,bicubic,-80.703,-54.837,0
fastvit_t12.apple_in1k,13.613,86.387,43.307,56.693,7.55,256,0.900,bicubic,-80.307,-55.303,+54
pit_xs_distilled_224.in1k,13.573,86.427,45.173,54.827,11.00,224,0.900,bicubic,-80.207,-53.327,+67
resnet152.a3_in1k,13.547,86.453,43.387,56.613,60.19,224,0.950,bicubic,-81.183,-55.293,-70
resmlp_36_224.fb_in1k,13.467,86.533,46.667,53.333,44.69,224,0.875,bicubic,-80.723,-51.943,+11
repvgg_b2g4.rvgg_in1k,13.467,86.533,43.853,56.147,61.76,224,0.875,bilinear,-80.413,-54.807,+57
efficientformerv2_s1.snap_dist_in1k,13.453,86.547,42.933,57.067,6.19,224,0.950,bicubic,-80.747,-55.707,+9
vit_small_patch16_224.augreg_in1k,13.387,86.613,41.427,58.573,22.05,224,0.900,bicubic,-80.503,-57.013,+52
eca_botnext26ts_256.c1_in1k,13.320,86.680,42.133,57.867,10.59,256,0.950,bicubic,-80.460,-56.487,+62
visformer_tiny.in1k,13.307,86.693,43.933,56.067,10.32,224,0.900,bicubic,-80.253,-54.457,+86
regnetx_320.pycls_in1k,13.293,86.707,40.747,59.253,107.81,224,0.875,bicubic,-81.157,-58.173,-22
resnet101d.gluon_in1k,13.200,86.800,41.547,58.453,44.57,224,0.875,bicubic,-81.030,-56.993,+1
efficientnet_b3_pruned.in1k,13.160,86.840,45.200,54.800,9.86,300,0.904,bicubic,-81.470,-53.560,-62
resnet50.b1k_in1k,13.093,86.907,43.947,56.053,25.56,288,1.000,bicubic,-81.767,-54.863,-101
mixnet_xl.ra_in1k,13.080,86.920,43.213,56.787,11.90,224,0.875,bicubic,-81.100,-55.107,+4
cspresnext50.ra_in1k,13.053,86.947,44.973,55.027,20.57,256,0.887,bilinear,-81.777,-53.837,-96
efficientformer_l1.snap_dist_in1k,13.013,86.987,45.600,54.400,12.29,224,0.950,bicubic,-81.467,-53.230,-35
regnetx_016.tv2_in1k,13.000,87.000,45.427,54.573,9.19,224,0.965,bicubic,-81.130,-53.193,+13
repvit_m1_0.dist_300e_in1k,13.000,87.000,44.413,55.587,7.30,224,0.950,bicubic,-81.300,-54.437,-13
nf_regnet_b1.ra2_in1k,12.947,87.053,44.373,55.627,10.22,288,0.900,bicubic,-81.163,-54.247,+12
eca_halonext26ts.c1_in1k,12.947,87.053,42.813,57.187,10.76,256,0.940,bicubic,-81.093,-55.677,+21
mobilevit_s.cvnets_in1k,12.907,87.093,40.760,59.240,5.58,256,0.900,bicubic,-80.253,-57.560,+120
pit_xs_224.in1k,12.827,87.173,42.813,57.187,10.62,224,0.900,bicubic,-80.273,-55.517,+126
tf_efficientnet_b3.in1k,12.787,87.213,43.627,56.373,12.23,300,0.904,bicubic,-81.753,-55.223,-53
resnet50.b2k_in1k,12.760,87.240,44.133,55.867,25.56,288,1.000,bicubic,-81.970,-54.797,-93
resnext50_32x4d.tv2_in1k,12.693,87.307,43.093,56.907,25.03,224,0.965,bilinear,-81.927,-55.617,-70
inception_v3.gluon_in1k,12.640,87.360,40.427,59.573,23.83,299,0.875,bicubic,-80.830,-58.143,+83
tresnet_m.miil_in1k,12.613,87.387,41.907,58.093,31.39,224,0.875,bilinear,-82.007,-56.643,-69
resnet50.a1h_in1k,12.587,87.413,44.240,55.760,25.56,224,1.000,bicubic,-82.183,-54.230,-106
crossvit_9_dagger_240.in1k,12.560,87.440,41.720,58.280,8.78,240,0.875,bicubic,-80.330,-56.520,+140
efficientvit_b1.r256_in1k,12.547,87.453,42.120,57.880,9.10,256,1.000,bicubic,-81.543,-56.240,+5
resnetblur50.bt_in1k,12.493,87.507,44.160,55.840,25.56,288,0.950,bicubic,-81.967,-54.680,-47
resmlp_24_224.fb_in1k,12.493,87.507,43.413,56.587,30.02,224,0.875,bicubic,-81.537,-55.247,+12
convnext_femto_ols.d1_in1k,12.480,87.520,43.933,56.067,5.23,288,0.950,bicubic,-81.440,-54.667,+20
coat_lite_tiny.in1k,12.467,87.533,41.053,58.947,5.72,224,0.900,bicubic,-80.773,-57.207,+101
efficientnet_em.ra2_in1k,12.400,87.600,43.933,56.067,6.90,240,0.882,bicubic,-81.430,-54.887,+28
regnety_120.pycls_in1k,12.400,87.600,42.173,57.827,51.82,224,0.875,bicubic,-82.070,-56.597,-53
regnety_160.pycls_in1k,12.227,87.773,41.387,58.613,83.59,224,0.875,bicubic,-82.133,-57.473,-41
resnet50.a2_in1k,12.173,87.827,40.453,59.547,25.56,288,1.000,bicubic,-82.457,-58.207,-87
ecaresnet50t.a3_in1k,12.147,87.853,41.573,58.427,25.57,224,0.950,bicubic,-82.203,-57.097,-41
hrnet_w64.ms_in1k,12.013,87.987,40.827,59.173,128.06,224,0.875,bilinear,-82.017,-57.503,+2
cspdarknet53.ra_in1k,11.960,88.040,43.267,56.733,27.64,256,0.887,bilinear,-82.700,-55.583,-97
xcit_tiny_12_p16_224.fb_dist_in1k,11.933,88.067,40.133,59.867,6.72,224,1.000,bicubic,-81.477,-58.377,+74
resnet101s.gluon_in1k,11.893,88.107,40.947,59.053,44.67,224,0.875,bicubic,-82.827,-57.873,-108
resnet101.a3_in1k,11.867,88.133,40.840,59.160,44.55,224,0.950,bicubic,-82.163,-57.750,-4
gmixer_24_224.ra3_in1k,11.867,88.133,37.800,62.200,24.72,224,0.875,bicubic,-80.963,-60.080,+128
nf_resnet50.ra2_in1k,11.773,88.227,45.907,54.093,25.56,288,0.940,bicubic,-82.767,-52.723,-75
fbnetv3_b.ra2_in1k,11.760,88.240,44.400,55.600,8.60,256,0.950,bilinear,-82.210,-54.090,-1
dpn92.mx_in1k,11.640,88.360,40.160,59.840,37.67,224,0.875,bicubic,-82.650,-58.590,-41
botnet26t_256.c1_in1k,11.613,88.387,40.107,59.893,12.49,256,0.950,bicubic,-81.897,-58.213,+53
convnextv2_femto.fcmae_ft_in1k,11.600,88.400,40.800,59.200,5.23,288,0.950,bicubic,-82.590,-57.860,-33
dla102x2.in1k,11.560,88.440,41.293,58.707,41.28,224,0.875,bilinear,-82.410,-57.227,-3
xception41.tf_in1k,11.560,88.440,39.067,60.933,26.97,299,0.903,bicubic,-81.870,-59.153,+61
vit_small_patch32_224.augreg_in21k_ft_in1k,11.507,88.493,39.547,60.453,22.88,224,0.900,bicubic,-80.533,-58.743,+176
efficientvit_b1.r224_in1k,11.493,88.507,40.200,59.800,9.10,224,0.950,bicubic,-82.017,-58.100,+47
levit_128.fb_dist_in1k,11.440,88.560,40.187,59.813,9.21,224,0.900,bicubic,-81.890,-58.333,+69
levit_conv_128.fb_dist_in1k,11.440,88.560,40.173,59.827,9.21,224,0.900,bicubic,-81.910,-58.197,+66
lambda_resnet26t.c1_in1k,11.387,88.613,40.187,59.813,10.96,256,0.940,bicubic,-82.443,-58.463,+8
seresnext26t_32x4d.bt_in1k,11.373,88.627,41.107,58.893,16.81,288,0.950,bicubic,-82.187,-57.383,+37
regnety_080.pycls_in1k,11.373,88.627,40.613,59.387,39.18,224,0.875,bicubic,-82.797,-57.987,-39
efficientnet_b2_pruned.in1k,11.333,88.667,42.040,57.960,8.31,260,0.890,bicubic,-82.807,-56.670,-31
resnet50.ra_in1k,11.333,88.667,41.013,58.987,25.56,288,0.950,bicubic,-82.877,-57.607,-48
tf_efficientnet_el.in1k,11.320,88.680,42.053,57.947,10.59,300,0.904,bicubic,-83.080,-56.657,-71
xcit_nano_12_p16_384.fb_dist_in1k,11.227,88.773,39.853,60.147,3.05,384,1.000,bicubic,-80.603,-58.167,+180
convnext_femto.d1_in1k,11.213,88.787,42.773,57.227,5.22,288,0.950,bicubic,-82.717,-55.747,-13
resnet152c.gluon_in1k,11.107,88.893,37.133,62.867,60.21,224,0.875,bicubic,-83.053,-61.457,-42
hrnet_w48.ms_in1k,11.093,88.907,40.307,59.693,77.47,224,0.875,bilinear,-82.827,-58.433,-13
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,11.093,88.907,39.933,60.067,6.36,384,1.000,bicubic,-80.947,-58.297,+160
halonet26t.a1h_in1k,11.093,88.907,38.800,61.200,12.48,256,0.950,bicubic,-82.907,-59.540,-22
mobilevitv2_100.cvnets_in1k,11.067,88.933,40.613,59.387,4.90,256,0.888,bicubic,-82.233,-57.667,+63
tf_efficientnet_b0.ns_jft_in1k,11.000,89.000,40.080,59.920,5.29,224,0.875,bicubic,-82.620,-58.560,+19
tf_efficientnetv2_b2.in1k,11.000,89.000,39.747,60.253,10.10,260,0.890,bicubic,-83.420,-58.833,-82
inception_v3.tf_adv_in1k,11.000,89.000,36.720,63.280,23.83,299,0.875,bicubic,-81.900,-61.420,+98
seresnext26d_32x4d.bt_in1k,10.987,89.013,41.347,58.653,16.81,288,0.950,bicubic,-82.453,-56.983,+39
xcit_tiny_12_p16_224.fb_in1k,10.987,89.013,37.067,62.933,6.72,224,1.000,bicubic,-81.523,-61.173,+126
regnety_008_tv.tv2_in1k,10.840,89.160,40.533,59.467,6.43,224,0.965,bicubic,-82.850,-57.957,+6
resnet34d.ra2_in1k,10.827,89.173,38.653,61.347,21.82,288,0.950,bicubic,-82.813,-59.887,+8
dpn107.mx_in1k,10.827,89.173,38.307,61.693,86.92,224,0.875,bicubic,-83.513,-60.193,-77
inception_v3.tf_in1k,10.827,89.173,36.853,63.147,23.83,299,0.875,bicubic,-82.493,-61.527,+51
mobileone_s4.apple_in1k,10.787,89.213,38.480,61.520,14.95,224,0.900,bilinear,-82.953,-59.590,-2
xcit_nano_12_p8_224.fb_dist_in1k,10.773,89.227,38.120,61.880,3.05,224,1.000,bicubic,-81.307,-59.790,+144
densenetblur121d.ra_in1k,10.547,89.453,39.707,60.293,8.00,288,0.950,bicubic,-82.073,-58.553,+109
tf_efficientnet_b2.ap_in1k,10.533,89.467,40.133,59.867,9.11,260,0.890,bicubic,-83.977,-58.487,-105
dpn131.mx_in1k,10.533,89.467,36.787,63.213,79.25,224,0.875,bicubic,-83.517,-61.923,-44
rexnet_130.nav_in1k,10.413,89.587,41.547,58.453,7.56,224,0.875,bicubic,-83.487,-56.853,-26
repvit_m0_9.dist_450e_in1k,10.400,89.600,40.120,59.880,5.49,224,0.950,bicubic,-83.200,-58.380,+7
hrnet_w44.ms_in1k,10.307,89.693,39.493,60.507,67.06,224,0.875,bilinear,-83.243,-59.107,+11
xcit_nano_12_p8_224.fb_in1k,10.307,89.693,36.973,63.027,3.05,224,1.000,bicubic,-80.703,-60.797,+180
resnext50_32x4d.a3_in1k,10.267,89.733,38.200,61.800,25.03,224,0.950,bicubic,-83.393,-60.320,-4
lambda_resnet26rpt_256.c1_in1k,10.227,89.773,38.133,61.867,10.99,256,0.940,bicubic,-83.483,-60.387,-9
resnext101_32x8d.tv_in1k,10.173,89.827,37.747,62.253,88.79,224,0.875,bilinear,-83.647,-60.833,-24
regnetx_160.pycls_in1k,10.133,89.867,38.053,61.947,54.28,224,0.875,bicubic,-84.007,-60.467,-64
legacy_seresnext50_32x4d.in1k,10.093,89.907,39.213,60.787,27.56,224,0.875,bilinear,-83.657,-59.367,-20
resnetrs50.tf_in1k,10.053,89.947,37.573,62.427,35.69,224,0.910,bicubic,-84.267,-61.067,-90
dpn98.mx_in1k,10.013,89.987,36.173,63.827,61.57,224,0.875,bicubic,-84.147,-62.467,-70
inception_v3.tv_in1k,10.013,89.987,35.227,64.773,23.83,299,0.875,bicubic,-82.717,-62.743,+88
efficientnet_b1.ft_in1k,10.000,90.000,37.600,62.400,7.79,256,1.000,bicubic,-83.250,-60.690,+38
legacy_xception.tf_in1k,9.987,90.013,37.987,62.013,22.86,299,0.897,bicubic,-83.473,-60.543,+13
resnet33ts.ra2_in1k,9.947,90.053,39.840,60.160,19.68,288,1.000,bicubic,-84.153,-58.810,-64
regnety_064.pycls_in1k,9.933,90.067,39.093,60.907,30.58,224,0.875,bicubic,-84.207,-59.657,-71
resnet152.gluon_in1k,9.733,90.267,36.093,63.907,60.19,224,0.875,bicubic,-84.337,-62.367,-63
tf_efficientnet_lite3.in1k,9.680,90.320,39.013,60.987,8.20,300,0.904,bilinear,-84.520,-59.617,-87
tf_efficientnet_b2.aa_in1k,9.667,90.333,38.893,61.107,9.11,260,0.890,bicubic,-84.713,-59.717,-109
tf_efficientnet_cc_b1_8e.in1k,9.573,90.427,36.840,63.160,39.72,240,0.882,bicubic,-84.347,-61.420,-47
res2net101_26w_4s.in1k,9.507,90.493,35.093,64.907,45.21,224,0.875,bilinear,-84.213,-63.217,-25
resnet50.ram_in1k,9.480,90.520,35.507,64.493,25.56,288,0.950,bicubic,-85.040,-63.143,-129
legacy_seresnet152.in1k,9.333,90.667,37.373,62.627,66.82,224,0.875,bilinear,-84.047,-60.967,+13
cspresnet50.ra_in1k,9.293,90.707,39.613,60.387,21.62,256,0.887,bilinear,-84.447,-59.027,-32
repvit_m0_9.dist_300e_in1k,9.293,90.707,38.840,61.160,5.49,224,0.950,bicubic,-84.147,-59.870,+3
resnet34.a1_in1k,9.267,90.733,34.947,65.053,21.80,288,1.000,bicubic,-83.833,-63.363,+38
hrnet_w40.ms_in1k,9.240,90.760,36.920,63.080,57.56,224,0.875,bilinear,-84.260,-61.620,-6
resnet32ts.ra2_in1k,9.213,90.787,38.600,61.400,17.96,288,1.000,bicubic,-84.617,-60.040,-48
regnetx_120.pycls_in1k,9.213,90.787,37.200,62.800,46.11,224,0.875,bicubic,-85.017,-61.470,-100
crossvit_tiny_240.in1k,9.133,90.867,34.600,65.400,7.01,240,0.875,bicubic,-81.097,-62.990,+179
resnest26d.gluon_in1k,9.053,90.947,37.840,62.160,17.07,224,0.875,bilinear,-84.267,-60.520,+11
vit_tiny_patch16_224.augreg_in21k_ft_in1k,9.053,90.947,34.627,65.373,5.72,224,0.900,bicubic,-82.717,-63.413,+130
resnet50d.a3_in1k,9.040,90.960,37.307,62.693,25.58,224,0.950,bicubic,-84.440,-61.143,-8
gcresnext26ts.ch_in1k,8.987,91.013,36.920,63.080,10.48,288,1.000,bicubic,-84.173,-61.450,+26
vit_base_patch16_224.sam_in1k,8.987,91.013,36.133,63.867,86.57,224,0.900,bicubic,-85.163,-62.367,-93
regnety_040.pycls_in1k,8.933,91.067,37.067,62.933,20.65,224,0.875,bicubic,-84.947,-61.453,-59
resnext50_32x4d.gluon_in1k,8.933,91.067,36.293,63.707,25.03,224,0.875,bicubic,-84.877,-62.127,-53
rexnet_100.nav_in1k,8.893,91.107,36.413,63.587,4.80,224,0.875,bicubic,-84.127,-61.777,+33
mixnet_l.ft_in1k,8.893,91.107,36.240,63.760,7.33,224,0.875,bicubic,-84.537,-62.190,-8
efficientvit_m5.r224_in1k,8.893,91.107,34.587,65.413,12.47,224,0.875,bicubic,-83.567,-63.403,+82
bat_resnext26ts.ch_in1k,8.880,91.120,36.453,63.547,10.73,256,0.900,bicubic,-84.440,-62.167,+3
convit_tiny.fb_in1k,8.867,91.133,34.307,65.693,5.71,224,0.875,bicubic,-81.793,-63.423,+156
mobilenetv3_large_100.miil_in21k_ft_in1k,8.853,91.147,33.080,66.920,5.48,224,0.875,bilinear,-83.407,-64.540,+90
hrnet_w18.ms_aug_in1k,8.747,91.253,38.787,61.213,21.30,224,0.950,bilinear,-84.803,-59.913,-29
resnet50.bt_in1k,8.653,91.347,38.720,61.280,25.56,288,0.950,bicubic,-85.667,-59.810,-127
levit_conv_128s.fb_dist_in1k,8.653,91.347,33.093,66.907,7.78,224,0.900,bicubic,-83.307,-64.967,+107
dla169.in1k,8.640,91.360,36.000,64.000,53.39,224,0.875,bilinear,-84.710,-62.600,-10
levit_128s.fb_dist_in1k,8.640,91.360,33.067,66.933,7.78,224,0.900,bicubic,-83.320,-64.823,+103
mixer_b16_224.goog_in21k_ft_in1k,8.627,91.373,29.413,70.587,59.88,224,0.875,bicubic,-83.253,-68.627,+109
repvit_m1.dist_in1k,8.613,91.387,37.293,62.707,5.49,224,0.950,bicubic,-84.677,-61.147,0
hrnet_w30.ms_in1k,8.587,91.413,37.067,62.933,37.71,224,0.875,bilinear,-84.613,-61.423,+3
eca_resnext26ts.ch_in1k,8.560,91.440,36.827,63.173,10.30,288,1.000,bicubic,-84.500,-61.573,+17
ghostnetv2_160.in1k,8.560,91.440,36.627,63.373,12.39,224,0.875,bicubic,-84.430,-61.603,+23
legacy_seresnet101.in1k,8.533,91.467,35.960,64.040,49.33,224,0.875,bilinear,-84.777,-62.560,-8
convnext_atto_ols.a2_in1k,8.533,91.467,35.000,65.000,3.70,288,0.950,bicubic,-84.557,-63.470,+13
tf_efficientnet_b2.in1k,8.520,91.480,36.520,63.480,9.11,260,0.890,bicubic,-85.590,-61.930,-106
tf_efficientnet_b1.ap_in1k,8.453,91.547,35.240,64.760,7.79,240,0.882,bicubic,-85.227,-63.120,-58
repvgg_b2.rvgg_in1k,8.440,91.560,36.480,63.520,89.02,224,0.875,bilinear,-85.060,-62.090,-38
ese_vovnet19b_dw.ra_in1k,8.307,91.693,36.973,63.027,6.54,288,0.950,bicubic,-84.843,-61.277,+2
resmlp_12_224.fb_distilled_in1k,8.307,91.693,36.853,63.147,15.35,224,0.875,bicubic,-84.513,-61.287,+31
crossvit_9_240.in1k,8.280,91.720,34.107,65.893,8.55,240,0.875,bicubic,-82.350,-63.623,+139
dla102x.in1k,8.187,91.813,37.067,62.933,26.31,224,0.875,bilinear,-85.303,-61.433,-39
seresnext26ts.ch_in1k,8.147,91.853,36.093,63.907,10.39,288,1.000,bicubic,-84.813,-62.087,+17
hrnet_w32.ms_in1k,8.053,91.947,37.560,62.440,41.23,224,0.875,bilinear,-85.477,-60.890,-48
resnet101c.gluon_in1k,8.027,91.973,33.320,66.680,44.57,224,0.875,bicubic,-85.643,-65.100,-65
vit_base_patch32_224.augreg_in1k,7.987,92.013,30.453,69.547,88.22,224,0.900,bicubic,-83.203,-66.937,+113
cs3darknet_m.c2ns_in1k,7.960,92.040,36.507,63.493,9.31,288,0.950,bicubic,-85.390,-62.103,-29
poolformerv2_s12.sail_in1k,7.960,92.040,34.560,65.440,11.89,224,1.000,bicubic,-85.000,-63.800,+13
resnet50d.gluon_in1k,7.947,92.053,34.987,65.013,25.58,224,0.875,bicubic,-85.803,-63.403,-79
resnet26t.ra2_in1k,7.893,92.107,36.720,63.280,16.01,320,1.000,bicubic,-85.307,-61.690,-17
fastvit_t8.apple_dist_in1k,7.853,92.147,34.667,65.333,4.03,256,0.900,bicubic,-84.687,-63.503,+43
res2net50_26w_8s.in1k,7.853,92.147,33.707,66.293,48.40,224,0.875,bilinear,-85.537,-64.463,-37
dla60_res2next.in1k,7.840,92.160,34.960,65.040,17.03,224,0.875,bilinear,-85.350,-63.440,-18
repghostnet_200.in1k,7.800,92.200,37.200,62.800,9.80,224,0.875,bicubic,-85.700,-61.530,-52
mobilevitv2_075.cvnets_in1k,7.773,92.227,33.720,66.280,2.87,256,0.888,bicubic,-83.987,-64.060,+87
convnextv2_atto.fcmae_ft_in1k,7.773,92.227,32.907,67.093,3.71,288,0.950,bicubic,-85.197,-65.253,+4
mobilevit_xs.cvnets_in1k,7.733,92.267,32.520,67.480,2.32,256,0.900,bicubic,-83.087,-65.410,+114
tf_efficientnetv2_b1.in1k,7.720,92.280,34.613,65.387,8.14,240,0.882,bicubic,-86.230,-64.007,-111
deit_tiny_distilled_patch16_224.fb_in1k,7.693,92.307,33.507,66.493,5.91,224,0.900,bicubic,-83.017,-64.053,+116
regnety_032.pycls_in1k,7.680,92.320,34.280,65.720,19.44,224,0.875,bicubic,-85.730,-64.360,-48
efficientformerv2_s0.snap_dist_in1k,7.667,92.333,32.653,67.347,3.60,224,0.950,bicubic,-84.293,-65.407,+72
convnext_atto.d2_in1k,7.613,92.387,35.053,64.947,3.70,288,0.950,bicubic,-85.177,-63.097,+12
dla60_res2net.in1k,7.600,92.400,34.613,65.387,20.85,224,0.875,bilinear,-85.570,-63.817,-27
efficientnet_b1_pruned.in1k,7.480,92.520,34.480,65.520,6.33,240,0.882,bicubic,-85.300,-63.560,+11
regnetx_064.pycls_in1k,7.373,92.627,34.360,65.640,26.21,224,0.875,bicubic,-86.527,-64.280,-111
wide_resnet101_2.tv_in1k,7.360,92.640,34.147,65.853,126.89,224,0.875,bilinear,-86.380,-64.083,-93
densenet121.ra_in1k,7.333,92.667,35.480,64.520,7.98,288,0.950,bicubic,-85.187,-62.740,+28
deit_tiny_patch16_224.fb_in1k,7.293,92.707,30.680,69.320,5.72,224,0.900,bicubic,-82.367,-66.770,+134
regnetx_008.tv2_in1k,7.280,92.720,34.133,65.867,7.26,224,0.965,bicubic,-85.270,-64.047,+22
resnet50s.gluon_in1k,7.280,92.720,33.453,66.547,25.68,224,0.875,bicubic,-86.360,-65.007,-86
resnet101.gluon_in1k,7.267,92.733,32.773,67.227,44.55,224,0.875,bicubic,-86.483,-65.607,-102
resnet34.a2_in1k,7.267,92.733,31.813,68.187,21.80,288,1.000,bicubic,-85.453,-66.357,+10
edgenext_x_small.in1k,7.267,92.733,30.920,69.080,2.34,288,1.000,bicubic,-84.443,-66.680,+76
hardcorenas_e.miil_green_in1k,7.240,92.760,33.307,66.693,8.07,224,0.875,bilinear,-85.330,-64.793,+16
efficientnet_b0.ra_in1k,7.213,92.787,33.987,66.013,5.29,224,0.875,bicubic,-85.477,-64.083,+8
tf_mixnet_l.in1k,7.173,92.827,31.667,68.333,7.33,224,0.875,bicubic,-86.147,-66.363,-50
tf_efficientnet_b1.aa_in1k,7.160,92.840,33.027,66.973,7.79,240,0.882,bicubic,-86.330,-65.333,-73
tf_efficientnet_cc_b0_8e.in1k,7.133,92.867,31.787,68.213,24.01,224,0.875,bicubic,-85.707,-66.393,-10
convmixer_1024_20_ks9_p14.in1k,7.093,92.907,33.053,66.947,24.38,224,0.960,bicubic,-85.307,-65.217,+25
resmlp_12_224.fb_in1k,7.013,92.987,33.933,66.067,15.35,224,0.875,bicubic,-85.207,-64.217,+35
cs3darknet_focus_m.c2ns_in1k,6.933,93.067,34.587,65.413,9.30,288,0.950,bicubic,-86.037,-63.473,-24
fastvit_t8.apple_in1k,6.893,93.107,33.400,66.600,4.03,256,0.900,bicubic,-85.167,-64.530,+42
hardcorenas_f.miil_green_in1k,6.880,93.120,34.067,65.933,8.20,224,0.875,bilinear,-86.090,-64.323,-25
pit_ti_distilled_224.in1k,6.840,93.160,30.947,69.053,5.10,224,0.900,bicubic,-83.930,-66.663,+88
ghostnetv2_130.in1k,6.707,93.293,32.960,67.040,8.96,224,0.875,bicubic,-85.613,-65.300,+25
efficientnet_es.ra_in1k,6.693,93.307,33.973,66.027,5.44,224,0.875,bicubic,-86.477,-64.437,-49
selecsls60b.in1k,6.693,93.307,33.293,66.707,32.77,224,0.875,bicubic,-86.617,-64.997,-60
res2net50_26w_6s.in1k,6.693,93.307,31.653,68.347,37.05,224,0.875,bilinear,-86.707,-66.627,-73
poolformer_s12.sail_in1k,6.653,93.347,34.520,65.480,11.92,224,0.900,bicubic,-85.957,-63.660,-2
mixnet_m.ft_in1k,6.653,93.347,32.053,67.947,5.01,224,0.875,bicubic,-85.757,-66.097,+14
dpn68b.mx_in1k,6.640,93.360,32.907,67.093,12.61,224,0.875,bicubic,-86.150,-65.173,-18
tinynet_a.in1k,6.640,93.360,32.227,67.773,6.19,192,0.875,bicubic,-85.800,-65.853,+9
legacy_seresnext26_32x4d.in1k,6.627,93.373,33.240,66.760,16.79,224,0.875,bicubic,-86.013,-64.880,-8
tf_efficientnet_b1.in1k,6.627,93.373,32.640,67.360,7.79,240,0.882,bicubic,-86.473,-65.660,-49
mobileone_s3.apple_in1k,6.627,93.373,32.147,67.853,10.17,224,0.900,bilinear,-86.313,-66.043,-30
resnet50.a3_in1k,6.587,93.413,32.053,67.947,25.56,224,0.950,bicubic,-86.133,-65.957,-15
regnety_004.tv2_in1k,6.533,93.467,30.480,69.520,4.34,224,0.965,bicubic,-85.047,-67.410,+51
dla60x.in1k,6.493,93.507,34.080,65.920,17.35,224,0.875,bilinear,-86.587,-64.420,-50
repvgg_b1.rvgg_in1k,6.467,93.533,33.800,66.200,57.42,224,0.875,bilinear,-86.863,-64.570,-79
skresnet34.ra_in1k,6.467,93.533,31.573,68.427,22.28,224,0.875,bicubic,-85.913,-66.577,+7
repghostnet_150.in1k,6.453,93.547,32.307,67.693,6.58,224,0.875,bicubic,-85.917,-65.743,+6
hardcorenas_d.miil_green_in1k,6.453,93.547,32.187,67.813,7.50,224,0.875,bilinear,-85.947,-65.893,+4
resnet26d.bt_in1k,6.307,93.693,32.747,67.253,16.01,288,0.950,bicubic,-86.213,-65.463,-7
regnetx_080.pycls_in1k,6.293,93.707,32.373,67.627,39.57,224,0.875,bicubic,-87.587,-66.217,-145
resnet18.fb_swsl_ig1b_ft_in1k,6.253,93.747,31.600,68.400,11.69,224,0.875,bilinear,-84.447,-66.100,+72
legacy_seresnet50.in1k,6.200,93.800,32.680,67.320,28.09,224,0.875,bilinear,-86.760,-65.730,-44
pit_ti_224.in1k,6.120,93.880,30.240,69.760,4.85,224,0.900,bicubic,-83.820,-67.210,+89
resnet152.tv_in1k,6.067,93.933,32.080,67.920,60.19,224,0.875,bilinear,-87.253,-65.960,-85
wide_resnet50_2.tv_in1k,6.013,93.987,32.160,67.840,68.88,224,0.875,bilinear,-87.147,-66.280,-70
tf_efficientnet_cc_b0_4e.in1k,5.987,94.013,29.587,70.413,13.31,224,0.875,bicubic,-86.613,-68.493,-21
regnetx_040.pycls_in1k,5.947,94.053,31.493,68.507,22.12,224,0.875,bicubic,-87.613,-67.037,-120
seresnet50.a3_in1k,5.947,94.053,30.827,69.173,28.09,224,0.950,bicubic,-86.123,-67.213,+11
mixer_l16_224.goog_in21k_ft_in1k,5.880,94.120,18.547,81.453,208.20,224,0.875,bicubic,-81.270,-74.973,+120
tf_efficientnetv2_b0.in1k,5.867,94.133,30.787,69.213,7.14,224,0.875,bicubic,-87.243,-67.603,-71
dla102.in1k,5.827,94.173,32.760,67.240,33.27,224,0.875,bilinear,-87.223,-65.790,-65
selecsls60.in1k,5.680,94.320,32.520,67.480,30.67,224,0.875,bicubic,-87.330,-65.780,-62
regnety_016.pycls_in1k,5.667,94.333,30.480,69.520,11.20,224,0.875,bicubic,-87.373,-67.890,-66
res2next50.in1k,5.653,94.347,30.893,69.107,24.67,224,0.875,bilinear,-87.187,-67.287,-52
hardcorenas_c.miil_green_in1k,5.653,94.347,30.427,69.573,5.52,224,0.875,bilinear,-86.367,-67.413,+9
hrnet_w18_small_v2.gluon_in1k,5.520,94.480,31.853,68.147,15.60,224,0.875,bicubic,-87.250,-66.557,-44
hrnet_w18.ms_in1k,5.493,94.507,30.907,69.093,21.30,224,0.875,bilinear,-86.827,-67.163,-12
resnest14d.gluon_in1k,5.453,94.547,28.547,71.453,10.61,224,0.875,bilinear,-86.267,-69.323,+24
ghostnetv2_100.in1k,5.387,94.613,28.560,71.440,6.16,224,0.875,bicubic,-85.513,-69.140,+45
tf_efficientnet_em.in1k,5.360,94.640,31.080,68.920,6.90,240,0.882,bicubic,-87.580,-67.100,-61
tf_efficientnet_lite2.in1k,5.333,94.667,30.880,69.120,6.09,260,0.890,bicubic,-87.317,-67.350,-41
gernet_s.idstcv_in1k,5.320,94.680,30.147,69.853,8.17,224,0.875,bilinear,-86.810,-68.043,-7
resnext26ts.ra2_in1k,5.307,94.693,29.680,70.320,10.30,288,1.000,bicubic,-86.823,-68.350,-8
tf_efficientnet_b0.ap_in1k,5.307,94.693,28.840,71.160,5.29,224,0.875,bicubic,-86.893,-69.180,-12
efficientvit_m4.r224_in1k,5.307,94.693,28.013,71.987,8.80,224,0.875,bicubic,-85.273,-69.517,+55
repvgg_b1g4.rvgg_in1k,5.240,94.760,30.760,69.240,39.97,224,0.875,bilinear,-87.760,-67.670,-75
resnet34.bt_in1k,5.213,94.787,29.440,70.560,21.80,288,0.950,bicubic,-87.197,-68.430,-29
xcit_nano_12_p16_224.fb_dist_in1k,5.200,94.800,26.493,73.507,3.05,224,1.000,bicubic,-84.500,-70.607,+68
res2net50_26w_4s.in1k,5.173,94.827,29.360,70.640,25.70,224,0.875,bilinear,-87.317,-68.670,-35
efficientvit_m3.r224_in1k,5.160,94.840,27.400,72.600,6.90,224,0.875,bicubic,-84.700,-70.140,+64
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,5.080,94.920,27.027,72.973,6.34,224,0.900,bicubic,-84.100,-70.193,+74
mobilenetv3_large_100.ra_in1k,5.067,94.933,28.200,71.800,5.48,224,0.875,bicubic,-86.283,-69.510,+17
tf_efficientnet_b0.aa_in1k,5.053,94.947,28.760,71.240,5.29,224,0.875,bicubic,-87.187,-69.240,-23
tf_mixnet_m.in1k,5.053,94.947,28.187,71.813,5.01,224,0.875,bicubic,-87.247,-69.703,-27
res2net50_14w_8s.in1k,5.040,94.960,28.733,71.267,25.06,224,0.875,bilinear,-87.730,-69.427,-62
regnetx_004_tv.tv2_in1k,5.000,95.000,27.560,72.440,5.50,224,0.965,bicubic,-85.640,-70.040,+39
repghostnet_130.in1k,4.987,95.013,29.653,70.347,5.48,224,0.875,bicubic,-86.903,-68.277,-5
mixnet_s.ft_in1k,4.947,95.053,28.573,71.427,4.13,224,0.875,bicubic,-86.873,-69.127,-2
hardcorenas_b.miil_green_in1k,4.947,95.053,28.040,71.960,5.18,224,0.875,bilinear,-86.813,-69.820,+2
mobilenetv3_rw.rmsp_in1k,4.933,95.067,29.853,70.147,5.48,224,0.875,bicubic,-86.277,-67.807,+13
hardcorenas_a.miil_green_in1k,4.893,95.107,28.093,71.907,5.26,224,0.875,bilinear,-86.457,-69.757,+7
regnetx_032.pycls_in1k,4.880,95.120,30.227,69.773,15.30,224,0.875,bicubic,-88.230,-68.163,-104
resnet50c.gluon_in1k,4.880,95.120,28.080,71.920,25.58,224,0.875,bicubic,-88.150,-70.320,-95
xcit_nano_12_p16_224.fb_in1k,4.853,95.147,25.467,74.533,3.05,224,1.000,bicubic,-83.767,-71.323,+73
resnext50_32x4d.tv_in1k,4.827,95.173,30.267,69.733,25.03,224,0.875,bilinear,-87.923,-68.003,-71
densenet161.tv_in1k,4.733,95.267,29.560,70.440,28.68,224,0.875,bicubic,-87.747,-68.740,-51
resnet101.tv_in1k,4.693,95.307,29.347,70.653,44.55,224,0.875,bilinear,-88.117,-68.903,-79
selecsls42b.in1k,4.680,95.320,28.573,71.427,32.46,224,0.875,bicubic,-87.610,-69.537,-40
tf_efficientnet_lite1.in1k,4.600,95.400,28.347,71.653,5.42,240,0.882,bicubic,-88.030,-69.713,-67
mobilenetv2_120d.ra_in1k,4.547,95.453,29.320,70.680,5.83,224,0.875,bicubic,-87.853,-68.740,-48
mobileone_s2.apple_in1k,4.520,95.480,29.133,70.867,7.88,224,0.900,bilinear,-88.300,-69.137,-85
tf_efficientnet_b0.in1k,4.427,95.573,26.680,73.320,5.29,224,0.875,bicubic,-87.653,-71.480,-34
pvt_v2_b0.in1k,4.347,95.653,25.960,74.040,3.67,224,0.900,bicubic,-84.433,-70.900,+61
vit_base_patch32_224.sam_in1k,4.333,95.667,24.387,75.613,88.22,224,0.900,bicubic,-85.407,-72.613,+41
resnet50.am_in1k,4.267,95.733,28.627,71.373,25.56,224,0.875,bicubic,-89.703,-70.003,-216
edgenext_xx_small.in1k,4.267,95.733,24.093,75.907,1.33,288,1.000,bicubic,-84.623,-72.887,+57
tinynet_b.in1k,4.200,95.800,26.787,73.213,3.73,188,0.875,bicubic,-86.720,-70.873,+5
efficientnet_es_pruned.in1k,4.200,95.800,26.453,73.547,5.44,224,0.875,bicubic,-86.970,-71.287,-1
repghostnet_111.in1k,4.147,95.853,26.187,73.813,4.54,224,0.875,bicubic,-86.563,-71.283,+13
densenet201.tv_in1k,4.133,95.867,27.547,72.453,20.01,224,0.875,bicubic,-88.607,-70.683,-85
resnet50.gluon_in1k,4.120,95.880,26.960,73.040,25.56,224,0.875,bicubic,-88.420,-71.080,-72
fbnetc_100.rmsp_in1k,4.107,95.893,25.907,74.093,5.57,224,0.875,bilinear,-86.623,-71.303,+8
semnasnet_100.rmsp_in1k,3.947,96.053,26.933,73.067,3.89,224,0.875,bicubic,-87.363,-70.627,-14
mobilevitv2_050.cvnets_in1k,3.947,96.053,23.947,76.053,1.37,256,0.888,bicubic,-84.233,-73.043,+59
resnet26.bt_in1k,3.933,96.067,28.213,71.787,16.00,288,0.950,bicubic,-88.057,-69.807,-42
repvgg_a2.rvgg_in1k,3.933,96.067,27.227,72.773,28.21,224,0.875,bilinear,-88.007,-70.873,-35
dpn68.mx_in1k,3.893,96.107,25.693,74.307,12.61,224,0.875,bicubic,-88.097,-72.527,-42
tf_mixnet_s.in1k,3.880,96.120,25.267,74.733,4.13,224,0.875,bicubic,-87.640,-72.353,-23
semnasnet_075.rmsp_in1k,3.867,96.133,27.080,72.920,2.91,224,0.875,bicubic,-86.203,-70.360,+21
tf_efficientnet_es.in1k,3.827,96.173,26.133,73.867,5.44,224,0.875,bicubic,-88.143,-71.747,-45
mobilevit_xxs.cvnets_in1k,3.827,96.173,21.733,78.267,1.27,256,0.900,bicubic,-83.333,-74.367,+58
resnet18d.ra2_in1k,3.813,96.187,26.013,73.987,11.71,288,0.950,bicubic,-86.467,-71.547,+12
regnety_008.pycls_in1k,3.787,96.213,27.160,72.840,6.26,224,0.875,bicubic,-87.943,-71.020,-32
dla60.in1k,3.747,96.253,27.947,72.053,22.04,224,0.875,bilinear,-88.453,-70.153,-62
resnet18.fb_ssl_yfcc100m_ft_in1k,3.747,96.253,25.373,74.627,11.69,224,0.875,bilinear,-86.463,-72.177,+11
mobilenetv2_140.ra_in1k,3.720,96.280,26.720,73.280,6.11,224,0.875,bicubic,-88.120,-71.140,-41
densenet169.tv_in1k,3.707,96.293,25.587,74.413,14.15,224,0.875,bicubic,-88.233,-72.553,-46
resnet18.a1_in1k,3.707,96.293,22.960,77.040,11.69,288,1.000,bicubic,-85.973,-74.140,+19
regnetx_016.pycls_in1k,3.613,96.387,26.320,73.680,9.19,224,0.875,bicubic,-88.567,-71.880,-66
efficientvit_m2.r224_in1k,3.613,96.387,21.853,78.147,4.19,224,0.875,bicubic,-84.857,-75.047,+40
spnasnet_100.rmsp_in1k,3.573,96.427,24.253,75.747,4.42,224,0.875,bilinear,-86.757,-72.937,+1
res2net50_48w_2s.in1k,3.560,96.440,26.613,73.387,25.29,224,0.875,bilinear,-88.990,-71.467,-94
tf_mobilenetv3_large_100.in1k,3.560,96.440,25.120,74.880,5.48,224,0.875,bilinear,-87.670,-72.540,-31
repghostnet_100.in1k,3.520,96.480,24.520,75.480,4.07,224,0.875,bicubic,-86.770,-72.960,-1
regnety_006.pycls_in1k,3.453,96.547,24.920,75.080,6.06,224,0.875,bicubic,-87.937,-73.080,-38
ghostnet_100.in1k,3.427,96.573,25.120,74.880,5.18,224,0.875,bicubic,-86.753,-72.170,+1
resnet34.a3_in1k,3.373,96.627,23.387,76.613,21.80,224,0.950,bicubic,-86.567,-73.793,+6
legacy_seresnet34.in1k,3.347,96.653,23.813,76.187,21.96,224,0.875,bilinear,-87.553,-73.767,-23
resnet18.a2_in1k,3.267,96.733,22.373,77.627,11.69,288,1.000,bicubic,-86.303,-74.587,+13
efficientnet_lite0.ra_in1k,3.240,96.760,25.947,74.053,4.65,224,0.875,bicubic,-87.880,-71.693,-34
dla34.in1k,3.240,96.760,23.547,76.453,15.74,224,0.875,bilinear,-87.520,-74.103,-21
efficientvit_b0.r224_in1k,3.200,96.800,19.533,80.467,3.41,224,0.950,bicubic,-84.740,-76.597,+31
mobilenetv2_110d.ra_in1k,3.187,96.813,24.573,75.427,4.52,224,0.875,bicubic,-87.773,-72.967,-31
regnety_004.pycls_in1k,3.187,96.813,22.680,77.320,4.34,224,0.875,bicubic,-87.303,-74.850,-14
tinynet_c.in1k,3.120,96.880,21.520,78.480,2.46,184,0.875,bicubic,-84.660,-74.850,+29
mnasnet_100.rmsp_in1k,3.107,96.893,24.227,75.773,4.38,224,0.875,bicubic,-87.393,-73.243,-17
repghostnet_080.in1k,3.080,96.920,21.973,78.027,3.28,224,0.875,bicubic,-85.760,-74.727,+16
tf_efficientnet_lite0.in1k,3.040,96.960,22.893,77.107,4.65,224,0.875,bicubic,-88.010,-74.687,-39
skresnet18.ra_in1k,3.027,96.973,22.813,77.187,11.96,224,0.875,bicubic,-86.633,-74.417,0
mobileone_s1.apple_in1k,2.947,97.053,24.947,75.053,4.83,224,0.900,bilinear,-88.333,-72.873,-49
vgg19_bn.tv_in1k,2.947,97.053,23.440,76.560,143.68,224,0.875,bilinear,-87.133,-74.140,-12
tinynet_d.in1k,2.853,97.147,17.787,82.213,2.34,152,0.875,bicubic,-81.867,-77.383,+40
tf_mobilenetv3_large_075.in1k,2.840,97.160,21.560,78.440,3.99,224,0.875,bilinear,-86.800,-75.630,-3
efficientvit_m1.r224_in1k,2.827,97.173,19.600,80.400,2.98,224,0.875,bicubic,-83.963,-76.430,+27
resnet14t.c3_in1k,2.760,97.240,20.213,79.787,10.08,224,0.950,bicubic,-86.230,-76.517,+3
hrnet_w18_small_v2.ms_in1k,2.720,97.280,23.720,76.280,15.60,224,0.875,bilinear,-88.480,-74.180,-52
regnetx_008.pycls_in1k,2.667,97.333,22.453,77.547,7.26,224,0.875,bicubic,-88.383,-75.267,-49
vgg16_bn.tv_in1k,2.653,97.347,23.800,76.200,138.37,224,0.875,bilinear,-87.437,-73.570,-21
resnet34.gluon_in1k,2.653,97.347,21.680,78.320,21.80,224,0.875,bicubic,-88.327,-75.950,-47
lcnet_100.ra2_in1k,2.627,97.373,20.760,79.240,2.95,224,0.875,bicubic,-86.123,-76.220,+6
vgg16.tv_in1k,2.627,97.373,20.360,79.640,138.36,224,0.875,bilinear,-85.933,-76.440,+7
repvgg_b0.rvgg_in1k,2.560,97.440,24.000,76.000,15.82,224,0.875,bilinear,-88.830,-73.700,-66
densenet121.tv_in1k,2.547,97.453,22.653,77.347,7.98,224,0.875,bicubic,-88.343,-75.057,-47
regnetx_006.pycls_in1k,2.533,97.467,20.627,79.373,6.20,224,0.875,bicubic,-87.817,-76.803,-33
hrnet_w18_small.gluon_in1k,2.507,97.493,20.653,79.347,13.19,224,0.875,bicubic,-86.963,-76.407,-12
legacy_seresnet18.in1k,2.480,97.520,20.067,79.933,11.78,224,0.875,bicubic,-86.410,-76.633,-5
lcnet_075.ra2_in1k,2.320,97.680,17.173,82.827,2.36,224,0.875,bicubic,-83.650,-78.507,+21
mobilenetv3_small_075.lamb_in1k,2.307,97.693,15.893,84.107,2.04,224,0.875,bicubic,-80.723,-78.207,+30
efficientvit_m0.r224_in1k,2.293,97.707,16.493,83.507,2.35,224,0.875,bicubic,-80.057,-77.937,+30
repghostnet_058.in1k,2.253,97.747,18.320,81.680,2.55,224,0.875,bicubic,-84.287,-77.580,+13
repvgg_a1.rvgg_in1k,2.240,97.760,21.333,78.667,14.09,224,0.875,bilinear,-88.360,-76.317,-45
mobileone_s0.apple_in1k,2.240,97.760,17.467,82.533,5.29,224,0.875,bilinear,-85.990,-78.933,0
resnet18.a3_in1k,2.227,97.773,17.773,82.227,11.69,224,0.950,bicubic,-84.223,-78.107,+11
mobilenetv2_100.ra_in1k,2.147,97.853,19.933,80.067,3.50,224,0.875,bicubic,-87.453,-77.217,-23
regnety_002.pycls_in1k,2.120,97.880,18.920,81.080,3.16,224,0.875,bicubic,-85.250,-77.690,+2
vgg19.tv_in1k,2.107,97.893,20.760,79.240,143.67,224,0.875,bilinear,-86.943,-76.110,-19
vgg13_bn.tv_in1k,2.093,97.907,20.333,79.667,133.05,224,0.875,bilinear,-86.657,-76.387,-12
tf_mobilenetv3_small_100.in1k,2.027,97.973,15.827,84.173,2.54,224,0.875,bilinear,-83.163,-79.943,+12
mobilenetv3_small_100.lamb_in1k,1.987,98.013,17.093,82.907,2.54,224,0.875,bicubic,-83.233,-78.557,+10
repghostnet_050.in1k,1.987,98.013,16.507,83.493,2.31,224,0.875,bicubic,-83.073,-78.693,+11
tf_mobilenetv3_small_075.in1k,1.987,98.013,14.840,85.160,2.04,224,0.875,bilinear,-81.513,-80.000,+16
regnetx_004.pycls_in1k,1.920,98.080,19.147,80.853,5.16,224,0.875,bicubic,-87.010,-77.973,-22
resnet34.tv_in1k,1.853,98.147,20.053,79.947,21.80,224,0.875,bilinear,-88.097,-77.287,-42
tinynet_e.in1k,1.853,98.147,14.013,85.987,2.04,106,0.875,bicubic,-77.067,-78.527,+18
vgg13.tv_in1k,1.840,98.160,18.027,81.973,133.05,224,0.875,bilinear,-85.200,-78.303,-5
mobilenetv3_small_050.lamb_in1k,1.813,98.187,12.533,87.467,1.59,224,0.875,bicubic,-75.207,-78.767,+17
lcnet_050.ra2_in1k,1.787,98.213,13.867,86.133,1.88,224,0.875,bicubic,-80.013,-79.843,+13
mnasnet_small.lamb_in1k,1.773,98.227,15.080,84.920,2.03,224,0.875,bicubic,-82.647,-80.110,+5
dla46x_c.in1k,1.747,98.253,16.387,83.613,1.07,224,0.875,bilinear,-82.483,-78.883,+5
vgg11_bn.tv_in1k,1.720,98.280,18.093,81.907,132.87,224,0.875,bilinear,-85.780,-78.727,-15
dla60x_c.in1k,1.613,98.387,18.013,81.987,1.32,224,0.875,bilinear,-84.677,-78.147,-6
tf_mobilenetv3_large_minimal_100.in1k,1.613,98.387,17.120,82.880,3.92,224,0.875,bilinear,-87.337,-79.740,-34
mobilenetv2_050.lamb_in1k,1.613,98.387,14.200,85.800,1.97,224,0.875,bicubic,-82.297,-80.520,+3
resnet10t.c3_in1k,1.600,98.400,16.053,83.947,5.44,224,0.950,bicubic,-84.620,-79.687,-8
vgg11.tv_in1k,1.560,98.440,16.187,83.813,132.86,224,0.875,bilinear,-85.020,-80.093,-13
resnet18.gluon_in1k,1.547,98.453,16.640,83.360,11.69,224,0.875,bicubic,-86.823,-80.030,-26
hrnet_w18_small.ms_in1k,1.533,98.467,18.093,81.907,13.19,224,0.875,bilinear,-87.517,-79.027,-41
dla46_c.in1k,1.493,98.507,15.227,84.773,1.30,224,0.875,bilinear,-82.117,-79.723,-2
repvgg_a0.rvgg_in1k,1.467,98.533,17.587,82.413,9.11,224,0.875,bilinear,-87.813,-79.303,-45
regnetx_002.pycls_in1k,1.373,98.627,15.053,84.947,2.68,224,0.875,bicubic,-84.767,-80.917,-13
resnet18.tv_in1k,1.160,98.840,16.227,83.773,11.69,224,0.875,bilinear,-86.220,-80.063,-25
tf_mobilenetv3_small_minimal_100.in1k,1.040,98.960,11.493,88.507,2.04,224,0.875,bilinear,-80.360,-82.187,-1
resnet50.tv_in1k,0.000,100.000,14.453,85.547,25.56,224,0.875,bilinear,-91.880,-82.807,-120
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt112-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,49285.12,20.767,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,43905.96,23.312,1024,224,0.03,0.92,1.59
lcnet_035,40961.84,24.988,1024,224,0.03,1.04,1.64
lcnet_050,36451.18,28.081,1024,224,0.05,1.26,1.88
mobilenetv3_small_075,32291.57,31.7,1024,224,0.05,1.3,2.04
mobilenetv3_small_100,28935.54,35.379,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,27926.5,36.657,1024,224,0.06,1.41,2.04
tinynet_d,27303.88,37.493,1024,152,0.05,1.42,2.34
tf_mobilenetv3_small_075,26850.04,38.127,1024,224,0.05,1.3,2.04
tf_mobilenetv3_small_100,24320.21,42.094,1024,224,0.06,1.42,2.54
lcnet_075,22627.19,45.245,1024,224,0.1,1.99,2.36
mnasnet_small,20150.91,50.806,1024,224,0.07,2.16,2.03
levit_128s,19458.78,52.613,1024,224,0.31,1.88,7.78
lcnet_100,18910.66,54.139,1024,224,0.16,2.52,2.95
mobilenetv2_035,18047.72,56.728,1024,224,0.07,2.86,1.68
regnetx_002,17921.55,57.126,1024,224,0.2,2.16,2.68
regnety_002,16656.92,61.462,1024,224,0.2,2.17,3.16
ghostnet_050,16494.57,62.071,1024,224,0.05,1.77,2.59
mnasnet_050,15574.97,65.736,1024,224,0.11,3.07,2.22
mobilenetv2_050,14533.98,70.445,1024,224,0.1,3.64,1.97
tinynet_c,14397.76,71.111,1024,184,0.11,2.87,2.46
semnasnet_050,14065.61,72.79,1024,224,0.11,3.44,2.08
levit_128,13348.5,76.702,1024,224,0.41,2.71,9.21
vit_small_patch32_224,12899.41,79.373,1024,224,1.15,2.5,22.88
mixer_s32_224,12823.61,79.842,1024,224,1.0,2.28,19.1
lcnet_150,12599.24,81.264,1024,224,0.34,3.79,4.5
regnetx_004,12314.46,83.141,1024,224,0.4,3.14,5.16
cs3darknet_focus_s,11852.98,86.381,1024,256,0.69,2.7,3.27
mobilenetv3_large_075,11687.27,87.605,1024,224,0.16,4.0,3.99
resnet10t,11549.51,88.651,1024,224,1.1,2.43,5.44
cs3darknet_s,11540.93,88.716,1024,256,0.72,2.97,3.28
vit_tiny_r_s16_p8_224,10917.33,93.785,1024,224,0.44,2.06,6.34
ese_vovnet19b_slim_dw,10530.7,97.229,1024,224,0.4,5.28,1.9
mobilenetv3_rw,10453.43,97.947,1024,224,0.23,4.41,5.48
hardcorenas_a,10387.47,98.569,1024,224,0.23,4.38,5.26
mobilenetv3_large_100_miil,10298.68,99.419,1024,224,0.23,4.41,5.48
mobilenetv3_large_100,10295.13,99.453,1024,224,0.23,4.41,5.48
tf_mobilenetv3_large_075,10277.2,99.627,1024,224,0.16,4.0,3.99
gernet_s,10228.24,100.105,1024,224,0.75,2.65,8.17
mnasnet_075,10209.23,100.29,1024,224,0.23,4.77,3.17
levit_192,10099.95,101.375,1024,224,0.66,3.2,10.95
tf_mobilenetv3_large_minimal_100,10021.88,102.166,1024,224,0.22,4.4,3.92
hardcorenas_b,9469.88,108.121,1024,224,0.26,5.09,5.18
regnetx_006,9309.45,109.982,1024,224,0.61,3.98,6.2
tinynet_b,9298.6,110.113,1024,188,0.21,4.44,3.73
regnety_004,9296.36,110.137,1024,224,0.41,3.89,4.34
ghostnet_100,9264.87,110.513,1024,224,0.15,3.55,5.18
hardcorenas_c,9196.31,111.338,1024,224,0.28,5.01,5.52
resnet18,9171.4,111.64,1024,224,1.82,2.48,11.69
tf_mobilenetv3_large_100,9170.64,111.649,1024,224,0.23,4.41,5.48
mobilenetv2_075,9151.72,111.88,1024,224,0.22,5.86,2.64
swsl_resnet18,9145.07,111.962,1024,224,1.82,2.48,11.69
mnasnet_100,9128.95,112.159,1024,224,0.33,5.46,4.38
mnasnet_b1,9096.68,112.558,1024,224,0.33,5.46,4.38
gluon_resnet18_v1b,9092.93,112.604,1024,224,1.82,2.48,11.69
ssl_resnet18,9043.33,113.221,1024,224,1.82,2.48,11.69
semnasnet_075,8958.16,114.297,1024,224,0.23,5.54,2.91
hardcorenas_d,8756.1,116.935,1024,224,0.3,4.93,7.5
seresnet18,8678.54,117.981,1024,224,1.82,2.49,11.78
regnety_006,8404.01,121.832,1024,224,0.61,4.33,6.06
mobilenetv2_100,8360.81,122.466,1024,224,0.31,6.68,3.5
legacy_seresnet18,8318.12,123.094,1024,224,1.82,2.49,11.78
spnasnet_100,8246.33,124.165,1024,224,0.35,6.03,4.42
semnasnet_100,8027.18,127.555,1024,224,0.32,6.23,3.89
mnasnet_a1,8013.14,127.779,1024,224,0.32,6.23,3.89
levit_256,7862.24,130.228,1024,224,1.13,4.23,18.89
resnet18d,7721.04,132.614,1024,224,2.06,3.29,11.71
hardcorenas_f,7642.68,133.973,1024,224,0.35,5.57,8.2
hardcorenas_e,7588.05,134.938,1024,224,0.35,5.65,8.07
ese_vovnet19b_slim,7530.8,135.964,1024,224,1.69,3.52,3.17
efficientnet_lite0,7530.79,135.964,1024,224,0.4,6.74,4.65
ghostnet_130,7411.84,138.146,1024,224,0.24,4.6,7.36
regnetx_008,7376.89,138.798,1024,224,0.81,5.15,7.26
tinynet_a,7260.16,141.032,1024,192,0.35,5.41,6.19
tf_efficientnetv2_b0,7117.22,143.865,1024,224,0.73,4.77,7.14
fbnetc_100,7115.49,143.899,1024,224,0.4,6.51,5.57
regnety_008,7108.36,144.037,1024,224,0.81,5.25,6.26
xcit_nano_12_p16_224_dist,7019.86,145.861,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,7000.29,146.268,1024,224,0.56,4.17,3.05
edgenext_xx_small,6963.01,147.05,1024,256,0.33,4.21,1.33
levit_256d,6856.41,149.338,1024,224,1.4,4.93,26.21
deit_tiny_patch16_224,6794.46,150.698,1024,224,1.26,5.97,5.72
vit_tiny_patch16_224,6769.81,151.248,1024,224,1.26,5.97,5.72
tf_efficientnet_lite0,6667.82,153.562,1024,224,0.4,6.74,4.65
deit_tiny_distilled_patch16_224,6647.4,154.032,1024,224,1.27,6.01,5.91
efficientnet_b0,6576.16,155.702,1024,224,0.4,6.75,5.29
dla46_c,6538.59,156.596,1024,224,0.58,4.5,1.3
rexnetr_100,6369.79,160.748,1024,224,0.43,7.72,4.88
mnasnet_140,6297.39,162.595,1024,224,0.6,7.71,7.12
rexnet_100,6295.89,162.634,1024,224,0.41,7.44,4.8
efficientnet_b1_pruned,6269.62,163.315,1024,240,0.4,6.21,6.33
mobilenetv2_110d,6263.44,163.477,1024,224,0.45,8.71,4.52
regnetz_005,6057.27,169.042,1024,224,0.52,5.86,7.12
resnetblur18,6056.25,169.07,1024,224,2.34,3.39,11.69
pit_ti_distilled_224,6026.49,169.903,1024,224,0.71,6.23,5.1
pit_ti_224,5988.08,170.993,1024,224,0.7,6.19,4.85
nf_regnet_b0,5936.35,172.485,1024,256,0.64,5.58,8.76
mobilevitv2_050,5906.65,173.353,1024,256,0.48,8.04,1.37
tf_efficientnet_b0_ap,5894.61,173.707,1024,224,0.4,6.75,5.29
tf_efficientnet_b0,5892.32,173.774,1024,224,0.4,6.75,5.29
tf_efficientnet_b0_ns,5891.52,173.799,1024,224,0.4,6.75,5.29
visformer_tiny,5845.93,175.153,1024,224,1.27,5.72,10.32
resnet14t,5834.5,175.495,1024,224,1.69,5.8,10.08
dla46x_c,5690.98,179.922,1024,224,0.54,5.66,1.07
skresnet18,5640.2,181.543,1024,224,1.82,3.24,11.96
semnasnet_140,5544.22,184.685,1024,224,0.6,8.87,6.11
hrnet_w18_small,5451.81,187.816,1024,224,1.61,5.72,13.19
mobilenetv2_140,5399.1,189.649,1024,224,0.6,9.57,6.11
resnet34,5356.43,191.161,1024,224,3.67,3.74,21.8
dla60x_c,5292.02,193.487,1024,224,0.59,6.01,1.32
mobilevit_xxs,5275.05,194.109,1024,256,0.42,8.34,1.27
ese_vovnet19b_dw,5260.83,194.634,1024,224,1.34,8.25,6.54
gluon_resnet34_v1b,5203.76,196.769,1024,224,3.67,3.74,21.8
tv_resnet34,5193.55,197.156,1024,224,3.67,3.74,21.8
efficientnet_lite1,5144.81,199.024,1024,240,0.62,10.14,5.42
mixnet_s,5054.78,202.566,1024,224,0.25,6.25,4.13
seresnet34,5051.53,202.699,1024,224,3.67,3.74,21.96
gernet_m,5028.39,203.632,1024,224,3.02,5.24,21.14
fbnetv3_b,4982.49,205.508,1024,256,0.55,9.1,8.6
selecsls42,4945.53,207.043,1024,224,2.94,4.62,30.35
selecsls42b,4942.3,207.179,1024,224,2.98,4.62,32.46
vit_base_patch32_224_sam,4921.3,208.063,1024,224,4.41,5.01,88.22
vit_base_patch32_224,4918.17,208.197,1024,224,4.41,5.01,88.22
resnet34d,4834.03,211.82,1024,224,3.91,4.54,21.82
rexnetr_130,4789.2,213.803,1024,224,0.68,9.81,7.61
pit_xs_224,4766.56,214.816,1024,224,1.4,7.71,10.62
tf_efficientnetv2_b1,4738.51,216.09,1024,240,1.21,7.34,8.14
legacy_seresnet34,4737.15,216.152,1024,224,3.67,3.74,21.96
pit_xs_distilled_224,4722.37,216.826,1024,224,1.41,7.76,11.0
mixer_b32_224,4707.3,217.523,1024,224,3.24,6.29,60.29
tf_mixnet_s,4706.57,217.551,1024,224,0.25,6.25,4.13
tf_efficientnet_lite1,4662.36,219.62,1024,240,0.62,10.14,5.42
xcit_tiny_12_p16_224_dist,4593.26,222.924,1024,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,4592.09,222.979,1024,224,1.24,6.29,6.72
rexnet_130,4578.43,223.646,1024,224,0.68,9.71,7.56
levit_384,4538.82,225.597,1024,224,2.36,6.26,39.13
mobilenetv2_120d,4530.56,226.009,1024,224,0.69,11.97,5.83
edgenext_x_small,4436.58,230.795,1024,256,0.68,7.5,2.34
cs3darknet_focus_m,4399.27,232.755,1024,288,2.51,6.19,9.3
efficientnet_b0_g16_evos,4394.3,233.017,1024,224,1.01,7.42,8.11
efficientnet_es,4389.85,233.253,1024,224,1.81,8.73,5.44
efficientnet_es_pruned,4389.6,233.266,1024,224,1.81,8.73,5.44
resnet26,4383.27,233.604,1024,224,2.36,7.35,16.0
cs3darknet_m,4330.89,236.429,1024,288,2.63,6.69,9.31
fbnetv3_d,4328.59,236.555,1024,256,0.68,11.1,10.31
repvgg_b0,4286.85,238.858,1024,224,3.41,6.15,15.82
selecsls60,4286.11,238.899,1024,224,3.59,5.52,30.67
darknet17,4270.14,179.843,768,256,3.26,7.18,14.3
selecsls60b,4265.59,240.05,1024,224,3.63,5.52,32.77
efficientnet_b2_pruned,4264.69,240.099,1024,260,0.73,9.13,8.31
tf_efficientnet_es,4239.07,241.551,1024,224,1.81,8.73,5.44
regnetx_016,4196.72,243.986,1024,224,1.62,7.93,9.19
rexnetr_150,4170.12,245.545,1024,224,0.89,11.13,9.78
crossvit_tiny_240,4122.19,248.4,1024,240,1.57,9.08,7.01
dla34,4120.09,248.525,1024,224,3.07,5.02,15.74
mixer_s16_224,4085.83,250.611,1024,224,3.79,5.97,18.53
vit_small_patch32_384,4015.79,254.982,1024,384,3.45,8.25,22.92
rexnet_150,3990.72,256.583,1024,224,0.9,11.21,9.73
resnet26d,3989.2,256.681,1024,224,2.6,8.15,16.01
ecaresnet50d_pruned,3983.23,257.066,1024,224,2.53,6.43,19.94
efficientnet_lite2,3977.91,257.41,1024,260,0.89,12.9,6.09
gmlp_ti16_224,3944.3,259.603,1024,224,1.34,7.55,5.87
mobilevitv2_075,3905.27,262.199,1024,256,1.05,12.06,2.87
crossvit_9_240,3875.46,264.215,1024,240,1.85,9.52,8.55
darknet21,3872.25,198.322,768,256,3.93,7.47,20.86
nf_resnet26,3857.21,265.465,1024,224,2.41,7.35,16.0
convnext_nano_ols,3756.44,272.585,1024,224,2.5,8.37,15.6
convnext_nano_hnf,3749.56,273.084,1024,224,2.46,8.37,15.59
sedarknet21,3744.18,205.107,768,256,3.93,7.47,20.95
efficientnet_b1,3742.67,273.59,1024,256,0.77,12.22,7.79
crossvit_9_dagger_240,3734.14,274.215,1024,240,1.99,9.97,8.78
tf_efficientnet_b1,3731.51,274.409,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,3731.48,274.411,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ap,3726.19,274.8,1024,240,0.71,10.88,7.79
resnest14d,3644.88,280.93,1024,224,2.76,7.33,10.61
regnety_016,3624.55,282.503,1024,224,1.63,8.04,11.2
tf_efficientnet_lite2,3624.06,282.543,1024,260,0.89,12.9,6.09
vit_tiny_r_s16_p8_384,3594.94,213.622,768,384,1.34,6.49,6.36
tf_efficientnetv2_b2,3593.98,284.91,1024,260,1.72,9.84,10.1
poolformer_s12,3483.41,293.951,1024,224,1.82,5.53,11.92
resmlp_12_224,3460.87,295.868,1024,224,3.01,5.5,15.35
resmlp_12_distilled_224,3458.54,296.067,1024,224,3.01,5.5,15.35
mixnet_m,3455.23,296.35,1024,224,0.36,8.19,5.01
gmixer_12_224,3401.29,301.051,1024,224,2.67,7.26,12.7
resnext26ts,3375.26,303.371,1024,256,2.43,10.52,10.3
nf_ecaresnet26,3365.9,304.215,1024,224,2.41,7.36,16.0
nf_seresnet26,3360.23,304.729,1024,224,2.41,7.36,17.4
gernet_l,3328.59,307.626,1024,256,4.57,8.0,31.08
repvgg_a2,3325.03,307.955,1024,224,5.7,6.26,28.21
tf_mixnet_m,3322.0,308.236,1024,224,0.36,8.19,5.01
efficientnet_b3_pruned,3297.4,310.535,1024,300,1.04,11.86,9.86
nf_regnet_b1,3293.07,310.944,1024,288,1.02,9.2,10.22
seresnext26ts,3291.26,311.115,1024,256,2.43,10.52,10.39
eca_resnext26ts,3290.56,311.182,1024,256,2.43,10.52,10.3
legacy_seresnext26_32x4d,3269.09,313.225,1024,224,2.49,9.39,16.79
skresnet34,3229.96,317.02,1024,224,3.67,5.13,22.28
gcresnext26ts,3229.79,317.037,1024,256,2.43,10.53,10.48
nf_regnet_b2,3193.49,320.64,1024,272,1.22,9.27,14.31
convit_tiny,3179.42,322.058,1024,224,1.26,7.94,5.71
resnet26t,3149.41,325.128,1024,256,3.35,10.52,16.01
rexnetr_200,3135.78,244.904,768,224,1.59,15.11,16.52
ecaresnet101d_pruned,3129.51,327.195,1024,224,3.48,7.69,24.88
seresnext26tn_32x4d,3050.2,335.704,1024,224,2.7,10.09,16.81
seresnext26t_32x4d,3050.01,335.724,1024,224,2.7,10.09,16.81
ecaresnext50t_32x4d,3049.83,335.744,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,3048.36,335.905,1024,224,2.7,10.09,15.41
seresnext26d_32x4d,3037.9,337.063,1024,224,2.73,10.19,16.81
deit_small_patch16_224,3002.36,341.052,1024,224,4.61,11.95,22.05
rexnet_200,3001.86,255.828,768,224,1.56,14.91,16.37
vit_small_patch16_224,3000.37,341.279,1024,224,4.61,11.95,22.05
mobilevit_xs,2981.72,257.559,768,256,1.05,16.33,2.32
deit_small_distilled_patch16_224,2950.87,347.001,1024,224,4.63,12.02,22.44
pit_s_224,2945.22,347.668,1024,224,2.88,11.56,23.46
ecaresnetlight,2941.7,348.085,1024,224,4.11,8.42,30.16
coat_lite_tiny,2932.4,349.189,1024,224,1.6,11.65,5.72
eca_botnext26ts_256,2930.99,349.358,1024,256,2.46,11.6,10.59
pit_s_distilled_224,2918.75,350.821,1024,224,2.9,11.64,24.04
tf_efficientnet_b2_ns,2903.13,352.71,1024,260,1.02,13.83,9.11
tf_efficientnet_b2,2902.67,352.766,1024,260,1.02,13.83,9.11
tf_efficientnet_b2_ap,2901.98,352.851,1024,260,1.02,13.83,9.11
eca_halonext26ts,2883.09,355.163,1024,256,2.44,11.46,10.76
tresnet_m,2870.7,356.694,1024,224,5.74,7.31,31.39
botnet26t_256,2862.72,357.688,1024,256,3.32,11.98,12.49
regnetx_032,2852.1,359.019,1024,224,3.2,11.37,15.3
hrnet_w18_small_v2,2845.04,359.912,1024,224,2.62,9.65,15.6
deit3_small_patch16_224_in21ft1k,2837.48,360.868,1024,224,4.61,11.95,22.06
halonet26t,2832.73,361.477,1024,256,3.19,11.69,12.48
resnetv2_50,2829.74,361.858,1024,224,4.11,11.11,25.55
deit3_small_patch16_224,2828.59,362.004,1024,224,4.61,11.95,22.06
vgg11,2795.7,183.125,512,224,7.61,7.44,132.86
haloregnetz_b,2794.73,366.391,1024,224,1.97,11.94,11.68
bat_resnext26ts,2793.93,366.495,1024,256,2.53,12.51,10.73
vit_relpos_base_patch32_plus_rpn_256,2775.87,368.882,1024,256,7.68,8.01,119.42
vit_base_patch32_plus_256,2773.66,369.174,1024,256,7.79,7.76,119.48
dpn68b,2762.53,370.662,1024,224,2.35,10.47,12.61
vit_small_resnet26d_224,2758.05,371.264,1024,224,5.07,11.12,63.61
efficientnet_b2,2753.12,371.929,1024,288,1.12,16.2,9.11
coat_lite_mini,2752.31,372.037,1024,224,2.0,12.25,11.01
efficientnet_b2a,2752.1,372.068,1024,288,1.12,16.2,9.11
efficientnet_b0_gn,2748.63,372.536,1024,224,0.42,6.75,5.29
resnet50,2733.34,374.621,1024,224,4.11,11.11,25.56
ssl_resnet50,2732.73,374.705,1024,224,4.11,11.11,25.56
tv_resnet50,2732.0,374.804,1024,224,4.11,11.11,25.56
gluon_resnet50_v1b,2731.92,374.815,1024,224,4.11,11.11,25.56
swsl_resnet50,2730.89,374.957,1024,224,4.11,11.11,25.56
cspresnet50,2720.51,376.385,1024,256,4.54,11.5,21.62
resnet32ts,2719.03,376.593,1024,256,4.63,11.58,17.96
dpn68,2711.28,377.669,1024,224,2.35,10.47,12.61
mobilevitv2_100,2710.59,283.322,768,256,1.84,16.08,4.9
vovnet39a,2706.48,378.339,1024,224,7.09,6.73,22.6
resnetv2_50t,2687.6,380.997,1024,224,4.32,11.82,25.57
resnet33ts,2683.32,381.605,1024,256,4.76,11.66,19.68
resnetv2_50d,2678.25,382.327,1024,224,4.35,11.92,25.57
efficientnet_em,2663.14,384.496,1024,240,3.04,14.34,6.9
mixnet_l,2651.17,289.672,768,224,0.58,10.84,7.33
visformer_small,2638.82,388.04,1024,224,4.88,11.43,40.22
ese_vovnet39b,2631.4,389.135,1024,224,7.09,6.74,24.57
resnest26d,2624.21,390.2,1024,224,3.64,9.97,17.07
vit_relpos_small_patch16_224,2615.53,391.496,1024,224,4.59,13.05,21.98
seresnet33ts,2613.57,391.79,1024,256,4.76,11.66,19.78
eca_resnet33ts,2609.68,392.373,1024,256,4.76,11.66,19.68
vit_srelpos_small_patch16_224,2607.7,392.67,1024,224,4.59,12.16,21.97
eca_vovnet39b,2607.14,392.755,1024,224,7.09,6.74,22.6
gluon_resnet50_v1c,2599.91,393.848,1024,224,4.35,11.92,25.58
tf_efficientnet_em,2599.16,393.961,1024,240,3.04,14.34,6.9
cspresnet50w,2589.44,395.44,1024,256,5.04,12.19,28.12
resnet50d,2584.02,396.27,1024,224,4.35,11.92,25.58
legacy_seresnet50,2582.34,396.527,1024,224,3.88,10.6,28.09
resnet50t,2580.37,396.829,1024,224,4.32,11.82,25.57
twins_svt_small,2576.63,397.407,1024,224,2.94,13.75,24.06
gluon_resnet50_v1d,2570.89,398.293,1024,224,4.35,11.92,25.58
gcresnet33ts,2569.2,398.556,1024,256,4.76,11.68,19.88
cspresnet50d,2560.0,399.988,1024,256,4.86,12.55,21.64
lambda_resnet26t,2551.69,401.29,1024,256,3.02,11.87,10.96
tf_mixnet_l,2550.79,301.072,768,224,0.58,10.84,7.33
selecsls84,2543.3,402.613,1024,224,5.9,7.57,50.95
vgg11_bn,2541.42,201.45,512,224,7.62,7.44,132.87
dla60,2525.2,405.498,1024,224,4.26,10.16,22.04
cs3darknet_focus_l,2520.03,406.331,1024,288,5.9,10.16,21.15
res2net50_48w_2s,2502.4,409.196,1024,224,4.18,11.72,25.29
cs3darknet_l,2485.99,411.896,1024,288,6.16,10.83,21.16
densenet121,2467.71,414.945,1024,224,2.87,6.9,7.98
xcit_nano_12_p16_384_dist,2466.74,415.111,1024,384,1.64,12.15,3.05
xcit_tiny_24_p16_224_dist,2463.21,415.705,1024,224,2.34,11.82,12.12
tv_densenet121,2461.4,416.011,1024,224,2.87,6.9,7.98
xcit_tiny_24_p16_224,2457.2,416.72,1024,224,2.34,11.82,12.12
seresnet50,2438.84,419.859,1024,224,4.11,11.13,28.09
convnext_tiny_hnfd,2399.93,426.664,1024,224,4.47,13.44,28.59
convnext_tiny_hnf,2395.62,427.433,1024,224,4.47,13.44,28.59
efficientnet_lite3,2383.15,214.83,512,300,1.65,21.85,8.2
efficientnet_b0_g8_gn,2375.8,431.002,1024,224,0.66,6.75,6.56
convnext_tiny_in22ft1k,2362.17,433.485,1024,224,4.47,13.44,28.59
densenet121d,2362.07,433.503,1024,224,3.11,7.7,8.0
convnext_tiny,2359.72,433.936,1024,224,4.47,13.44,28.59
cs3sedarknet_l,2353.08,435.161,1024,288,6.16,10.83,21.91
resnetaa50d,2350.26,435.685,1024,224,5.39,12.44,25.58
efficientnet_cc_b0_4e,2334.0,438.721,1024,224,0.41,9.42,13.31
seresnet50t,2333.68,438.78,1024,224,4.32,11.83,28.1
ecaresnet50d,2316.33,442.066,1024,224,4.35,11.93,25.58
resnetblur50,2298.66,445.465,1024,224,5.16,12.02,25.56
mobilevit_s,2279.76,336.866,768,256,2.03,19.94,5.58
convnext_nano,2276.19,449.862,1024,288,4.06,13.84,15.59
resnetrs50,2276.18,449.864,1024,224,4.48,12.14,35.69
vit_base_resnet26d_224,2262.15,452.654,1024,224,6.97,13.16,101.4
gluon_resnet50_v1s,2257.16,453.655,1024,224,5.47,13.52,25.68
vovnet57a,2253.6,454.372,1024,224,8.95,7.52,36.64
adv_inception_v3,2250.27,455.041,1024,299,5.73,8.97,23.83
gluon_inception_v3,2249.35,455.229,1024,299,5.73,8.97,23.83
tf_inception_v3,2245.22,456.064,1024,299,5.73,8.97,23.83
tf_efficientnet_cc_b0_4e,2243.01,456.518,1024,224,0.41,9.42,13.31
inception_v3,2240.78,456.965,1024,299,5.73,8.97,23.83
tf_efficientnet_cc_b0_8e,2240.71,456.986,1024,224,0.42,9.42,24.01
densenetblur121d,2235.56,458.037,1024,224,3.11,7.9,8.0
resnest50d_1s4x24d,2213.57,462.589,1024,224,4.43,13.57,25.68
res2net50_26w_4s,2209.54,463.432,1024,224,4.28,12.61,25.7
ssl_resnext50_32x4d,2205.13,464.359,1024,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2204.8,464.429,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,2203.2,464.765,1024,224,4.26,14.4,25.03
resnext50_32x4d,2199.44,465.561,1024,224,4.26,14.4,25.03
tv_resnext50_32x4d,2198.23,465.818,1024,224,4.26,14.4,25.03
regnetx_040,2190.95,467.362,1024,224,3.99,12.2,22.12
cspresnext50,2182.4,469.194,1024,256,4.05,15.86,20.57
resnetblur50d,2182.09,469.263,1024,224,5.4,12.82,25.58
regnetz_b16,2180.8,469.54,1024,288,2.39,16.43,9.72
ese_vovnet57b,2171.57,471.535,1024,224,8.95,7.52,38.61
tf_efficientnet_lite3,2166.77,236.285,512,300,1.65,21.85,8.2
mobilevitv2_125,2151.63,356.926,768,256,2.86,20.1,7.48
efficientnet_cc_b0_8e,2149.58,476.36,1024,224,0.42,9.42,24.01
semobilevit_s,2143.19,358.331,768,256,2.03,19.95,5.74
twins_pcpvt_small,2142.01,478.043,1024,224,3.83,18.08,24.11
nf_regnet_b3,2133.81,479.88,1024,320,2.05,14.61,18.59
tf_efficientnetv2_b3,2121.62,482.639,1024,300,3.04,15.74,14.36
seresnetaa50d,2118.99,483.236,1024,224,5.4,12.46,28.11
efficientnetv2_rw_t,2117.33,483.616,1024,288,3.19,16.42,13.65
gcresnext50ts,2113.73,484.438,1024,256,3.75,15.46,15.67
edgenext_small,2107.47,485.876,1024,320,1.97,14.16,5.59
resnext50d_32x4d,2094.1,488.98,1024,224,4.5,15.2,25.05
dla60x,2080.97,492.062,1024,224,3.54,13.8,17.35
res2net50_14w_8s,2066.79,495.441,1024,224,4.21,13.28,25.06
gc_efficientnetv2_rw_t,2061.37,496.743,1024,288,3.2,16.45,13.68
sehalonet33ts,2057.14,373.322,768,256,3.55,14.7,13.69
gcresnet50t,2055.89,498.068,1024,256,5.42,14.67,25.9
skresnet50,2048.81,499.79,1024,224,4.11,12.5,25.8
fbnetv3_g,2047.87,500.019,1024,288,1.77,21.09,16.62
nf_ecaresnet50,2039.26,502.129,1024,224,4.21,11.13,25.56
nf_seresnet50,2037.45,502.576,1024,224,4.21,11.13,28.09
cs3darknet_focus_x,2026.01,505.415,1024,256,8.03,10.69,35.02
dla60_res2net,2015.55,508.035,1024,224,4.15,12.34,20.85
lambda_resnet26rpt_256,2015.24,190.536,384,256,3.16,11.87,10.99
seresnext50_32x4d,2010.4,509.339,1024,224,4.26,14.42,27.56
legacy_seresnext50_32x4d,2003.57,511.076,1024,224,4.26,14.42,27.56
repvgg_b1g4,2003.2,511.169,1024,224,8.15,10.64,39.97
gluon_seresnext50_32x4d,2002.45,511.358,1024,224,4.26,14.42,27.56
densenet169,1987.41,515.228,1024,224,3.4,7.3,14.15
res2next50,1967.78,520.369,1024,224,4.2,13.71,24.67
vit_relpos_small_patch16_rpn_224,1966.99,520.579,1024,224,4.59,13.05,21.97
skresnet50d,1957.14,523.201,1024,224,4.36,13.31,25.82
xcit_small_12_p16_224_dist,1952.72,524.382,1024,224,4.82,12.58,26.25
crossvit_small_240,1952.54,524.431,1024,240,5.63,18.17,26.86
xcit_small_12_p16_224,1952.1,524.55,1024,224,4.82,12.58,26.25
cs3sedarknet_xdw,1919.73,533.397,1024,256,5.97,17.18,21.6
swin_tiny_patch4_window7_224,1915.52,534.569,1024,224,4.51,17.06,28.29
vit_relpos_medium_patch16_cls_224,1909.56,536.236,1024,224,8.03,18.24,38.76
mixnet_xl,1903.31,268.993,512,224,0.93,14.57,11.9
dla60_res2next,1893.19,540.873,1024,224,3.49,13.17,17.03
xcit_nano_12_p8_224_dist,1887.0,542.649,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,1883.21,543.74,1024,224,2.16,15.71,3.05
cspdarknet53,1881.33,408.211,768,256,6.57,16.81,27.64
gmlp_s16_224,1873.82,546.464,1024,224,4.42,15.1,19.42
edgenext_small_rw,1831.96,558.95,1024,320,2.46,14.85,7.83
ecaresnet26t,1828.87,559.898,1024,320,5.24,16.44,16.01
vit_small_r26_s32_224,1825.94,560.792,1024,224,3.56,9.85,36.43
vgg13,1819.73,281.346,512,224,11.31,12.25,133.05
poolformer_s24,1804.4,567.487,1024,224,3.41,10.68,21.39
crossvit_15_240,1799.06,569.173,1024,240,5.81,19.77,27.53
vit_relpos_medium_patch16_224,1794.09,570.75,1024,224,7.97,17.02,38.75
vit_srelpos_medium_patch16_224,1787.05,573.0,1024,224,7.96,16.21,38.74
mobilevitv2_150,1774.77,288.477,512,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,1773.43,288.695,512,256,4.09,24.11,10.59
sebotnet33ts_256,1762.47,217.864,384,256,3.89,17.46,13.7
resmlp_24_224,1761.92,581.171,1024,224,5.96,10.91,30.02
efficientnet_b3,1761.71,290.615,512,320,2.01,26.52,12.23
efficientnet_b3a,1761.71,290.614,512,320,2.01,26.52,12.23
resmlp_24_distilled_224,1760.69,581.576,1024,224,5.96,10.91,30.02
regnetx_064,1757.88,436.877,768,224,6.49,16.37,26.21
resnest50d,1750.78,584.87,1024,224,5.4,14.36,27.48
gmixer_24_224,1741.35,588.036,1024,224,5.28,14.45,24.72
swin_s3_tiny_224,1737.42,589.369,1024,224,4.64,19.13,28.33
crossvit_15_dagger_240,1736.98,589.517,1024,240,6.13,20.43,28.21
vit_base_resnet50d_224,1722.65,594.42,1024,224,8.73,16.92,110.97
resnetv2_101,1717.18,596.314,1024,224,7.83,16.23,44.54
tf_efficientnet_b3_ap,1706.97,299.935,512,300,1.87,23.83,12.23
tf_efficientnet_b3,1705.74,300.151,512,300,1.87,23.83,12.23
tf_efficientnet_b3_ns,1705.51,300.191,512,300,1.87,23.83,12.23
lambda_resnet50ts,1694.68,604.231,1024,256,5.07,17.48,21.54
dla102,1693.75,604.56,1024,224,7.19,14.18,33.27
darknetaa53,1689.16,454.651,768,288,10.08,15.68,36.02
gluon_resnet101_v1b,1679.75,609.599,1024,224,7.83,16.23,44.55
tv_resnet101,1679.18,609.808,1024,224,7.83,16.23,44.55
resnet101,1676.67,610.719,1024,224,7.83,16.23,44.55
repvgg_b1,1663.53,615.546,1024,224,13.16,10.64,57.42
resnetv2_101d,1653.14,619.414,1024,224,8.07,17.04,44.56
gluon_resnet101_v1c,1649.02,620.96,1024,224,8.08,17.04,44.57
vgg13_bn,1642.15,311.774,512,224,11.33,12.25,133.05
cait_xxs24_224,1641.65,623.749,1024,224,2.53,20.29,11.96
res2net50_26w_6s,1639.34,624.627,1024,224,6.33,15.28,37.05
hrnet_w18,1631.65,627.569,1024,224,4.32,16.31,21.3
vit_large_patch32_224,1623.29,630.805,1024,224,15.39,13.3,306.54
wide_resnet50_2,1618.04,632.851,1024,224,11.43,14.4,68.88
gluon_resnet101_v1d,1616.88,633.307,1024,224,8.08,17.04,44.57
xcit_tiny_12_p16_384_dist,1614.06,634.414,1024,384,3.64,18.26,6.72
regnetv_040,1604.78,478.557,768,288,6.6,20.3,20.64
halonet50ts,1600.56,639.764,1024,256,5.3,19.2,22.73
regnety_040,1597.66,480.688,768,288,6.61,20.3,20.65
darknet53,1585.01,484.528,768,288,11.78,15.68,41.61
efficientnet_cc_b1_8e,1576.34,649.593,1024,240,0.75,15.44,39.72
coat_lite_small,1576.03,649.72,1024,224,3.96,22.09,19.84
regnety_032,1576.03,649.722,1024,288,5.29,18.61,19.44
resnetv2_50x1_bit_distilled,1575.9,649.775,1024,224,4.23,11.11,25.55
swinv2_cr_tiny_224,1574.62,650.304,1024,224,4.66,28.45,28.33
legacy_seresnet101,1569.43,652.454,1024,224,7.61,15.74,49.33
vit_base_patch32_384,1551.76,659.885,1024,384,13.06,16.5,88.3
ese_vovnet39b_evos,1551.37,660.05,1024,224,7.07,6.74,24.58
swinv2_cr_tiny_ns_224,1546.02,662.33,1024,224,4.66,28.45,28.33
vit_tiny_patch16_384,1542.96,663.648,1024,384,4.7,25.39,5.79
lamhalobotnet50ts_256,1533.2,667.873,1024,256,5.02,18.44,22.57
tf_efficientnet_cc_b1_8e,1527.24,670.479,1024,240,0.75,15.44,39.72
resnetaa101d,1521.49,673.009,1024,224,9.12,17.56,44.57
densenet201,1515.85,675.514,1024,224,4.34,7.85,20.01
resnetaa50,1510.7,677.817,1024,288,8.52,19.24,25.56
mixer_l32_224,1508.54,678.791,1024,224,11.27,19.86,206.94
seresnet101,1502.48,681.526,1024,224,7.84,16.27,49.33
vit_base_r26_s32_224,1492.4,686.129,1024,224,6.81,12.36,101.38
gluon_resnet101_v1s,1485.37,689.375,1024,224,9.19,18.64,44.67
twins_pcpvt_base,1484.93,689.584,1024,224,6.68,25.25,43.83
mobilevitv2_175,1472.18,347.77,512,256,5.54,28.13,14.25
mobilevitv2_175_in22ft1k,1472.06,347.8,512,256,5.54,28.13,14.25
nf_resnet101,1469.16,696.987,1024,224,8.01,16.23,44.55
resnest50d_4s2x40d,1467.36,697.84,1024,224,4.4,17.94,30.42
vgg16,1464.57,349.576,512,224,15.47,13.56,138.36
resnetv2_50d_frn,1463.93,699.474,1024,224,4.33,11.92,25.59
resnetblur101d,1458.09,702.276,1024,224,9.12,17.94,44.57
ecaresnet101d,1457.01,702.796,1024,224,8.08,17.07,44.57
sequencer2d_s,1455.29,703.627,1024,224,4.96,11.31,27.65
nf_resnet50,1445.9,708.195,1024,288,6.88,18.37,25.56
convnext_small,1445.85,708.22,1024,224,8.71,21.56,50.22
convnext_small_in22ft1k,1443.98,709.135,1024,224,8.71,21.56,50.22
regnetz_c16,1437.42,356.181,512,320,3.92,25.88,13.46
tresnet_l,1432.52,714.812,1024,224,10.88,11.9,55.99
cs3darknet_x,1429.24,716.453,1024,288,10.6,14.36,35.05
dla102x,1397.97,732.475,1024,224,5.89,19.42,26.31
ssl_resnext101_32x4d,1392.96,735.11,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1392.73,735.231,1024,224,8.01,21.23,44.18
resnext101_32x4d,1390.48,736.423,1024,224,8.01,21.23,44.18
botnet50ts_256,1389.99,276.247,384,256,5.54,22.23,22.74
skresnext50_32x4d,1389.9,736.732,1024,224,4.5,17.18,27.48
gluon_resnext101_32x4d,1389.41,736.987,1024,224,8.01,21.23,44.18
nest_tiny,1388.05,553.283,768,224,5.83,25.48,17.06
resnet50_gn,1386.72,738.422,1024,224,4.14,11.11,25.56
resnetv2_50d_evob,1383.3,740.244,1024,224,4.33,11.92,25.59
res2net50_26w_8s,1373.33,745.622,1024,224,8.37,17.95,48.4
halo2botnet50ts_256,1372.33,559.619,768,256,5.02,21.78,22.64
regnetx_080,1370.56,747.125,1024,224,8.02,14.06,39.57
cs3sedarknet_x,1368.84,748.067,1024,288,10.6,14.37,35.4
jx_nest_tiny,1362.62,563.605,768,224,5.83,25.48,17.06
convit_small,1355.18,755.603,1024,224,5.76,17.87,27.78
res2net101_26w_4s,1353.43,756.586,1024,224,8.1,18.45,45.21
xception,1340.72,572.814,768,299,8.4,35.83,22.86
mixer_b16_224_miil,1340.03,764.147,1024,224,12.62,14.53,59.88
repvgg_b2g4,1335.06,766.992,1024,224,12.63,12.9,61.76
vgg16_bn,1335.02,383.503,512,224,15.5,13.56,138.37
mixer_b16_224,1328.05,771.041,1024,224,12.62,14.53,59.88
twins_svt_base,1307.2,783.34,1024,224,8.59,26.33,56.07
dpn92,1299.67,787.878,1024,224,6.54,18.21,37.67
cs3edgenet_x,1289.05,794.37,1024,288,14.59,16.36,47.82
ese_vovnet99b_iabn,1282.63,798.345,1024,224,16.49,11.27,63.2
crossvit_18_240,1272.74,804.553,1024,240,9.05,26.26,43.27
regnety_040s_gn,1271.39,805.405,1024,224,4.03,12.29,20.65
eca_nfnet_l0,1271.38,805.411,1024,288,7.12,17.29,24.14
nfnet_l0,1269.37,806.681,1024,288,7.13,17.29,35.07
seresnext101_32x4d,1268.1,807.494,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1267.59,807.817,1024,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1265.67,809.045,1024,224,8.02,21.26,48.96
nf_ecaresnet101,1264.2,809.986,1024,224,8.01,16.27,44.55
vit_relpos_medium_patch16_rpn_224,1263.66,810.331,1024,224,7.97,17.02,38.73
nf_seresnet101,1261.42,811.77,1024,224,8.02,16.27,49.33
mobilevitv2_200,1256.15,305.684,384,256,7.22,32.15,18.45
mobilevitv2_200_in22ft1k,1255.83,305.762,384,256,7.22,32.15,18.45
xception41p,1254.65,408.071,512,299,9.25,39.86,26.91
resnet51q,1254.6,816.185,1024,288,8.07,20.94,35.7
efficientnet_el,1254.42,408.143,512,300,8.0,30.7,10.59
efficientnet_el_pruned,1254.28,408.188,512,300,8.0,30.7,10.59
ese_vovnet99b,1240.88,825.205,1024,224,16.51,11.27,63.2
xcit_tiny_12_p8_224_dist,1237.16,827.688,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,1235.05,829.105,1024,224,4.81,23.6,6.71
crossvit_18_dagger_240,1235.02,829.126,1024,240,9.5,27.03,44.27
vgg19,1227.1,417.229,512,224,19.63,14.86,143.67
tf_efficientnet_el,1226.94,417.286,512,300,8.0,30.7,10.59
poolformer_s36,1217.09,841.334,1024,224,5.0,15.82,30.86
hrnet_w32,1204.83,849.897,1024,224,8.97,22.02,41.23
hrnet_w30,1202.88,851.275,1024,224,8.15,21.21,37.71
resnetv2_152,1196.21,856.023,1024,224,11.55,22.56,60.19
nfnet_f0,1193.84,857.722,1024,256,12.62,18.05,71.49
swin_small_patch4_window7_224,1179.92,867.841,1024,224,8.77,27.47,49.61
resmlp_36_224,1179.88,867.87,1024,224,8.91,16.33,44.69
vit_small_resnet50d_s16_224,1179.15,868.406,1024,224,13.48,24.82,57.53
resmlp_36_distilled_224,1179.01,868.509,1024,224,8.91,16.33,44.69
efficientnet_lite4,1178.02,325.958,384,380,4.04,45.66,13.01
tv_resnet152,1172.68,873.198,1024,224,11.56,22.56,60.19
gluon_resnet152_v1b,1172.67,873.208,1024,224,11.56,22.56,60.19
resnet152,1170.69,874.682,1024,224,11.56,22.56,60.19
mixnet_xxl,1163.99,329.888,384,224,2.04,23.43,23.96
resnetv2_152d,1163.57,880.032,1024,224,11.8,23.36,60.2
ecaresnet50t,1162.34,880.97,1024,320,8.82,24.13,25.57
resnet61q,1160.54,882.331,1024,288,9.87,21.52,36.85
vit_base_patch16_224_miil,1154.75,886.763,1024,224,17.58,23.9,86.54
repvgg_b2,1154.25,887.146,1024,224,20.45,12.9,89.02
inception_v4,1153.57,887.661,1024,299,12.28,15.09,42.68
swinv2_tiny_window8_256,1152.79,888.266,1024,256,5.96,24.57,28.35
densenet161,1147.8,892.122,1024,224,7.79,11.06,28.68
gluon_resnet152_v1c,1146.71,892.979,1024,224,11.8,23.36,60.21
gluon_resnet152_v1d,1141.31,897.204,1024,224,11.8,23.36,60.21
sequencer2d_m,1138.06,899.765,1024,224,6.55,14.26,38.31
vit_base_patch16_224_sam,1132.42,904.242,1024,224,17.58,23.9,86.57
deit_base_patch16_224,1132.42,904.245,1024,224,17.58,23.9,86.57
vit_base_patch16_224,1132.21,904.413,1024,224,17.58,23.9,86.57
dla169,1130.13,906.071,1024,224,11.6,20.2,53.39
regnetx_120,1129.55,453.263,512,224,12.13,21.37,46.11
volo_d1_224,1126.62,908.904,1024,224,6.94,24.43,26.63
vgg19_bn,1122.31,456.189,512,224,19.66,14.86,143.68
deit_base_distilled_patch16_224,1116.6,917.056,1024,224,17.68,24.05,87.34
xception41,1110.46,461.057,512,299,9.28,39.86,26.97
cait_xxs36_224,1104.66,926.97,1024,224,3.77,30.34,17.3
tf_efficientnet_lite4,1091.59,351.767,384,380,4.04,45.66,13.01
convmixer_1024_20_ks9_p14,1091.56,938.092,1024,224,5.55,5.51,24.38
deit3_base_patch16_224,1090.26,939.213,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1088.57,940.667,1024,224,17.58,23.9,86.59
legacy_seresnet152,1086.41,942.544,1024,224,11.33,22.08,66.82
tnt_s_patch16_224,1079.54,948.54,1024,224,5.24,24.37,23.76
regnety_120,1077.58,475.125,512,224,12.14,21.38,51.82
repvgg_b3g4,1077.28,950.524,1024,224,17.89,15.1,83.83
vit_relpos_base_patch16_clsgap_224,1077.01,950.767,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1076.19,951.489,1024,224,17.6,25.12,86.43
gluon_resnet152_v1s,1074.28,953.181,1024,224,12.92,24.96,60.32
twins_pcpvt_large,1061.77,964.416,1024,224,9.84,35.82,60.99
seresnet152,1047.32,977.721,1024,224,11.57,22.61,66.82
beit_base_patch16_224,1045.12,979.774,1024,224,17.58,23.9,86.53
xcit_small_24_p16_224_dist,1038.39,986.125,1024,224,9.1,23.64,47.67
xcit_small_24_p16_224,1037.69,986.793,1024,224,9.1,23.64,47.67
coat_tiny,1036.7,987.731,1024,224,4.35,27.2,5.5
dm_nfnet_f0,1035.11,989.253,1024,256,12.62,18.05,71.49
nf_regnet_b4,1027.0,997.065,1024,384,4.7,28.61,30.21
vit_relpos_base_patch16_224,1017.61,1006.263,1024,224,17.51,24.97,86.43
convnext_base_in22ft1k,1006.85,1017.02,1024,224,15.38,28.75,88.59
convnext_base,1006.73,1017.126,1024,224,15.38,28.75,88.59
pit_b_224,993.61,515.277,512,224,12.42,32.94,73.76
pit_b_distilled_224,985.16,519.696,512,224,12.5,33.07,74.79
tresnet_xl,983.38,1041.292,1024,224,15.17,15.34,78.44
efficientnetv2_s,976.0,1049.166,1024,384,8.44,35.77,21.46
dla102x2,973.1,526.138,512,224,9.34,29.91,41.28
cs3se_edgenet_x,972.26,1053.196,1024,320,18.01,20.21,50.72
vit_small_patch16_36x1_224,972.14,1053.329,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,966.28,1059.712,1024,224,9.07,50.27,49.7
swinv2_cr_small_ns_224,955.69,1071.465,1024,224,9.08,50.27,49.7
tf_efficientnetv2_s_in21ft1k,955.24,1071.964,1024,384,8.44,35.77,21.46
tf_efficientnetv2_s,955.13,1072.086,1024,384,8.44,35.77,21.46
vit_small_patch16_18x2_224,948.32,1079.793,1024,224,13.71,35.69,64.67
wide_resnet101_2,939.08,1090.412,1024,224,22.8,21.23,126.89
regnetx_160,936.53,546.684,512,224,15.99,25.52,54.28
regnety_080,933.52,548.447,512,288,13.22,29.69,39.18
regnetz_b16_evos,933.51,822.691,768,288,2.36,16.43,9.74
efficientnetv2_rw_s,931.24,1099.596,1024,384,8.72,38.03,23.94
resnetv2_50d_gn,920.9,1111.946,1024,288,7.24,19.7,25.57
twins_svt_large,918.22,1115.185,1024,224,15.15,35.1,99.27
efficientnet_b4,917.89,418.339,384,384,4.51,50.04,19.34
regnetz_040,913.72,420.249,384,320,6.35,37.78,27.12
xception65p,910.71,562.184,512,299,13.91,52.48,39.82
regnetz_040h,909.33,422.274,384,320,6.43,37.94,28.94
dpn98,906.73,1129.316,1024,224,11.73,25.2,61.57
repvgg_b3,901.67,1135.661,1024,224,29.16,15.1,123.09
resnetrs101,898.53,1139.62,1024,288,13.56,28.53,63.62
gluon_resnext101_64x4d,887.37,1153.955,1024,224,15.52,31.21,83.46
nest_small,885.28,867.51,768,224,10.35,40.04,38.35
poolformer_m36,879.83,1163.84,1024,224,8.8,22.02,56.17
regnetz_d8,877.84,1166.489,1024,320,6.19,37.08,23.37
jx_nest_small,874.11,878.596,768,224,10.35,40.04,38.35
ssl_resnext101_32x8d,874.01,1171.597,1024,224,16.48,31.21,88.79
swsl_resnext101_32x8d,873.31,1172.532,1024,224,16.48,31.21,88.79
resnext101_32x8d,873.01,1172.932,1024,224,16.48,31.21,88.79
ig_resnext101_32x8d,872.81,1173.211,1024,224,16.48,31.21,88.79
regnetz_d32,869.58,1177.564,1024,320,9.33,37.08,27.58
inception_resnet_v2,868.78,1178.653,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,868.32,1179.275,1024,299,13.18,25.06,55.84
xcit_tiny_24_p16_384_dist,866.54,1181.7,1024,384,6.87,34.29,12.12
cait_s24_224,865.33,1183.354,1024,224,9.35,40.58,46.92
resnest101e,858.93,894.122,768,256,13.38,28.66,48.28
tf_efficientnet_b4,858.91,447.067,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,858.7,447.171,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,858.52,447.267,384,380,4.49,49.49,19.34
swin_s3_small_224,853.54,899.766,768,224,9.43,37.84,49.74
regnetv_064,852.1,600.857,512,288,10.55,27.11,30.58
regnety_064,851.33,601.396,512,288,10.56,27.11,30.58
resnet200,847.44,1208.333,1024,224,15.07,32.19,64.67
gluon_seresnext101_64x4d,834.87,1226.518,1024,224,15.53,31.25,88.23
coat_mini,833.41,1228.669,1024,224,6.82,33.68,10.34
swin_base_patch4_window7_224,832.6,1229.869,1024,224,15.47,36.63,87.77
resnet101d,816.8,1253.661,1024,320,16.48,34.77,44.57
gluon_xception65,816.5,627.052,512,299,13.96,52.48,39.92
xception65,811.16,631.185,512,299,13.96,52.48,39.92
resnetv2_50d_evos,810.51,947.543,768,288,7.15,19.7,25.59
convnext_tiny_384_in22ft1k,807.27,634.218,512,384,13.14,39.48,28.59
gmlp_b16_224,789.84,1296.449,1024,224,15.78,30.21,73.08
hrnet_w40,787.85,1299.728,1024,224,12.75,25.29,57.56
crossvit_base_240,787.17,975.639,768,240,21.22,36.33,105.03
hrnet_w44,771.15,1327.87,1024,224,14.94,26.92,67.06
swinv2_tiny_window16_256,763.4,670.672,512,256,6.68,39.02,28.35
mobilevitv2_150_384_in22ft1k,757.55,337.918,256,384,9.2,54.25,10.59
xcit_medium_24_p16_224_dist,748.7,1367.689,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,748.18,1368.635,1024,224,16.13,31.71,84.4
tresnet_m_448,743.16,1377.885,1024,448,22.94,29.21,31.39
vit_large_r50_s32_224,742.19,1379.692,1024,224,19.58,24.41,328.99
hrnet_w48,738.63,1386.343,1024,224,17.34,28.56,77.47
vit_base_patch16_plus_240,738.11,1387.321,1024,240,27.41,33.08,117.56
sequencer2d_l,736.17,1390.978,1024,224,9.74,22.12,54.3
xcit_small_12_p16_384_dist,715.91,1430.327,1024,384,14.14,36.51,26.25
swinv2_small_window8_256,710.32,1441.594,1024,256,11.58,40.14,49.73
swin_s3_base_224,693.67,1476.198,1024,224,13.69,48.26,71.13
vit_small_patch16_384,692.4,1109.164,768,384,15.52,50.78,22.2
vit_relpos_base_patch16_plus_240,691.79,1480.194,1024,240,27.3,34.33,117.38
tnt_b_patch16_224,691.78,1480.223,1024,224,14.09,39.01,65.41
swinv2_cr_base_224,688.11,1488.125,1024,224,15.86,59.66,87.88
densenet264d_iabn,687.57,1489.287,1024,224,13.47,14.0,72.74
convit_base,685.88,1492.962,1024,224,17.52,31.77,86.54
swinv2_cr_base_ns_224,682.58,1500.17,1024,224,15.86,59.66,87.88
vit_base_patch16_rpn_224,667.73,1533.544,1024,224,17.49,23.75,86.54
densenet264,664.62,1540.716,1024,224,12.95,12.8,72.69
deit3_small_patch16_384,664.03,1156.564,768,384,15.52,50.78,22.21
poolformer_m48,663.83,1542.547,1024,224,11.59,29.17,73.47
deit3_small_patch16_384_in21ft1k,663.62,1157.274,768,384,15.52,50.78,22.21
efficientnet_b3_gn,662.87,386.187,256,320,2.14,28.83,11.73
dpn131,660.11,1551.238,1024,224,16.09,32.97,79.25
eca_nfnet_l1,655.87,1561.27,1024,320,14.92,34.42,41.41
vit_relpos_base_patch16_rpn_224,655.49,1562.186,1024,224,17.51,24.97,86.41
xcit_tiny_24_p8_224,650.45,1574.283,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,649.22,1577.262,1024,224,9.21,45.39,12.11
xcit_nano_12_p8_384_dist,643.06,1592.369,1024,384,6.34,46.08,3.05
nest_base,629.02,813.95,512,224,17.96,53.39,67.72
volo_d2_224,627.91,1630.781,1024,224,14.34,41.34,58.68
mobilevitv2_175_384_in22ft1k,627.52,407.942,256,384,12.47,63.29,14.25
jx_nest_base,621.88,823.3,512,224,17.96,53.39,67.72
vit_small_r26_s32_384,619.54,619.804,384,384,10.43,29.85,36.47
senet154,618.82,1654.743,1024,224,20.77,38.69,115.09
gluon_senet154,618.51,1655.586,1024,224,20.77,38.69,115.09
legacy_senet154,618.16,1656.503,1024,224,20.77,38.69,115.09
xception71,616.97,829.852,512,299,18.09,69.92,42.34
vit_base_r50_s16_224,613.11,1670.152,1024,224,21.66,35.29,98.66
hrnet_w64,609.7,1679.491,1024,224,28.97,35.09,128.06
regnety_320,607.61,842.637,512,224,32.34,30.26,145.05
dpn107,606.08,1689.539,1024,224,18.38,33.46,86.92
regnetz_c16_evos,598.89,854.904,512,320,3.86,25.88,13.49
ecaresnet200d,592.5,1728.248,1024,256,20.0,43.15,64.69
seresnet200d,591.19,1732.085,1024,256,20.01,43.15,71.86
resnet152d,576.9,1774.999,1024,320,24.08,47.67,60.21
convnext_large,559.02,1831.761,1024,224,34.4,43.13,197.77
convnext_large_in22ft1k,558.96,1831.941,1024,224,34.4,43.13,197.77
regnety_160,558.21,687.896,384,288,26.37,38.07,83.59
efficientnet_b3_g8_gn,557.9,458.854,256,320,3.2,28.83,14.25
xcit_small_12_p8_224,546.6,1873.371,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,546.45,1873.905,1024,224,18.69,47.21,26.21
resnext101_64x4d,541.68,1417.803,768,288,25.66,51.59,83.46
mobilevitv2_200_384_in22ft1k,527.08,364.262,192,384,16.24,72.34,18.45
halonet_h1,518.76,493.471,256,256,3.0,51.17,8.1
vit_large_patch32_384,517.18,1979.967,1024,384,45.31,43.86,306.63
seresnet152d,516.02,1984.399,1024,320,24.09,47.72,66.84
resnetrs152,512.78,1996.941,1024,320,24.34,48.14,86.62
swinv2_base_window8_256,507.32,1513.812,768,256,20.37,52.59,87.92
seresnext101_32x8d,503.19,1526.235,768,288,27.24,51.63,93.57
convnext_small_384_in22ft1k,494.64,1035.087,512,384,25.58,63.37,50.22
seresnext101d_32x8d,494.43,1553.287,768,288,27.64,52.95,93.59
swin_large_patch4_window7_224,478.67,1604.435,768,224,34.53,54.94,196.53
swinv2_small_window16_256,476.38,1074.753,512,256,12.82,66.29,49.73
regnetz_e8,474.49,1618.577,768,320,15.46,63.94,57.7
regnetx_320,471.27,814.799,384,224,31.81,36.3,107.81
ssl_resnext101_32x16d,471.02,1086.983,512,224,36.27,51.18,194.03
swsl_resnext101_32x16d,470.83,1087.428,512,224,36.27,51.18,194.03
ig_resnext101_32x16d,470.74,1087.624,512,224,36.27,51.18,194.03
mixer_l16_224,470.73,2175.315,1024,224,44.6,41.69,208.2
seresnextaa101d_32x8d,463.39,1657.351,768,288,28.51,56.44,93.59
seresnet269d,463.29,2210.273,1024,256,26.59,53.6,113.67
nf_regnet_b5,450.96,1135.344,512,456,11.7,61.95,49.74
efficientnetv2_m,449.82,2276.453,1024,416,18.6,67.5,54.14
volo_d3_224,439.99,2327.294,1024,224,20.78,60.09,86.33
efficientnet_b5,425.78,601.238,256,456,10.46,98.86,30.39
xcit_large_24_p16_224_dist,423.07,2420.403,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,422.98,2420.908,1024,224,35.86,47.27,189.1
xcit_tiny_12_p8_384_dist,419.35,2441.847,1024,384,14.13,69.14,6.71
resnet200d,417.0,2455.593,1024,320,31.25,67.33,64.69
efficientnetv2_rw_m,411.82,1864.879,768,416,21.49,79.62,53.24
tf_efficientnet_b5_ns,408.16,627.186,256,456,10.46,98.86,30.39
swinv2_cr_tiny_384,408.1,627.286,256,384,15.34,161.01,28.33
tf_efficientnet_b5,407.78,627.773,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,407.68,627.936,256,456,10.46,98.86,30.39
swinv2_cr_large_224,405.25,1895.127,768,224,35.1,78.42,196.68
resnetv2_50x1_bitm,401.93,955.37,384,448,16.62,44.46,25.55
nfnet_f1,399.69,2561.946,1024,320,35.97,46.77,132.63
xcit_small_24_p16_384_dist,382.57,2676.633,1024,384,26.72,68.58,47.67
regnetz_d8_evos,376.87,2037.797,768,320,7.03,38.92,23.46
tresnet_l_448,371.52,2756.242,1024,448,43.5,47.56,55.99
vit_large_patch16_224,369.7,2769.802,1024,224,61.6,63.52,304.33
resnetrs200,368.58,2778.22,1024,320,31.51,67.81,93.21
convnext_xlarge_in22ft1k,368.02,1391.221,512,224,60.98,57.5,350.2
crossvit_15_dagger_408,366.37,698.731,256,408,21.45,95.05,28.5
vit_base_patch16_18x2_224,361.96,2829.064,1024,224,52.51,71.38,256.73
deit3_large_patch16_224,358.07,2859.733,1024,224,61.6,63.52,304.37
deit3_large_patch16_224_in21ft1k,357.9,2861.143,1024,224,61.6,63.52,304.37
dm_nfnet_f1,357.87,2146.026,768,320,35.97,46.77,132.63
tf_efficientnetv2_m,350.54,2190.896,768,480,24.76,89.84,54.14
tf_efficientnetv2_m_in21ft1k,350.14,2193.372,768,480,24.76,89.84,54.14
swinv2_base_window16_256,345.6,1111.087,384,256,22.02,84.71,87.92
swinv2_base_window12to16_192to256_22kft1k,345.47,1111.525,384,256,22.02,84.71,87.92
convnext_base_384_in22ft1k,344.56,1485.926,512,384,45.21,84.49,88.59
beit_large_patch16_224,342.32,2991.347,1024,224,61.6,63.52,304.43
eca_nfnet_l2,322.02,2384.947,768,384,30.05,68.28,56.72
volo_d1_384,293.04,1747.159,512,384,22.75,108.55,26.78
convmixer_768_32,292.83,3496.872,1024,224,19.55,25.95,21.11
resnetv2_152x2_bit_teacher,291.46,2634.992,768,224,46.95,45.11,236.34
deit_base_patch16_384,288.65,1330.327,384,384,55.54,101.56,86.86
vit_base_patch16_384,288.47,1331.141,384,384,55.54,101.56,86.86
resnest200e,288.19,1776.58,512,320,35.69,82.78,70.2
xcit_small_24_p8_224,286.12,3578.848,1024,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,286.06,3579.677,1024,224,35.81,90.78,47.63
deit_base_distilled_patch16_384,284.56,1349.413,384,384,55.65,101.82,87.63
volo_d4_224,282.61,3623.333,1024,224,44.34,80.22,192.96
deit3_base_patch16_384,277.81,1382.217,384,384,55.54,101.56,86.88
deit3_base_patch16_384_in21ft1k,277.78,1382.367,384,384,55.54,101.56,86.88
tresnet_xl_448,277.15,2771.052,768,448,60.65,61.31,78.44
nasnetalarge,276.88,1386.877,384,331,23.89,90.56,88.75
vit_large_patch14_224,271.51,3771.489,1024,224,81.08,88.79,304.2
cait_xxs24_384,269.82,3795.14,1024,384,9.63,122.66,12.03
crossvit_18_dagger_408,269.4,950.247,256,408,32.47,124.87,44.61
xcit_medium_24_p16_384_dist,269.2,2852.889,768,384,47.39,91.64,84.4
pnasnet5large,264.84,1449.925,384,331,25.04,92.89,86.06
resnetv2_101x1_bitm,252.59,1520.226,384,448,31.65,64.93,44.54
efficientnet_b6,252.26,507.392,128,528,19.4,167.39,43.04
swinv2_cr_small_384,250.03,1023.876,256,384,29.7,298.03,49.7
beit_base_patch16_384,247.68,1550.363,384,384,55.54,101.56,86.74
vit_large_r50_s32_384,246.17,1559.866,384,384,57.43,76.52,329.09
tf_efficientnet_b6_ns,242.42,527.986,128,528,19.4,167.39,43.04
tf_efficientnet_b6,242.34,528.179,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ap,242.3,528.255,128,528,19.4,167.39,43.04
ecaresnet269d,241.69,4236.816,1024,352,50.25,101.25,102.09
resnetrs270,234.11,4373.986,1024,352,51.13,105.48,129.86
nfnet_f2,224.73,4556.614,1024,352,63.22,79.06,193.78
swin_base_patch4_window12_384,220.36,871.278,192,384,47.19,134.78,87.9
xcit_tiny_24_p8_384_dist,219.9,4656.678,1024,384,27.05,132.95,12.11
resmlp_big_24_224,218.18,4693.363,1024,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,217.68,4704.164,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,217.65,4704.831,1024,224,100.23,87.31,129.14
swinv2_large_window12to16_192to256_22kft1k,211.96,1207.756,256,256,47.81,121.53,196.74
efficientnetv2_l,206.63,2477.808,512,480,56.4,157.99,118.52
tf_efficientnetv2_l,204.52,2503.355,512,480,56.4,157.99,118.52
tf_efficientnetv2_l_in21ft1k,204.48,2503.917,512,480,56.4,157.99,118.52
ig_resnext101_32x32d,202.59,1263.594,256,224,87.29,91.12,468.53
xcit_medium_24_p8_224,202.12,5066.293,1024,224,63.53,121.23,84.32
xcit_medium_24_p8_224_dist,201.88,5072.196,1024,224,63.53,121.23,84.32
dm_nfnet_f2,200.18,3836.576,768,352,63.22,79.06,193.78
convnext_large_384_in22ft1k,190.55,1343.472,256,384,101.1,126.74,197.77
vit_base_patch8_224,188.25,1359.85,256,224,78.22,161.69,86.58
volo_d5_224,187.56,5459.662,1024,224,72.4,118.11,295.46
cait_xs24_384,186.33,4121.716,768,384,19.28,183.98,26.67
xcit_small_12_p8_384_dist,183.57,2091.823,384,384,54.92,138.29,26.21
eca_nfnet_l3,182.91,2799.141,512,448,52.55,118.4,72.04
cait_xxs36_384,180.41,5675.791,1024,384,14.35,183.7,17.37
swinv2_cr_base_384,178.38,1435.085,256,384,50.57,333.68,87.88
vit_base_resnet50_384,177.85,2159.087,384,384,67.43,135.03,98.95
vit_base_r50_s16_384,177.6,2162.196,384,384,67.43,135.03,98.95
swinv2_cr_huge_224,175.47,2188.347,384,224,115.97,121.08,657.83
convmixer_1536_20,167.1,6128.044,1024,224,48.68,33.03,51.63
volo_d2_384,164.75,1553.889,256,384,46.17,184.51,58.87
resnetrs350,156.77,4898.75,768,384,77.59,154.74,163.96
xcit_large_24_p16_384_dist,154.33,3317.602,512,384,105.35,137.17,189.1
vit_huge_patch14_224,146.32,6998.359,1024,224,167.4,139.41,632.05
efficientnet_b7,145.11,661.558,96,600,38.33,289.94,66.35
cait_s24_384,144.99,3531.336,512,384,32.17,245.31,47.06
deit3_huge_patch14_224,142.26,7197.843,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224_in21ft1k,142.17,7202.758,1024,224,167.4,139.41,632.13
tf_efficientnet_b7_ns,140.64,682.566,96,600,38.33,289.94,66.35
tf_efficientnet_b7_ap,140.61,682.704,96,600,38.33,289.94,66.35
tf_efficientnet_b7,140.6,682.756,96,600,38.33,289.94,66.35
efficientnetv2_xl,139.56,2751.573,384,512,93.85,247.32,208.12
tf_efficientnetv2_xl_in21ft1k,138.42,2774.117,384,512,93.85,247.32,208.12
resnest269e,135.65,2830.833,384,416,77.69,171.98,110.93
swin_large_patch4_window12_384,130.35,981.936,128,384,104.08,202.16,196.74
convnext_xlarge_384_in22ft1k,125.25,1532.9,192,384,179.2,168.99,350.2
nfnet_f3,124.74,4104.555,512,416,115.58,141.78,254.92
ig_resnext101_32x48d,118.28,1623.193,192,224,153.57,131.06,828.41
xcit_large_24_p8_224,115.22,4443.765,512,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,115.18,4445.056,512,224,141.23,181.56,188.93
resnetrs420,112.12,6849.78,768,416,108.45,213.79,191.89
dm_nfnet_f3,110.18,4647.097,512,416,115.58,141.78,254.92
swinv2_cr_large_384,108.04,1184.75,128,384,108.95,404.96,196.68
resnetv2_50x3_bitm,102.09,1253.798,128,448,145.7,133.37,217.32
resnetv2_152x2_bit_teacher_384,98.91,2588.163,256,384,136.16,132.56,236.34
vit_large_patch16_384,97.45,2626.88,256,384,191.21,270.24,304.72
cait_s36_384,97.05,5275.469,512,384,47.99,367.4,68.37
xcit_small_24_p8_384_dist,96.34,3985.916,384,384,105.24,265.91,47.63
vit_giant_patch14_224,95.73,8022.929,768,224,267.18,192.64,1012.61
deit3_large_patch16_384,94.64,2704.996,256,384,191.21,270.24,304.76
deit3_large_patch16_384_in21ft1k,94.52,2708.314,256,384,191.21,270.24,304.76
swinv2_base_window12to24_192to384_22kft1k,94.37,678.174,64,384,55.25,280.36,87.92
efficientnet_b8,91.29,1051.594,96,672,63.48,442.89,87.41
tf_efficientnet_b8,88.95,1079.277,96,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,88.84,1080.533,96,672,63.48,442.89,87.41
beit_large_patch16_384,84.67,3023.634,256,384,191.21,270.24,305.0
resnetv2_152x2_bitm,73.09,2626.956,192,448,184.99,180.43,236.34
volo_d3_448,72.41,2651.496,192,448,96.33,446.83,86.63
nfnet_f4,69.91,5493.031,384,512,216.26,262.26,316.07
xcit_medium_24_p8_384_dist,67.93,3768.466,256,384,186.67,354.73,84.32
dm_nfnet_f4,62.55,4092.528,256,512,216.26,262.26,316.07
resnetv2_101x3_bitm,61.05,2096.759,128,448,280.33,194.78,387.93
swinv2_large_window12to24_192to384_22kft1k,59.71,803.821,48,384,116.15,407.83,196.74
vit_gigantic_patch14_224,57.59,8890.782,512,224,483.95,275.37,1844.44
tf_efficientnet_l2_ns_475,56.35,1135.833,64,475,172.11,609.89,480.31
volo_d4_448,52.92,2418.622,128,448,197.13,527.35,193.41
swinv2_cr_giant_224,50.53,2532.906,128,224,483.85,309.15,2598.76
nfnet_f5,49.64,5157.064,256,544,290.97,349.71,377.21
swinv2_cr_huge_384,47.06,1360.056,64,384,352.04,583.18,657.94
dm_nfnet_f5,44.17,5795.363,256,544,290.97,349.71,377.21
xcit_large_24_p8_384_dist,38.64,4968.379,192,384,415.0,531.82,188.93
nfnet_f6,37.99,6738.223,256,576,378.69,452.2,438.36
volo_d5_448,36.49,3507.831,128,448,315.06,737.92,295.91
beit_large_patch16_512,33.88,2833.282,96,512,362.24,656.39,305.67
dm_nfnet_f6,33.83,7567.962,256,576,378.69,452.2,438.36
cait_m36_384,31.72,8071.786,256,384,173.11,734.81,271.22
nfnet_f7,30.38,8426.213,256,608,480.39,570.85,499.5
volo_d5_512,25.58,3752.221,96,512,425.09,1105.37,296.09
resnetv2_152x4_bitm,22.67,4234.474,96,480,844.84,414.26,936.53
efficientnet_l2,20.51,1169.975,24,800,479.12,1707.39,480.31
tf_efficientnet_l2_ns,20.15,1191.261,24,800,479.12,1707.39,480.31
swinv2_cr_giant_384,14.62,2188.205,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.47,9503.031,128,448,329.41,1708.23,356.46
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet-r-clean.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,98.150,1.850,99.880,0.120,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,98.030,1.970,99.890,0.110,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,98.000,2.000,99.860,0.140,"1,014.45",560,1.000,bicubic
eva_giant_patch14_336.m30m_ft_in22k_in1k,97.990,2.010,99.900,0.100,"1,013.01",336,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_384,97.870,2.130,99.910,0.090,660.29,384,1.000,bicubic
eva_large_patch14_336.in22k_ft_in22k_in1k,97.860,2.140,99.880,0.120,304.53,336,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,97.860,2.140,99.800,0.200,305.08,448,1.000,bicubic
eva_giant_patch14_336.clip_ft_in1k,97.860,2.140,99.790,0.210,"1,013.01",336,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in1k,97.830,2.170,99.820,0.180,305.08,448,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_512,97.810,2.190,99.860,0.140,660.29,512,1.000,bicubic
eva_large_patch14_336.in22k_ft_in1k,97.810,2.190,99.840,0.160,304.53,336,1.000,bicubic
beit_large_patch16_384.in22k_ft_in22k_in1k,97.810,2.190,99.790,0.210,305.00,384,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k,97.780,2.220,99.890,0.110,480.31,800,0.960,bicubic
regnety_1280.swag_ft_in1k,97.780,2.220,99.860,0.140,644.81,384,1.000,bicubic
beit_large_patch16_512.in22k_ft_in22k_in1k,97.780,2.220,99.820,0.180,305.67,512,1.000,bicubic
maxvit_base_tf_512.in21k_ft_in1k,97.760,2.240,99.860,0.140,119.88,512,1.000,bicubic
maxvit_xlarge_tf_512.in21k_ft_in1k,97.760,2.240,99.820,0.180,475.77,512,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic
convnext_xxlarge.clip_laion2b_soup_ft_in1k,97.750,2.250,99.810,0.190,846.47,256,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in22k_in1k,97.750,2.250,99.790,0.210,304.43,224,0.950,bicubic
maxvit_xlarge_tf_384.in21k_ft_in1k,97.740,2.260,99.850,0.150,475.32,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in1k,97.720,2.280,99.760,0.240,87.12,448,1.000,bicubic
maxvit_large_tf_512.in21k_ft_in1k,97.670,2.330,99.730,0.270,212.33,512,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k_384,97.660,2.340,99.860,0.140,98.75,384,1.000,bicubic
maxvit_large_tf_384.in21k_ft_in1k,97.660,2.340,99.820,0.180,212.03,384,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k_384,97.630,2.370,99.800,0.200,197.96,384,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,97.610,2.390,99.820,0.180,87.12,448,1.000,bicubic
eva_large_patch14_196.in22k_ft_in22k_in1k,97.610,2.390,99.810,0.190,304.14,196,1.000,bicubic
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,97.610,2.390,99.780,0.220,632.46,336,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in12k_in1k,97.610,2.390,99.730,0.270,304.20,224,1.000,bicubic
vit_large_patch14_clip_336.openai_ft_in12k_in1k,97.600,2.400,99.730,0.270,304.53,336,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k_384,97.590,2.410,99.770,0.230,350.20,384,1.000,bicubic
deit3_large_patch16_384.fb_in22k_ft_in1k,97.580,2.420,99.710,0.290,304.76,384,1.000,bicubic
eva_giant_patch14_224.clip_ft_in1k,97.570,2.430,99.710,0.290,"1,012.56",224,0.900,bicubic
maxvit_base_tf_384.in21k_ft_in1k,97.560,2.440,99.760,0.240,119.65,384,1.000,bicubic
eva_large_patch14_196.in22k_ft_in1k,97.520,2.480,99.790,0.210,304.14,196,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k_384,97.490,2.510,99.760,0.240,99.88,384,1.000,bicubic
beit_large_patch16_224.in22k_ft_in22k_in1k,97.480,2.520,99.690,0.310,304.43,224,0.900,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,97.470,2.530,99.760,0.240,200.13,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,97.460,2.540,99.780,0.220,304.53,336,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k,97.450,2.550,99.820,0.180,350.20,288,1.000,bicubic
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,97.450,2.550,99.760,0.240,116.09,384,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in1k,97.440,2.560,99.680,0.320,304.20,224,1.000,bicubic
vit_large_patch16_384.augreg_in21k_ft_in1k,97.410,2.590,99.780,0.220,304.72,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,97.390,2.610,99.740,0.260,304.20,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,97.390,2.610,99.730,0.270,200.13,384,1.000,bicubic
regnety_1280.swag_lc_in1k,97.390,2.610,99.730,0.270,644.81,224,0.965,bicubic
regnety_320.swag_ft_in1k,97.380,2.620,99.760,0.240,145.05,384,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k_384,97.380,2.620,99.720,0.280,88.72,384,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k_384,97.370,2.630,99.790,0.210,56.20,384,1.000,bicubic
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,97.370,2.630,99.700,0.300,73.88,384,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k_384,97.370,2.630,99.680,0.320,57.05,384,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k,97.360,2.640,99.830,0.170,98.75,224,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,97.360,2.640,99.800,0.200,632.05,224,1.000,bicubic
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,97.340,2.660,99.690,0.310,116.14,384,1.000,bicubic
tf_efficientnetv2_xl.in21k_ft_in1k,97.330,2.670,99.600,0.400,208.12,512,1.000,bicubic
beit_base_patch16_384.in22k_ft_in22k_in1k,97.320,2.680,99.720,0.280,86.74,384,1.000,bicubic
tf_efficientnetv2_l.in21k_ft_in1k,97.320,2.680,99.640,0.360,118.52,480,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k,97.310,2.690,99.760,0.240,197.96,288,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in1k,97.310,2.690,99.740,0.260,304.43,224,0.950,bicubic
deit3_large_patch16_224.fb_in22k_ft_in1k,97.310,2.690,99.680,0.320,304.37,224,1.000,bicubic
convnext_large.fb_in22k_ft_in1k_384,97.300,2.700,99.760,0.240,197.77,384,1.000,bicubic
volo_d5_512.sail_in1k,97.300,2.700,99.760,0.240,296.09,512,1.150,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,97.290,2.710,99.780,0.220,93.59,320,1.000,bicubic
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,97.290,2.710,99.750,0.250,149.39,384,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k_384,97.290,2.710,99.720,0.280,39.30,384,1.000,bicubic
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,97.280,2.720,99.780,0.220,196.74,384,1.000,bicubic
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,97.270,2.730,99.790,0.210,87.92,384,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k,97.260,2.740,99.750,0.250,99.88,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,97.260,2.740,99.740,0.260,200.13,320,1.000,bicubic
convnext_base.fb_in22k_ft_in1k_384,97.260,2.740,99.710,0.290,88.59,384,1.000,bicubic
convnextv2_huge.fcmae_ft_in1k,97.250,2.750,99.720,0.280,660.29,288,1.000,bicubic
deit3_huge_patch14_224.fb_in22k_ft_in1k,97.250,2.750,99.720,0.280,632.13,224,1.000,bicubic
volo_d5_448.sail_in1k,97.240,2.760,99.740,0.260,295.91,448,1.150,bicubic
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,97.240,2.760,99.710,0.290,196.74,256,0.900,bicubic
deit3_base_patch16_384.fb_in22k_ft_in1k,97.240,2.760,99.670,0.330,86.88,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in1k,97.230,2.770,99.720,0.280,304.53,336,1.000,bicubic
convnext_large.fb_in22k_ft_in1k,97.220,2.780,99.730,0.270,197.77,288,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,97.220,2.780,99.700,0.300,86.86,384,1.000,bicubic
convnext_base.fb_in22k_ft_in1k,97.200,2.800,99.760,0.240,88.59,288,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k,97.200,2.800,99.760,0.240,88.72,288,1.000,bicubic
maxvit_small_tf_512.in1k,97.200,2.800,99.620,0.380,69.13,512,1.000,bicubic
tf_efficientnet_b7.ns_jft_in1k,97.190,2.810,99.700,0.300,66.35,600,0.949,bicubic
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,97.180,2.820,99.650,0.350,73.88,224,0.950,bicubic
regnety_160.swag_ft_in1k,97.170,2.830,99.780,0.220,83.59,384,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k,97.170,2.830,99.740,0.260,93.59,288,1.000,bicubic
swin_large_patch4_window12_384.ms_in22k_ft_in1k,97.170,2.830,99.680,0.320,196.74,384,1.000,bicubic
maxvit_base_tf_512.in1k,97.170,2.830,99.640,0.360,119.88,512,1.000,bicubic
caformer_b36.sail_in1k_384,97.160,2.840,99.610,0.390,98.75,384,1.000,bicubic
swin_base_patch4_window12_384.ms_in22k_ft_in1k,97.130,2.870,99.780,0.220,87.90,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,97.130,2.870,99.720,0.280,200.13,256,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in12k_in1k,97.120,2.880,99.640,0.360,86.86,384,0.950,bicubic
maxvit_base_tf_384.in1k,97.120,2.880,99.570,0.430,119.65,384,1.000,bicubic
convnext_small.fb_in22k_ft_in1k_384,97.110,2.890,99.640,0.360,50.22,384,1.000,bicubic
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,97.110,2.890,99.600,0.400,116.14,224,0.950,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in1k,97.100,2.900,99.690,0.310,632.05,224,1.000,bicubic
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,97.090,2.910,99.680,0.320,116.09,224,0.950,bicubic
vit_base_patch8_224.augreg_in21k_ft_in1k,97.090,2.910,99.610,0.390,86.58,224,0.900,bicubic
volo_d4_448.sail_in1k,97.070,2.930,99.750,0.250,193.41,448,1.150,bicubic
convformer_m36.sail_in22k_ft_in1k,97.070,2.930,99.630,0.370,57.05,224,1.000,bicubic
convformer_s36.sail_in22k_ft_in1k_384,97.060,2.940,99.710,0.290,40.01,384,1.000,bicubic
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,97.050,2.950,99.660,0.340,87.92,256,0.900,bicubic
maxvit_large_tf_512.in1k,97.050,2.950,99.590,0.410,212.33,512,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,97.040,2.960,99.670,0.330,88.59,384,1.000,bicubic
caformer_m36.sail_in1k_384,97.030,2.970,99.710,0.290,56.20,384,1.000,bicubic
volo_d3_448.sail_in1k,97.030,2.970,99.680,0.320,86.63,448,1.000,bicubic
dm_nfnet_f5.dm_in1k,97.030,2.970,99.670,0.330,377.21,544,0.954,bicubic
caformer_m36.sail_in22k_ft_in1k,97.020,2.980,99.730,0.270,56.20,224,1.000,bicubic
tf_efficientnet_b6.ns_jft_in1k,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic
vit_base_patch16_384.augreg_in21k_ft_in1k,97.020,2.980,99.710,0.290,86.86,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in1k,97.020,2.980,99.670,0.330,304.20,224,1.000,bicubic
tf_efficientnetv2_m.in21k_ft_in1k,97.000,3.000,99.630,0.370,54.14,480,1.000,bicubic
convnext_small.in12k_ft_in1k_384,96.990,3.010,99.660,0.340,50.22,384,1.000,bicubic
coatnet_2_rw_224.sw_in12k_ft_in1k,96.990,3.010,99.650,0.350,73.87,224,0.950,bicubic
dm_nfnet_f6.dm_in1k,96.970,3.030,99.760,0.240,438.36,576,0.956,bicubic
maxvit_tiny_tf_512.in1k,96.970,3.030,99.670,0.330,31.05,512,1.000,bicubic
vit_large_r50_s32_384.augreg_in21k_ft_in1k,96.950,3.050,99.710,0.290,329.09,384,1.000,bicubic
vit_base_patch8_224.augreg2_in21k_ft_in1k,96.950,3.050,99.640,0.360,86.58,224,0.900,bicubic
dm_nfnet_f4.dm_in1k,96.950,3.050,99.630,0.370,316.07,512,0.951,bicubic
tiny_vit_21m_384.dist_in22k_ft_in1k,96.950,3.050,99.610,0.390,21.23,384,1.000,bicubic
swin_large_patch4_window7_224.ms_in22k_ft_in1k,96.940,3.060,99.670,0.330,196.53,224,0.900,bicubic
xcit_large_24_p16_384.fb_dist_in1k,96.940,3.060,99.510,0.490,189.10,384,1.000,bicubic
maxvit_large_tf_384.in1k,96.930,3.070,99.570,0.430,212.03,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in22k_in1k,96.910,3.090,99.730,0.270,86.53,224,0.900,bicubic
tiny_vit_21m_512.dist_in22k_ft_in1k,96.900,3.100,99.690,0.310,21.27,512,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in1k,96.900,3.100,99.670,0.330,86.86,384,1.000,bicubic
caformer_s36.sail_in1k_384,96.880,3.120,99.670,0.330,39.30,384,1.000,bicubic
volo_d5_224.sail_in1k,96.880,3.120,99.670,0.330,295.46,224,0.960,bicubic
resnetv2_152x4_bit.goog_in21k_ft_in1k,96.880,3.120,99.660,0.340,936.53,480,1.000,bilinear
cait_m48_448.fb_dist_in1k,96.880,3.120,99.620,0.380,356.46,448,1.000,bicubic
convformer_b36.sail_in1k_384,96.870,3.130,99.650,0.350,99.88,384,1.000,bicubic
tf_efficientnet_b5.ns_jft_in1k,96.870,3.130,99.640,0.360,30.39,456,0.934,bicubic
convnext_base.clip_laiona_augreg_ft_in1k_384,96.860,3.140,99.690,0.310,88.59,384,1.000,bicubic
deit3_base_patch16_224.fb_in22k_ft_in1k,96.860,3.140,99.620,0.380,86.59,224,1.000,bicubic
deit3_large_patch16_384.fb_in1k,96.850,3.150,99.620,0.380,304.76,384,1.000,bicubic
cait_m36_384.fb_dist_in1k,96.840,3.160,99.660,0.340,271.22,384,1.000,bicubic
convnextv2_large.fcmae_ft_in1k,96.830,3.170,99.760,0.240,197.96,288,1.000,bicubic
regnety_160.sw_in12k_ft_in1k,96.820,3.180,99.690,0.310,83.59,288,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k,96.820,3.180,99.620,0.380,39.30,224,1.000,bicubic
regnety_160.lion_in12k_ft_in1k,96.810,3.190,99.710,0.290,83.59,288,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in1k,96.810,3.190,99.660,0.340,86.86,384,1.000,bicubic
xcit_small_24_p8_384.fb_dist_in1k,96.810,3.190,99.630,0.370,47.63,384,1.000,bicubic
convnext_small.fb_in22k_ft_in1k,96.810,3.190,99.510,0.490,50.22,288,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k_384,96.790,3.210,99.710,0.290,26.77,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,96.790,3.210,99.680,0.320,88.59,256,1.000,bicubic
regnety_320.swag_lc_in1k,96.780,3.220,99.730,0.270,145.05,224,0.965,bicubic
volo_d4_224.sail_in1k,96.780,3.220,99.670,0.330,192.96,224,0.960,bicubic
convformer_m36.sail_in1k_384,96.780,3.220,99.620,0.380,57.05,384,1.000,bicubic
flexivit_large.1200ep_in1k,96.780,3.220,99.610,0.390,304.36,240,0.950,bicubic
xcit_medium_24_p8_384.fb_dist_in1k,96.770,3.230,99.620,0.380,84.32,384,1.000,bicubic
efficientnet_b5.sw_in12k_ft_in1k,96.770,3.230,99.600,0.400,30.39,448,1.000,bicubic
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,96.770,3.230,99.530,0.470,468.53,224,0.875,bilinear
xcit_large_24_p8_384.fb_dist_in1k,96.760,3.240,99.560,0.440,188.93,384,1.000,bicubic
maxvit_small_tf_384.in1k,96.750,3.250,99.600,0.400,69.02,384,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in1k,96.750,3.250,99.540,0.460,86.53,224,0.900,bicubic
tf_efficientnetv2_l.in1k,96.740,3.260,99.550,0.450,118.52,480,1.000,bicubic
flexivit_large.600ep_in1k,96.730,3.270,99.560,0.440,304.36,240,0.950,bicubic
inception_next_base.sail_in1k_384,96.720,3.280,99.610,0.390,86.67,384,1.000,bicubic
tf_efficientnet_b4.ns_jft_in1k,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic
volo_d2_384.sail_in1k,96.710,3.290,99.600,0.400,58.87,384,1.000,bicubic
vit_large_patch16_224.augreg_in21k_ft_in1k,96.700,3.300,99.650,0.350,304.33,224,0.900,bicubic
flexivit_large.300ep_in1k,96.700,3.300,99.580,0.420,304.36,240,0.950,bicubic
convformer_s36.sail_in1k_384,96.700,3.300,99.570,0.430,40.01,384,1.000,bicubic
tf_efficientnet_b8.ra_in1k,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic
eva02_small_patch14_336.mim_in22k_ft_in1k,96.690,3.310,99.610,0.390,22.13,336,1.000,bicubic
xcit_medium_24_p16_384.fb_dist_in1k,96.690,3.310,99.600,0.400,84.40,384,1.000,bicubic
swin_base_patch4_window7_224.ms_in22k_ft_in1k,96.680,3.320,99.670,0.330,87.77,224,0.900,bicubic
deit3_small_patch16_384.fb_in22k_ft_in1k,96.670,3.330,99.640,0.360,22.21,384,1.000,bicubic
beit_base_patch16_224.in22k_ft_in22k_in1k,96.660,3.340,99.660,0.340,86.53,224,0.900,bicubic
cait_s36_384.fb_dist_in1k,96.630,3.370,99.610,0.390,68.37,384,1.000,bicubic
xcit_large_24_p8_224.fb_dist_in1k,96.630,3.370,99.460,0.540,188.93,224,1.000,bicubic
dm_nfnet_f3.dm_in1k,96.620,3.380,99.630,0.370,254.92,416,0.940,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k_384,96.620,3.380,99.580,0.420,28.64,384,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,96.620,3.380,99.560,0.440,86.57,224,0.950,bicubic
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,96.610,3.390,99.480,0.520,88.30,384,1.000,bicubic
regnetz_e8.ra3_in1k,96.600,3.400,99.620,0.380,57.70,320,1.000,bicubic
convnext_small.in12k_ft_in1k,96.600,3.400,99.580,0.420,50.22,288,1.000,bicubic
maxvit_tiny_tf_384.in1k,96.600,3.400,99.560,0.440,30.98,384,1.000,bicubic
deit3_huge_patch14_224.fb_in1k,96.580,3.420,99.520,0.480,632.13,224,0.900,bicubic
cait_s24_384.fb_dist_in1k,96.570,3.430,99.550,0.450,47.06,384,1.000,bicubic
tf_efficientnet_b7.ra_in1k,96.570,3.430,99.520,0.480,66.35,600,0.949,bicubic
coat_lite_medium_384.in1k,96.570,3.430,99.470,0.530,44.57,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in1k,96.560,3.440,99.650,0.350,88.59,256,1.000,bicubic
convnext_tiny.in12k_ft_in1k_384,96.560,3.440,99.630,0.370,28.59,384,1.000,bicubic
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,96.560,3.440,99.520,0.480,88.34,448,1.000,bicubic
regnety_120.sw_in12k_ft_in1k,96.550,3.450,99.680,0.320,51.82,288,1.000,bicubic
xcit_small_24_p8_224.fb_dist_in1k,96.550,3.450,99.560,0.440,47.63,224,1.000,bicubic
tf_efficientnet_b8.ap_in1k,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic
hrnet_w48_ssld.paddle_in1k,96.540,3.460,99.640,0.360,77.47,288,1.000,bilinear
caformer_s18.sail_in22k_ft_in1k_384,96.530,3.470,99.580,0.420,26.34,384,1.000,bicubic
regnety_2560.seer_ft_in1k,96.530,3.470,99.520,0.480,"1,282.60",384,1.000,bicubic
xcit_medium_24_p8_224.fb_dist_in1k,96.530,3.470,99.510,0.490,84.32,224,1.000,bicubic
resnetv2_152x2_bit.goog_in21k_ft_in1k,96.520,3.480,99.590,0.410,236.34,448,1.000,bilinear
dm_nfnet_f2.dm_in1k,96.520,3.480,99.570,0.430,193.78,352,0.920,bicubic
deit_base_distilled_patch16_384.fb_in1k,96.510,3.490,99.590,0.410,87.63,384,1.000,bicubic
vit_base_patch16_224.augreg2_in21k_ft_in1k,96.510,3.490,99.560,0.440,86.57,224,0.900,bicubic
vit_base_patch16_clip_224.openai_ft_in12k_in1k,96.510,3.490,99.550,0.450,86.57,224,0.950,bicubic
convformer_s36.sail_in22k_ft_in1k,96.500,3.500,99.630,0.370,40.01,224,1.000,bicubic
caformer_b36.sail_in1k,96.500,3.500,99.460,0.540,98.75,224,1.000,bicubic
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,96.490,3.510,99.620,0.380,39.03,384,0.950,bicubic
tf_efficientnetv2_m.in1k,96.480,3.520,99.610,0.390,54.14,480,1.000,bicubic
volo_d1_384.sail_in1k,96.480,3.520,99.550,0.450,26.78,384,1.000,bicubic
convnextv2_base.fcmae_ft_in1k,96.480,3.520,99.520,0.480,88.72,288,1.000,bicubic
tf_efficientnetv2_s.in21k_ft_in1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic
xcit_small_12_p8_384.fb_dist_in1k,96.470,3.530,99.490,0.510,26.21,384,1.000,bicubic
regnety_160.swag_lc_in1k,96.450,3.550,99.750,0.250,83.59,224,0.965,bicubic
vit_base_r50_s16_384.orig_in21k_ft_in1k,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic
eca_nfnet_l2.ra3_in1k,96.450,3.550,99.610,0.390,56.72,384,1.000,bicubic
ecaresnet269d.ra2_in1k,96.450,3.550,99.610,0.390,102.09,352,1.000,bicubic
seresnextaa101d_32x8d.ah_in1k,96.440,3.560,99.510,0.490,93.59,288,1.000,bicubic
volo_d3_224.sail_in1k,96.430,3.570,99.630,0.370,86.33,224,0.960,bicubic
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,96.430,3.570,99.540,0.460,194.03,224,0.875,bilinear
volo_d2_224.sail_in1k,96.420,3.580,99.500,0.500,58.68,224,0.960,bicubic
vit_base_patch32_clip_384.openai_ft_in12k_in1k,96.420,3.580,99.460,0.540,88.30,384,0.950,bicubic
caformer_s18.sail_in1k_384,96.410,3.590,99.560,0.440,26.34,384,1.000,bicubic
caformer_m36.sail_in1k,96.410,3.590,99.530,0.470,56.20,224,1.000,bicubic
resnetrs420.tf_in1k,96.400,3.600,99.540,0.460,191.89,416,1.000,bicubic
convnext_large.fb_in1k,96.400,3.600,99.530,0.470,197.77,288,1.000,bicubic
mvitv2_large.fb_in1k,96.400,3.600,99.450,0.550,217.99,224,0.900,bicubic
tiny_vit_21m_224.dist_in22k_ft_in1k,96.380,3.620,99.500,0.500,21.20,224,0.950,bicubic
swin_base_patch4_window12_384.ms_in1k,96.380,3.620,99.420,0.580,87.90,384,1.000,bicubic
tf_efficientnet_b6.ap_in1k,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic
seresnext101d_32x8d.ah_in1k,96.360,3.640,99.470,0.530,93.59,288,1.000,bicubic
resnetaa101d.sw_in12k_ft_in1k,96.360,3.640,99.440,0.560,44.57,288,1.000,bicubic
tf_efficientnet_b7.ap_in1k,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic
resnetrs200.tf_in1k,96.350,3.650,99.550,0.450,93.21,320,1.000,bicubic
resmlp_big_24_224.fb_in22k_ft_in1k,96.350,3.650,99.520,0.480,129.14,224,0.875,bicubic
xcit_small_24_p16_384.fb_dist_in1k,96.340,3.660,99.580,0.420,47.67,384,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k,96.340,3.660,99.550,0.450,28.64,288,1.000,bicubic
xcit_small_12_p16_384.fb_dist_in1k,96.340,3.660,99.490,0.510,26.25,384,1.000,bicubic
maxvit_base_tf_224.in1k,96.340,3.660,99.370,0.630,119.47,224,0.950,bicubic
maxvit_large_tf_224.in1k,96.330,3.670,99.410,0.590,211.79,224,0.950,bicubic
vit_base_patch16_clip_224.laion2b_ft_in1k,96.320,3.680,99.540,0.460,86.57,224,1.000,bicubic
regnetz_040_h.ra3_in1k,96.320,3.680,99.520,0.480,28.94,320,1.000,bicubic
xcit_large_24_p16_224.fb_dist_in1k,96.320,3.680,99.500,0.500,189.10,224,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in1k,96.310,3.690,99.550,0.450,86.57,224,0.900,bicubic
seresnet152d.ra2_in1k,96.310,3.690,99.510,0.490,66.84,320,1.000,bicubic
convnext_base.fb_in1k,96.310,3.690,99.500,0.500,88.59,288,1.000,bicubic
regnety_1280.seer_ft_in1k,96.310,3.690,99.410,0.590,644.81,384,1.000,bicubic
vit_base_patch16_224.augreg_in21k_ft_in1k,96.300,3.700,99.560,0.440,86.57,224,0.900,bicubic
dm_nfnet_f1.dm_in1k,96.300,3.700,99.530,0.470,132.63,320,0.910,bicubic
tf_efficientnet_b6.aa_in1k,96.300,3.700,99.530,0.470,43.04,528,0.942,bicubic
fastvit_ma36.apple_dist_in1k,96.300,3.700,99.500,0.500,44.07,256,0.950,bicubic
resnetv2_50x3_bit.goog_in21k_ft_in1k,96.270,3.730,99.630,0.370,217.32,448,1.000,bilinear
efficientnetv2_rw_m.agc_in1k,96.270,3.730,99.560,0.440,53.24,416,1.000,bicubic
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,96.270,3.730,99.500,0.500,194.03,224,0.875,bilinear
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,96.250,3.750,99.590,0.410,88.79,224,0.875,bilinear
resnetv2_101x3_bit.goog_in21k_ft_in1k,96.250,3.750,99.580,0.420,387.93,448,1.000,bilinear
convformer_s18.sail_in1k_384,96.250,3.750,99.540,0.460,26.77,384,1.000,bicubic
resnetrs350.tf_in1k,96.250,3.750,99.470,0.530,163.96,384,1.000,bicubic
xcit_medium_24_p16_224.fb_dist_in1k,96.250,3.750,99.410,0.590,84.40,224,1.000,bicubic
convnext_tiny.in12k_ft_in1k,96.240,3.760,99.640,0.360,28.59,288,1.000,bicubic
davit_base.msft_in1k,96.240,3.760,99.410,0.590,87.95,224,0.950,bicubic
convformer_b36.sail_in1k,96.240,3.760,99.290,0.710,99.88,224,1.000,bicubic
xcit_tiny_24_p8_384.fb_dist_in1k,96.230,3.770,99.440,0.560,12.11,384,1.000,bicubic
deit3_base_patch16_384.fb_in1k,96.230,3.770,99.400,0.600,86.88,384,1.000,bicubic
maxxvit_rmlp_small_rw_256.sw_in1k,96.210,3.790,99.480,0.520,66.01,256,0.950,bicubic
coatnet_rmlp_2_rw_224.sw_in1k,96.210,3.790,99.280,0.720,73.88,224,0.950,bicubic
vit_base_patch16_384.orig_in21k_ft_in1k,96.200,3.800,99.530,0.470,86.86,384,1.000,bicubic
edgenext_base.in21k_ft_in1k,96.200,3.800,99.470,0.530,18.51,320,1.000,bicubic
maxvit_small_tf_224.in1k,96.200,3.800,99.460,0.540,68.93,224,0.950,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,96.190,3.810,99.500,0.500,236.34,384,1.000,bicubic
deit3_large_patch16_224.fb_in1k,96.190,3.810,99.300,0.700,304.37,224,0.900,bicubic
vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.180,3.820,99.530,0.470,328.99,224,0.900,bicubic
regnetz_040.ra3_in1k,96.180,3.820,99.510,0.490,27.12,320,1.000,bicubic
convnext_tiny.fb_in22k_ft_in1k_384,96.170,3.830,99.500,0.500,28.59,384,1.000,bicubic
swinv2_base_window16_256.ms_in1k,96.170,3.830,99.390,0.610,87.92,256,0.900,bicubic
crossvit_18_dagger_408.in1k,96.150,3.850,99.470,0.530,44.61,408,1.000,bicubic
regnetz_d8_evos.ch_in1k,96.140,3.860,99.490,0.510,23.46,320,1.000,bicubic
deit3_medium_patch16_224.fb_in22k_ft_in1k,96.140,3.860,99.480,0.520,38.85,224,1.000,bicubic
seresnext101_32x8d.ah_in1k,96.140,3.860,99.360,0.640,93.57,288,1.000,bicubic
efficientvit_b3.r288_in1k,96.140,3.860,99.290,0.710,48.65,288,1.000,bicubic
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,96.130,3.870,99.340,0.660,41.72,224,0.950,bicubic
flexivit_base.1200ep_in1k,96.120,3.880,99.410,0.590,86.59,240,0.950,bicubic
resnest269e.in1k,96.110,3.890,99.520,0.480,110.93,416,0.928,bicubic
resnet200d.ra2_in1k,96.110,3.890,99.460,0.540,64.69,320,1.000,bicubic
convformer_s36.sail_in1k,96.110,3.890,99.310,0.690,40.01,224,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k,96.100,3.900,99.490,0.510,26.77,224,1.000,bicubic
tf_efficientnet_b3.ns_jft_in1k,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic
rexnetr_300.sw_in12k_ft_in1k,96.090,3.910,99.540,0.460,34.81,288,1.000,bicubic
tf_efficientnet_b5.ap_in1k,96.090,3.910,99.540,0.460,30.39,456,0.934,bicubic
xcit_large_24_p8_224.fb_in1k,96.090,3.910,99.140,0.860,188.93,224,1.000,bicubic
caformer_s36.sail_in1k,96.080,3.920,99.510,0.490,39.30,224,1.000,bicubic
gcvit_base.in1k,96.080,3.920,99.390,0.610,90.32,224,0.875,bicubic
convformer_m36.sail_in1k,96.080,3.920,99.250,0.750,57.05,224,1.000,bicubic
resnest200e.in1k,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic
tf_efficientnet_b7.aa_in1k,96.070,3.930,99.450,0.550,66.35,600,0.949,bicubic
swinv2_small_window16_256.ms_in1k,96.070,3.930,99.340,0.660,49.73,256,0.900,bicubic
vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.060,3.940,99.550,0.450,36.47,384,1.000,bicubic
regnety_640.seer_ft_in1k,96.060,3.940,99.500,0.500,281.38,384,1.000,bicubic
resnetrs270.tf_in1k,96.060,3.940,99.480,0.520,129.86,352,1.000,bicubic
swin_small_patch4_window7_224.ms_in22k_ft_in1k,96.060,3.940,99.480,0.520,49.61,224,0.900,bicubic
swinv2_base_window8_256.ms_in1k,96.060,3.940,99.410,0.590,87.92,256,0.900,bicubic
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,96.040,3.960,99.540,0.460,44.18,224,0.875,bilinear
maxvit_rmlp_tiny_rw_256.sw_in1k,96.040,3.960,99.410,0.590,29.15,256,0.950,bicubic
swin_s3_base_224.ms_in1k,96.040,3.960,99.350,0.650,71.13,224,0.900,bicubic
vit_base_patch16_224_miil.in21k_ft_in1k,96.040,3.960,99.350,0.650,86.54,224,0.875,bilinear
davit_small.msft_in1k,96.030,3.970,99.400,0.600,49.75,224,0.950,bicubic
volo_d1_224.sail_in1k,96.030,3.970,99.390,0.610,26.63,224,0.960,bicubic
caformer_s18.sail_in22k_ft_in1k,96.010,3.990,99.550,0.450,26.34,224,1.000,bicubic
regnetz_d8.ra3_in1k,96.010,3.990,99.520,0.480,23.37,320,1.000,bicubic
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,96.000,4.000,99.500,0.500,38.86,256,0.950,bicubic
cs3se_edgenet_x.c2ns_in1k,96.000,4.000,99.430,0.570,50.72,320,1.000,bicubic
cait_xs24_384.fb_dist_in1k,96.000,4.000,99.420,0.580,26.67,384,1.000,bicubic
coat_lite_medium.in1k,96.000,4.000,99.350,0.650,44.57,224,0.900,bicubic
repvit_m2_3.dist_450e_in1k,95.990,4.010,99.400,0.600,23.69,224,0.950,bicubic
vit_small_patch16_384.augreg_in21k_ft_in1k,95.980,4.020,99.590,0.410,22.20,384,1.000,bicubic
mvitv2_base.fb_in1k,95.980,4.020,99.330,0.670,51.47,224,0.900,bicubic
tf_efficientnet_b5.ra_in1k,95.970,4.030,99.460,0.540,30.39,456,0.934,bicubic
convnext_small.fb_in1k,95.970,4.030,99.430,0.570,50.22,288,1.000,bicubic
fastvit_ma36.apple_in1k,95.970,4.030,99.360,0.640,44.07,256,0.950,bicubic
xcit_small_12_p8_224.fb_dist_in1k,95.960,4.040,99.430,0.570,26.21,224,1.000,bicubic
flexivit_base.600ep_in1k,95.960,4.040,99.420,0.580,86.59,240,0.950,bicubic
resnetrs152.tf_in1k,95.960,4.040,99.380,0.620,86.62,320,1.000,bicubic
fastvit_sa36.apple_dist_in1k,95.960,4.040,99.370,0.630,31.53,256,0.900,bicubic
maxvit_rmlp_small_rw_224.sw_in1k,95.960,4.040,99.350,0.650,64.90,224,0.900,bicubic
repvgg_d2se.rvgg_in1k,95.950,4.050,99.470,0.530,133.33,320,1.000,bilinear
flexivit_base.300ep_in1k,95.950,4.050,99.370,0.630,86.59,240,0.950,bicubic
pvt_v2_b5.in1k,95.940,4.060,99.390,0.610,81.96,224,0.900,bicubic
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,95.940,4.060,99.380,0.620,88.79,224,0.875,bilinear
eca_nfnet_l1.ra2_in1k,95.930,4.070,99.490,0.510,41.41,320,1.000,bicubic
pvt_v2_b4.in1k,95.920,4.080,99.360,0.640,62.56,224,0.900,bicubic
inception_next_base.sail_in1k,95.920,4.080,99.220,0.780,86.67,224,0.950,bicubic
gcvit_small.in1k,95.910,4.090,99.280,0.720,51.09,224,0.875,bicubic
vit_base_patch32_384.augreg_in21k_ft_in1k,95.900,4.100,99.440,0.560,88.30,384,1.000,bicubic
focalnet_base_srf.ms_in1k,95.900,4.100,99.340,0.660,88.15,224,0.900,bicubic
swin_base_patch4_window7_224.ms_in1k,95.900,4.100,99.310,0.690,87.77,224,0.900,bicubic
xcit_small_24_p8_224.fb_in1k,95.900,4.100,99.180,0.820,47.63,224,1.000,bicubic
mvitv2_small.fb_in1k,95.890,4.110,99.360,0.640,34.87,224,0.900,bicubic
tf_efficientnet_b5.aa_in1k,95.880,4.120,99.350,0.650,30.39,456,0.934,bicubic
regnety_160.deit_in1k,95.870,4.130,99.560,0.440,83.59,288,1.000,bicubic
sequencer2d_l.in1k,95.870,4.130,99.470,0.530,54.30,224,0.875,bicubic
regnety_080.ra3_in1k,95.870,4.130,99.440,0.560,39.18,288,1.000,bicubic
resmlp_big_24_224.fb_distilled_in1k,95.870,4.130,99.440,0.560,129.14,224,0.875,bicubic
regnetz_d32.ra3_in1k,95.870,4.130,99.430,0.570,27.58,320,0.950,bicubic
resnet152d.ra2_in1k,95.870,4.130,99.430,0.570,60.21,320,1.000,bicubic
tf_efficientnet_b5.in1k,95.870,4.130,99.390,0.610,30.39,456,0.934,bicubic
xcit_medium_24_p8_224.fb_in1k,95.860,4.140,99.080,0.920,84.32,224,1.000,bicubic
deit3_small_patch16_224.fb_in22k_ft_in1k,95.830,4.170,99.390,0.610,22.06,224,1.000,bicubic
convnextv2_tiny.fcmae_ft_in1k,95.830,4.170,99.340,0.660,28.64,288,1.000,bicubic
efficientvit_b3.r256_in1k,95.830,4.170,99.220,0.780,48.65,256,1.000,bicubic
swin_s3_small_224.ms_in1k,95.830,4.170,99.200,0.800,49.74,224,0.900,bicubic
focalnet_base_lrf.ms_in1k,95.830,4.170,99.180,0.820,88.75,224,0.900,bicubic
resnext101_64x4d.tv_in1k,95.820,4.180,99.320,0.680,83.46,224,0.875,bilinear
crossvit_15_dagger_408.in1k,95.820,4.180,99.310,0.690,28.50,408,1.000,bicubic
tresnet_v2_l.miil_in21k_ft_in1k,95.820,4.180,99.290,0.710,46.17,224,0.875,bilinear
pit_b_distilled_224.in1k,95.820,4.180,99.210,0.790,74.79,224,0.900,bicubic
maxvit_tiny_tf_224.in1k,95.810,4.190,99.260,0.740,30.92,224,0.950,bicubic
edgenext_base.usi_in1k,95.790,4.210,99.570,0.430,18.51,320,1.000,bicubic
regnety_320.seer_ft_in1k,95.790,4.210,99.390,0.610,145.05,384,1.000,bicubic
xcit_small_24_p16_224.fb_dist_in1k,95.790,4.210,99.350,0.650,47.67,224,1.000,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k_384,95.790,4.210,99.300,0.700,15.62,384,1.000,bicubic
regnety_064.ra3_in1k,95.790,4.210,99.290,0.710,30.58,288,1.000,bicubic
regnetv_064.ra3_in1k,95.780,4.220,99.420,0.580,30.58,288,1.000,bicubic
deit3_base_patch16_224.fb_in1k,95.770,4.230,99.270,0.730,86.59,224,0.900,bicubic
efficientformerv2_l.snap_dist_in1k,95.760,4.240,99.370,0.630,26.32,224,0.950,bicubic
resnet152.a1h_in1k,95.750,4.250,99.430,0.570,60.19,288,1.000,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,95.750,4.250,99.430,0.570,236.34,224,0.875,bicubic
deit_base_distilled_patch16_224.fb_in1k,95.750,4.250,99.280,0.720,87.34,224,0.900,bicubic
resnet101d.ra2_in1k,95.740,4.260,99.440,0.560,44.57,320,1.000,bicubic
focalnet_small_lrf.ms_in1k,95.740,4.260,99.210,0.790,50.34,224,0.900,bicubic
maxvit_tiny_rw_224.sw_in1k,95.740,4.260,99.160,0.840,29.06,224,0.950,bicubic
regnetv_040.ra3_in1k,95.730,4.270,99.380,0.620,20.64,288,1.000,bicubic
swinv2_small_window8_256.ms_in1k,95.730,4.270,99.360,0.640,49.73,256,0.900,bicubic
xcit_small_12_p16_224.fb_dist_in1k,95.730,4.270,99.300,0.700,26.25,224,1.000,bicubic
twins_pcpvt_large.in1k,95.720,4.280,99.490,0.510,60.99,224,0.900,bicubic
tf_efficientnetv2_s.in1k,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic
efficientnetv2_rw_s.ra2_in1k,95.710,4.290,99.380,0.620,23.94,384,1.000,bicubic
hrnet_w18_ssld.paddle_in1k,95.710,4.290,99.340,0.660,21.30,288,1.000,bilinear
swin_small_patch4_window7_224.ms_in1k,95.710,4.290,99.290,0.710,49.61,224,0.900,bicubic
swinv2_cr_small_ns_224.sw_in1k,95.710,4.290,99.290,0.710,49.70,224,0.900,bicubic
tiny_vit_11m_224.dist_in22k_ft_in1k,95.710,4.290,99.260,0.740,11.00,224,0.950,bicubic
twins_svt_large.in1k,95.700,4.300,99.370,0.630,99.27,224,0.900,bicubic
dm_nfnet_f0.dm_in1k,95.690,4.310,99.350,0.650,71.49,256,0.900,bicubic
xception65.ra3_in1k,95.690,4.310,99.320,0.680,39.92,299,0.940,bicubic
caformer_s18.sail_in1k,95.680,4.320,99.290,0.710,26.34,224,1.000,bicubic
inception_next_small.sail_in1k,95.680,4.320,99.250,0.750,49.37,224,0.875,bicubic
cait_s24_224.fb_dist_in1k,95.660,4.340,99.390,0.610,46.92,224,1.000,bicubic
gcvit_tiny.in1k,95.660,4.340,99.330,0.670,28.22,224,0.875,bicubic
xception65p.ra3_in1k,95.660,4.340,99.270,0.730,39.82,299,0.940,bicubic
tiny_vit_21m_224.in1k,95.650,4.350,99.250,0.750,21.20,224,0.950,bicubic
regnetz_c16_evos.ch_in1k,95.640,4.360,99.420,0.580,13.49,320,0.950,bicubic
deit_base_patch16_384.fb_in1k,95.640,4.360,99.240,0.760,86.86,384,1.000,bicubic
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,95.630,4.370,99.440,0.560,25.03,224,0.875,bilinear
ecaresnet101d.miil_in1k,95.630,4.370,99.410,0.590,44.57,288,0.950,bicubic
focalnet_small_srf.ms_in1k,95.630,4.370,99.290,0.710,49.89,224,0.900,bicubic
coatnet_1_rw_224.sw_in1k,95.620,4.380,99.220,0.780,41.72,224,0.950,bicubic
fastvit_sa36.apple_in1k,95.610,4.390,99.320,0.680,31.53,256,0.900,bicubic
efficientformer_l7.snap_dist_in1k,95.600,4.400,99.440,0.560,82.23,224,0.950,bicubic
deit3_small_patch16_384.fb_in1k,95.600,4.400,99.390,0.610,22.21,384,1.000,bicubic
tf_efficientnetv2_b3.in21k_ft_in1k,95.600,4.400,99.280,0.720,14.36,300,0.900,bicubic
repvit_m2_3.dist_300e_in1k,95.590,4.410,99.390,0.610,23.69,224,0.950,bicubic
tf_efficientnet_b4.aa_in1k,95.590,4.410,99.330,0.670,19.34,380,0.922,bicubic
sequencer2d_m.in1k,95.580,4.420,99.270,0.730,38.31,224,0.875,bicubic
resnet101.a1h_in1k,95.580,4.420,99.250,0.750,44.55,288,1.000,bicubic
efficientvit_b2.r288_in1k,95.580,4.420,99.220,0.780,24.33,288,1.000,bicubic
resnetv2_101.a1h_in1k,95.570,4.430,99.370,0.630,44.54,288,1.000,bicubic
resnest101e.in1k,95.570,4.430,99.270,0.730,48.28,256,0.875,bilinear
regnety_320.tv2_in1k,95.560,4.440,99.390,0.610,145.05,224,0.965,bicubic
twins_svt_base.in1k,95.560,4.440,99.230,0.770,56.07,224,0.900,bicubic
fastvit_sa24.apple_dist_in1k,95.550,4.450,99.310,0.690,21.55,256,0.900,bicubic
rexnet_300.nav_in1k,95.540,4.460,99.320,0.680,34.71,224,0.875,bicubic
nest_base_jx.goog_in1k,95.540,4.460,99.290,0.710,67.72,224,0.875,bicubic
nest_small_jx.goog_in1k,95.540,4.460,99.220,0.780,38.35,224,0.875,bicubic
efficientvit_b3.r224_in1k,95.540,4.460,99.190,0.810,48.65,224,0.950,bicubic
efficientnet_b4.ra2_in1k,95.530,4.470,99.400,0.600,19.34,384,1.000,bicubic
resnext101_64x4d.c1_in1k,95.530,4.470,99.290,0.710,83.46,288,1.000,bicubic
tf_efficientnet_b2.ns_jft_in1k,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic
tresnet_xl.miil_in1k_448,95.510,4.490,99.340,0.660,78.44,448,0.875,bilinear
regnety_040.ra3_in1k,95.490,4.510,99.420,0.580,20.65,288,1.000,bicubic
tf_efficientnet_b4.ap_in1k,95.490,4.510,99.390,0.610,19.34,380,0.922,bicubic
xcit_tiny_24_p16_384.fb_dist_in1k,95.490,4.510,99.360,0.640,12.12,384,1.000,bicubic
coatnet_rmlp_1_rw_224.sw_in1k,95.490,4.510,99.250,0.750,41.69,224,0.950,bicubic
tf_efficientnet_b4.in1k,95.480,4.520,99.270,0.730,19.34,380,0.922,bicubic
twins_pcpvt_base.in1k,95.470,4.530,99.390,0.610,43.83,224,0.900,bicubic
pvt_v2_b3.in1k,95.470,4.530,99.310,0.690,45.24,224,0.900,bicubic
maxvit_nano_rw_256.sw_in1k,95.470,4.530,99.120,0.880,15.45,256,0.950,bicubic
eca_nfnet_l0.ra2_in1k,95.460,4.540,99.390,0.610,24.14,288,1.000,bicubic
regnety_032.ra_in1k,95.460,4.540,99.320,0.680,19.44,288,1.000,bicubic
cs3edgenet_x.c2_in1k,95.460,4.540,99.280,0.720,47.82,288,1.000,bicubic
sequencer2d_s.in1k,95.460,4.540,99.260,0.740,27.65,224,0.875,bicubic
xcit_tiny_24_p8_224.fb_dist_in1k,95.450,4.550,99.360,0.640,12.11,224,1.000,bicubic
maxvit_rmlp_nano_rw_256.sw_in1k,95.440,4.560,99.060,0.940,15.50,256,0.950,bicubic
maxxvitv2_nano_rw_256.sw_in1k,95.430,4.570,99.190,0.810,23.70,256,0.950,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k,95.420,4.580,99.310,0.690,15.62,288,1.000,bicubic
xcit_small_12_p8_224.fb_in1k,95.420,4.580,99.190,0.810,26.21,224,1.000,bicubic
resnetv2_50x1_bit.goog_distilled_in1k,95.410,4.590,99.430,0.570,25.55,224,0.875,bicubic
cs3sedarknet_x.c2ns_in1k,95.410,4.590,99.320,0.680,35.40,288,1.000,bicubic
swinv2_cr_small_224.sw_in1k,95.410,4.590,99.060,0.940,49.70,224,0.900,bicubic
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,95.400,4.600,99.400,0.600,194.03,224,0.875,bilinear
tresnet_l.miil_in1k_448,95.400,4.600,99.300,0.700,55.99,448,0.875,bilinear
mvitv2_tiny.fb_in1k,95.400,4.600,99.160,0.840,24.17,224,0.900,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k_384,95.390,4.610,99.280,0.720,18.45,384,1.000,bicubic
nfnet_l0.ra2_in1k,95.380,4.620,99.420,0.580,35.07,288,1.000,bicubic
regnetz_c16.ra3_in1k,95.380,4.620,99.350,0.650,13.46,320,1.000,bicubic
deit3_medium_patch16_224.fb_in1k,95.380,4.620,99.210,0.790,38.85,224,0.900,bicubic
tresnet_m.miil_in21k_ft_in1k,95.380,4.620,99.150,0.850,31.39,224,0.875,bilinear
pnasnet5large.tf_in1k,95.360,4.640,99.130,0.870,86.06,331,0.911,bicubic
convnext_nano.in12k_ft_in1k,95.350,4.650,99.450,0.550,15.59,288,1.000,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k_384,95.350,4.650,99.120,0.880,10.59,384,1.000,bicubic
xcit_tiny_12_p8_384.fb_dist_in1k,95.340,4.660,99.340,0.660,6.71,384,1.000,bicubic
maxxvit_rmlp_nano_rw_256.sw_in1k,95.340,4.660,99.310,0.690,16.78,256,0.950,bicubic
swinv2_tiny_window16_256.ms_in1k,95.330,4.670,99.300,0.700,28.35,256,0.900,bicubic
convformer_s18.sail_in1k,95.330,4.670,99.150,0.850,26.77,224,1.000,bicubic
resnetv2_101x1_bit.goog_in21k_ft_in1k,95.320,4.680,99.370,0.630,44.54,448,1.000,bilinear
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,95.320,4.680,99.320,0.680,88.79,224,0.875,bilinear
rexnetr_200.sw_in12k_ft_in1k,95.310,4.690,99.470,0.530,16.52,288,1.000,bicubic
resnext101_32x8d.tv2_in1k,95.300,4.700,99.360,0.640,88.79,224,0.965,bilinear
regnety_080_tv.tv2_in1k,95.300,4.700,99.230,0.770,39.38,224,0.965,bicubic
vit_relpos_medium_patch16_cls_224.sw_in1k,95.300,4.700,99.100,0.900,38.76,224,0.900,bicubic
resnetaa50d.sw_in12k_ft_in1k,95.290,4.710,99.380,0.620,25.58,288,1.000,bicubic
gc_efficientnetv2_rw_t.agc_in1k,95.290,4.710,99.220,0.780,13.68,288,1.000,bicubic
fastvit_sa24.apple_in1k,95.280,4.720,99.310,0.690,21.55,256,0.900,bicubic
cs3darknet_x.c2ns_in1k,95.280,4.720,99.290,0.710,35.05,288,1.000,bicubic
regnetx_320.tv2_in1k,95.280,4.720,99.290,0.710,107.81,224,0.965,bicubic
repvit_m1_5.dist_450e_in1k,95.280,4.720,99.230,0.770,14.64,224,0.950,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k_384,95.260,4.740,99.380,0.620,14.25,384,1.000,bicubic
flexivit_small.600ep_in1k,95.260,4.740,99.160,0.840,22.06,240,0.950,bicubic
resnetrs101.tf_in1k,95.250,4.750,99.210,0.790,63.62,288,0.940,bicubic
vit_relpos_base_patch16_clsgap_224.sw_in1k,95.250,4.750,99.200,0.800,86.43,224,0.900,bicubic
convnext_tiny_hnf.a2h_in1k,95.250,4.750,98.980,1.020,28.59,288,1.000,bicubic
cait_xxs36_384.fb_dist_in1k,95.240,4.760,99.320,0.680,17.37,384,1.000,bicubic
vit_large_patch32_384.orig_in21k_ft_in1k,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic
tiny_vit_11m_224.in1k,95.240,4.760,99.230,0.770,11.00,224,0.950,bicubic
wide_resnet101_2.tv2_in1k,95.240,4.760,99.200,0.800,126.89,224,0.965,bilinear
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,95.230,4.770,99.240,0.760,88.22,224,0.900,bicubic
pvt_v2_b2_li.in1k,95.220,4.780,99.260,0.740,22.55,224,0.900,bicubic
efficientvit_b2.r256_in1k,95.220,4.780,99.130,0.870,24.33,256,1.000,bicubic
resnetv2_50d_gn.ah_in1k,95.220,4.780,99.030,0.970,25.57,288,1.000,bicubic
convnext_tiny.fb_in1k,95.210,4.790,99.310,0.690,28.59,288,1.000,bicubic
efficientformer_l3.snap_dist_in1k,95.210,4.790,99.310,0.690,31.41,224,0.950,bicubic
regnetx_160.tv2_in1k,95.210,4.790,99.280,0.720,54.28,224,0.965,bicubic
levit_384.fb_dist_in1k,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic
levit_conv_384.fb_dist_in1k,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic
resnet50.fb_swsl_ig1b_ft_in1k,95.200,4.800,99.390,0.610,25.56,224,0.875,bilinear
resnet51q.ra2_in1k,95.200,4.800,99.280,0.720,35.70,288,1.000,bilinear
vit_base_patch16_224.orig_in21k_ft_in1k,95.200,4.800,99.230,0.770,86.57,224,0.900,bicubic
coat_small.in1k,95.190,4.810,99.280,0.720,21.69,224,0.900,bicubic
focalnet_tiny_lrf.ms_in1k,95.190,4.810,99.220,0.780,28.65,224,0.900,bicubic
vit_relpos_medium_patch16_224.sw_in1k,95.190,4.810,99.220,0.780,38.75,224,0.900,bicubic
flexivit_small.1200ep_in1k,95.190,4.810,99.180,0.820,22.06,240,0.950,bicubic
poolformerv2_m48.sail_in1k,95.180,4.820,99.160,0.840,73.35,224,1.000,bicubic
crossvit_18_dagger_240.in1k,95.180,4.820,99.120,0.880,44.27,240,0.875,bicubic
regnety_160.tv2_in1k,95.160,4.840,99.250,0.750,83.59,224,0.965,bicubic
repvit_m1_5.dist_300e_in1k,95.150,4.850,99.270,0.730,14.64,224,0.950,bicubic
flexivit_small.300ep_in1k,95.150,4.850,99.150,0.850,22.06,240,0.950,bicubic
nasnetalarge.tf_in1k,95.150,4.850,99.130,0.870,88.75,331,0.911,bicubic
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,95.140,4.860,99.300,0.700,44.18,224,0.875,bilinear
convnextv2_nano.fcmae_ft_in1k,95.140,4.860,99.220,0.780,15.62,288,1.000,bicubic
efficientnet_b3.ra2_in1k,95.140,4.860,99.210,0.790,12.23,320,1.000,bicubic
vit_relpos_base_patch16_224.sw_in1k,95.130,4.870,99.290,0.710,86.43,224,0.900,bicubic
wide_resnet50_2.racm_in1k,95.130,4.870,99.260,0.740,68.88,288,0.950,bicubic
resnet61q.ra2_in1k,95.130,4.870,99.080,0.920,36.85,288,1.000,bicubic
xcit_medium_24_p16_224.fb_in1k,95.130,4.870,98.940,1.060,84.40,224,1.000,bicubic
vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.120,4.880,99.220,0.780,36.43,224,0.900,bicubic
fbnetv3_g.ra2_in1k,95.120,4.880,99.200,0.800,16.62,288,0.950,bilinear
tf_efficientnetv2_b3.in1k,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic
cs3sedarknet_l.c2ns_in1k,95.110,4.890,99.210,0.790,21.91,288,0.950,bicubic
efficientformerv2_s2.snap_dist_in1k,95.110,4.890,99.120,0.880,12.71,224,0.950,bicubic
convit_base.fb_in1k,95.100,4.900,99.150,0.850,86.54,224,0.875,bicubic
inception_next_tiny.sail_in1k,95.100,4.900,99.140,0.860,28.06,224,0.875,bicubic
poolformer_m48.sail_in1k,95.100,4.900,99.100,0.900,73.47,224,0.950,bicubic
coatnet_rmlp_nano_rw_224.sw_in1k,95.090,4.910,99.170,0.830,15.15,224,0.900,bicubic
resnet152.a1_in1k,95.090,4.910,98.990,1.010,60.19,288,1.000,bicubic
ecaresnet50t.ra2_in1k,95.080,4.920,99.290,0.710,25.57,320,0.950,bicubic
tresnet_xl.miil_in1k,95.080,4.920,99.260,0.740,78.44,224,0.875,bilinear
davit_tiny.msft_in1k,95.080,4.920,99.140,0.860,28.36,224,0.950,bicubic
crossvit_18_240.in1k,95.070,4.930,99.120,0.880,43.27,240,0.875,bicubic
coat_lite_small.in1k,95.070,4.930,99.030,0.970,19.84,224,0.900,bicubic
crossvit_base_240.in1k,95.070,4.930,98.980,1.020,105.03,240,0.875,bicubic
efficientnetv2_rw_t.ra2_in1k,95.060,4.940,99.220,0.780,13.65,288,1.000,bicubic
vit_relpos_medium_patch16_rpn_224.sw_in1k,95.060,4.940,99.200,0.800,38.73,224,0.900,bicubic
xcit_small_24_p16_224.fb_in1k,95.060,4.940,99.070,0.930,47.67,224,1.000,bicubic
xception41p.ra3_in1k,95.050,4.950,99.160,0.840,26.91,299,0.940,bicubic
poolformerv2_m36.sail_in1k,95.050,4.950,99.150,0.850,56.08,224,1.000,bicubic
coatnet_nano_rw_224.sw_in1k,95.050,4.950,99.140,0.860,15.14,224,0.900,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k,95.050,4.950,99.080,0.920,18.45,256,0.888,bicubic
focalnet_tiny_srf.ms_in1k,95.040,4.960,99.280,0.720,28.43,224,0.900,bicubic
resnet152.tv2_in1k,95.040,4.960,99.170,0.830,60.19,224,0.965,bilinear
poolformer_m36.sail_in1k,95.030,4.970,99.100,0.900,56.17,224,0.950,bicubic
swinv2_tiny_window8_256.ms_in1k,95.020,4.980,99.170,0.830,28.35,256,0.900,bicubic
gcvit_xtiny.in1k,95.020,4.980,99.160,0.840,19.98,224,0.875,bicubic
deit_base_patch16_224.fb_in1k,95.020,4.980,98.970,1.030,86.57,224,0.900,bicubic
pvt_v2_b2.in1k,95.010,4.990,99.140,0.860,25.36,224,0.900,bicubic
halo2botnet50ts_256.a1h_in1k,95.010,4.990,99.050,0.950,22.64,256,0.950,bicubic
ecaresnet101d_pruned.miil_in1k,95.000,5.000,99.230,0.770,24.88,288,0.950,bicubic
seresnext50_32x4d.racm_in1k,95.000,5.000,99.190,0.810,27.56,288,0.950,bicubic
resnext50_32x4d.a1h_in1k,94.990,5.010,99.190,0.810,25.03,288,1.000,bicubic
crossvit_15_dagger_240.in1k,94.990,5.010,99.160,0.840,28.21,240,0.875,bicubic
coatnet_bn_0_rw_224.sw_in1k,94.980,5.020,99.230,0.770,27.44,224,0.950,bicubic
visformer_small.in1k,94.970,5.030,99.210,0.790,40.22,224,0.900,bicubic
convmixer_1536_20.in1k,94.970,5.030,99.170,0.830,51.63,224,0.960,bicubic
tf_efficientnet_b3.ap_in1k,94.970,5.030,99.110,0.890,12.23,300,0.904,bicubic
resnet152.a2_in1k,94.970,5.030,99.070,0.930,60.19,288,1.000,bicubic
xcit_large_24_p16_224.fb_in1k,94.960,5.040,98.830,1.170,189.10,224,1.000,bicubic
cait_xxs24_384.fb_dist_in1k,94.950,5.050,99.130,0.870,12.03,384,1.000,bicubic
vit_srelpos_medium_patch16_224.sw_in1k,94.940,5.060,99.200,0.800,38.74,224,0.900,bicubic
resnet101.a1_in1k,94.940,5.060,99.040,0.960,44.55,288,1.000,bicubic
gernet_l.idstcv_in1k,94.930,5.070,99.200,0.800,31.08,256,0.875,bilinear
resnetv2_50d_evos.ah_in1k,94.920,5.080,99.180,0.820,25.59,288,1.000,bicubic
swin_s3_tiny_224.ms_in1k,94.920,5.080,99.170,0.830,28.33,224,0.900,bicubic
convit_small.fb_in1k,94.920,5.080,99.100,0.900,27.78,224,0.875,bicubic
nest_tiny_jx.goog_in1k,94.920,5.080,99.100,0.900,17.06,224,0.875,bicubic
tf_efficientnet_b3.aa_in1k,94.910,5.090,99.110,0.890,12.23,300,0.904,bicubic
xcit_tiny_24_p8_224.fb_in1k,94.900,5.100,99.190,0.810,12.11,224,1.000,bicubic
tresnet_l.miil_in1k,94.900,5.100,99.030,0.970,55.99,224,0.875,bilinear
coatnet_0_rw_224.sw_in1k,94.900,5.100,99.020,0.980,27.44,224,0.950,bicubic
vit_small_patch16_224.augreg_in21k_ft_in1k,94.890,5.110,99.270,0.730,22.05,224,0.900,bicubic
ecaresnet50t.a1_in1k,94.890,5.110,99.070,0.930,25.57,288,1.000,bicubic
resnet101.a2_in1k,94.890,5.110,99.060,0.940,44.55,288,1.000,bicubic
mixer_b16_224.miil_in21k_ft_in1k,94.880,5.120,99.080,0.920,59.88,224,0.875,bilinear
regnety_032.tv2_in1k,94.870,5.130,99.230,0.770,19.44,224,0.965,bicubic
convnext_nano.d1h_in1k,94.870,5.130,99.140,0.860,15.59,288,1.000,bicubic
tf_efficientnet_lite4.in1k,94.870,5.130,99.100,0.900,13.01,380,0.920,bilinear
tf_efficientnet_b1.ns_jft_in1k,94.860,5.140,99.250,0.750,7.79,240,0.882,bicubic
coatnext_nano_rw_224.sw_in1k,94.850,5.150,99.200,0.800,14.70,224,0.900,bicubic
resnetaa50.a1h_in1k,94.850,5.150,99.120,0.880,25.56,288,1.000,bicubic
efficientvit_b2.r224_in1k,94.850,5.150,98.970,1.030,24.33,224,0.950,bicubic
resnet101.tv2_in1k,94.840,5.160,99.030,0.970,44.55,224,0.965,bilinear
edgenext_small.usi_in1k,94.830,5.170,99.410,0.590,5.59,320,1.000,bicubic
vit_base_patch16_rpn_224.sw_in1k,94.820,5.180,99.090,0.910,86.54,224,0.900,bicubic
xcit_small_12_p16_224.fb_in1k,94.820,5.180,99.060,0.940,26.25,224,1.000,bicubic
wide_resnet50_2.tv2_in1k,94.810,5.190,99.260,0.740,68.88,224,0.965,bilinear
resnet50d.ra2_in1k,94.810,5.190,99.230,0.770,25.58,288,0.950,bicubic
lamhalobotnet50ts_256.a1h_in1k,94.810,5.190,98.980,1.020,22.57,256,0.950,bicubic
pit_b_224.in1k,94.810,5.190,98.820,1.180,73.76,224,0.900,bicubic
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,94.800,5.200,99.290,0.710,28.29,224,0.900,bicubic
cs3darknet_focus_l.c2ns_in1k,94.790,5.210,99.160,0.840,21.15,288,0.950,bicubic
gcresnet50t.ra2_in1k,94.780,5.220,99.120,0.880,25.90,288,1.000,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k,94.780,5.220,99.090,0.910,14.25,256,0.888,bicubic
swinv2_cr_tiny_ns_224.sw_in1k,94.770,5.230,99.110,0.890,28.33,224,0.900,bicubic
twins_svt_small.in1k,94.760,5.240,99.090,0.910,24.06,224,0.900,bicubic
coat_mini.in1k,94.760,5.240,98.950,1.050,10.34,224,0.900,bicubic
vit_base_patch32_clip_224.laion2b_ft_in1k,94.750,5.250,99.070,0.930,88.22,224,0.900,bicubic
resnetv2_50x1_bit.goog_in21k_ft_in1k,94.740,5.260,99.180,0.820,25.55,448,1.000,bilinear
seresnet50.ra2_in1k,94.740,5.260,99.110,0.890,28.09,288,0.950,bicubic
legacy_senet154.in1k,94.730,5.270,99.100,0.900,115.09,224,0.875,bilinear
regnetx_080.tv2_in1k,94.730,5.270,99.030,0.970,39.57,224,0.965,bicubic
repvit_m3.dist_in1k,94.720,5.280,99.060,0.940,10.68,224,0.950,bicubic
halonet50ts.a1h_in1k,94.720,5.280,98.830,1.170,22.73,256,0.940,bicubic
resnet152s.gluon_in1k,94.710,5.290,99.060,0.940,60.32,224,0.875,bicubic
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,94.700,5.300,99.240,0.760,25.03,224,0.875,bilinear
poolformerv2_s36.sail_in1k,94.700,5.300,99.230,0.770,30.79,224,1.000,bicubic
resnest50d_4s2x40d.in1k,94.700,5.300,99.130,0.870,30.42,224,0.875,bicubic
crossvit_15_240.in1k,94.700,5.300,99.080,0.920,27.53,240,0.875,bicubic
senet154.gluon_in1k,94.700,5.300,98.970,1.030,115.09,224,0.875,bicubic
xcit_tiny_12_p8_224.fb_dist_in1k,94.690,5.310,99.180,0.820,6.71,224,1.000,bicubic
pit_s_distilled_224.in1k,94.690,5.310,99.150,0.850,24.04,224,0.900,bicubic
deit3_small_patch16_224.fb_in1k,94.690,5.310,98.760,1.240,22.06,224,0.900,bicubic
vit_relpos_small_patch16_224.sw_in1k,94.680,5.320,99.110,0.890,21.98,224,0.900,bicubic
fastvit_sa12.apple_dist_in1k,94.680,5.320,99.100,0.900,11.58,256,0.900,bicubic
resnetv2_50.a1h_in1k,94.680,5.320,99.090,0.910,25.55,288,1.000,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k,94.680,5.320,98.920,1.080,10.59,256,0.888,bicubic
ecaresnet50d.miil_in1k,94.670,5.330,99.260,0.740,25.58,288,0.950,bicubic
cs3darknet_l.c2ns_in1k,94.670,5.330,99.230,0.770,21.16,288,0.950,bicubic
efficientnet_el.ra_in1k,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic
regnetz_b16.ra3_in1k,94.670,5.330,99.130,0.870,9.72,288,1.000,bicubic
rexnet_200.nav_in1k,94.670,5.330,99.090,0.910,16.37,224,0.875,bicubic
tresnet_m.miil_in1k_448,94.660,5.340,99.150,0.850,31.39,448,0.875,bilinear
seresnext101_64x4d.gluon_in1k,94.650,5.350,98.970,1.030,88.23,224,0.875,bicubic
tiny_vit_5m_224.dist_in22k_ft_in1k,94.630,5.370,99.140,0.860,5.39,224,0.950,bicubic
resnet50_gn.a1h_in1k,94.620,5.380,99.150,0.850,25.56,288,0.950,bicubic
swin_tiny_patch4_window7_224.ms_in1k,94.620,5.380,99.120,0.880,28.29,224,0.900,bicubic
poolformer_s36.sail_in1k,94.620,5.380,99.050,0.950,30.86,224,0.900,bicubic
vit_small_patch16_384.augreg_in1k,94.610,5.390,99.140,0.860,22.20,384,1.000,bicubic
twins_pcpvt_small.in1k,94.600,5.400,99.150,0.850,24.11,224,0.900,bicubic
deit_small_distilled_patch16_224.fb_in1k,94.600,5.400,99.100,0.900,22.44,224,0.900,bicubic
resnet50.tv2_in1k,94.600,5.400,99.090,0.910,25.56,224,0.965,bilinear
efficientnet_b3_pruned.in1k,94.600,5.400,99.070,0.930,9.86,300,0.904,bicubic
resnest50d.in1k,94.600,5.400,99.030,0.970,27.48,224,0.875,bilinear
vit_small_patch32_384.augreg_in21k_ft_in1k,94.590,5.410,99.140,0.860,22.92,384,1.000,bicubic
pit_s_224.in1k,94.590,5.410,98.930,1.070,23.46,224,0.900,bicubic
crossvit_small_240.in1k,94.580,5.420,99.120,0.880,26.86,240,0.875,bicubic
convnext_nano_ols.d1h_in1k,94.580,5.420,99.050,0.950,15.65,288,1.000,bicubic
ecaresnet50t.a2_in1k,94.570,5.430,99.040,0.960,25.57,288,1.000,bicubic
repvgg_b3.rvgg_in1k,94.570,5.430,98.910,1.090,123.09,224,0.875,bilinear
lambda_resnet50ts.a1h_in1k,94.570,5.430,98.650,1.350,21.54,256,0.950,bicubic
tnt_s_patch16_224,94.560,5.440,99.170,0.830,23.76,224,0.900,bicubic
resmlp_36_224.fb_distilled_in1k,94.560,5.440,99.160,0.840,44.69,224,0.875,bicubic
convnextv2_pico.fcmae_ft_in1k,94.560,5.440,99.140,0.860,9.07,288,0.950,bicubic
vit_srelpos_small_patch16_224.sw_in1k,94.550,5.450,99.150,0.850,21.97,224,0.900,bicubic
repvit_m1_1.dist_450e_in1k,94.550,5.450,99.090,0.910,8.80,224,0.950,bicubic
gernet_m.idstcv_in1k,94.540,5.460,98.920,1.080,21.14,224,0.875,bilinear
ecaresnetlight.miil_in1k,94.530,5.470,99.180,0.820,30.16,288,0.950,bicubic
regnety_320.pycls_in1k,94.520,5.480,99.170,0.830,145.05,224,0.875,bicubic
xcit_tiny_12_p16_384.fb_dist_in1k,94.520,5.480,99.170,0.830,6.72,384,1.000,bicubic
res2net101d.in1k,94.520,5.480,98.980,1.020,45.23,224,0.875,bilinear
mobilevitv2_200.cvnets_in1k,94.520,5.480,98.970,1.030,18.45,256,0.888,bicubic
haloregnetz_b.ra3_in1k,94.520,5.480,98.960,1.040,11.68,224,0.940,bicubic
regnetx_032.tv2_in1k,94.520,5.480,98.910,1.090,15.30,224,0.965,bicubic
resnet50.c1_in1k,94.510,5.490,99.070,0.930,25.56,288,1.000,bicubic
resnet50.b1k_in1k,94.510,5.490,99.000,1.000,25.56,288,1.000,bicubic
sehalonet33ts.ra2_in1k,94.510,5.490,98.760,1.240,13.69,256,0.940,bicubic
repvgg_b3g4.rvgg_in1k,94.500,5.500,99.020,0.980,83.83,224,0.875,bilinear
gcresnext50ts.ch_in1k,94.490,5.510,99.010,0.990,15.67,288,1.000,bicubic
ese_vovnet39b.ra_in1k,94.480,5.520,99.060,0.940,24.57,288,0.950,bicubic
poolformerv2_s24.sail_in1k,94.470,5.530,99.010,0.990,21.34,224,1.000,bicubic
resnet50.d_in1k,94.470,5.530,99.000,1.000,25.56,288,1.000,bicubic
resnext50_32x4d.tv2_in1k,94.460,5.540,99.030,0.970,25.03,224,0.965,bilinear
resnet50d.a2_in1k,94.460,5.540,98.900,1.100,25.58,288,1.000,bicubic
eva02_tiny_patch14_336.mim_in22k_ft_in1k,94.450,5.550,99.100,0.900,5.76,336,1.000,bicubic
seresnet50.a2_in1k,94.450,5.550,98.890,1.110,28.09,288,1.000,bicubic
vit_base_patch32_clip_224.openai_ft_in1k,94.440,5.560,99.180,0.820,88.22,224,0.900,bicubic
convmixer_768_32.in1k,94.440,5.560,99.110,0.890,21.11,224,0.960,bicubic
vit_base_patch16_384.augreg_in1k,94.440,5.560,99.030,0.970,86.86,384,1.000,bicubic
resnet152.a3_in1k,94.440,5.560,98.880,1.120,60.19,224,0.950,bicubic
seresnext101_32x4d.gluon_in1k,94.430,5.570,99.090,0.910,48.96,224,0.875,bicubic
fastvit_sa12.apple_in1k,94.430,5.570,99.030,0.970,11.58,256,0.900,bicubic
resnet152d.gluon_in1k,94.430,5.570,99.000,1.000,60.21,224,0.875,bicubic
regnety_016.tv2_in1k,94.410,5.590,99.040,0.960,11.20,224,0.965,bicubic
levit_256.fb_dist_in1k,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic
levit_conv_256.fb_dist_in1k,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic
vit_base_patch32_224.augreg_in21k_ft_in1k,94.400,5.600,99.060,0.940,88.22,224,0.900,bicubic
resnext50d_32x4d.bt_in1k,94.400,5.600,99.050,0.950,25.05,288,0.950,bicubic
repvit_m2.dist_in1k,94.400,5.600,99.040,0.960,8.80,224,0.950,bicubic
resnet50d.a1_in1k,94.400,5.600,98.790,1.210,25.58,288,1.000,bicubic
poolformer_s24.sail_in1k,94.390,5.610,99.060,0.940,21.39,224,0.900,bicubic
nf_resnet50.ra2_in1k,94.380,5.620,99.070,0.930,25.56,288,0.940,bicubic
resnest50d_1s4x24d.in1k,94.380,5.620,99.070,0.930,25.68,224,0.875,bicubic
inception_v4.tf_in1k,94.380,5.620,98.820,1.180,42.68,299,0.875,bicubic
resnext50_32x4d.a1_in1k,94.380,5.620,98.780,1.220,25.03,288,1.000,bicubic
darknet53.c2ns_in1k,94.360,5.640,99.050,0.950,41.61,288,1.000,bicubic
efficientnet_b2.ra_in1k,94.360,5.640,99.050,0.950,9.11,288,1.000,bicubic
edgenext_small_rw.sw_in1k,94.360,5.640,99.040,0.960,7.83,320,1.000,bicubic
inception_resnet_v2.tf_in1k,94.360,5.640,98.800,1.200,55.84,299,0.897,bicubic
tf_efficientnet_el.in1k,94.350,5.650,99.100,0.900,10.59,300,0.904,bicubic
xcit_tiny_12_p8_224.fb_in1k,94.350,5.650,99.070,0.930,6.71,224,1.000,bicubic
gcresnet33ts.ra2_in1k,94.350,5.650,98.960,1.040,19.88,288,1.000,bicubic
resnext101_64x4d.gluon_in1k,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic
resmlp_24_224.fb_distilled_in1k,94.330,5.670,99.090,0.910,30.02,224,0.875,bicubic
resnext50_32x4d.ra_in1k,94.330,5.670,99.030,0.970,25.03,288,0.950,bicubic
resnet50.fb_ssl_yfcc100m_ft_in1k,94.310,5.690,99.150,0.850,25.56,224,0.875,bilinear
sebotnet33ts_256.a1h_in1k,94.310,5.690,98.600,1.400,13.70,256,0.940,bicubic
ecaresnet50d_pruned.miil_in1k,94.300,5.700,99.200,0.800,19.94,288,0.950,bicubic
resnet50.b2k_in1k,94.300,5.700,98.930,1.070,25.56,288,1.000,bicubic
tf_efficientnet_b3.in1k,94.290,5.710,99.100,0.900,12.23,300,0.904,bicubic
rexnet_150.nav_in1k,94.280,5.720,99.090,0.910,9.73,224,0.875,bicubic
fastvit_s12.apple_dist_in1k,94.280,5.720,98.980,1.020,9.47,256,0.900,bicubic
res2net50d.in1k,94.280,5.720,98.860,1.140,25.72,224,0.875,bilinear
repvit_m1_0.dist_450e_in1k,94.270,5.730,99.040,0.960,7.30,224,0.950,bicubic
resnet50.c2_in1k,94.270,5.730,99.040,0.960,25.56,288,1.000,bicubic
tf_efficientnet_b2.ap_in1k,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic
resmlp_big_24_224.fb_in1k,94.270,5.730,98.820,1.180,129.14,224,0.875,bicubic
regnetx_120.pycls_in1k,94.260,5.740,99.170,0.830,46.11,224,0.875,bicubic
seresnet33ts.ra2_in1k,94.260,5.740,99.000,1.000,19.78,288,1.000,bicubic
eca_resnet33ts.ra2_in1k,94.250,5.750,99.030,0.970,19.68,288,1.000,bicubic
cspresnext50.ra_in1k,94.240,5.760,99.050,0.950,20.57,256,0.887,bilinear
xcit_tiny_24_p16_224.fb_dist_in1k,94.240,5.760,98.960,1.040,12.12,224,1.000,bicubic
regnetx_320.pycls_in1k,94.230,5.770,99.050,0.950,107.81,224,0.875,bicubic
efficientvit_b1.r288_in1k,94.230,5.770,98.950,1.050,9.10,288,1.000,bicubic
mobilevitv2_175.cvnets_in1k,94.230,5.770,98.930,1.070,14.25,256,0.888,bicubic
mixnet_xl.ra_in1k,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic
tf_efficientnet_b2.aa_in1k,94.220,5.780,99.040,0.960,9.11,260,0.890,bicubic
resnet50.a1_in1k,94.220,5.780,98.930,1.070,25.56,288,1.000,bicubic
resnext50_32x4d.a2_in1k,94.220,5.780,98.750,1.250,25.03,288,1.000,bicubic
maxvit_rmlp_pico_rw_256.sw_in1k,94.210,5.790,99.000,1.000,7.52,256,0.950,bicubic
darknetaa53.c2ns_in1k,94.210,5.790,98.950,1.050,36.02,288,1.000,bilinear
resnet50.a1h_in1k,94.200,5.800,98.920,1.080,25.56,224,1.000,bicubic
repvit_m1_1.dist_300e_in1k,94.180,5.820,99.080,0.920,8.80,224,0.950,bicubic
resnet101s.gluon_in1k,94.180,5.820,99.010,0.990,44.67,224,0.875,bicubic
resnet101d.gluon_in1k,94.180,5.820,98.940,1.060,44.57,224,0.875,bicubic
resnetblur50.bt_in1k,94.170,5.830,99.010,0.990,25.56,288,0.950,bicubic
seresnext50_32x4d.gluon_in1k,94.170,5.830,98.920,1.080,27.56,224,0.875,bicubic
seresnet50.a1_in1k,94.160,5.840,98.850,1.150,28.09,288,1.000,bicubic
dpn92.mx_in1k,94.150,5.850,98.950,1.050,37.67,224,0.875,bicubic
regnety_064.pycls_in1k,94.130,5.870,99.030,0.970,30.58,224,0.875,bicubic
regnety_160.pycls_in1k,94.130,5.870,99.020,0.980,83.59,224,0.875,bicubic
resnext101_32x4d.gluon_in1k,94.130,5.870,98.940,1.060,44.18,224,0.875,bicubic
legacy_seresnext101_32x4d.in1k,94.120,5.880,98.970,1.030,48.96,224,0.875,bilinear
resnet50.a2_in1k,94.120,5.880,98.850,1.150,25.56,288,1.000,bicubic
inception_resnet_v2.tf_ens_adv_in1k,94.120,5.880,98.790,1.210,55.84,299,0.897,bicubic
cspdarknet53.ra_in1k,94.100,5.900,98.980,1.020,27.64,256,0.887,bilinear
fastvit_t12.apple_dist_in1k,94.100,5.900,98.950,1.050,7.55,256,0.900,bicubic
efficientnet_el_pruned.in1k,94.090,5.910,99.020,0.980,10.59,300,0.904,bicubic
tf_efficientnet_lite3.in1k,94.090,5.910,98.960,1.040,8.20,300,0.904,bilinear
resnet50.ra_in1k,94.090,5.910,98.840,1.160,25.56,288,0.950,bicubic
tresnet_m.miil_in1k,94.080,5.920,98.830,1.170,31.39,224,0.875,bilinear
tf_efficientnetv2_b2.in1k,94.060,5.940,98.930,1.070,10.10,260,0.890,bicubic
gcvit_xxtiny.in1k,94.050,5.950,99.080,0.920,12.00,224,0.875,bicubic
mobilevitv2_150.cvnets_in1k,94.050,5.950,98.900,1.100,10.59,256,0.888,bicubic
convnext_pico.d1_in1k,94.030,5.970,99.010,0.990,9.05,288,0.950,bicubic
resnetrs50.tf_in1k,94.030,5.970,98.850,1.150,35.69,224,0.910,bicubic
resnet152.gluon_in1k,94.030,5.970,98.740,1.260,60.19,224,0.875,bicubic
regnetx_016.tv2_in1k,94.020,5.980,98.930,1.070,9.19,224,0.965,bicubic
hrnet_w48.ms_in1k,94.010,5.990,99.030,0.970,77.47,224,0.875,bilinear
regnety_120.pycls_in1k,94.010,5.990,99.030,0.970,51.82,224,0.875,bicubic
convnext_pico_ols.d1_in1k,94.010,5.990,98.930,1.070,9.06,288,1.000,bicubic
dpn107.mx_in1k,94.010,5.990,98.820,1.180,86.92,224,0.875,bicubic
resnet50.ram_in1k,94.000,6.000,98.880,1.120,25.56,288,0.950,bicubic
dla102x2.in1k,93.990,6.010,99.040,0.960,41.28,224,0.875,bilinear
deit_small_patch16_224.fb_in1k,93.990,6.010,98.960,1.040,22.05,224,0.900,bicubic
skresnext50_32x4d.ra_in1k,93.970,6.030,98.830,1.170,27.48,224,0.875,bicubic
resnet50.bt_in1k,93.960,6.040,98.930,1.070,25.56,288,0.950,bicubic
efficientformer_l1.snap_dist_in1k,93.940,6.060,99.030,0.970,12.29,224,0.950,bicubic
ecaresnet26t.ra2_in1k,93.940,6.060,98.930,1.070,16.01,320,0.950,bicubic
dpn98.mx_in1k,93.930,6.070,98.910,1.090,61.57,224,0.875,bicubic
resnet33ts.ra2_in1k,93.930,6.070,98.880,1.120,19.68,288,1.000,bicubic
xception71.tf_in1k,93.920,6.080,98.950,1.050,42.34,299,0.903,bicubic
regnetx_160.pycls_in1k,93.910,6.090,99.090,0.910,54.28,224,0.875,bicubic
cait_xxs36_224.fb_dist_in1k,93.910,6.090,98.890,1.110,17.30,224,1.000,bicubic
vit_base_patch16_224.sam_in1k,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic
nf_regnet_b1.ra2_in1k,93.890,6.110,98.740,1.260,10.22,288,0.900,bicubic
regnety_080.pycls_in1k,93.880,6.120,99.000,1.000,39.18,224,0.875,bicubic
fbnetv3_d.ra2_in1k,93.870,6.130,98.890,1.110,10.31,256,0.950,bilinear
cspresnet50.ra_in1k,93.870,6.130,98.870,1.130,21.62,256,0.887,bilinear
resnet152c.gluon_in1k,93.870,6.130,98.800,1.200,60.21,224,0.875,bicubic
ecaresnet50t.a3_in1k,93.860,6.140,98.850,1.150,25.57,224,0.950,bicubic
resnet101.a3_in1k,93.860,6.140,98.760,1.240,44.55,224,0.950,bicubic
xcit_tiny_24_p16_224.fb_in1k,93.850,6.150,98.750,1.250,12.12,224,1.000,bicubic
efficientformerv2_s1.snap_dist_in1k,93.840,6.160,98.890,1.110,6.19,224,0.950,bicubic
hrnet_w64.ms_in1k,93.830,6.170,98.940,1.060,128.06,224,0.875,bilinear
repvgg_b2g4.rvgg_in1k,93.830,6.170,98.930,1.070,61.76,224,0.875,bilinear
efficientnet_b2_pruned.in1k,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic
dla169.in1k,93.800,6.200,98.860,1.140,53.39,224,0.875,bilinear
tiny_vit_5m_224.in1k,93.790,6.210,98.940,1.060,5.39,224,0.950,bicubic
regnetx_080.pycls_in1k,93.790,6.210,98.910,1.090,39.57,224,0.875,bicubic
dpn68b.ra_in1k,93.790,6.210,98.540,1.460,12.61,288,1.000,bicubic
resnext101_32x8d.tv_in1k,93.780,6.220,98.960,1.040,88.79,224,0.875,bilinear
dpn131.mx_in1k,93.780,6.220,98.850,1.150,79.25,224,0.875,bicubic
repvit_m1_0.dist_300e_in1k,93.760,6.240,98.920,1.080,7.30,224,0.950,bicubic
resnet101.gluon_in1k,93.760,6.240,98.700,1.300,44.55,224,0.875,bicubic
tf_efficientnet_b0.ns_jft_in1k,93.750,6.250,98.970,1.030,5.29,224,0.875,bicubic
convnextv2_femto.fcmae_ft_in1k,93.750,6.250,98.930,1.070,5.23,288,0.950,bicubic
xception65.tf_in1k,93.750,6.250,98.870,1.130,39.92,299,0.903,bicubic
efficientnet_em.ra2_in1k,93.730,6.270,98.930,1.070,6.90,240,0.882,bicubic
hrnet_w40.ms_in1k,93.730,6.270,98.800,1.200,57.56,224,0.875,bilinear
wide_resnet101_2.tv_in1k,93.720,6.280,98.810,1.190,126.89,224,0.875,bilinear
tf_efficientnet_b1.aa_in1k,93.720,6.280,98.800,1.200,7.79,240,0.882,bicubic
tf_efficientnet_b2.in1k,93.710,6.290,98.930,1.070,9.11,260,0.890,bicubic
tf_efficientnetv2_b1.in1k,93.710,6.290,98.820,1.180,8.14,240,0.882,bicubic
efficientvit_b1.r256_in1k,93.710,6.290,98.810,1.190,9.10,256,1.000,bicubic
levit_192.fb_dist_in1k,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic
levit_conv_192.fb_dist_in1k,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic
resnet101c.gluon_in1k,93.700,6.300,98.760,1.240,44.57,224,0.875,bicubic
fastvit_s12.apple_in1k,93.700,6.300,98.720,1.280,9.47,256,0.900,bicubic
regnetx_040.pycls_in1k,93.690,6.310,98.930,1.070,22.12,224,0.875,bicubic
rexnet_130.nav_in1k,93.680,6.320,98.700,1.300,7.56,224,0.875,bicubic
resnext50_32x4d.gluon_in1k,93.670,6.330,98.690,1.310,25.03,224,0.875,bicubic
resmlp_36_224.fb_in1k,93.650,6.350,98.950,1.050,44.69,224,0.875,bicubic
mobileone_s4.apple_in1k,93.650,6.350,98.650,1.350,14.95,224,0.900,bilinear
regnetx_064.pycls_in1k,93.640,6.360,99.040,0.960,26.21,224,0.875,bicubic
resnet50.am_in1k,93.640,6.360,98.870,1.130,25.56,224,0.875,bicubic
regnety_040.pycls_in1k,93.630,6.370,98.950,1.050,20.65,224,0.875,bicubic
fbnetv3_b.ra2_in1k,93.630,6.370,98.910,1.090,8.60,256,0.950,bilinear
tf_efficientnet_b1.ap_in1k,93.630,6.370,98.800,1.200,7.79,240,0.882,bicubic
hrnet_w44.ms_in1k,93.620,6.380,98.960,1.040,67.06,224,0.875,bilinear
legacy_xception.tf_in1k,93.620,6.380,98.770,1.230,22.86,299,0.897,bicubic
resnet34d.ra2_in1k,93.600,6.400,98.760,1.240,21.82,288,0.950,bicubic
res2net50_26w_6s.in1k,93.590,6.410,98.740,1.260,37.05,224,0.875,bilinear
resnet32ts.ra2_in1k,93.590,6.410,98.740,1.260,17.96,288,1.000,bicubic
halonet26t.a1h_in1k,93.590,6.410,98.630,1.370,12.48,256,0.950,bicubic
repvgg_b2.rvgg_in1k,93.580,6.420,99.070,0.930,89.02,224,0.875,bilinear
dla60_res2next.in1k,93.580,6.420,98.790,1.210,17.03,224,0.875,bilinear
tf_efficientnet_cc_b1_8e.in1k,93.580,6.420,98.690,1.310,39.72,240,0.882,bicubic
resnet50s.gluon_in1k,93.570,6.430,98.840,1.160,25.68,224,0.875,bicubic
inception_v3.gluon_in1k,93.560,6.440,98.840,1.160,23.83,299,0.875,bicubic
resnext50_32x4d.a3_in1k,93.550,6.450,98.820,1.180,25.03,224,0.950,bicubic
eca_halonext26ts.c1_in1k,93.550,6.450,98.680,1.320,10.76,256,0.940,bicubic
repghostnet_200.in1k,93.550,6.450,98.600,1.400,9.80,224,0.875,bicubic
dla102x.in1k,93.540,6.460,98.850,1.150,26.31,224,0.875,bilinear
resnet50d.gluon_in1k,93.540,6.460,98.710,1.290,25.58,224,0.875,bicubic
res2net101_26w_4s.in1k,93.530,6.470,98.600,1.400,45.21,224,0.875,bilinear
convnext_tiny.fb_in22k_ft_in1k,93.530,6.470,98.570,1.430,28.59,288,1.000,bicubic
coat_tiny.in1k,93.510,6.490,98.680,1.320,5.50,224,0.900,bicubic
selecsls60b.in1k,93.500,6.500,98.840,1.160,32.77,224,0.875,bicubic
gmlp_s16_224.ra3_in1k,93.500,6.500,98.780,1.220,19.42,224,0.875,bicubic
pvt_v2_b1.in1k,93.490,6.510,98.860,1.140,14.01,224,0.900,bicubic
fastvit_t12.apple_in1k,93.490,6.510,98.710,1.290,7.55,256,0.900,bicubic
hrnet_w18.ms_aug_in1k,93.480,6.520,98.980,1.020,21.30,224,0.950,bilinear
mobilevitv2_125.cvnets_in1k,93.480,6.520,98.840,1.160,7.48,256,0.888,bicubic
xception41.tf_in1k,93.480,6.520,98.750,1.250,26.97,299,0.903,bicubic
coat_lite_mini.in1k,93.470,6.530,98.770,1.230,11.01,224,0.900,bicubic
regnety_032.pycls_in1k,93.460,6.540,98.950,1.050,19.44,224,0.875,bicubic
wide_resnet50_2.tv_in1k,93.460,6.540,98.950,1.050,68.88,224,0.875,bilinear
repvit_m0_9.dist_300e_in1k,93.460,6.540,98.820,1.180,5.49,224,0.950,bicubic
legacy_seresnext50_32x4d.in1k,93.450,6.550,98.800,1.200,27.56,224,0.875,bilinear
cait_xxs24_224.fb_dist_in1k,93.450,6.550,98.780,1.220,11.96,224,1.000,bicubic
vit_small_patch16_224.augreg_in1k,93.450,6.550,98.780,1.220,22.05,224,0.900,bicubic
repvit_m0_9.dist_450e_in1k,93.440,6.560,98.910,1.090,5.49,224,0.950,bicubic
convnext_femto.d1_in1k,93.440,6.560,98.820,1.180,5.22,288,0.950,bicubic
repvgg_b1.rvgg_in1k,93.440,6.560,98.790,1.210,57.42,224,0.875,bilinear
botnet26t_256.c1_in1k,93.440,6.560,98.650,1.350,12.49,256,0.950,bicubic
lambda_resnet26rpt_256.c1_in1k,93.430,6.570,98.880,1.120,10.99,256,0.940,bicubic
lambda_resnet26t.c1_in1k,93.430,6.570,98.730,1.270,10.96,256,0.940,bicubic
vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.420,6.580,98.830,1.170,5.79,384,1.000,bicubic
resmlp_24_224.fb_in1k,93.420,6.580,98.810,1.190,30.02,224,0.875,bicubic
legacy_seresnet152.in1k,93.410,6.590,98.850,1.150,66.82,224,0.875,bilinear
hrnet_w30.ms_in1k,93.410,6.590,98.830,1.170,37.71,224,0.875,bilinear
resnet50d.a3_in1k,93.410,6.590,98.750,1.250,25.58,224,0.950,bicubic
res2net50_26w_8s.in1k,93.410,6.590,98.690,1.310,48.40,224,0.875,bilinear
convnext_femto_ols.d1_in1k,93.390,6.610,98.910,1.090,5.23,288,0.950,bicubic
repvit_m1.dist_in1k,93.380,6.620,98.650,1.350,5.49,224,0.950,bicubic
dla60_res2net.in1k,93.370,6.630,98.840,1.160,20.85,224,0.875,bilinear
xcit_tiny_12_p16_224.fb_dist_in1k,93.350,6.650,98.760,1.240,6.72,224,1.000,bicubic
eca_botnext26ts_256.c1_in1k,93.350,6.650,98.690,1.310,10.59,256,0.950,bicubic
seresnext26t_32x4d.bt_in1k,93.350,6.650,98.690,1.310,16.81,288,0.950,bicubic
vit_base_patch16_224.augreg_in1k,93.350,6.650,98.660,1.340,86.57,224,0.900,bicubic
efficientvit_b1.r224_in1k,93.330,6.670,98.570,1.430,9.10,224,0.950,bicubic
xcit_nano_12_p8_384.fb_dist_in1k,93.300,6.700,98.860,1.140,3.05,384,1.000,bicubic
pit_xs_distilled_224.in1k,93.290,6.710,98.790,1.210,11.00,224,0.900,bicubic
cs3darknet_m.c2ns_in1k,93.280,6.720,98.720,1.280,9.31,288,0.950,bicubic
dla102.in1k,93.270,6.730,98.790,1.210,33.27,224,0.875,bilinear
legacy_seresnet101.in1k,93.270,6.730,98.740,1.260,49.33,224,0.875,bilinear
resnet152.tv_in1k,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear
regnetx_032.pycls_in1k,93.240,6.760,98.720,1.280,15.30,224,0.875,bicubic
mixnet_l.ft_in1k,93.240,6.760,98.700,1.300,7.33,224,0.875,bicubic
resnest26d.gluon_in1k,93.220,6.780,98.850,1.150,17.07,224,0.875,bilinear
dla60x.in1k,93.190,6.810,98.720,1.280,17.35,224,0.875,bilinear
tf_efficientnet_em.in1k,93.190,6.810,98.660,1.340,6.90,240,0.882,bicubic
inception_v3.tf_in1k,93.190,6.810,98.490,1.510,23.83,299,0.875,bicubic
res2net50_26w_4s.in1k,93.180,6.820,98.660,1.340,25.70,224,0.875,bilinear
vit_base_patch32_384.augreg_in1k,93.160,6.840,98.610,1.390,88.30,384,1.000,bicubic
mobilevit_s.cvnets_in1k,93.150,6.850,98.780,1.220,5.58,256,0.900,bicubic
regnety_008_tv.tv2_in1k,93.150,6.850,98.680,1.320,6.43,224,0.965,bicubic
res2next50.in1k,93.150,6.850,98.640,1.360,24.67,224,0.875,bilinear
mobilevitv2_100.cvnets_in1k,93.140,6.860,98.760,1.240,4.90,256,0.888,bicubic
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.140,6.860,98.310,1.690,119.42,256,0.900,bicubic
bat_resnext26ts.ch_in1k,93.120,6.880,98.730,1.270,10.73,256,0.900,bicubic
cs3darknet_focus_m.c2ns_in1k,93.100,6.900,98.750,1.250,9.30,288,0.950,bicubic
ghostnetv2_160.in1k,93.090,6.910,98.740,1.260,12.39,224,0.875,bicubic
seresnext26d_32x4d.bt_in1k,93.060,6.940,98.710,1.290,16.81,288,0.950,bicubic
tf_efficientnetv2_b0.in1k,93.060,6.940,98.700,1.300,7.14,224,0.875,bicubic
repvgg_b1g4.rvgg_in1k,93.030,6.970,98.820,1.180,39.97,224,0.875,bilinear
levit_128.fb_dist_in1k,93.030,6.970,98.710,1.290,9.21,224,0.900,bicubic
levit_conv_128.fb_dist_in1k,93.030,6.970,98.700,1.300,9.21,224,0.900,bicubic
res2net50_14w_8s.in1k,93.030,6.970,98.700,1.300,25.06,224,0.875,bilinear
densenetblur121d.ra_in1k,93.030,6.970,98.600,1.400,8.00,288,0.950,bicubic
tf_mixnet_l.in1k,93.030,6.970,98.530,1.470,7.33,224,0.875,bicubic
efficientnet_b1.ft_in1k,93.020,6.980,98.710,1.290,7.79,256,1.000,bicubic
selecsls60.in1k,93.010,6.990,98.820,1.180,30.67,224,0.875,bicubic
regnety_016.pycls_in1k,93.010,6.990,98.670,1.330,11.20,224,0.875,bicubic
inception_v3.tf_adv_in1k,93.010,6.990,98.490,1.510,23.83,299,0.875,bicubic
hrnet_w18_small_v2.gluon_in1k,93.000,7.000,98.760,1.240,15.60,224,0.875,bicubic
resnet34.a1_in1k,93.000,7.000,98.630,1.370,21.80,288,1.000,bicubic
visformer_tiny.in1k,92.980,7.020,98.730,1.270,10.32,224,0.900,bicubic
convnext_atto_ols.a2_in1k,92.980,7.020,98.670,1.330,3.70,288,0.950,bicubic
mobileone_s3.apple_in1k,92.980,7.020,98.630,1.370,10.17,224,0.900,bilinear
hardcorenas_f.miil_green_in1k,92.980,7.020,98.620,1.380,8.20,224,0.875,bilinear
efficientnet_b1_pruned.in1k,92.980,7.020,98.540,1.460,6.33,240,0.882,bicubic
hrnet_w32.ms_in1k,92.950,7.050,98.850,1.150,41.23,224,0.875,bilinear
seresnext26ts.ch_in1k,92.940,7.060,98.670,1.330,10.39,288,1.000,bicubic
hardcorenas_e.miil_green_in1k,92.940,7.060,98.580,1.420,8.07,224,0.875,bilinear
resnet50.a3_in1k,92.940,7.060,98.510,1.490,25.56,224,0.950,bicubic
tf_efficientnet_b1.in1k,92.930,7.070,98.660,1.340,7.79,240,0.882,bicubic
convnextv2_atto.fcmae_ft_in1k,92.920,7.080,98.560,1.440,3.71,288,0.950,bicubic
resnet50c.gluon_in1k,92.910,7.090,98.700,1.300,25.58,224,0.875,bicubic
efficientnet_es.ra_in1k,92.910,7.090,98.690,1.310,5.44,224,0.875,bicubic
resnet26t.ra2_in1k,92.910,7.090,98.680,1.320,16.01,320,1.000,bicubic
resnext50_32x4d.tv_in1k,92.900,7.100,98.730,1.270,25.03,224,0.875,bilinear
inception_v3.tv_in1k,92.900,7.100,98.320,1.680,23.83,299,0.875,bicubic
densenet161.tv_in1k,92.890,7.110,98.790,1.210,28.68,224,0.875,bicubic
pit_xs_224.in1k,92.890,7.110,98.780,1.220,10.62,224,0.900,bicubic
poolformerv2_s12.sail_in1k,92.890,7.110,98.530,1.470,11.89,224,1.000,bicubic
resnet101.tv_in1k,92.880,7.120,98.660,1.340,44.55,224,0.875,bilinear
resmlp_12_224.fb_distilled_in1k,92.870,7.130,98.620,1.380,15.35,224,0.875,bicubic
tf_efficientnet_cc_b0_8e.in1k,92.870,7.130,98.460,1.540,24.01,224,0.875,bicubic
coat_lite_tiny.in1k,92.860,7.140,98.640,1.360,5.72,224,0.900,bicubic
rexnet_100.nav_in1k,92.830,7.170,98.600,1.400,4.80,224,0.875,bicubic
tf_efficientnet_cc_b0_4e.in1k,92.820,7.180,98.440,1.560,13.31,224,0.875,bicubic
tinynet_a.in1k,92.810,7.190,98.560,1.440,6.19,192,0.875,bicubic
dpn68b.mx_in1k,92.780,7.220,98.520,1.480,12.61,224,0.875,bicubic
res2net50_48w_2s.in1k,92.780,7.220,98.470,1.530,25.29,224,0.875,bilinear
hrnet_w18.ms_in1k,92.770,7.230,98.660,1.340,21.30,224,0.875,bilinear
convnext_atto.d2_in1k,92.770,7.230,98.620,1.380,3.70,288,0.950,bicubic
crossvit_9_dagger_240.in1k,92.770,7.230,98.490,1.510,8.78,240,0.875,bicubic
ese_vovnet19b_dw.ra_in1k,92.760,7.240,98.650,1.350,6.54,288,0.950,bicubic
eca_resnext26ts.ch_in1k,92.750,7.250,98.710,1.290,10.30,288,1.000,bicubic
gcresnext26ts.ch_in1k,92.740,7.260,98.610,1.390,10.48,288,1.000,bicubic
densenet201.tv_in1k,92.700,7.300,98.640,1.360,20.01,224,0.875,bicubic
densenet121.ra_in1k,92.700,7.300,98.600,1.400,7.98,288,0.950,bicubic
repvgg_a2.rvgg_in1k,92.680,7.320,98.530,1.470,28.21,224,0.875,bilinear
gmixer_24_224.ra3_in1k,92.680,7.320,98.280,1.720,24.72,224,0.875,bicubic
mobileone_s2.apple_in1k,92.660,7.340,98.680,1.320,7.88,224,0.900,bilinear
legacy_seresnet50.in1k,92.660,7.340,98.650,1.350,28.09,224,0.875,bilinear
dla60.in1k,92.650,7.350,98.630,1.370,22.04,224,0.875,bilinear
tf_efficientnet_b0.ap_in1k,92.620,7.380,98.370,1.630,5.29,224,0.875,bicubic
mobilenetv2_120d.ra_in1k,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic
hardcorenas_d.miil_green_in1k,92.610,7.390,98.430,1.570,7.50,224,0.875,bilinear
legacy_seresnext26_32x4d.in1k,92.600,7.400,98.410,1.590,16.79,224,0.875,bicubic
tf_efficientnet_lite2.in1k,92.590,7.410,98.540,1.460,6.09,260,0.890,bicubic
fastvit_t8.apple_dist_in1k,92.590,7.410,98.430,1.570,4.03,256,0.900,bicubic
resnet34.a2_in1k,92.570,7.430,98.570,1.430,21.80,288,1.000,bicubic
skresnet34.ra_in1k,92.570,7.430,98.520,1.480,22.28,224,0.875,bicubic
resnet50.gluon_in1k,92.560,7.440,98.550,1.450,25.56,224,0.875,bicubic
resnet26d.bt_in1k,92.550,7.450,98.650,1.350,16.01,288,0.950,bicubic
regnetx_016.pycls_in1k,92.530,7.470,98.550,1.450,9.19,224,0.875,bicubic
poolformer_s12.sail_in1k,92.500,7.500,98.390,1.610,11.92,224,0.900,bicubic
regnetx_008.tv2_in1k,92.490,7.510,98.430,1.570,7.26,224,0.965,bicubic
efficientnet_b0.ra_in1k,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic
xcit_tiny_12_p16_224.fb_in1k,92.480,7.520,98.630,1.370,6.72,224,1.000,bicubic
selecsls42b.in1k,92.480,7.520,98.430,1.570,32.46,224,0.875,bicubic
gernet_s.idstcv_in1k,92.440,7.560,98.500,1.500,8.17,224,0.875,bilinear
xcit_nano_12_p8_224.fb_dist_in1k,92.430,7.570,98.540,1.460,3.05,224,1.000,bicubic
tf_efficientnet_b0.aa_in1k,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic
repghostnet_150.in1k,92.380,7.620,98.530,1.470,6.58,224,0.875,bicubic
resnext26ts.ra2_in1k,92.380,7.620,98.390,1.610,10.30,288,1.000,bicubic
seresnet50.a3_in1k,92.360,7.640,98.330,1.670,28.09,224,0.950,bicubic
hardcorenas_c.miil_green_in1k,92.350,7.650,98.340,1.660,5.52,224,0.875,bilinear
convmixer_1024_20_ks9_p14.in1k,92.330,7.670,98.430,1.570,24.38,224,0.960,bicubic
dpn68.mx_in1k,92.300,7.700,98.610,1.390,12.61,224,0.875,bicubic
densenet169.tv_in1k,92.300,7.700,98.590,1.410,14.15,224,0.875,bicubic
tf_efficientnet_lite1.in1k,92.290,7.710,98.500,1.500,5.42,240,0.882,bicubic
resnet34.bt_in1k,92.280,7.720,98.600,1.400,21.80,288,0.950,bicubic
tf_efficientnet_b0.in1k,92.280,7.720,98.550,1.450,5.29,224,0.875,bicubic
mixnet_m.ft_in1k,92.270,7.730,98.360,1.640,5.01,224,0.875,bicubic
mobilenetv3_large_100.miil_in21k_ft_in1k,92.260,7.740,98.240,1.760,5.48,224,0.875,bilinear
ghostnetv2_130.in1k,92.240,7.760,98.380,1.620,8.96,224,0.875,bicubic
tf_mixnet_m.in1k,92.210,7.790,98.420,1.580,5.01,224,0.875,bicubic
efficientvit_m5.r224_in1k,92.150,7.850,98.520,1.480,12.47,224,0.875,bicubic
vit_small_patch32_224.augreg_in21k_ft_in1k,92.140,7.860,98.520,1.480,22.88,224,0.900,bicubic
xcit_nano_12_p16_384.fb_dist_in1k,92.130,7.870,98.510,1.490,3.05,384,1.000,bicubic
resmlp_12_224.fb_in1k,92.120,7.880,98.570,1.430,15.35,224,0.875,bicubic
resnet50.tv_in1k,92.120,7.880,98.410,1.590,25.56,224,0.875,bilinear
resnet26.bt_in1k,92.110,7.890,98.550,1.450,16.00,288,0.950,bicubic
tf_efficientnet_es.in1k,92.110,7.890,98.430,1.570,5.44,224,0.875,bicubic
mobilenetv2_140.ra_in1k,92.050,7.950,98.250,1.750,6.11,224,0.875,bicubic
mobilevitv2_075.cvnets_in1k,91.970,8.030,98.300,1.700,2.87,256,0.888,bicubic
repghostnet_130.in1k,91.940,8.060,98.390,1.610,5.48,224,0.875,bicubic
fastvit_t8.apple_in1k,91.930,8.070,98.380,1.620,4.03,256,0.900,bicubic
hardcorenas_b.miil_green_in1k,91.920,8.080,98.410,1.590,5.18,224,0.875,bilinear
vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.920,8.080,98.340,1.660,5.72,224,0.900,bicubic
regnety_008.pycls_in1k,91.900,8.100,98.410,1.590,6.26,224,0.875,bicubic
efficientformerv2_s0.snap_dist_in1k,91.860,8.140,98.370,1.630,3.60,224,0.950,bicubic
mobileone_s1.apple_in1k,91.790,8.210,98.460,1.540,4.83,224,0.900,bilinear
mixnet_s.ft_in1k,91.780,8.220,98.300,1.700,4.13,224,0.875,bicubic
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,91.730,8.270,98.430,1.570,6.36,384,1.000,bicubic
efficientnet_es_pruned.in1k,91.700,8.300,98.410,1.590,5.44,224,0.875,bicubic
repvgg_b0.rvgg_in1k,91.680,8.320,98.450,1.550,15.82,224,0.875,bilinear
tf_mixnet_s.in1k,91.680,8.320,98.240,1.760,4.13,224,0.875,bicubic
semnasnet_100.rmsp_in1k,91.670,8.330,98.290,1.710,3.89,224,0.875,bicubic
ghostnetv2_100.in1k,91.630,8.370,98.290,1.710,6.16,224,0.875,bicubic
regnety_004.tv2_in1k,91.620,8.380,98.280,1.720,4.34,224,0.965,bicubic
hardcorenas_a.miil_green_in1k,91.620,8.380,98.170,1.830,5.26,224,0.875,bilinear
edgenext_x_small.in1k,91.580,8.420,98.190,1.810,2.34,288,1.000,bicubic
regnety_006.pycls_in1k,91.560,8.440,98.430,1.570,6.06,224,0.875,bicubic
mobilenetv3_rw.rmsp_in1k,91.550,8.450,98.280,1.720,5.48,224,0.875,bicubic
levit_128s.fb_dist_in1k,91.510,8.490,98.400,1.600,7.78,224,0.900,bicubic
levit_conv_128s.fb_dist_in1k,91.500,8.500,98.400,1.600,7.78,224,0.900,bicubic
legacy_seresnet34.in1k,91.490,8.510,98.200,1.800,21.96,224,0.875,bilinear
mobilenetv3_large_100.ra_in1k,91.470,8.530,98.320,1.680,5.48,224,0.875,bicubic
tf_mobilenetv3_large_100.in1k,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear
densenet121.tv_in1k,91.400,8.600,98.250,1.750,7.98,224,0.875,bicubic
mobilenetv2_110d.ra_in1k,91.330,8.670,98.190,1.810,4.52,224,0.875,bicubic
tf_efficientnet_lite0.in1k,91.280,8.720,98.080,1.920,4.65,224,0.875,bicubic
fbnetc_100.rmsp_in1k,91.280,8.720,97.840,2.160,5.57,224,0.875,bilinear
efficientnet_lite0.ra_in1k,91.250,8.750,98.240,1.760,4.65,224,0.875,bicubic
dla34.in1k,91.220,8.780,98.170,1.830,15.74,224,0.875,bilinear
mobilevit_xs.cvnets_in1k,91.210,8.790,98.220,1.780,2.32,256,0.900,bicubic
mnasnet_100.rmsp_in1k,91.210,8.790,98.050,1.950,4.38,224,0.875,bicubic
regnetx_008.pycls_in1k,91.170,8.830,98.370,1.630,7.26,224,0.875,bicubic
hrnet_w18_small_v2.ms_in1k,91.160,8.840,98.340,1.660,15.60,224,0.875,bilinear
regnetx_004_tv.tv2_in1k,91.160,8.840,98.100,1.900,5.50,224,0.965,bicubic
resnest14d.gluon_in1k,91.150,8.850,98.330,1.670,10.61,224,0.875,bilinear
mixer_b16_224.goog_in21k_ft_in1k,91.140,8.860,97.400,2.600,59.88,224,0.875,bicubic
repvgg_a1.rvgg_in1k,91.120,8.880,98.160,1.840,14.09,224,0.875,bilinear
tinynet_b.in1k,91.110,8.890,98.060,1.940,3.73,188,0.875,bicubic
xcit_nano_12_p8_224.fb_in1k,91.100,8.900,98.230,1.770,3.05,224,1.000,bicubic
resnet18.fb_swsl_ig1b_ft_in1k,91.100,8.900,98.200,1.800,11.69,224,0.875,bilinear
repghostnet_111.in1k,91.100,8.900,98.050,1.950,4.54,224,0.875,bicubic
resnet34.gluon_in1k,91.090,8.910,98.180,1.820,21.80,224,0.875,bicubic
deit_tiny_distilled_patch16_224.fb_in1k,91.080,8.920,98.270,1.730,5.91,224,0.900,bicubic
crossvit_9_240.in1k,91.050,8.950,98.320,1.680,8.55,240,0.875,bicubic
vgg19_bn.tv_in1k,90.990,9.010,98.100,1.900,143.68,224,0.875,bilinear
resnet18d.ra2_in1k,90.800,9.200,98.160,1.840,11.71,288,0.950,bicubic
regnetx_006.pycls_in1k,90.790,9.210,98.090,1.910,6.20,224,0.875,bicubic
regnety_004.pycls_in1k,90.770,9.230,98.070,1.930,4.34,224,0.875,bicubic
efficientvit_m4.r224_in1k,90.740,9.260,98.040,1.960,8.80,224,0.875,bicubic
pit_ti_distilled_224.in1k,90.730,9.270,98.250,1.750,5.10,224,0.900,bicubic
resnet18.fb_ssl_yfcc100m_ft_in1k,90.700,9.300,98.020,1.980,11.69,224,0.875,bilinear
repghostnet_100.in1k,90.690,9.310,98.120,1.880,4.07,224,0.875,bicubic
spnasnet_100.rmsp_in1k,90.590,9.410,97.950,2.050,4.42,224,0.875,bilinear
vit_base_patch32_224.augreg_in1k,90.590,9.410,97.720,2.280,88.22,224,0.900,bicubic
convit_tiny.fb_in1k,90.550,9.450,98.190,1.810,5.71,224,0.875,bicubic
vgg16_bn.tv_in1k,90.540,9.460,97.990,2.010,138.37,224,0.875,bilinear
crossvit_tiny_240.in1k,90.530,9.470,97.950,2.050,7.01,240,0.875,bicubic
ghostnet_100.in1k,90.460,9.540,97.910,2.090,5.18,224,0.875,bicubic
pit_ti_224.in1k,90.420,9.580,98.010,1.990,4.85,224,0.900,bicubic
tf_mobilenetv3_large_075.in1k,90.330,9.670,97.870,2.130,3.99,224,0.875,bilinear
hrnet_w18_small.gluon_in1k,90.310,9.690,97.750,2.250,13.19,224,0.875,bicubic
resnet34.tv_in1k,90.300,9.700,97.970,2.030,21.80,224,0.875,bilinear
resnet34.a3_in1k,90.240,9.760,97.880,2.120,21.80,224,0.950,bicubic
semnasnet_075.rmsp_in1k,90.220,9.780,97.950,2.050,2.91,224,0.875,bicubic
resnet18.a1_in1k,90.200,9.800,97.760,2.240,11.69,288,1.000,bicubic
xcit_nano_12_p16_224.fb_dist_in1k,90.190,9.810,97.750,2.250,3.05,224,1.000,bicubic
skresnet18.ra_in1k,90.180,9.820,97.780,2.220,11.96,224,0.875,bicubic
efficientvit_m3.r224_in1k,90.000,10.000,97.830,2.170,6.90,224,0.875,bicubic
hrnet_w18_small.ms_in1k,89.870,10.130,97.890,2.110,13.19,224,0.875,bilinear
mobilenetv2_100.ra_in1k,89.870,10.130,97.830,2.170,3.50,224,0.875,bicubic
vit_base_patch32_224.sam_in1k,89.870,10.130,97.600,2.400,88.22,224,0.900,bicubic
edgenext_xx_small.in1k,89.800,10.200,97.500,2.500,1.33,288,1.000,bicubic
repvgg_a0.rvgg_in1k,89.680,10.320,97.760,2.240,9.11,224,0.875,bilinear
vgg19.tv_in1k,89.680,10.320,97.550,2.450,143.67,224,0.875,bilinear
deit_tiny_patch16_224.fb_in1k,89.610,10.390,97.960,2.040,5.72,224,0.900,bicubic
regnetx_004.pycls_in1k,89.470,10.530,97.760,2.240,5.16,224,0.875,bicubic
resnet18.a2_in1k,89.470,10.530,97.630,2.370,11.69,288,1.000,bicubic
repghostnet_080.in1k,89.470,10.530,97.410,2.590,3.28,224,0.875,bicubic
vgg16.tv_in1k,89.370,10.630,97.520,2.480,138.36,224,0.875,bilinear
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.340,10.660,97.700,2.300,6.34,224,0.900,bicubic
legacy_seresnet18.in1k,89.260,10.740,97.690,2.310,11.78,224,0.875,bicubic
resnet14t.c3_in1k,89.250,10.750,97.440,2.560,10.08,224,0.950,bicubic
vgg13_bn.tv_in1k,89.200,10.800,97.530,2.470,133.05,224,0.875,bilinear
tf_mobilenetv3_large_minimal_100.in1k,89.160,10.840,97.310,2.690,3.92,224,0.875,bilinear
mobilevitv2_050.cvnets_in1k,89.030,10.970,97.600,2.400,1.37,256,0.888,bicubic
pvt_v2_b0.in1k,88.970,11.030,97.690,2.310,3.67,224,0.900,bicubic
xcit_nano_12_p16_224.fb_in1k,88.970,11.030,97.410,2.590,3.05,224,1.000,bicubic
efficientvit_m2.r224_in1k,88.910,11.090,97.390,2.610,4.19,224,0.875,bicubic
lcnet_100.ra2_in1k,88.910,11.090,97.380,2.620,2.95,224,0.875,bicubic
mobileone_s0.apple_in1k,88.810,11.190,97.220,2.780,5.29,224,0.875,bilinear
resnet18.gluon_in1k,88.660,11.340,97.100,2.900,11.69,224,0.875,bicubic
tinynet_c.in1k,88.400,11.600,97.260,2.740,2.46,184,0.875,bicubic
vgg11_bn.tv_in1k,88.400,11.600,97.250,2.750,132.87,224,0.875,bilinear
efficientvit_b0.r224_in1k,88.300,11.700,96.880,3.120,3.41,224,0.950,bicubic
regnety_002.pycls_in1k,88.170,11.830,97.440,2.560,3.16,224,0.875,bicubic
resnet18.tv_in1k,88.150,11.850,97.120,2.880,11.69,224,0.875,bilinear
mobilevit_xxs.cvnets_in1k,87.940,12.060,97.190,2.810,1.27,256,0.900,bicubic
vgg13.tv_in1k,87.570,12.430,97.110,2.890,133.05,224,0.875,bilinear
regnetx_002.pycls_in1k,87.370,12.630,97.000,3.000,2.68,224,0.875,bicubic
vgg11.tv_in1k,87.350,12.650,97.100,2.900,132.86,224,0.875,bilinear
efficientvit_m1.r224_in1k,87.220,12.780,97.020,2.980,2.98,224,0.875,bicubic
repghostnet_058.in1k,87.150,12.850,96.780,3.220,2.55,224,0.875,bicubic
dla60x_c.in1k,87.110,12.890,97.140,2.860,1.32,224,0.875,bilinear
resnet18.a3_in1k,87.070,12.930,96.660,3.340,11.69,224,0.950,bicubic
mixer_l16_224.goog_in21k_ft_in1k,86.990,13.010,94.070,5.930,208.20,224,0.875,bicubic
lcnet_075.ra2_in1k,86.960,13.040,96.550,3.450,2.36,224,0.875,bicubic
resnet10t.c3_in1k,86.680,13.320,96.730,3.270,5.44,224,0.950,bicubic
mobilenetv3_small_100.lamb_in1k,86.180,13.820,96.450,3.550,2.54,224,0.875,bicubic
tf_mobilenetv3_small_100.in1k,85.950,14.050,96.400,3.600,2.54,224,0.875,bilinear
mnasnet_small.lamb_in1k,85.480,14.520,96.000,4.000,2.03,224,0.875,bicubic
repghostnet_050.in1k,85.450,14.550,96.150,3.850,2.31,224,0.875,bicubic
dla46x_c.in1k,85.440,14.560,96.420,3.580,1.07,224,0.875,bilinear
tinynet_d.in1k,85.440,14.560,96.020,3.980,2.34,152,0.875,bicubic
mobilenetv2_050.lamb_in1k,84.990,15.010,95.620,4.380,1.97,224,0.875,bicubic
dla46_c.in1k,84.700,15.300,96.210,3.790,1.30,224,0.875,bilinear
tf_mobilenetv3_small_075.in1k,84.500,15.500,95.880,4.120,2.04,224,0.875,bilinear
mobilenetv3_small_075.lamb_in1k,84.120,15.880,95.520,4.480,2.04,224,0.875,bicubic
efficientvit_m0.r224_in1k,83.220,16.780,95.700,4.300,2.35,224,0.875,bicubic
lcnet_050.ra2_in1k,83.040,16.960,95.020,4.980,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100.in1k,82.660,17.340,95.030,4.970,2.04,224,0.875,bilinear
tinynet_e.in1k,79.810,20.190,93.970,6.030,2.04,106,0.875,bicubic
mobilenetv3_small_050.lamb_in1k,78.080,21.920,93.010,6.990,1.59,224,0.875,bicubic
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nhwc-pt112-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,11915.85,41.681,512,106,2.04
mobilenetv3_small_050,11290.99,44.293,512,224,1.59
lcnet_035,10015.98,50.125,512,224,1.64
lcnet_050,9286.37,54.37,512,224,1.88
tf_mobilenetv3_small_minimal_100,9042.22,55.986,512,224,2.04
mobilenetv3_small_075,8679.98,58.254,512,224,2.04
mobilenetv3_small_100,8035.08,62.981,512,224,2.54
tinynet_d,7990.69,63.223,512,152,2.34
tf_mobilenetv3_small_075,7930.1,63.8,512,224,2.04
tf_mobilenetv3_small_100,7330.24,69.047,512,224,2.54
lcnet_075,6950.91,73.156,512,224,2.36
levit_128s,6539.16,77.346,512,224,7.78
resnet10t,6318.63,80.774,512,176,5.44
mnasnet_small,5607.09,90.422,512,224,2.03
lcnet_100,5354.67,95.126,512,224,2.95
mixer_s32_224,4943.04,103.013,512,224,19.1
mobilenetv2_035,4789.43,106.101,512,224,1.68
mnasnet_050,4680.08,108.62,512,224,2.22
levit_128,4558.28,111.213,512,224,9.21
cs3darknet_focus_s,4469.48,114.041,512,256,3.27
vit_small_patch32_224,4445.76,114.324,512,224,22.88
tinynet_c,4167.16,121.826,512,184,2.46
gernet_s,4165.03,122.198,512,224,8.17
cs3darknet_s,4110.51,124.007,512,256,3.28
regnetx_002,4105.04,124.027,512,224,2.68
mobilenetv2_050,4051.14,125.606,512,224,1.97
vit_tiny_r_s16_p8_224,4025.23,126.328,512,224,6.34
semnasnet_050,3904.91,130.185,512,224,2.08
regnety_002,3777.81,134.562,512,224,3.16
levit_192,3727.29,136.213,512,224,10.95
ghostnet_050,3670.99,138.144,512,224,2.59
ese_vovnet19b_slim_dw,3629.92,140.575,512,224,1.9
lcnet_150,3576.28,142.665,512,224,4.5
gluon_resnet18_v1b,3482.17,146.691,512,224,11.69
resnet18,3481.78,146.713,512,224,11.69
swsl_resnet18,3480.5,146.765,512,224,11.69
ssl_resnet18,3477.04,146.904,512,224,11.69
resnet14t,3472.37,147.102,512,176,10.08
tf_efficientnetv2_b0,3428.08,148.143,512,192,7.14
tf_mobilenetv3_large_minimal_100,3366.45,151.356,512,224,3.92
mnasnet_075,3238.88,157.273,512,224,3.17
tf_mobilenetv3_large_075,3189.08,159.67,512,224,3.99
seresnet18,3138.91,162.608,512,224,11.78
mobilenetv3_large_075,3095.0,164.56,512,224,3.99
legacy_seresnet18,3076.04,165.928,512,224,11.78
hardcorenas_a,2971.63,171.576,512,224,5.26
levit_256,2956.43,172.043,512,224,18.89
mnasnet_b1,2930.02,173.933,512,224,4.38
mnasnet_100,2929.31,173.976,512,224,4.38
tf_mobilenetv3_large_100,2907.93,175.204,512,224,5.48
resnet18d,2875.3,177.69,512,224,11.71
tinynet_b,2851.82,178.435,512,188,3.73
hardcorenas_b,2772.42,183.73,512,224,5.18
hardcorenas_c,2763.94,184.272,512,224,5.52
mobilenetv3_rw,2754.46,184.981,512,224,5.48
nf_regnet_b0,2740.89,185.595,512,192,8.76
mobilenetv3_large_100_miil,2733.62,186.4,512,224,5.48
mobilenetv3_large_100,2732.43,186.472,512,224,5.48
ese_vovnet19b_slim,2684.58,190.344,512,224,3.17
spnasnet_100,2610.47,195.171,512,224,4.42
mobilenetv2_075,2609.91,195.379,512,224,2.64
semnasnet_075,2603.1,195.762,512,224,2.91
hardcorenas_d,2566.48,198.271,512,224,7.5
tf_efficientnetv2_b1,2548.95,199.349,512,192,8.14
levit_256d,2522.09,201.424,512,224,26.21
fbnetc_100,2397.58,212.548,512,224,5.57
tinynet_a,2334.41,218.035,512,192,6.19
mobilenetv2_100,2313.1,220.563,512,224,3.5
vit_tiny_patch16_224,2299.56,221.804,512,224,5.72
mnasnet_a1,2291.94,222.453,512,224,3.89
deit_tiny_patch16_224,2290.33,222.697,512,224,5.72
semnasnet_100,2279.15,223.737,512,224,3.89
edgenext_xx_small,2271.04,224.572,512,256,1.33
dla46_c,2266.89,225.115,512,224,1.3
hardcorenas_f,2252.64,226.141,512,224,8.2
deit_tiny_distilled_patch16_224,2248.67,226.799,512,224,5.91
hardcorenas_e,2245.94,226.861,512,224,8.07
xcit_nano_12_p16_224_dist,2177.52,233.052,512,224,3.05
xcit_nano_12_p16_224,2170.17,234.054,512,224,3.05
tf_efficientnet_lite0,2134.89,239.057,512,224,4.65
ghostnet_100,2129.82,239.0,512,224,5.18
hrnet_w18_small,2121.96,239.906,512,224,13.19
regnety_004,2085.76,244.311,512,224,4.34
efficientnet_lite0,2079.28,245.485,512,224,4.65
cs3darknet_focus_m,2062.98,247.547,512,256,9.3
pit_ti_distilled_224,2061.94,247.414,512,224,5.1
mnasnet_140,2060.59,247.645,512,224,7.12
pit_ti_224,2057.02,247.989,512,224,4.85
gluon_resnet34_v1b,2039.68,250.446,512,224,21.8
tv_resnet34,2038.39,250.573,512,224,21.8
resnet34,2036.51,250.813,512,224,21.8
ese_vovnet19b_dw,1999.58,255.562,512,224,6.54
resnet26,1962.0,260.488,512,224,16.0
tf_efficientnetv2_b2,1951.52,260.748,512,208,10.1
skresnet18,1943.81,262.753,512,224,11.96
cs3darknet_m,1940.79,263.122,512,256,9.31
regnetz_005,1916.17,265.765,512,224,7.12
resnetblur18,1897.99,269.406,512,224,11.69
rexnetr_100,1893.12,201.724,384,224,4.88
nf_resnet26,1869.64,273.344,512,224,16.0
mobilenetv2_110d,1868.27,204.505,384,224,4.52
visformer_tiny,1861.63,274.356,512,224,10.32
mixer_b32_224,1856.75,274.965,512,224,60.29
seresnet34,1837.21,277.783,512,224,21.96
fbnetv3_b,1825.5,278.744,512,224,8.6
mobilevitv2_050,1824.87,279.552,512,256,1.37
gernet_m,1822.12,280.293,512,224,21.14
resnet34d,1813.16,281.758,512,224,21.82
levit_384,1801.0,283.153,512,224,39.13
legacy_seresnet34,1781.32,286.529,512,224,21.96
regnetx_004,1780.61,286.47,512,224,5.16
tf_efficientnet_b0_ns,1779.24,214.77,384,224,5.29
tf_efficientnet_b0,1779.02,214.765,384,224,5.29
tf_efficientnet_b0_ap,1777.73,214.898,384,224,5.29
efficientnet_b0,1751.72,291.183,512,224,5.29
selecsls42,1718.76,297.231,512,224,30.35
selecsls42b,1710.18,298.726,512,224,32.46
vit_base_patch32_224,1708.63,298.818,512,224,88.22
vit_base_patch32_224_sam,1707.5,298.997,512,224,88.22
efficientnet_es_pruned,1687.32,302.688,512,224,5.44
resnetrs50,1686.45,302.42,512,160,35.69
efficientnet_es,1686.11,302.906,512,224,5.44
mixer_s16_224,1660.76,307.737,512,224,18.53
darknet17,1654.01,309.253,512,256,14.3
mobilenetv2_140,1637.57,233.691,384,224,6.11
fbnetv3_d,1634.54,233.136,384,224,10.31
tf_efficientnet_es,1623.9,314.542,512,224,5.44
resnet26d,1623.31,314.899,512,224,16.01
mobilevit_xxs,1602.81,238.427,384,256,1.27
resmlp_12_distilled_224,1577.54,323.769,512,224,15.35
resmlp_12_224,1577.31,323.803,512,224,15.35
pit_xs_224,1555.66,328.198,512,224,10.62
pit_xs_distilled_224,1555.48,328.255,512,224,11.0
semnasnet_140,1546.19,330.184,512,224,6.11
ghostnet_130,1542.47,330.535,512,224,7.36
repvgg_b0,1538.07,331.828,512,224,15.82
efficientnet_lite1,1530.99,166.26,256,240,5.42
dla34,1524.02,335.337,512,224,15.74
edgenext_x_small,1512.48,337.399,512,256,2.34
darknet21,1486.14,344.159,512,256,20.86
selecsls60,1482.76,344.397,512,224,30.67
selecsls60b,1478.62,345.378,512,224,32.77
nf_seresnet26,1473.71,346.754,512,224,17.4
vit_small_patch32_384,1455.89,350.818,512,384,22.92
gmixer_12_224,1448.32,352.721,512,224,12.7
efficientnet_b1_pruned,1446.82,352.35,512,240,6.33
tf_efficientnet_lite1,1443.47,176.394,256,240,5.42
nf_ecaresnet26,1440.41,354.896,512,224,16.0
xcit_tiny_12_p16_224_dist,1426.36,357.157,512,224,6.72
xcit_tiny_12_p16_224,1426.18,357.168,512,224,6.72
sedarknet21,1401.98,364.696,512,256,20.95
rexnetr_130,1388.84,183.199,256,224,7.61
dla46x_c,1388.59,367.953,512,224,1.07
gmlp_ti16_224,1381.11,276.449,384,224,5.87
mixnet_s,1365.54,373.667,512,224,4.13
rexnet_100,1364.31,280.319,384,224,4.8
regnety_006,1361.43,374.963,512,224,6.06
mobilenetv2_120d,1352.9,188.013,256,224,5.83
legacy_seresnext26_32x4d,1349.26,378.798,512,224,16.79
crossvit_tiny_240,1348.01,378.219,512,240,7.01
vit_tiny_r_s16_p8_384,1345.31,284.562,384,384,6.36
poolformer_s12,1342.54,380.659,512,224,11.92
dla60x_c,1341.77,380.621,512,224,1.32
efficientnet_b1,1325.85,191.544,256,224,7.79
resnetv2_50,1288.61,396.553,512,224,25.55
regnetx_006,1286.44,397.176,512,224,6.2
crossvit_9_240,1258.73,303.637,384,240,8.55
convnext_nano_ols,1252.33,408.151,512,224,15.6
convnext_nano,1249.89,408.864,512,224,15.59
convnext_nano_hnf,1249.05,409.138,512,224,15.59
resnet26t,1237.34,413.275,512,256,16.01
tf_mixnet_s,1236.15,412.905,512,224,4.13
nf_regnet_b2,1229.56,414.759,512,240,14.31
rexnetr_150,1224.57,207.878,256,224,9.78
gluon_resnet50_v1b,1219.23,419.12,512,224,25.56
tv_resnet50,1218.99,419.17,512,224,25.56
crossvit_9_dagger_240,1218.38,313.701,384,240,8.78
resnet50,1218.01,419.528,512,224,25.56
swsl_resnet50,1217.39,419.737,512,224,25.56
ssl_resnet50,1217.38,419.757,512,224,25.56
cs3darknet_focus_l,1216.61,314.788,384,256,21.15
repvgg_a2,1214.87,420.579,512,224,28.21
cs3darknet_l,1203.14,318.267,384,256,21.16
gernet_l,1201.09,425.379,512,256,31.08
efficientnet_lite2,1191.67,213.855,256,260,6.09
nf_regnet_b1,1181.15,431.966,512,256,10.22
seresnext26d_32x4d,1178.86,325.051,384,224,16.81
botnet26t_256,1178.34,325.281,384,256,12.49
seresnext26tn_32x4d,1177.85,325.355,384,224,16.81
seresnext26t_32x4d,1176.65,325.669,384,224,16.81
mobilevitv2_075,1174.29,217.001,256,256,2.87
ecaresnext50t_32x4d,1159.52,330.605,384,224,15.41
ecaresnext26t_32x4d,1158.26,330.961,384,224,15.41
gluon_resnet50_v1c,1147.86,333.697,384,224,25.58
halonet26t,1136.15,337.402,384,256,12.48
resnetv2_50d,1134.86,450.316,512,224,25.57
resnetv2_50t,1132.89,451.133,512,224,25.57
edgenext_small,1127.71,452.849,512,256,5.59
tf_efficientnet_lite2,1121.02,227.403,256,260,6.09
convit_tiny,1118.98,456.53,512,224,5.71
skresnet34,1113.08,458.799,512,224,22.28
tf_efficientnet_b1,1099.77,231.299,256,240,7.79
tf_efficientnet_b1_ap,1099.37,231.402,256,240,7.79
efficientnetv2_rw_t,1098.86,230.78,256,224,13.65
tf_efficientnet_b1_ns,1098.29,231.567,256,240,7.79
ecaresnetlight,1091.16,468.275,512,224,30.16
gluon_resnet50_v1d,1084.38,353.226,384,224,25.58
dpn68b,1083.77,353.123,384,224,12.61
cs3sedarknet_l,1083.42,353.12,384,256,21.91
resnet50d,1078.0,355.348,384,224,25.58
resnet50t,1076.81,355.721,384,224,25.57
resnet32ts,1075.86,237.337,256,256,17.96
resnet33ts,1061.36,240.599,256,256,19.68
vit_small_patch16_224,1057.92,362.157,384,224,22.05
resnetaa50,1057.73,362.204,384,224,25.56
vit_small_resnet26d_224,1057.57,362.04,384,224,63.61
deit_small_patch16_224,1050.7,364.638,384,224,22.05
cspresnet50,1042.19,367.617,384,256,21.62
tf_efficientnetv2_b3,1041.71,243.94,256,240,14.36
regnetx_008,1034.73,493.971,512,224,7.26
ecaresnet26t,1033.34,371.048,384,256,16.01
deit_small_distilled_patch16_224,1028.8,372.398,384,224,22.44
vit_relpos_base_patch32_plus_rpn_256,1021.86,499.989,512,256,119.42
dla60,1020.05,375.488,384,224,22.04
res2net50_48w_2s,1018.83,376.079,384,224,25.29
gc_efficientnetv2_rw_t,1014.65,249.524,256,224,13.68
vit_relpos_small_patch16_rpn_224,1013.69,377.786,384,224,21.97
edgenext_small_rw,1011.18,505.339,512,256,7.83
pit_s_224,1010.83,378.943,384,224,23.46
seresnet33ts,1007.26,253.362,256,256,19.78
efficientnet_em,1007.19,253.179,256,240,6.9
vovnet39a,1006.62,507.995,512,224,22.6
legacy_seresnet50,1003.5,381.52,384,224,28.09
gluon_resnext50_32x4d,1001.3,382.689,384,224,25.03
tv_resnext50_32x4d,1001.18,382.711,384,224,25.03
resnext50_32x4d,1001.03,382.776,384,224,25.03
ssl_resnext50_32x4d,1000.68,382.908,384,224,25.03
eca_resnet33ts,999.77,255.368,256,256,19.68
swsl_resnext50_32x4d,997.37,384.186,384,224,25.03
regnety_008,993.3,514.408,512,224,6.26
dpn68,992.27,385.859,384,224,12.61
deit3_small_patch16_224,987.86,387.777,384,224,22.06
deit3_small_patch16_224_in21ft1k,987.15,388.058,384,224,22.06
gcresnet33ts,985.12,258.855,256,256,19.88
efficientnet_b2a,980.29,259.63,256,256,9.11
tf_efficientnet_em,980.0,260.253,256,240,6.9
efficientnet_b2,978.68,260.092,256,256,9.11
seresnet50,971.79,394.011,384,224,28.09
gluon_resnet50_v1s,970.71,394.714,384,224,25.68
vit_srelpos_small_patch16_224,969.18,395.281,384,224,21.97
vit_relpos_small_patch16_224,965.13,396.742,384,224,21.98
ecaresnet50d_pruned,964.18,530.07,512,224,19.94
cspresnet50d,956.82,266.672,256,256,21.64
vgg11,954.03,536.508,512,224,132.86
cspresnet50w,952.27,267.927,256,256,28.12
ese_vovnet39b,951.93,537.173,512,224,24.57
vit_base_patch32_plus_256,951.5,537.138,512,256,119.48
resnetaa50d,950.79,403.026,384,224,25.58
eca_vovnet39b,948.4,539.184,512,224,22.6
lambda_resnet26rpt_256,942.15,203.17,192,256,10.99
pit_s_distilled_224,934.29,273.079,256,224,24.04
mobilevit_xs,924.5,275.792,256,256,2.32
tv_densenet121,917.93,277.067,256,224,7.98
densenet121,913.65,278.353,256,224,7.98
resnetblur50,911.91,420.254,384,224,25.56
hrnet_w18_small_v2,910.26,559.998,512,224,15.6
coat_lite_tiny,909.29,421.406,384,224,5.72
mobilevitv2_100,907.45,281.094,256,256,4.9
nf_resnet50,900.11,425.722,384,256,25.56
resnext50d_32x4d,894.57,285.293,256,224,25.05
nf_seresnet50,892.73,428.967,384,224,28.09
rexnetr_200,890.57,214.407,192,224,16.52
efficientnet_cc_b0_4e,890.34,430.073,384,224,13.31
efficientnet_cc_b0_8e,889.37,430.553,384,224,24.01
dla60x,886.5,287.775,256,224,17.35
twins_svt_small,885.48,432.048,384,224,24.06
seresnet50t,879.71,435.29,384,224,28.1
mixnet_m,878.04,581.529,512,224,5.01
nf_ecaresnet50,875.38,437.674,384,224,25.56
efficientnet_b2_pruned,873.9,291.355,256,260,8.31
densenet121d,873.44,291.238,256,224,8.0
cspresnext50,868.23,294.006,256,256,20.57
rexnet_150,866.26,294.391,256,224,9.73
ecaresnet50d,862.65,444.205,384,224,25.58
fbnetv3_g,862.32,220.642,192,240,16.62
regnetz_b16,862.05,295.457,256,224,9.72
tf_efficientnet_cc_b0_4e,861.1,444.691,384,224,13.31
tf_efficientnet_cc_b0_8e,857.16,446.822,384,224,24.01
gcresnet50t,854.99,447.633,384,256,25.9
res2net50_26w_4s,851.03,449.921,384,224,25.7
coat_lite_mini,849.82,450.985,384,224,11.01
tf_efficientnet_b2_ap,849.52,224.466,192,260,9.11
tf_efficientnet_b2,848.58,224.736,192,260,9.11
tf_efficientnet_b2_ns,847.86,224.983,192,260,9.11
vit_base_resnet26d_224,844.62,453.315,384,224,101.4
vgg11_bn,832.74,460.889,384,224,132.87
vovnet57a,832.06,614.449,512,224,36.64
selecsls84,830.17,615.492,512,224,50.95
resnetblur50d,826.31,308.964,256,224,25.58
convnext_tiny_hnfd,820.9,310.941,256,224,28.59
convnext_tiny_hnf,819.46,311.471,256,224,28.59
convnext_tiny,819.24,311.536,256,224,28.59
convnext_tiny_in22ft1k,818.81,311.724,256,224,28.59
rexnet_130,816.78,312.226,256,224,7.56
seresnext50_32x4d,814.69,313.102,256,224,27.56
legacy_seresnext50_32x4d,813.61,313.477,256,224,27.56
gluon_seresnext50_32x4d,813.13,313.678,256,224,27.56
skresnet50,808.8,473.357,384,224,25.8
visformer_small,806.27,475.588,384,224,40.22
res2net50_14w_8s,794.56,319.93,256,224,25.06
densenetblur121d,789.33,322.521,256,224,8.0
seresnetaa50d,785.32,324.779,256,224,28.11
gluon_inception_v3,782.59,489.263,384,299,23.83
inception_v3,782.35,489.427,384,299,23.83
adv_inception_v3,778.18,491.976,384,299,23.83
resmlp_24_distilled_224,777.24,327.895,256,224,30.02
resmlp_24_224,776.95,327.972,256,224,30.02
tf_inception_v3,775.41,493.776,384,299,23.83
ese_vovnet57b,774.18,495.058,384,224,38.61
tf_mixnet_m,773.08,495.127,384,224,5.01
resnetv2_101,772.45,329.834,256,224,44.54
dla60_res2net,767.35,332.099,256,224,20.85
nf_regnet_b3,766.23,499.321,384,288,18.59
sehalonet33ts,763.66,334.4,256,256,13.69
ecaresnet101d_pruned,754.9,676.449,512,224,24.88
darknet53,753.16,339.081,256,256,41.61
densenet169,752.52,337.551,256,224,14.15
resnet101,747.89,340.74,256,224,44.55
gluon_resnet101_v1b,747.04,341.055,256,224,44.55
tv_resnet101,746.84,341.219,256,224,44.55
skresnet50d,739.17,344.891,256,224,25.82
twins_pcpvt_small,738.11,345.194,256,224,24.11
vit_small_r26_s32_224,733.9,347.477,256,224,36.43
mobilevit_s,733.0,260.821,192,256,5.58
darknetaa53,732.7,348.577,256,256,36.02
xcit_tiny_24_p16_224_dist,727.98,348.335,256,224,12.12
xcit_tiny_24_p16_224,727.1,348.63,256,224,12.12
efficientnet_b0_gn,724.56,352.174,256,224,5.29
efficientnet_b3_pruned,722.23,352.701,256,300,9.86
gluon_resnet101_v1c,717.66,355.143,256,224,44.57
resnext26ts,717.15,534.946,384,256,10.3
resnetv2_101d,715.45,356.238,256,224,44.56
gmixer_24_224,714.67,356.582,256,224,24.72
resnetrs101,714.37,356.071,256,192,63.62
nf_resnet101,712.1,537.603,384,224,44.55
efficientnet_lite3,702.44,181.104,128,300,8.2
mixnet_l,702.18,545.327,384,224,7.33
eca_resnext26ts,694.05,368.289,256,256,10.3
semobilevit_s,692.92,368.16,256,256,5.74
seresnext26ts,691.18,369.699,256,256,10.39
poolformer_s24,689.84,369.792,256,224,21.39
gluon_resnet101_v1d,688.26,370.323,256,224,44.57
dla102,688.03,370.524,256,224,33.27
vit_relpos_medium_patch16_rpn_224,687.13,371.514,256,224,38.73
sebotnet33ts_256,686.07,279.058,192,256,13.7
gcresnext26ts,683.09,373.929,256,256,10.48
regnetx_016,682.73,749.012,512,224,9.19
haloregnetz_b,680.78,374.495,256,224,11.68
cspdarknet53,679.01,375.961,256,256,27.64
vgg13,677.45,566.653,384,224,133.05
xcit_nano_12_p16_384_dist,671.72,379.231,256,384,3.05
wide_resnet50_2,668.78,573.358,384,224,68.88
tf_efficientnet_lite3,665.78,191.165,128,300,8.2
vit_relpos_medium_patch16_cls_224,661.77,385.665,256,224,38.76
vit_srelpos_medium_patch16_224,659.88,386.996,256,224,38.74
rexnet_200,659.06,290.146,192,224,16.37
vit_base_resnet50d_224,658.84,386.945,256,224,110.97
ecaresnet50t,657.78,388.237,256,256,25.57
gmlp_s16_224,657.63,290.408,192,224,19.42
vit_relpos_medium_patch16_224,657.05,388.484,256,224,38.75
tf_efficientnet_cc_b1_8e,654.82,389.25,256,240,39.72
regnety_016,650.07,785.757,512,224,11.2
swin_tiny_patch4_window7_224,648.69,393.641,256,224,28.29
xcit_small_12_p16_224,640.82,397.688,256,224,26.25
gluon_resnet101_v1s,640.79,397.908,256,224,44.67
xcit_small_12_p16_224_dist,639.99,398.193,256,224,26.25
crossvit_small_240,638.8,399.076,256,240,26.86
efficientnet_cc_b1_8e,637.42,399.94,256,240,39.72
resnetaa101d,634.86,401.619,256,224,44.57
cs3sedarknet_xdw,630.82,302.41,192,256,21.6
repvgg_b1,623.55,820.034,512,224,57.42
mobilevitv2_125,620.51,308.406,192,256,7.48
bat_resnext26ts,613.61,415.954,256,256,10.73
gluon_resnext101_32x4d,609.67,418.333,256,224,44.18
swsl_resnext101_32x4d,609.02,418.731,256,224,44.18
resnext101_32x4d,609.01,418.74,256,224,44.18
tf_mixnet_l,606.88,420.297,256,224,7.33
ssl_resnext101_32x4d,606.28,420.718,256,224,44.18
legacy_seresnet101,601.55,423.316,256,224,49.33
cs3darknet_focus_x,600.02,425.715,256,256,35.02
dla102x,598.42,319.231,192,224,26.31
halonet50ts,597.8,320.205,192,256,22.73
xcit_nano_12_p8_224,595.07,428.358,256,224,3.05
xcit_nano_12_p8_224_dist,593.27,429.695,256,224,3.05
cait_xxs24_224,593.22,428.92,256,224,11.96
seresnet101,590.42,431.41,256,224,49.33
swin_s3_tiny_224,588.57,433.98,256,224,28.33
resnetv2_50x1_bit_distilled,585.83,326.889,192,224,25.55
efficientnet_b0_g8_gn,582.67,438.264,256,224,6.56
crossvit_15_240,580.46,328.975,192,240,27.53
resnetblur101d,576.87,442.155,256,224,44.57
res2net50_26w_6s,573.8,444.339,256,224,37.05
vgg13_bn,573.47,446.125,256,224,133.05
efficientnet_b3a,572.29,221.925,128,288,12.23
efficientnet_b3,572.18,221.941,128,288,12.23
cs3darknet_x,571.12,447.259,256,256,35.05
densenet201,562.52,338.221,192,224,20.01
crossvit_15_dagger_240,562.49,339.489,192,240,28.21
efficientnetv2_s,559.15,226.702,128,288,21.46
eca_botnext26ts_256,558.6,457.666,256,256,10.59
mixer_b16_224,556.6,459.152,256,224,59.88
mixer_b16_224_miil,556.5,459.202,256,224,59.88
eca_halonext26ts,547.96,466.574,256,256,10.76
ecaresnet101d,546.58,466.555,256,224,44.57
vgg16,546.06,702.994,384,224,138.36
mixer_l32_224,543.38,351.819,192,224,206.94
vit_base_patch32_384,543.37,470.294,256,384,88.3
nf_seresnet101,540.53,471.014,256,224,49.33
resnetv2_152,536.63,474.697,256,224,60.19
botnet50ts_256,534.71,238.412,128,256,22.74
mobilevitv2_150,533.38,238.97,128,256,10.59
vit_base_r26_s32_224,533.28,358.697,192,224,101.38
mobilevitv2_150_in22ft1k,532.99,239.183,128,256,10.59
cs3sedarknet_x,531.85,479.872,256,256,35.4
nf_ecaresnet101,531.3,479.947,256,224,44.55
cs3edgenet_x,529.37,482.632,256,256,47.82
res2next50,528.59,483.023,256,224,24.67
res2net101_26w_4s,527.01,483.179,256,224,45.21
vit_large_patch32_224,524.74,486.172,256,224,306.54
resnet101d,523.85,364.964,192,256,44.57
efficientnetv2_rw_s,520.77,243.564,128,288,23.94
halo2botnet50ts_256,517.51,369.975,192,256,22.64
resmlp_36_distilled_224,513.23,371.84,192,224,44.69
vit_tiny_patch16_384,510.99,249.657,128,384,5.79
resmlp_36_224,509.53,374.55,192,224,44.69
swinv2_cr_tiny_224,506.82,503.861,256,224,28.33
mixnet_xl,505.67,504.387,256,224,11.9
resnetv2_50d_gn,505.25,379.149,192,224,25.57
swinv2_cr_tiny_ns_224,504.1,506.527,256,224,28.33
gluon_resnet152_v1b,502.02,380.204,192,224,60.19
regnetz_d8,501.8,253.463,128,256,23.37
resnet152,501.44,380.547,192,224,60.19
tv_resnet152,501.12,380.811,192,224,60.19
xception,497.64,256.367,128,299,22.86
regnety_032,496.85,771.405,384,224,19.44
tf_efficientnet_b3_ap,496.0,256.348,128,300,12.23
tf_efficientnet_b3,494.58,257.101,128,300,12.23
tf_efficientnet_b3_ns,492.45,258.213,128,300,12.23
convnext_small_in22ft1k,490.79,389.411,192,224,50.22
res2net50_26w_8s,489.22,520.921,256,224,48.4
tf_efficientnetv2_s_in21ft1k,488.77,259.622,128,300,21.46
tf_efficientnetv2_s,488.25,259.918,128,300,21.46
gluon_resnet152_v1c,488.2,390.894,192,224,60.21
convnext_small,487.15,392.394,192,224,50.22
twins_pcpvt_base,487.13,391.314,192,224,43.83
resnetv2_152d,486.82,392.059,192,224,60.2
legacy_seresnext101_32x4d,484.7,393.829,192,224,48.96
resnet50_gn,482.45,397.141,192,224,25.56
gluon_seresnext101_32x4d,480.63,397.197,192,224,48.96
hrnet_w32,480.24,528.215,256,224,41.23
sequencer2d_s,480.16,264.213,128,224,27.65
seresnext101_32x4d,479.03,398.526,192,224,48.96
nest_tiny,477.97,266.889,128,224,17.06
dla60_res2next,477.74,534.408,256,224,17.03
gluon_resnet152_v1d,476.32,400.788,192,224,60.21
regnetz_c16,475.79,402.061,192,256,13.46
hrnet_w18,473.42,535.867,256,224,21.3
jx_nest_tiny,472.84,269.807,128,224,17.06
regnetz_d32,471.85,269.596,128,256,27.58
regnetz_040,471.81,269.412,128,256,27.12
xception41p,471.79,270.424,128,299,26.91
vgg16_bn,470.3,543.999,256,224,138.37
regnetz_040h,469.27,270.846,128,256,28.94
poolformer_s36,467.39,408.832,192,224,30.86
resnet51q,463.81,551.102,256,256,35.7
efficientnet_el_pruned,461.98,275.957,128,300,10.59
efficientnet_el,461.97,275.94,128,300,10.59
coat_lite_small,461.36,414.606,192,224,19.84
nf_regnet_b4,457.97,417.049,192,320,30.21
vgg19,457.37,839.347,384,224,143.67
cs3se_edgenet_x,457.05,418.615,192,256,50.72
dla169,455.51,419.044,192,224,53.39
convit_small,454.72,421.197,192,224,27.78
gluon_resnet152_v1s,452.53,421.917,192,224,60.32
tf_efficientnet_el,449.8,283.446,128,300,10.59
gcresnext50ts,445.12,429.834,192,256,15.67
regnetx_040,442.35,866.937,384,224,22.12
vit_small_resnet50d_s16_224,437.26,437.826,192,224,57.53
volo_d1_224,437.04,437.842,192,224,26.63
mobilevitv2_175_in22ft1k,434.9,293.339,128,256,14.25
mobilevitv2_175,434.88,293.341,128,256,14.25
resnet61q,433.95,441.405,192,256,36.85
ese_vovnet99b,433.24,589.371,256,224,63.2
ese_vovnet39b_evos,430.54,296.328,128,224,24.58
twins_svt_base,425.02,449.64,192,224,56.07
resnest14d,411.87,1242.634,512,224,10.61
dla102x2,405.87,313.766,128,224,41.28
mobilevitv2_200_in22ft1k,405.83,314.414,128,256,18.45
mobilevitv2_200,405.76,314.447,128,256,18.45
inception_v4,405.43,471.399,192,299,42.68
crossvit_18_240,404.58,314.332,128,240,43.27
swin_small_patch4_window7_224,400.37,477.632,192,224,49.61
densenet161,399.17,318.189,128,224,28.68
vgg19_bn,398.5,642.012,256,224,143.68
legacy_seresnet152,398.4,478.588,192,224,66.82
vit_base_patch16_224_miil,397.86,481.79,192,224,86.54
sequencer2d_m,396.83,480.626,192,224,38.31
crossvit_18_dagger_240,396.31,320.926,128,240,44.27
resnetv2_50d_frn,394.18,323.553,128,224,25.59
vit_base_patch16_224,392.99,487.729,192,224,86.57
vit_base_patch16_224_sam,392.92,487.774,192,224,86.57
vit_base_patch16_rpn_224,391.32,489.846,192,224,86.54
xception41,391.05,326.045,128,299,26.97
deit_base_patch16_224,390.04,491.437,192,224,86.57
cait_xxs36_224,387.24,492.066,192,224,17.3
efficientnet_b0_g16_evos,386.23,993.13,384,224,8.11
deit_base_distilled_patch16_224,384.06,499.086,192,224,87.34
xcit_tiny_12_p16_384_dist,383.09,499.371,192,384,6.72
vit_relpos_base_patch16_rpn_224,382.95,500.328,192,224,86.41
seresnet152,379.38,334.127,128,224,66.82
resnetv2_50d_evos,374.27,340.812,128,224,25.59
deit3_base_patch16_224,374.05,512.341,192,224,86.59
deit3_base_patch16_224_in21ft1k,373.84,512.639,192,224,86.59
vit_relpos_base_patch16_clsgap_224,370.76,516.704,192,224,86.43
vit_relpos_base_patch16_cls_224,370.22,517.437,192,224,86.43
hrnet_w30,369.35,688.202,256,224,37.71
vit_relpos_base_patch16_224,368.93,519.279,192,224,86.43
gluon_resnext101_64x4d,363.93,350.084,128,224,83.46
resnext101_64x4d,363.79,350.21,128,224,83.46
beit_base_patch16_224,358.77,534.02,192,224,86.53
ens_adv_inception_resnet_v2,358.6,532.08,192,299,55.84
wide_resnet101_2,358.56,533.868,192,224,126.89
inception_resnet_v2,358.54,532.143,192,299,55.84
resnet200,357.55,355.04,128,224,64.67
resnet152d,357.54,355.729,128,256,60.21
swinv2_tiny_window8_256,357.0,536.56,192,256,28.35
efficientnet_b4,354.99,268.381,96,320,19.34
dpn92,353.0,723.721,256,224,37.67
repvgg_b2,352.05,1453.222,512,224,89.02
resnest50d_1s4x24d,349.97,730.142,256,224,25.68
regnetz_b16_evos,347.19,366.83,128,224,9.74
tnt_s_patch16_224,342.71,558.25,192,224,23.76
xception65p,341.43,373.588,128,299,39.82
convnext_base_in22ft1k,339.67,374.996,128,224,88.59
convnext_base,338.68,376.119,128,224,88.59
efficientnet_lite4,338.39,187.783,64,380,13.01
twins_pcpvt_large,333.52,379.633,128,224,60.99
pit_b_224,331.08,385.636,128,224,73.76
pit_b_distilled_224,328.87,388.208,128,224,74.79
xcit_small_24_p16_224_dist,326.41,388.817,128,224,47.67
xcit_small_24_p16_224,326.38,388.806,128,224,47.67
tf_efficientnet_lite4,324.6,195.748,64,380,13.01
eca_nfnet_l0,319.84,1599.745,512,224,24.14
nfnet_l0,319.69,1600.317,512,224,35.07
gluon_seresnext101_64x4d,319.51,398.398,128,224,88.23
repvgg_b3,316.57,1211.922,384,224,123.09
skresnext50_32x4d,315.84,809.121,256,224,27.48
poolformer_m36,315.69,403.496,128,224,56.17
ssl_resnext101_32x8d,313.35,406.924,128,224,88.79
resnext101_32x8d,312.8,407.622,128,224,88.79
swsl_resnext101_32x8d,312.76,407.724,128,224,88.79
ig_resnext101_32x8d,311.13,409.865,128,224,88.79
vit_small_patch16_36x1_224,309.04,411.365,128,224,64.67
regnetx_032,308.88,1241.936,384,224,15.3
vit_small_patch16_18x2_224,306.57,414.654,128,224,64.67
xcit_tiny_12_p8_224,306.37,415.93,128,224,6.71
cait_s24_224,305.74,415.965,128,224,46.92
xcit_tiny_12_p8_224_dist,304.23,418.886,128,224,6.71
swinv2_cr_small_ns_224,300.99,422.86,128,224,49.7
twins_svt_large,300.69,423.548,128,224,99.27
swinv2_cr_small_224,299.81,424.482,128,224,49.7
coat_tiny,298.83,426.275,128,224,5.5
resnest26d,298.23,1286.829,384,224,17.07
nest_small,296.79,321.765,96,224,38.35
jx_nest_small,293.75,325.094,96,224,38.35
swin_s3_small_224,290.56,438.612,128,224,49.74
dpn98,290.11,439.591,128,224,61.57
resnetv2_50d_evob,289.65,330.197,96,224,25.59
seresnet152d,283.9,447.414,128,256,66.84
gluon_xception65,283.18,337.068,96,299,39.92
convnext_tiny_384_in22ft1k,282.39,338.982,96,384,28.59
resnetrs152,282.26,450.046,128,256,86.62
xception65,281.11,339.548,96,299,39.92
swin_base_patch4_window7_224,281.0,453.662,128,224,87.77
hrnet_w48,279.44,682.135,192,224,77.47
mixnet_xxl,278.4,457.833,128,224,23.96
seresnext101_32x8d,278.13,458.033,128,224,93.57
gmlp_b16_224,275.97,346.253,96,224,73.08
seresnext101d_32x8d,270.35,471.144,128,224,93.59
resnet200d,267.1,476.272,128,256,64.69
nfnet_f0,265.61,1926.394,512,192,71.49
regnetz_e8,256.51,247.489,64,256,57.7
xcit_tiny_24_p16_384_dist,255.73,371.975,96,384,12.12
crossvit_base_240,254.6,375.374,96,240,105.03
dm_nfnet_f0,251.38,1526.301,384,192,71.49
hrnet_w40,249.23,765.525,192,224,57.56
vit_base_patch16_plus_240,246.96,517.368,128,240,117.56
efficientnetv2_m,246.55,256.379,64,320,54.14
vit_relpos_base_patch16_plus_240,244.88,521.493,128,240,117.38
seresnextaa101d_32x8d,243.89,522.629,128,224,93.59
tf_efficientnet_b4_ap,242.14,262.218,64,380,19.34
tf_efficientnet_b4,241.83,262.52,64,380,19.34
tf_efficientnet_b4_ns,241.46,263.01,64,380,19.34
xcit_medium_24_p16_224,241.39,526.926,128,224,84.4
xcit_medium_24_p16_224_dist,241.08,527.466,128,224,84.4
xcit_small_12_p16_384_dist,240.61,397.192,96,384,26.25
vit_small_patch16_384,239.06,266.856,64,384,22.2
volo_d2_224,238.89,400.019,96,224,58.68
swinv2_tiny_window16_256,238.79,400.76,96,256,28.35
mobilevitv2_150_384_in22ft1k,238.1,267.77,64,384,10.59
vit_large_r50_s32_224,236.27,403.797,96,224,328.99
tresnet_m,233.24,2192.365,512,224,31.39
hrnet_w44,232.48,820.975,192,224,67.06
poolformer_m48,232.43,410.471,96,224,73.47
densenet264,231.27,411.12,96,224,72.69
convit_base,231.06,552.947,128,224,86.54
nf_regnet_b5,228.54,417.354,96,384,49.74
deit3_small_patch16_384,226.74,281.318,64,384,22.21
deit3_small_patch16_384_in21ft1k,226.44,281.652,64,384,22.21
vit_small_r26_s32_384,226.15,281.726,64,384,36.47
coat_mini,225.14,566.497,128,224,10.34
efficientnetv2_rw_m,224.46,281.565,64,320,53.24
swin_s3_base_224,224.0,425.728,96,224,71.13
tnt_b_patch16_224,223.52,570.669,128,224,65.41
hrnet_w64,223.29,568.417,128,224,128.06
sequencer2d_l,220.03,286.022,64,224,54.3
dpn131,216.53,588.962,128,224,79.25
vit_base_r50_s16_224,215.49,443.851,96,224,98.66
swinv2_cr_base_ns_224,214.73,444.647,96,224,87.88
xception71,214.09,296.77,64,299,42.34
swinv2_cr_base_224,213.21,447.81,96,224,87.88
swinv2_small_window8_256,213.06,448.048,96,256,49.73
nest_base,210.25,302.717,64,224,67.72
jx_nest_base,209.06,304.441,64,224,67.72
seresnet200d,203.53,467.209,96,256,71.86
resnetrs200,201.84,471.293,96,256,93.21
resnest50d,201.6,1268.493,256,224,27.48
ecaresnet200d,201.55,472.938,96,256,64.69
xcit_nano_12_p8_384_dist,201.45,315.854,64,384,3.05
efficientnet_b3_gn,197.65,322.123,64,288,11.73
xcit_tiny_24_p8_224_dist,195.37,488.075,96,224,12.11
xcit_tiny_24_p8_224,195.11,488.622,96,224,12.11
dpn107,194.08,492.913,96,224,86.92
regnetz_c16_evos,193.89,328.188,64,256,13.49
regnety_040,190.14,2017.916,384,224,20.65
mobilevitv2_175_384_in22ft1k,189.6,336.534,64,384,14.25
regnetv_040,188.29,1358.084,256,224,20.64
convnext_large,187.93,509.087,96,224,197.77
convnext_large_in22ft1k,187.83,509.365,96,224,197.77
convmixer_768_32,187.17,511.603,96,224,21.11
regnetx_080,181.41,1409.979,256,224,39.57
resnest50d_4s2x40d,180.44,1417.38,256,224,30.42
xcit_small_12_p8_224,179.5,354.768,64,224,26.21
xcit_small_12_p8_224_dist,179.34,355.047,64,224,26.21
halonet_h1,176.7,360.706,64,256,8.1
tf_efficientnetv2_m_in21ft1k,175.14,270.794,48,384,54.14
mobilevitv2_200_384_in22ft1k,175.13,273.08,48,384,18.45
tf_efficientnetv2_m,173.37,273.617,48,384,54.14
mixer_l16_224,171.41,558.471,96,224,208.2
efficientnet_b3_g8_gn,168.79,377.376,64,288,14.25
repvgg_b1g4,167.59,3053.943,512,224,39.97
vit_large_patch32_384,167.04,573.058,96,384,306.63
convnext_small_384_in22ft1k,165.65,384.557,64,384,50.22
volo_d3_224,162.19,392.021,64,224,86.33
regnetz_d8_evos,155.31,307.002,48,256,23.46
swin_large_patch4_window7_224,153.79,414.289,64,224,196.53
swinv2_base_window8_256,151.21,420.663,64,256,87.92
convmixer_1024_20_ks9_p14,149.3,1713.726,256,224,24.38
resnetv2_50x1_bitm,147.75,215.764,32,448,25.55
seresnet269d,145.59,433.61,64,256,113.67
resnetrs270,144.14,437.83,64,256,129.86
swinv2_small_window16_256,143.52,443.487,64,256,49.73
regnety_040s_gn,142.58,896.132,128,224,20.65
repvgg_b2g4,133.72,3827.892,512,224,61.76
eca_nfnet_l1,133.59,1435.413,192,256,41.41
xcit_large_24_p16_224,132.6,479.222,64,224,189.1
swinv2_cr_tiny_384,131.94,483.86,64,384,28.33
xcit_large_24_p16_224_dist,131.66,482.65,64,224,189.1
xcit_tiny_12_p8_384_dist,131.64,362.75,48,384,6.71
regnetx_064,129.82,1970.916,256,224,26.21
swinv2_cr_large_224,124.15,513.018,64,224,196.68
xcit_small_24_p16_384_dist,120.64,394.328,48,384,47.67
regnety_064,119.44,2141.523,256,224,30.58
regnety_080,117.88,2170.37,256,224,39.18
crossvit_15_dagger_408,117.86,269.618,32,408,28.5
vit_large_patch16_224,117.2,544.512,64,224,304.33
regnetv_064,117.03,1638.944,192,224,30.58
ese_vovnet99b_iabn,117.02,3278.167,384,224,63.2
convnext_xlarge_in22ft1k,116.37,548.167,64,224,350.2
vit_base_patch16_18x2_224,116.0,548.972,64,224,256.73
convnext_base_384_in22ft1k,115.58,413.454,48,384,88.59
efficientnet_b5,113.63,279.129,32,456,30.39
deit3_large_patch16_224_in21ft1k,112.51,567.041,64,224,304.37
deit3_large_patch16_224,112.48,567.139,64,224,304.37
tf_efficientnet_b5,111.42,284.665,32,456,30.39
tf_efficientnet_b5_ap,111.14,285.451,32,456,30.39
tf_efficientnet_b5_ns,111.14,285.33,32,456,30.39
legacy_senet154,110.98,861.567,96,224,115.09
senet154,110.82,862.828,96,224,115.09
gluon_senet154,110.77,863.12,96,224,115.09
beit_large_patch16_224,109.02,584.818,64,224,304.43
repvgg_b3g4,108.77,3529.239,384,224,83.83
regnetx_160,107.6,1783.261,192,224,54.28
nfnet_f1,107.01,1791.907,192,224,132.63
volo_d1_384,105.69,301.347,32,384,26.78
swinv2_base_window16_256,103.88,459.56,48,256,87.92
swinv2_base_window12to16_192to256_22kft1k,103.79,460.002,48,256,87.92
tresnet_l,102.82,4975.916,512,224,55.99
dm_nfnet_f1,101.59,1257.525,128,224,132.63
volo_d4_224,101.08,472.359,48,224,192.96
cait_xxs24_384,99.39,480.268,48,384,12.03
ecaresnet269d,99.06,479.988,48,320,102.09
efficientnetv2_l,98.76,319.521,32,384,118.52
tf_efficientnetv2_l_in21ft1k,98.35,320.759,32,384,118.52
tf_efficientnetv2_l,97.56,323.47,32,384,118.52
deit_base_patch16_384,97.3,328.042,32,384,86.86
vit_base_patch16_384,97.1,328.712,32,384,86.86
resnest101e,96.09,1329.413,128,256,48.28
deit_base_distilled_patch16_384,94.63,337.315,32,384,87.63
regnetx_120,94.03,2721.558,256,224,46.11
deit3_base_patch16_384,93.5,341.294,32,384,86.88
deit3_base_patch16_384_in21ft1k,93.49,341.309,32,384,86.88
xcit_small_24_p8_224_dist,92.61,514.968,48,224,47.63
xcit_small_24_p8_224,92.51,515.466,48,224,47.63
regnety_120,92.07,2083.952,192,224,51.82
tresnet_xl,91.15,4209.119,384,224,78.44
crossvit_18_dagger_408,89.17,356.787,32,408,44.61
resnetv2_152x2_bit_teacher,89.16,356.538,32,224,236.34
vit_large_patch14_224,85.06,562.673,48,224,304.2
resnetv2_101x1_bitm,84.72,187.286,16,448,44.54
resnetrs350,84.14,372.211,32,288,163.96
beit_base_patch16_384,83.87,380.424,32,384,86.74
regnety_160,83.24,2305.144,192,224,83.59
pnasnet5large,83.16,380.801,32,331,86.06
xcit_medium_24_p16_384_dist,82.74,383.266,32,384,84.4
vit_large_r50_s32_384,77.34,411.186,32,384,329.09
nasnetalarge,77.32,408.633,32,331,88.75
swinv2_cr_small_384,76.42,416.277,32,384,49.7
swin_base_patch4_window12_384,74.73,426.327,32,384,87.9
resmlp_big_24_distilled_224,70.88,449.95,32,224,129.14
resmlp_big_24_224_in22ft1k,70.88,449.933,32,224,129.14
resmlp_big_24_224,70.41,452.99,32,224,129.14
regnety_320,66.33,1928.357,128,224,145.05
xcit_tiny_24_p8_384_dist,66.29,479.361,32,384,12.11
cait_xs24_384,66.24,480.525,32,384,26.67
ig_resnext101_32x16d,65.9,1455.165,96,224,194.03
ssl_resnext101_32x16d,65.74,1458.688,96,224,194.03
swsl_resnext101_32x16d,65.74,1458.738,96,224,194.03
volo_d5_224,64.41,493.535,32,224,295.46
cait_xxs36_384,64.34,493.602,32,384,17.37
efficientnet_b6,64.08,246.77,16,528,43.04
xcit_medium_24_p8_224,63.96,496.86,32,224,84.32
xcit_medium_24_p8_224_dist,63.93,497.194,32,224,84.32
convnext_large_384_in22ft1k,63.85,499.388,32,384,197.77
vit_base_patch8_224,63.45,377.425,24,224,86.58
tf_efficientnet_b6_ns,63.1,250.577,16,528,43.04
tf_efficientnet_b6,62.84,251.669,16,528,43.04
tf_efficientnet_b6_ap,62.76,252.073,16,528,43.04
efficientnetv2_xl,62.18,251.438,16,384,208.12
tf_efficientnetv2_xl_in21ft1k,62.14,251.721,16,384,208.12
xcit_small_12_p8_384_dist,61.84,386.224,24,384,26.21
vit_base_r50_s16_384,61.01,391.67,24,384,98.95
vit_base_resnet50_384,60.98,391.903,24,384,98.95
swinv2_large_window12to16_192to256_22kft1k,60.98,391.098,24,256,196.74
eca_nfnet_l2,58.72,1632.112,96,320,56.72
volo_d2_384,58.5,271.766,16,384,58.87
resnetrs420,56.49,415.629,24,320,191.89
swinv2_cr_base_384,55.08,433.269,24,384,87.88
nfnet_f2,54.73,1750.573,96,256,193.78
tresnet_m_448,53.52,3584.333,192,448,31.39
dm_nfnet_f2,51.26,1245.084,64,256,193.78
cait_s24_384,50.14,476.064,24,384,47.06
swinv2_cr_huge_224,49.63,481.092,24,224,657.83
regnetx_320,48.06,2662.024,128,224,107.81
xcit_large_24_p16_384_dist,48.02,496.416,24,384,189.1
swin_large_patch4_window12_384,41.75,381.31,16,384,196.74
convnext_xlarge_384_in22ft1k,40.45,591.485,24,384,350.2
deit3_huge_patch14_224_in21ft1k,38.41,414.036,16,224,632.13
deit3_huge_patch14_224,38.4,414.103,16,224,632.13
efficientnet_b7,37.97,207.232,8,600,66.35
tf_efficientnet_b7_ap,37.27,210.986,8,600,66.35
tf_efficientnet_b7_ns,37.25,211.249,8,600,66.35
tf_efficientnet_b7,37.22,211.329,8,600,66.35
eca_nfnet_l3,35.61,1344.526,48,352,72.04
xcit_large_24_p8_224_dist,35.32,449.68,16,224,188.93
xcit_large_24_p8_224,35.06,452.952,16,224,188.93
resnetv2_50x3_bitm,34.68,460.605,16,448,217.32
swinv2_cr_large_384,32.56,488.949,16,384,196.68
cait_s36_384,32.3,491.641,16,384,68.37
densenet264d_iabn,32.11,3982.48,128,224,72.74
resnetv2_152x2_bit_teacher_384,31.17,382.508,12,384,236.34
xcit_small_24_p8_384_dist,31.12,510.761,16,384,47.63
resnest200e,30.3,1579.149,48,320,70.2
vit_large_patch16_384,29.14,410.147,12,384,304.72
deit3_large_patch16_384,28.26,422.768,12,384,304.76
deit3_large_patch16_384_in21ft1k,28.25,422.94,12,384,304.76
swinv2_base_window12to24_192to384_22kft1k,28.19,423.281,12,384,87.92
nfnet_f3,26.1,1834.868,48,320,254.92
beit_large_patch16_384,25.3,472.219,12,384,305.0
tresnet_l_448,25.28,5060.321,128,448,55.99
volo_d3_448,24.7,321.403,8,448,86.63
dm_nfnet_f3,24.56,1297.967,32,320,254.92
tresnet_xl_448,23.27,4122.323,96,448,78.44
efficientnet_b8,22.93,257.589,6,672,87.41
tf_efficientnet_b8,22.71,260.18,6,672,87.41
tf_efficientnet_b8_ap,22.69,260.555,6,672,87.41
resnetv2_152x2_bitm,22.58,351.774,8,448,236.34
vit_giant_patch14_224,22.32,355.766,8,224,1012.61
ig_resnext101_32x32d,21.03,1519.993,32,224,468.53
xcit_medium_24_p8_384_dist,21.03,376.997,8,384,84.32
convmixer_1536_20,20.83,2303.059,48,224,51.63
resnetv2_101x3_bitm,18.05,441.706,8,448,387.93
volo_d4_448,17.57,338.789,6,448,193.41
swinv2_large_window12to24_192to384_22kft1k,16.62,358.548,6,384,196.74
resnest269e,16.0,1493.085,24,416,110.93
nfnet_f4,14.17,1687.74,24,384,316.07
swinv2_cr_huge_384,13.13,454.627,6,384,657.94
dm_nfnet_f4,12.96,1229.019,16,384,316.07
xcit_large_24_p8_384_dist,12.19,488.758,6,384,188.93
cait_m36_384,11.91,500.148,6,384,271.22
volo_d5_448,11.43,346.654,4,448,295.91
ig_resnext101_32x48d,11.2,1427.437,16,224,828.41
tf_efficientnet_l2_ns_475,10.96,267.912,3,475,480.31
dm_nfnet_f5,9.76,1222.345,12,416,377.21
beit_large_patch16_512,9.42,422.548,4,512,305.67
volo_d5_512,8.0,371.847,3,512,296.09
nfnet_f5,8.0,1992.337,16,416,377.21
dm_nfnet_f6,7.45,1065.231,8,448,438.36
nfnet_f6,5.82,2052.248,12,448,438.36
nfnet_f7,5.73,1387.07,8,480,499.5
resnetv2_152x4_bitm,4.89,406.668,2,480,936.53
cait_m48_448,4.76,414.936,2,448,356.46
efficientnet_l2,3.95,247.515,1,800,480.31
tf_efficientnet_l2_ns,3.93,248.975,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/README.md | # Validation and Benchmark Results
This folder contains validation and benchmark results for the models in this collection. Validation scores are currently only run for models with pretrained weights and ImageNet-1k heads, benchmark numbers are run for all.
## Datasets
There are currently results for the ImageNet validation set and 5 additional test / label sets.
The test set results include rank and top-1/top-5 differences from clean validation. For the "Real Labels", ImageNetV2, and Sketch test sets, the differences were calculated against the full 1000 class ImageNet-1k validation set. For both the Adversarial and Rendition sets, the differences were calculated against 'clean' runs on the ImageNet-1k validation set with the same 200 classes used in each test set respectively.
### ImageNet Validation - [`results-imagenet.csv`](results-imagenet.csv)
The standard 50,000 image ImageNet-1k validation set. Model selection during training utilizes this validation set, so it is not a true test set. Question: Does anyone have the official ImageNet-1k test set classification labels now that challenges are done?
* Source: http://image-net.org/challenges/LSVRC/2012/index
* Paper: "ImageNet Large Scale Visual Recognition Challenge" - https://arxiv.org/abs/1409.0575
### ImageNet-"Real Labels" - [`results-imagenet-real.csv`](results-imagenet-real.csv)
The usual ImageNet-1k validation set with a fresh new set of labels intended to improve on mistakes in the original annotation process.
* Source: https://github.com/google-research/reassessed-imagenet
* Paper: "Are we done with ImageNet?" - https://arxiv.org/abs/2006.07159
### ImageNetV2 Matched Frequency - [`results-imagenetv2-matched-frequency.csv`](results-imagenetv2-matched-frequency.csv)
An ImageNet test set of 10,000 images sampled from new images roughly 10 years after the original. Care was taken to replicate the original ImageNet curation/sampling process.
* Source: https://github.com/modestyachts/ImageNetV2
* Paper: "Do ImageNet Classifiers Generalize to ImageNet?" - https://arxiv.org/abs/1902.10811
### ImageNet-Sketch - [`results-sketch.csv`](results-sketch.csv)
50,000 non photographic (or photos of such) images (sketches, doodles, mostly monochromatic) covering all 1000 ImageNet classes.
* Source: https://github.com/HaohanWang/ImageNet-Sketch
* Paper: "Learning Robust Global Representations by Penalizing Local Predictive Power" - https://arxiv.org/abs/1905.13549
### ImageNet-Adversarial - [`results-imagenet-a.csv`](results-imagenet-a.csv)
A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occurring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1.
For clean validation with same 200 classes, see [`results-imagenet-a-clean.csv`](results-imagenet-a-clean.csv)
* Source: https://github.com/hendrycks/natural-adv-examples
* Paper: "Natural Adversarial Examples" - https://arxiv.org/abs/1907.07174
### ImageNet-Rendition - [`results-imagenet-r.csv`](results-imagenet-r.csv)
Renditions of 200 ImageNet classes resulting in 30,000 images for testing robustness.
For clean validation with same 200 classes, see [`results-imagenet-r-clean.csv`](results-imagenet-r-clean.csv)
* Source: https://github.com/hendrycks/imagenet-r
* Paper: "The Many Faces of Robustness" - https://arxiv.org/abs/2006.16241
### TODO
* Explore adding a reduced version of ImageNet-C (Corruptions) and ImageNet-P (Perturbations) from https://github.com/hendrycks/robustness. The originals are huge and image size specific.
## Benchmark
CSV files with a `model_benchmark` prefix include benchmark numbers for models on various accelerators with different precision. Currently only run on RTX 3090 w/ AMP for inference, I intend to add more in the future.
## Metadata
CSV files with `model_metadata` prefix contain extra information about the source training, currently the pretraining dataset and technique (ie distillation, SSL, WSL, etc). Eventually I'd like to have metadata about augmentation, regularization, etc. but that will be a challenge to source consistently.
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenetv2-matched-frequency.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva_giant_patch14_336.clip_ft_in1k,82.200,17.800,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.266,-2.536,+6
eva02_large_patch14_448.mim_in22k_ft_in1k,82.130,17.870,96.260,3.740,305.08,448,1.000,bicubic,-7.492,-2.690,+2
eva02_large_patch14_448.mim_m38m_ft_in1k,82.130,17.870,96.160,3.840,305.08,448,1.000,bicubic,-7.444,-2.764,+2
eva_giant_patch14_560.m30m_ft_in22k_in1k,82.040,17.960,96.440,3.560,"1,014.45",560,1.000,bicubic,-7.746,-2.552,-1
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,81.900,18.100,96.150,3.850,305.08,448,1.000,bicubic,-8.070,-2.862,-3
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,81.890,18.110,96.370,3.630,305.08,448,1.000,bicubic,-8.162,-2.678,-5
eva_giant_patch14_336.m30m_ft_in22k_in1k,81.820,18.180,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.746,-2.662,-1
eva_giant_patch14_224.clip_ft_in1k,81.750,18.250,96.080,3.920,"1,012.56",224,0.900,bicubic,-7.130,-2.600,+1
eva_large_patch14_336.in22k_ft_in1k,81.190,18.810,95.880,4.120,304.53,336,1.000,bicubic,-7.480,-2.842,+4
eva_large_patch14_336.in22k_ft_in22k_in1k,80.930,19.070,96.010,3.990,304.53,336,1.000,bicubic,-8.276,-2.844,-2
vit_large_patch14_clip_336.openai_ft_in12k_in1k,80.520,19.480,95.500,4.500,304.53,336,1.000,bicubic,-7.748,-3.026,+13
regnety_1280.swag_ft_in1k,80.480,19.520,96.180,3.820,644.81,384,1.000,bicubic,-7.750,-2.506,+16
tf_efficientnet_l2.ns_jft_in1k_475,80.470,19.530,95.730,4.270,480.31,475,0.936,bicubic,-7.764,-2.816,+14
convnext_xxlarge.clip_laion2b_soup_ft_in1k,80.450,19.550,95.780,4.220,846.47,256,1.000,bicubic,-8.154,-2.928,0
beitv2_large_patch16_224.in1k_ft_in22k_in1k,80.270,19.730,95.150,4.850,304.43,224,0.950,bicubic,-8.124,-3.448,+5
tf_efficientnet_l2.ns_jft_in1k,80.250,19.750,95.860,4.140,480.31,800,0.960,bicubic,-8.102,-2.788,+5
eva_large_patch14_196.in22k_ft_in22k_in1k,80.170,19.830,95.380,4.620,304.14,196,1.000,bicubic,-8.404,-3.278,0
maxvit_base_tf_512.in21k_ft_in1k,80.150,19.850,95.480,4.520,119.88,512,1.000,bicubic,-8.070,-3.050,+11
eva_large_patch14_196.in22k_ft_in1k,80.150,19.850,95.450,4.550,304.14,196,1.000,bicubic,-7.782,-3.048,+19
maxvit_xlarge_tf_512.in21k_ft_in1k,80.100,19.900,95.480,4.520,475.77,512,1.000,bicubic,-8.438,-3.164,-2
convnextv2_huge.fcmae_ft_in22k_in1k_512,79.990,20.010,95.900,4.100,660.29,512,1.000,bicubic,-8.868,-2.848,-11
maxvit_large_tf_512.in21k_ft_in1k,79.980,20.020,95.160,4.840,212.33,512,1.000,bicubic,-8.244,-3.438,+7
beit_large_patch16_512.in22k_ft_in22k_in1k,79.950,20.050,95.350,4.650,305.67,512,1.000,bicubic,-8.646,-3.306,-8
convnextv2_huge.fcmae_ft_in22k_in1k_384,79.940,20.060,95.690,4.310,660.29,384,1.000,bicubic,-8.730,-3.048,-12
maxvit_xlarge_tf_384.in21k_ft_in1k,79.710,20.290,95.160,4.840,475.32,384,1.000,bicubic,-8.604,-3.384,-3
vit_large_patch14_clip_224.openai_ft_in1k,79.620,20.380,95.000,5.000,304.20,224,1.000,bicubic,-8.234,-3.426,+15
maxvit_large_tf_384.in21k_ft_in1k,79.590,20.410,95.070,4.930,212.03,384,1.000,bicubic,-8.396,-3.498,+8
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,79.520,20.480,95.200,4.800,87.12,448,1.000,bicubic,-9.170,-3.524,-17
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,79.520,20.480,95.000,5.000,632.46,336,1.000,bicubic,-9.072,-3.662,-13
beit_large_patch16_384.in22k_ft_in22k_in1k,79.500,20.500,95.180,4.820,305.00,384,1.000,bicubic,-8.902,-3.428,-11
vit_large_patch14_clip_224.openai_ft_in12k_in1k,79.400,20.600,95.070,4.930,304.20,224,1.000,bicubic,-8.774,-3.476,+2
vit_huge_patch14_clip_224.laion2b_ft_in1k,79.370,20.630,94.920,5.080,632.05,224,1.000,bicubic,-8.218,-3.298,+16
maxvit_base_tf_384.in21k_ft_in1k,79.340,20.660,95.080,4.920,119.65,384,1.000,bicubic,-8.582,-3.464,+5
vit_large_patch14_clip_336.laion2b_ft_in1k,79.240,20.760,95.000,5.000,304.53,336,1.000,bicubic,-8.616,-3.368,+6
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,79.200,20.800,95.120,4.880,632.05,224,1.000,bicubic,-9.056,-3.432,-10
deit3_huge_patch14_224.fb_in22k_ft_in1k,79.190,20.810,94.870,5.130,632.13,224,1.000,bicubic,-7.996,-3.390,+27
eva02_base_patch14_448.mim_in22k_ft_in1k,79.150,20.850,95.110,4.890,87.12,448,1.000,bicubic,-9.102,-3.454,-11
deit3_large_patch16_384.fb_in22k_ft_in1k,79.080,20.920,94.870,5.130,304.76,384,1.000,bicubic,-8.640,-3.642,+7
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,79.040,20.960,95.010,4.990,200.13,384,1.000,bicubic,-9.266,-3.572,-17
caformer_b36.sail_in22k_ft_in1k_384,79.040,20.960,94.920,5.080,98.75,384,1.000,bicubic,-9.018,-3.662,-5
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.980,21.020,94.890,5.110,304.53,336,1.000,bicubic,-9.200,-3.682,-9
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,78.900,21.100,94.580,5.420,116.14,384,1.000,bicubic,-8.928,-3.792,+1
beit_large_patch16_224.in22k_ft_in22k_in1k,78.830,21.170,94.610,5.390,304.43,224,0.900,bicubic,-8.648,-3.694,+7
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,78.750,21.250,94.950,5.050,200.13,384,1.000,bicubic,-9.098,-3.496,-2
beitv2_large_patch16_224.in1k_ft_in1k,78.730,21.270,94.230,5.770,304.43,224,0.950,bicubic,-8.682,-4.004,+11
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,78.650,21.350,94.660,5.340,200.13,320,1.000,bicubic,-9.308,-3.816,-10
deit3_large_patch16_224.fb_in22k_ft_in1k,78.630,21.370,94.720,5.280,304.37,224,1.000,bicubic,-8.352,-3.516,+26
convnextv2_large.fcmae_ft_in22k_in1k_384,78.570,21.430,94.840,5.160,197.96,384,1.000,bicubic,-9.628,-3.688,-17
tf_efficientnet_b7.ns_jft_in1k,78.530,21.470,94.370,5.630,66.35,600,0.949,bicubic,-8.310,-3.722,+30
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.490,21.510,94.660,5.340,304.20,224,1.000,bicubic,-9.404,-3.748,-11
vit_large_patch14_clip_224.laion2b_ft_in1k,78.440,21.560,94.580,5.420,304.20,224,1.000,bicubic,-8.846,-3.664,+10
regnety_320.swag_ft_in1k,78.380,21.620,95.200,4.800,145.05,384,1.000,bicubic,-8.454,-3.162,+28
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,78.240,21.760,94.350,5.650,73.88,384,1.000,bicubic,-9.142,-3.962,+4
caformer_m36.sail_in22k_ft_in1k_384,78.170,21.830,94.180,5.820,56.20,384,1.000,bicubic,-9.276,-4.128,0
caformer_b36.sail_in22k_ft_in1k,78.110,21.890,94.400,5.600,98.75,224,1.000,bicubic,-9.310,-3.928,0
convnextv2_huge.fcmae_ft_in1k,78.060,21.940,94.060,5.940,660.29,288,1.000,bicubic,-8.520,-3.912,+36
convformer_b36.sail_in22k_ft_in1k_384,78.040,21.960,94.170,5.830,99.88,384,1.000,bicubic,-9.562,-4.264,-10
convnext_xlarge.fb_in22k_ft_in1k_384,77.990,22.010,94.480,5.520,350.20,384,1.000,bicubic,-9.762,-4.076,-14
volo_d5_512.sail_in1k,77.970,22.030,94.160,5.840,296.09,512,1.150,bicubic,-9.088,-3.810,+9
vit_large_patch16_384.augreg_in21k_ft_in1k,77.940,22.060,94.460,5.540,304.72,384,1.000,bicubic,-9.144,-3.842,+6
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,77.940,22.060,94.390,5.610,200.13,256,1.000,bicubic,-9.396,-3.828,-2
convnextv2_large.fcmae_ft_in22k_in1k,77.900,22.100,94.390,5.610,197.96,288,1.000,bicubic,-9.584,-3.966,-13
deit3_base_patch16_384.fb_in22k_ft_in1k,77.870,22.130,94.030,5.970,86.88,384,1.000,bicubic,-8.870,-4.086,+23
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,77.770,22.230,94.160,5.840,86.86,384,1.000,bicubic,-9.436,-3.874,-2
volo_d5_448.sail_in1k,77.750,22.250,94.050,5.950,295.91,448,1.150,bicubic,-9.202,-3.888,+10
volo_d4_448.sail_in1k,77.740,22.260,93.940,6.060,193.41,448,1.150,bicubic,-9.052,-3.944,+18
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,77.710,22.290,94.330,5.670,116.09,384,1.000,bicubic,-9.754,-4.044,-15
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,77.690,22.310,94.070,5.930,116.14,224,0.950,bicubic,-9.204,-3.944,+8
tf_efficientnetv2_xl.in21k_ft_in1k,77.630,22.370,93.960,6.040,208.12,512,1.000,bicubic,-9.118,-4.054,+16
tf_efficientnetv2_l.in21k_ft_in1k,77.580,22.420,94.280,5.720,118.52,480,1.000,bicubic,-9.222,-3.856,+11
caformer_s36.sail_in22k_ft_in1k_384,77.540,22.460,94.070,5.930,39.30,384,1.000,bicubic,-9.318,-4.142,+7
convnextv2_base.fcmae_ft_in22k_in1k_384,77.490,22.510,94.410,5.590,88.72,384,1.000,bicubic,-10.154,-4.006,-27
caformer_b36.sail_in1k_384,77.490,22.510,93.530,6.470,98.75,384,1.000,bicubic,-8.918,-4.284,+32
convnext_base.clip_laiona_augreg_ft_in1k_384,77.480,22.520,93.960,6.040,88.59,384,1.000,bicubic,-9.022,-4.008,+22
maxvit_base_tf_512.in1k,77.440,22.560,93.970,6.030,119.88,512,1.000,bicubic,-9.162,-3.948,+14
caformer_m36.sail_in22k_ft_in1k,77.440,22.560,93.590,6.410,56.20,224,1.000,bicubic,-9.154,-4.434,+16
convnext_large.fb_in22k_ft_in1k_384,77.420,22.580,94.200,5.800,197.77,384,1.000,bicubic,-10.052,-4.186,-26
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,77.420,22.580,93.990,6.010,149.39,384,1.000,bicubic,-9.868,-4.344,-18
regnety_1280.swag_lc_in1k,77.380,22.620,94.500,5.500,644.81,224,0.965,bicubic,-8.602,-3.350,+56
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,77.340,22.660,93.910,6.090,196.74,384,1.000,bicubic,-10.124,-4.340,-27
vit_base_patch16_clip_384.laion2b_ft_in1k,77.320,22.680,93.860,6.140,86.86,384,1.000,bicubic,-9.298,-4.148,+8
maxvit_large_tf_512.in1k,77.300,22.700,93.790,6.210,212.33,512,1.000,bicubic,-9.226,-4.090,+12
convnextv2_base.fcmae_ft_in22k_in1k,77.290,22.710,94.110,5.890,88.72,288,1.000,bicubic,-9.708,-4.058,-11
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,77.290,22.710,94.050,5.950,93.59,320,1.000,bicubic,-9.434,-4.126,+3
tf_efficientnet_b6.ns_jft_in1k,77.290,22.710,93.890,6.110,43.04,528,0.942,bicubic,-9.168,-4.000,+17
convformer_b36.sail_in22k_ft_in1k,77.270,22.730,93.970,6.030,99.88,224,1.000,bicubic,-9.728,-4.202,-15
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,77.240,22.760,94.250,5.750,87.92,384,1.000,bicubic,-9.856,-3.984,-21
regnety_160.swag_ft_in1k,77.230,22.770,94.600,5.400,83.59,384,1.000,bicubic,-8.790,-3.452,+41
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,77.180,22.820,94.170,5.830,88.59,384,1.000,bicubic,-9.954,-4.052,-25
caformer_m36.sail_in1k_384,77.160,22.840,93.630,6.370,56.20,384,1.000,bicubic,-9.006,-4.190,+28
maxvit_large_tf_384.in1k,77.130,22.870,93.460,6.540,212.03,384,1.000,bicubic,-9.100,-4.228,+21
beitv2_base_patch16_224.in1k_ft_in22k_in1k,77.120,22.880,94.020,5.980,86.53,224,0.900,bicubic,-9.354,-4.032,+8
volo_d3_448.sail_in1k,77.090,22.910,94.110,5.890,86.63,448,1.000,bicubic,-9.412,-3.600,+4
swin_large_patch4_window12_384.ms_in22k_ft_in1k,77.070,22.930,93.760,6.240,196.74,384,1.000,bicubic,-10.062,-4.474,-29
vit_large_r50_s32_384.augreg_in21k_ft_in1k,77.070,22.930,93.710,6.290,329.09,384,1.000,bicubic,-9.112,-4.212,+21
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,77.010,22.990,93.320,6.680,73.88,224,0.950,bicubic,-9.494,-4.574,-1
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,76.970,23.030,93.520,6.480,196.74,256,0.900,bicubic,-9.982,-4.586,-23
vit_base_patch16_clip_384.openai_ft_in1k,76.960,23.040,93.750,6.250,86.86,384,1.000,bicubic,-9.246,-4.126,+15
convformer_m36.sail_in22k_ft_in1k_384,76.960,23.040,93.690,6.310,57.05,384,1.000,bicubic,-9.932,-4.426,-21
beit_base_patch16_384.in22k_ft_in22k_in1k,76.930,23.070,93.910,6.090,86.74,384,1.000,bicubic,-9.870,-4.226,-18
seresnextaa101d_32x8d.sw_in12k_ft_in1k,76.900,23.100,93.850,6.150,93.59,288,1.000,bicubic,-9.584,-4.180,-2
tf_efficientnetv2_m.in21k_ft_in1k,76.890,23.110,93.640,6.360,54.14,480,1.000,bicubic,-9.102,-4.304,+30
vit_base_patch16_clip_384.openai_ft_in12k_in1k,76.870,23.130,93.780,6.220,86.86,384,0.950,bicubic,-10.156,-4.402,-34
cait_m48_448.fb_dist_in1k,76.870,23.130,93.380,6.620,356.46,448,1.000,bicubic,-9.622,-4.372,-5
tf_efficientnet_b5.ns_jft_in1k,76.830,23.170,93.580,6.420,30.39,456,0.934,bicubic,-9.258,-4.176,+19
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,76.810,23.190,93.210,6.790,468.53,224,0.875,bilinear,-8.288,-4.228,+92
maxvit_base_tf_384.in1k,76.790,23.210,93.440,6.560,119.65,384,1.000,bicubic,-9.512,-4.358,+2
convnext_large.fb_in22k_ft_in1k,76.750,23.250,93.710,6.290,197.77,288,1.000,bicubic,-10.276,-4.494,-39
tiny_vit_21m_512.dist_in22k_ft_in1k,76.710,23.290,93.470,6.530,21.27,512,1.000,bicubic,-9.748,-4.414,-8
deit3_large_patch16_384.fb_in1k,76.690,23.310,93.350,6.650,304.76,384,1.000,bicubic,-9.122,-4.248,+32
convnextv2_large.fcmae_ft_in1k,76.660,23.340,93.570,6.430,197.96,288,1.000,bicubic,-9.458,-4.252,+10
convnext_base.fb_in22k_ft_in1k_384,76.650,23.350,93.700,6.300,88.59,384,1.000,bicubic,-10.146,-4.564,-29
convnext_xlarge.fb_in22k_ft_in1k,76.630,23.370,93.860,6.140,350.20,288,1.000,bicubic,-10.700,-4.468,-55
beitv2_base_patch16_224.in1k_ft_in1k,76.630,23.370,92.930,7.070,86.53,224,0.900,bicubic,-8.964,-4.576,+43
coatnet_2_rw_224.sw_in12k_ft_in1k,76.620,23.380,93.380,6.620,73.87,224,0.950,bicubic,-9.944,-4.516,-22
xcit_large_24_p8_384.fb_dist_in1k,76.620,23.380,93.090,6.910,188.93,384,1.000,bicubic,-9.376,-4.600,+14
regnety_160.sw_in12k_ft_in1k,76.610,23.390,93.560,6.440,83.59,288,1.000,bicubic,-9.376,-4.274,+17
vit_base_patch8_224.augreg2_in21k_ft_in1k,76.590,23.410,93.340,6.660,86.58,224,0.900,bicubic,-9.628,-4.492,-5
caformer_s36.sail_in22k_ft_in1k,76.580,23.420,93.620,6.380,39.30,224,1.000,bicubic,-9.210,-4.206,+25
caformer_s36.sail_in1k_384,76.550,23.450,93.450,6.550,39.30,384,1.000,bicubic,-9.192,-4.222,+28
volo_d5_224.sail_in1k,76.550,23.450,93.330,6.670,295.46,224,0.960,bicubic,-9.520,-4.246,+5
deit3_base_patch16_224.fb_in22k_ft_in1k,76.530,23.470,93.570,6.430,86.59,224,1.000,bicubic,-9.170,-4.176,+29
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,76.530,23.470,93.370,6.630,116.09,224,0.950,bicubic,-10.112,-4.650,-35
dm_nfnet_f6.dm_in1k,76.510,23.490,93.340,6.660,438.36,576,0.956,bicubic,-9.852,-4.556,-17
maxvit_small_tf_512.in1k,76.500,23.500,93.360,6.640,69.13,512,1.000,bicubic,-9.584,-4.404,0
vit_base_patch16_384.augreg_in21k_ft_in1k,76.490,23.510,93.760,6.240,86.86,384,1.000,bicubic,-9.504,-4.242,+5
tiny_vit_21m_384.dist_in22k_ft_in1k,76.470,23.530,93.130,6.870,21.23,384,1.000,bicubic,-9.638,-4.580,-5
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,76.450,23.550,93.700,6.300,87.92,256,0.900,bicubic,-9.818,-4.182,-17
convformer_m36.sail_in1k_384,76.370,23.630,93.100,6.900,57.05,384,1.000,bicubic,-9.210,-4.442,+28
convformer_s36.sail_in22k_ft_in1k_384,76.360,23.640,93.530,6.470,40.01,384,1.000,bicubic,-10.018,-4.454,-26
convformer_m36.sail_in22k_ft_in1k,76.360,23.640,93.450,6.550,57.05,224,1.000,bicubic,-9.788,-4.400,-10
convnext_small.in12k_ft_in1k_384,76.340,23.660,93.420,6.580,50.22,384,1.000,bicubic,-9.842,-4.502,-17
cait_m36_384.fb_dist_in1k,76.330,23.670,93.060,6.940,271.22,384,1.000,bicubic,-9.728,-4.670,-6
swin_large_patch4_window7_224.ms_in22k_ft_in1k,76.310,23.690,93.390,6.610,196.53,224,0.900,bicubic,-10.002,-4.512,-26
vit_large_patch16_224.augreg_in21k_ft_in1k,76.300,23.700,93.610,6.390,304.33,224,0.900,bicubic,-9.536,-4.054,+4
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,76.290,23.710,93.810,6.190,88.59,256,1.000,bicubic,-10.080,-4.174,-30
swin_base_patch4_window12_384.ms_in22k_ft_in1k,76.250,23.750,93.300,6.700,87.90,384,1.000,bicubic,-10.188,-4.766,-34
cait_s36_384.fb_dist_in1k,76.220,23.780,92.960,7.040,68.37,384,1.000,bicubic,-9.234,-4.518,+27
regnety_160.lion_in12k_ft_in1k,76.190,23.810,93.670,6.330,83.59,288,1.000,bicubic,-9.798,-4.164,-6
dm_nfnet_f4.dm_in1k,76.150,23.850,93.050,6.950,316.07,512,0.951,bicubic,-9.686,-4.768,0
xcit_medium_24_p8_384.fb_dist_in1k,76.130,23.870,92.980,7.020,84.32,384,1.000,bicubic,-9.686,-4.612,0
maxvit_small_tf_384.in1k,76.120,23.880,92.610,7.390,69.02,384,1.000,bicubic,-9.420,-4.852,+18
flexivit_large.1200ep_in1k,76.100,23.900,93.010,6.990,304.36,240,0.950,bicubic,-9.544,-4.530,+12
volo_d2_384.sail_in1k,76.090,23.910,93.130,6.870,58.87,384,1.000,bicubic,-9.952,-4.444,-17
tf_efficientnet_b8.ap_in1k,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.274,-4.562,+32
tf_efficientnet_b7.ap_in1k,76.080,23.920,92.970,7.030,66.35,600,0.949,bicubic,-9.044,-4.282,+48
convformer_b36.sail_in1k_384,76.070,23.930,92.720,7.280,99.88,384,1.000,bicubic,-9.670,-4.804,+2
maxvit_tiny_tf_512.in1k,76.060,23.940,93.160,6.840,31.05,512,1.000,bicubic,-9.604,-4.424,+5
flexivit_large.600ep_in1k,76.050,23.950,92.960,7.040,304.36,240,0.950,bicubic,-9.490,-4.528,+10
volo_d4_224.sail_in1k,76.020,23.980,92.980,7.020,192.96,224,0.960,bicubic,-9.852,-4.492,-12
tf_efficientnetv2_l.in1k,76.000,24.000,93.070,6.930,118.52,480,1.000,bicubic,-9.664,-4.404,+3
vit_base_patch8_224.augreg_in21k_ft_in1k,75.970,24.030,93.370,6.630,86.58,224,0.900,bicubic,-9.828,-4.420,-9
xcit_large_24_p8_224.fb_dist_in1k,75.970,24.030,92.710,7.290,188.93,224,1.000,bicubic,-9.432,-4.692,+18
flexivit_large.300ep_in1k,75.940,24.060,92.650,7.350,304.36,240,0.950,bicubic,-9.348,-4.750,+25
convnext_base.fb_in22k_ft_in1k,75.930,24.070,93.580,6.420,88.59,288,1.000,bicubic,-10.344,-4.512,-45
convnext_base.clip_laion2b_augreg_ft_in1k,75.840,24.160,93.210,6.790,88.59,256,1.000,bicubic,-10.318,-4.470,-37
xcit_large_24_p16_384.fb_dist_in1k,75.820,24.180,92.740,7.260,189.10,384,1.000,bicubic,-9.934,-4.798,-10
dm_nfnet_f3.dm_in1k,75.790,24.210,92.910,7.090,254.92,416,0.940,bicubic,-9.896,-4.660,-6
eva02_small_patch14_336.mim_in22k_ft_in1k,75.790,24.210,92.820,7.180,22.13,336,1.000,bicubic,-9.928,-4.814,-9
convformer_s36.sail_in1k_384,75.780,24.220,93.090,6.910,40.01,384,1.000,bicubic,-9.598,-4.386,+13
deit3_huge_patch14_224.fb_in1k,75.780,24.220,92.740,7.260,632.13,224,0.900,bicubic,-9.444,-4.620,+24
xcit_small_24_p8_384.fb_dist_in1k,75.770,24.230,92.970,7.030,47.63,384,1.000,bicubic,-9.784,-4.600,-4
caformer_b36.sail_in1k,75.740,24.260,92.690,7.310,98.75,224,1.000,bicubic,-9.764,-4.620,-1
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.446,-4.288,+119
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,75.710,24.290,92.770,7.230,86.57,224,0.950,bicubic,-10.460,-4.986,-48
tf_efficientnet_b4.ns_jft_in1k,75.690,24.310,93.050,6.950,19.34,380,0.922,bicubic,-9.470,-4.418,+26
caformer_s18.sail_in22k_ft_in1k_384,75.680,24.320,93.510,6.490,26.34,384,1.000,bicubic,-9.734,-4.192,+1
efficientnet_b5.sw_in12k_ft_in1k,75.680,24.320,93.040,6.960,30.39,448,1.000,bicubic,-10.216,-4.696,-31
hrnet_w48_ssld.paddle_in1k,75.670,24.330,92.830,7.170,77.47,288,1.000,bilinear,-8.810,-4.404,+79
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,75.660,24.340,92.980,7.020,39.03,384,0.950,bicubic,-9.870,-4.656,-9
volo_d1_384.sail_in1k,75.650,24.350,93.060,6.940,26.78,384,1.000,bicubic,-9.594,-4.134,+11
volo_d3_224.sail_in1k,75.620,24.380,92.980,7.020,86.33,224,0.960,bicubic,-9.794,-4.296,-3
convnextv2_base.fcmae_ft_in1k,75.620,24.380,92.850,7.150,88.72,288,1.000,bicubic,-9.854,-4.534,-9
dm_nfnet_f5.dm_in1k,75.610,24.390,92.780,7.220,377.21,544,0.954,bicubic,-10.490,-4.908,-51
caformer_m36.sail_in1k,75.600,24.400,92.400,7.600,56.20,224,1.000,bicubic,-9.632,-4.800,+9
vit_base_patch16_clip_224.openai_ft_in1k,75.590,24.410,92.960,7.040,86.57,224,0.900,bicubic,-9.702,-4.476,+2
vit_base_r50_s16_384.orig_in21k_ft_in1k,75.570,24.430,92.790,7.210,98.95,384,1.000,bicubic,-9.406,-4.500,+33
deit_base_distilled_patch16_384.fb_in1k,75.550,24.450,92.500,7.500,87.63,384,1.000,bicubic,-9.874,-4.906,-11
inception_next_base.sail_in1k_384,75.530,24.470,92.560,7.440,86.67,384,1.000,bicubic,-9.672,-4.854,+10
regnetz_e8.ra3_in1k,75.510,24.490,92.690,7.310,57.70,320,1.000,bicubic,-9.524,-4.582,+26
cait_s24_384.fb_dist_in1k,75.500,24.500,92.610,7.390,47.06,384,1.000,bicubic,-9.548,-4.736,+24
convformer_s36.sail_in22k_ft_in1k,75.480,24.520,93.190,6.810,40.01,224,1.000,bicubic,-9.934,-4.378,-13
xcit_medium_24_p8_224.fb_dist_in1k,75.470,24.530,92.890,7.110,84.32,224,1.000,bicubic,-9.604,-4.360,+19
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,75.470,24.530,92.730,7.270,88.79,224,0.875,bilinear,-8.832,-4.446,+86
vit_base_patch16_clip_224.openai_ft_in12k_in1k,75.460,24.540,92.750,7.250,86.57,224,0.950,bicubic,-10.482,-4.978,-49
regnety_2560.seer_ft_in1k,75.440,24.560,92.830,7.170,"1,282.60",384,1.000,bicubic,-9.710,-4.608,+7
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,75.420,24.580,92.710,7.290,88.34,448,1.000,bicubic,-10.360,-4.928,-42
regnety_320.swag_lc_in1k,75.400,24.600,93.680,6.320,145.05,224,0.965,bicubic,-9.148,-3.762,+50
beit_base_patch16_224.in22k_ft_in22k_in1k,75.390,24.610,93.020,6.980,86.53,224,0.900,bicubic,-9.822,-4.638,-2
tf_efficientnetv2_m.in1k,75.380,24.620,92.760,7.240,54.14,480,1.000,bicubic,-9.824,-4.604,-3
tf_efficientnet_b6.ap_in1k,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.408,-4.698,+34
vit_base_patch16_224.augreg2_in21k_ft_in1k,75.360,24.640,93.230,6.770,86.57,224,0.900,bicubic,-9.734,-4.300,+7
regnety_120.sw_in12k_ft_in1k,75.330,24.670,92.970,7.030,51.82,288,1.000,bicubic,-10.070,-4.612,-21
volo_d2_224.sail_in1k,75.310,24.690,92.520,7.480,58.68,224,0.960,bicubic,-9.892,-4.670,-4
mvitv2_large.fb_in1k,75.270,24.730,92.360,7.640,217.99,224,0.900,bicubic,-9.974,-4.854,-13
coat_lite_medium_384.in1k,75.270,24.730,92.230,7.770,44.57,384,1.000,bicubic,-9.608,-5.142,+22
dm_nfnet_f2.dm_in1k,75.260,24.740,92.450,7.550,193.78,352,0.920,bicubic,-9.932,-4.896,-6
caformer_s18.sail_in1k_384,75.210,24.790,92.700,7.300,26.34,384,1.000,bicubic,-9.816,-4.658,+9
convnext_small.fb_in22k_ft_in1k_384,75.160,24.840,93.060,6.940,50.22,384,1.000,bicubic,-10.618,-4.830,-53
efficientnetv2_rw_m.agc_in1k,75.150,24.850,92.570,7.430,53.24,416,1.000,bicubic,-9.660,-4.582,+23
ecaresnet269d.ra2_in1k,75.130,24.870,92.830,7.170,102.09,352,1.000,bicubic,-9.838,-4.392,+10
vit_base_patch16_clip_224.laion2b_ft_in1k,75.120,24.880,92.700,7.300,86.57,224,1.000,bicubic,-10.350,-4.876,-39
deit3_large_patch16_224.fb_in1k,75.120,24.880,92.280,7.720,304.37,224,0.900,bicubic,-9.654,-4.756,+23
deit3_small_patch16_384.fb_in22k_ft_in1k,75.090,24.910,92.810,7.190,22.21,384,1.000,bicubic,-9.734,-4.676,+17
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,75.090,24.910,92.580,7.420,88.30,384,1.000,bicubic,-10.276,-5.080,-30
xcit_medium_24_p16_384.fb_dist_in1k,75.090,24.910,92.440,7.560,84.40,384,1.000,bicubic,-10.334,-4.890,-40
tiny_vit_21m_224.dist_in22k_ft_in1k,75.070,24.930,92.590,7.410,21.20,224,0.950,bicubic,-10.016,-4.776,-6
maxvit_tiny_tf_384.in1k,75.020,24.980,92.450,7.550,30.98,384,1.000,bicubic,-10.080,-4.928,-11
convformer_b36.sail_in1k,74.980,25.020,91.600,8.400,99.88,224,1.000,bicubic,-9.838,-5.346,+13
xcit_small_24_p8_224.fb_dist_in1k,74.970,25.030,92.320,7.680,47.63,224,1.000,bicubic,-9.898,-4.870,+8
convnext_small.in12k_ft_in1k,74.960,25.040,92.520,7.480,50.22,288,1.000,bicubic,-10.370,-5.026,-34
tf_efficientnet_b8.ra_in1k,74.920,25.080,92.330,7.670,87.41,672,0.954,bicubic,-10.448,-5.064,-38
xcit_small_12_p8_384.fb_dist_in1k,74.850,25.150,92.450,7.550,26.21,384,1.000,bicubic,-10.228,-4.832,-11
eca_nfnet_l2.ra3_in1k,74.820,25.180,92.650,7.350,56.72,384,1.000,bicubic,-9.880,-4.616,+15
deit3_base_patch16_384.fb_in1k,74.800,25.200,92.240,7.760,86.88,384,1.000,bicubic,-10.274,-5.034,-11
convnext_tiny.in12k_ft_in1k_384,74.730,25.270,92.810,7.190,28.59,384,1.000,bicubic,-10.392,-4.796,-21
tf_efficientnet_b7.ra_in1k,74.730,25.270,92.220,7.780,66.35,600,0.949,bicubic,-10.202,-4.988,-4
deit3_medium_patch16_224.fb_in22k_ft_in1k,74.690,25.310,92.470,7.530,38.85,224,1.000,bicubic,-9.860,-4.718,+19
convnext_large.fb_in1k,74.650,25.350,91.970,8.030,197.77,288,1.000,bicubic,-10.196,-5.244,+1
xcit_large_24_p16_224.fb_dist_in1k,74.650,25.350,91.860,8.140,189.10,224,1.000,bicubic,-10.266,-5.268,-5
convformer_s18.sail_in1k_384,74.640,25.360,92.480,7.520,26.77,384,1.000,bicubic,-9.762,-4.632,+42
caformer_s36.sail_in1k,74.640,25.360,92.080,7.920,39.30,224,1.000,bicubic,-9.866,-4.916,+23
xcit_small_24_p16_384.fb_dist_in1k,74.590,25.410,92.460,7.540,47.67,384,1.000,bicubic,-10.500,-4.852,-24
tf_efficientnet_b5.ap_in1k,74.590,25.410,91.990,8.010,30.39,456,0.934,bicubic,-9.668,-4.984,+50
maxvit_large_tf_224.in1k,74.580,25.420,91.700,8.300,211.79,224,0.950,bicubic,-10.362,-5.270,-13
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,74.570,25.430,91.960,8.040,38.86,256,0.950,bicubic,-9.876,-5.250,+26
swin_base_patch4_window7_224.ms_in22k_ft_in1k,74.560,25.440,92.540,7.460,87.77,224,0.900,bicubic,-10.712,-5.024,-48
dm_nfnet_f1.dm_in1k,74.560,25.440,92.170,7.830,132.63,320,0.910,bicubic,-10.142,-5.012,+1
convformer_s18.sail_in22k_ft_in1k_384,74.550,25.450,92.760,7.240,26.77,384,1.000,bicubic,-10.448,-4.810,-20
maxxvit_rmlp_small_rw_256.sw_in1k,74.520,25.480,92.000,8.000,66.01,256,0.950,bicubic,-10.104,-5.068,+2
regnetz_040.ra3_in1k,74.510,25.490,91.880,8.120,27.12,320,1.000,bicubic,-9.730,-5.052,+45
seresnet152d.ra2_in1k,74.500,25.500,92.090,7.910,66.84,320,1.000,bicubic,-9.860,-4.950,+35
convnext_small.fb_in22k_ft_in1k,74.490,25.510,92.700,7.300,50.22,288,1.000,bicubic,-10.772,-4.982,-52
davit_base.msft_in1k,74.490,25.510,91.750,8.250,87.95,224,0.950,bicubic,-10.152,-5.270,-3
fastvit_ma36.apple_dist_in1k,74.480,25.520,91.960,8.040,44.07,256,0.950,bicubic,-10.118,-5.042,0
gcvit_base.in1k,74.480,25.520,91.770,8.230,90.32,224,0.875,bicubic,-9.964,-5.312,+18
regnetz_040_h.ra3_in1k,74.460,25.540,92.250,7.750,28.94,320,1.000,bicubic,-10.032,-4.508,+8
resnest200e.in1k,74.460,25.540,91.880,8.120,70.20,320,0.909,bicubic,-9.384,-5.004,+74
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,74.460,25.540,91.640,8.360,41.72,224,0.950,bicubic,-10.450,-5.318,-21
tf_efficientnetv2_s.in21k_ft_in1k,74.440,25.560,92.500,7.500,21.46,384,1.000,bicubic,-9.846,-4.752,+30
maxvit_rmlp_small_rw_224.sw_in1k,74.400,25.600,91.390,8.610,64.90,224,0.900,bicubic,-10.092,-5.620,+6
caformer_s18.sail_in22k_ft_in1k,74.380,25.620,92.500,7.500,26.34,224,1.000,bicubic,-9.694,-4.698,+49
convformer_m36.sail_in1k,74.380,25.620,91.480,8.520,57.05,224,1.000,bicubic,-10.114,-5.386,+2
flexivit_base.1200ep_in1k,74.350,25.650,91.800,8.200,86.59,240,0.950,bicubic,-10.326,-5.194,-14
resnetrs200.tf_in1k,74.340,25.660,91.940,8.060,93.21,320,1.000,bicubic,-10.104,-4.902,+8
seresnextaa101d_32x8d.ah_in1k,74.320,25.680,91.720,8.280,93.59,288,1.000,bicubic,-10.246,-5.356,-10
maxvit_base_tf_224.in1k,74.290,25.710,91.760,8.240,119.47,224,0.950,bicubic,-10.570,-5.228,-28
regnety_160.swag_lc_in1k,74.230,25.770,92.870,7.130,83.59,224,0.965,bicubic,-9.552,-4.410,+76
convnextv2_tiny.fcmae_ft_in22k_in1k_384,74.220,25.780,92.470,7.530,28.64,384,1.000,bicubic,-10.886,-5.158,-53
mvitv2_base.fb_in1k,74.220,25.780,91.650,8.350,51.47,224,0.900,bicubic,-10.230,-5.208,+1
resnest269e.in1k,74.210,25.790,91.960,8.040,110.93,416,0.928,bicubic,-10.298,-5.030,-9
convformer_s36.sail_in1k,74.210,25.790,91.230,8.770,40.01,224,1.000,bicubic,-9.850,-5.516,+42
convnext_tiny.in12k_ft_in1k,74.200,25.800,92.690,7.310,28.59,288,1.000,bicubic,-10.250,-4.650,-3
seresnext101d_32x8d.ah_in1k,74.180,25.820,91.850,8.150,93.59,288,1.000,bicubic,-10.178,-5.070,+13
coatnet_rmlp_2_rw_224.sw_in1k,74.180,25.820,91.300,8.700,73.88,224,0.950,bicubic,-10.428,-5.440,-21
cait_xs24_384.fb_dist_in1k,74.170,25.830,91.930,8.070,26.67,384,1.000,bicubic,-9.892,-4.954,+36
efficientnetv2_rw_s.ra2_in1k,74.160,25.840,91.710,8.290,23.94,384,1.000,bicubic,-9.646,-5.022,+63
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,74.130,25.870,91.990,8.010,44.18,224,0.875,bilinear,-9.096,-4.770,+126
vit_base_patch16_384.orig_in21k_ft_in1k,74.120,25.880,92.360,7.640,86.86,384,1.000,bicubic,-10.080,-4.858,+20
flexivit_base.600ep_in1k,74.120,25.880,91.750,8.250,86.59,240,0.950,bicubic,-10.404,-5.186,-19
flexivit_base.300ep_in1k,74.120,25.880,91.390,8.610,86.59,240,0.950,bicubic,-10.286,-5.494,+4
vit_large_r50_s32_224.augreg_in21k_ft_in1k,74.110,25.890,92.390,7.610,328.99,224,0.900,bicubic,-10.308,-4.782,-4
xcit_small_12_p16_384.fb_dist_in1k,74.110,25.890,92.090,7.910,26.25,384,1.000,bicubic,-10.602,-5.028,-38
eca_nfnet_l1.ra2_in1k,74.110,25.890,92.070,7.930,41.41,320,1.000,bicubic,-9.902,-4.956,+38
convnext_base.fb_in1k,74.110,25.890,91.710,8.290,88.59,288,1.000,bicubic,-10.318,-5.258,-5
volo_d1_224.sail_in1k,74.100,25.900,92.030,7.970,26.63,224,0.960,bicubic,-10.062,-4.746,+18
inception_next_base.sail_in1k,74.080,25.920,91.380,8.620,86.67,224,0.950,bicubic,-10.012,-5.416,+22
vit_base_patch32_clip_384.openai_ft_in12k_in1k,74.050,25.950,92.410,7.590,88.30,384,0.950,bicubic,-11.164,-4.994,-82
vit_base_patch16_224_miil.in21k_ft_in1k,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.226,-5.104,+3
xcit_large_24_p8_224.fb_in1k,74.040,25.960,90.890,9.110,188.93,224,1.000,bicubic,-10.354,-5.774,-4
tf_efficientnet_b7.aa_in1k,74.030,25.970,91.870,8.130,66.35,600,0.949,bicubic,-10.386,-5.038,-9
vit_base_patch16_224.augreg_in21k_ft_in1k,74.010,25.990,92.470,7.530,86.57,224,0.900,bicubic,-10.522,-4.824,-33
resnetv2_152x4_bit.goog_in21k_ft_in1k,74.010,25.990,92.350,7.650,936.53,480,1.000,bilinear,-10.906,-5.088,-58
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,73.990,26.010,92.180,7.820,194.03,224,0.875,bilinear,-9.346,-4.666,+101
tf_efficientnetv2_s.in1k,73.990,26.010,91.540,8.460,21.46,384,1.000,bicubic,-9.908,-5.156,+32
swinv2_base_window16_256.ms_in1k,73.980,26.020,91.750,8.250,87.92,256,0.900,bicubic,-10.620,-5.340,-42
regnetz_d32.ra3_in1k,73.960,26.040,91.950,8.050,27.58,320,0.950,bicubic,-10.062,-4.918,+22
seresnext101_32x8d.ah_in1k,73.960,26.040,91.450,8.550,93.57,288,1.000,bicubic,-10.226,-5.424,+4
resnetv2_152x2_bit.goog_in21k_ft_in1k,73.950,26.050,92.670,7.330,236.34,448,1.000,bilinear,-10.560,-4.764,-37
crossvit_18_dagger_408.in1k,73.930,26.070,91.410,8.590,44.61,408,1.000,bicubic,-10.272,-5.408,0
rexnetr_300.sw_in12k_ft_in1k,73.920,26.080,92.210,7.790,34.81,288,1.000,bicubic,-10.626,-5.046,-43
resnetrs420.tf_in1k,73.920,26.080,91.780,8.220,191.89,416,1.000,bicubic,-11.084,-5.344,-73
xcit_small_12_p8_224.fb_dist_in1k,73.920,26.080,91.720,8.280,26.21,224,1.000,bicubic,-10.316,-5.150,-6
resmlp_big_24_224.fb_in22k_ft_in1k,73.900,26.100,91.760,8.240,129.14,224,0.875,bicubic,-10.498,-5.352,-20
pit_b_distilled_224.in1k,73.900,26.100,90.780,9.220,74.79,224,0.900,bicubic,-9.866,-5.688,+42
tf_efficientnet_b6.aa_in1k,73.890,26.110,91.750,8.250,43.04,528,0.942,bicubic,-10.222,-5.134,+2
edgenext_base.usi_in1k,73.880,26.120,91.760,8.240,18.51,320,1.000,bicubic,-10.078,-5.010,+16
regnety_1280.seer_ft_in1k,73.860,26.140,91.900,8.100,644.81,384,1.000,bicubic,-10.572,-5.192,-32
deit3_small_patch16_224.fb_in22k_ft_in1k,73.850,26.150,91.970,8.030,22.06,224,1.000,bicubic,-9.226,-4.806,+113
tf_efficientnet_b3.ns_jft_in1k,73.850,26.150,91.860,8.140,12.23,300,0.904,bicubic,-10.202,-5.058,+6
maxvit_rmlp_tiny_rw_256.sw_in1k,73.830,26.170,91.440,8.560,29.15,256,0.950,bicubic,-10.394,-5.428,-12
vit_small_r26_s32_384.augreg_in21k_ft_in1k,73.790,26.210,92.300,7.700,36.47,384,1.000,bicubic,-10.258,-5.028,+5
davit_small.msft_in1k,73.780,26.220,91.560,8.440,49.75,224,0.950,bicubic,-10.472,-5.380,-19
fastvit_ma36.apple_in1k,73.780,26.220,91.490,8.510,44.07,256,0.950,bicubic,-10.102,-5.252,+16
regnety_080.ra3_in1k,73.760,26.240,91.800,8.200,39.18,288,1.000,bicubic,-10.166,-5.090,+8
maxvit_small_tf_224.in1k,73.760,26.240,91.410,8.590,68.93,224,0.950,bicubic,-10.666,-5.414,-36
regnetz_d8.ra3_in1k,73.750,26.250,92.000,8.000,23.37,320,1.000,bicubic,-10.302,-4.996,-2
focalnet_base_lrf.ms_in1k,73.740,26.260,90.990,9.010,88.75,224,0.900,bicubic,-10.098,-5.618,+17
fastvit_sa36.apple_dist_in1k,73.730,26.270,91.730,8.270,31.53,256,0.900,bicubic,-10.296,-5.124,-1
resnetrs270.tf_in1k,73.730,26.270,91.580,8.420,129.86,352,1.000,bicubic,-10.698,-5.388,-42
efficientvit_b3.r288_in1k,73.720,26.280,91.450,8.550,48.65,288,1.000,bicubic,-10.434,-5.286,-16
gcvit_small.in1k,73.700,26.300,91.230,8.770,51.09,224,0.875,bicubic,-10.192,-5.428,+7
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,73.670,26.330,92.140,7.860,88.79,224,0.875,bilinear,-9.028,-4.004,+130
resnet200d.ra2_in1k,73.670,26.330,91.570,8.430,64.69,320,1.000,bicubic,-10.294,-5.256,-1
resnetv2_101x3_bit.goog_in21k_ft_in1k,73.660,26.340,92.470,7.530,387.93,448,1.000,bilinear,-10.778,-4.912,-50
xcit_medium_24_p16_224.fb_dist_in1k,73.650,26.350,91.570,8.430,84.40,224,1.000,bicubic,-10.636,-5.362,-35
convnextv2_tiny.fcmae_ft_in22k_in1k,73.640,26.360,91.960,8.040,28.64,288,1.000,bicubic,-10.776,-5.300,-46
inception_next_small.sail_in1k,73.620,26.380,91.240,8.760,49.37,224,0.875,bicubic,-9.958,-5.358,+40
edgenext_base.in21k_ft_in1k,73.610,26.390,91.860,8.140,18.51,320,1.000,bicubic,-10.444,-5.336,-15
repvgg_d2se.rvgg_in1k,73.610,26.390,91.380,8.620,133.33,320,1.000,bilinear,-9.950,-5.278,+38
regnety_064.ra3_in1k,73.610,26.390,91.330,8.670,30.58,288,1.000,bicubic,-10.110,-5.392,+23
tresnet_v2_l.miil_in21k_ft_in1k,73.590,26.410,90.990,9.010,46.17,224,0.875,bilinear,-10.304,-5.500,-4
swinv2_base_window8_256.ms_in1k,73.550,26.450,91.510,8.490,87.92,256,0.900,bicubic,-10.700,-5.414,-38
tf_efficientnet_b5.ra_in1k,73.540,26.460,91.460,8.540,30.39,456,0.934,bicubic,-10.274,-5.292,+5
resnetaa101d.sw_in12k_ft_in1k,73.520,26.480,91.750,8.250,44.57,288,1.000,bicubic,-10.604,-5.356,-28
cs3se_edgenet_x.c2ns_in1k,73.510,26.490,91.480,8.520,50.72,320,1.000,bicubic,-10.036,-5.190,+34
resnet152d.ra2_in1k,73.510,26.490,91.240,8.760,60.21,320,1.000,bicubic,-10.174,-5.498,+23
regnetz_d8_evos.ch_in1k,73.490,26.510,91.710,8.290,23.46,320,1.000,bicubic,-10.636,-5.302,-33
deit3_base_patch16_224.fb_in1k,73.490,26.510,91.280,8.720,86.59,224,0.900,bicubic,-10.296,-5.306,+5
repvit_m2_3.dist_450e_in1k,73.480,26.520,91.260,8.740,23.69,224,0.950,bicubic,-10.262,-5.384,+10
regnetv_064.ra3_in1k,73.460,26.540,91.600,8.400,30.58,288,1.000,bicubic,-10.256,-5.142,+13
convnext_small.fb_in1k,73.450,26.550,91.330,8.670,50.22,288,1.000,bicubic,-10.250,-5.478,+14
coat_lite_medium.in1k,73.450,26.550,91.230,8.770,44.57,224,0.900,bicubic,-10.150,-5.498,+23
sequencer2d_l.in1k,73.450,26.550,91.090,8.910,54.30,224,0.875,bicubic,-9.944,-5.406,+43
xcit_tiny_24_p8_384.fb_dist_in1k,73.430,26.570,91.560,8.440,12.11,384,1.000,bicubic,-10.316,-4.840,+3
twins_svt_large.in1k,73.420,26.580,90.890,9.110,99.27,224,0.900,bicubic,-10.258,-5.698,+15
tiny_vit_21m_224.in1k,73.410,26.590,91.480,8.520,21.20,224,0.950,bicubic,-9.844,-5.112,+54
resnetrs350.tf_in1k,73.400,26.600,91.300,8.700,163.96,384,1.000,bicubic,-11.314,-5.692,-103
pvt_v2_b4.in1k,73.400,26.600,91.070,8.930,62.56,224,0.900,bicubic,-10.312,-5.600,+7
tf_efficientnet_b5.aa_in1k,73.390,26.610,91.210,8.790,30.39,456,0.934,bicubic,-10.298,-5.502,+9
regnety_160.deit_in1k,73.380,26.620,91.700,8.300,83.59,288,1.000,bicubic,-10.310,-5.080,+7
swin_s3_base_224.ms_in1k,73.350,26.650,91.180,8.820,71.13,224,0.900,bicubic,-10.570,-5.492,-27
convformer_s18.sail_in22k_ft_in1k,73.340,26.660,91.910,8.090,26.77,224,1.000,bicubic,-10.398,-5.138,-1
efficientnet_b4.ra2_in1k,73.340,26.660,91.270,8.730,19.34,384,1.000,bicubic,-10.074,-5.328,+30
gcvit_tiny.in1k,73.330,26.670,90.970,9.030,28.22,224,0.875,bicubic,-10.054,-5.428,+34
repvit_m2_3.dist_300e_in1k,73.320,26.680,90.890,9.110,23.69,224,0.950,bicubic,-10.184,-5.614,+16
mvitv2_small.fb_in1k,73.300,26.700,91.230,8.770,34.87,224,0.900,bicubic,-10.470,-5.346,-12
resnet152.a1h_in1k,73.300,26.700,91.180,8.820,60.19,288,1.000,bicubic,-10.150,-5.358,+21
resmlp_big_24_224.fb_distilled_in1k,73.300,26.700,91.170,8.830,129.14,224,0.875,bicubic,-10.292,-5.480,+9
swin_small_patch4_window7_224.ms_in22k_ft_in1k,73.290,26.710,92.030,7.970,49.61,224,0.900,bicubic,-10.008,-4.934,+39
vit_small_patch16_384.augreg_in21k_ft_in1k,73.290,26.710,91.990,8.010,22.20,384,1.000,bicubic,-10.514,-5.110,-20
xcit_small_24_p16_224.fb_dist_in1k,73.270,26.730,91.460,8.540,47.67,224,1.000,bicubic,-10.604,-5.276,-32
focalnet_base_srf.ms_in1k,73.270,26.730,91.260,8.740,88.15,224,0.900,bicubic,-10.550,-5.420,-24
swinv2_small_window16_256.ms_in1k,73.250,26.750,91.290,8.710,49.73,256,0.900,bicubic,-10.974,-5.488,-68
pvt_v2_b5.in1k,73.250,26.750,91.080,8.920,81.96,224,0.900,bicubic,-10.490,-5.556,-14
deit_base_distilled_patch16_224.fb_in1k,73.250,26.750,91.010,8.990,87.34,224,0.900,bicubic,-10.140,-5.478,+24
maxvit_tiny_rw_224.sw_in1k,73.230,26.770,90.770,9.230,29.06,224,0.950,bicubic,-10.274,-5.744,+6
pvt_v2_b3.in1k,73.210,26.790,91.010,8.990,45.24,224,0.900,bicubic,-9.908,-5.546,+49
resnetrs152.tf_in1k,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.502,-5.352,-13
fastvit_sa24.apple_dist_in1k,73.180,26.820,91.350,8.650,21.55,256,0.900,bicubic,-10.162,-5.202,+24
swin_base_patch4_window12_384.ms_in1k,73.160,26.840,91.130,8.870,87.90,384,1.000,bicubic,-11.316,-5.762,-102
efficientvit_b3.r256_in1k,73.150,26.850,91.110,8.890,48.65,256,1.000,bicubic,-10.652,-5.406,-30
vit_base_patch32_384.augreg_in21k_ft_in1k,73.130,26.870,91.250,8.750,88.30,384,1.000,bicubic,-10.222,-5.590,+19
nest_base_jx.goog_in1k,73.130,26.870,91.070,8.930,67.72,224,0.875,bicubic,-10.404,-5.304,-2
xcit_medium_24_p8_224.fb_in1k,73.130,26.870,90.280,9.720,84.32,224,1.000,bicubic,-10.616,-6.430,-26
regnety_640.seer_ft_in1k,73.110,26.890,91.520,8.480,281.38,384,1.000,bicubic,-10.798,-5.402,-50
swinv2_small_window8_256.ms_in1k,73.100,26.900,90.950,9.050,49.73,256,0.900,bicubic,-10.754,-5.694,-45
deit3_small_patch16_384.fb_in1k,73.090,26.910,91.220,8.780,22.21,384,1.000,bicubic,-10.338,-5.454,+4
xcit_small_24_p8_224.fb_in1k,73.090,26.910,91.160,8.840,47.63,224,1.000,bicubic,-10.744,-5.472,-42
cait_s24_224.fb_dist_in1k,73.060,26.940,91.120,8.880,46.92,224,1.000,bicubic,-10.382,-5.454,+1
efficientformerv2_l.snap_dist_in1k,73.060,26.940,90.880,9.120,26.32,224,0.950,bicubic,-10.572,-5.678,-18
focalnet_small_srf.ms_in1k,73.060,26.940,90.730,9.270,49.89,224,0.900,bicubic,-10.356,-5.708,+1
coatnet_rmlp_1_rw_224.sw_in1k,73.040,26.960,90.880,9.120,41.69,224,0.950,bicubic,-10.322,-5.570,+9
fastvit_sa36.apple_in1k,73.030,26.970,90.930,9.070,31.53,256,0.900,bicubic,-10.470,-5.700,-10
efficientvit_b3.r224_in1k,72.980,27.020,90.570,9.430,48.65,224,0.950,bicubic,-10.480,-5.760,-6
crossvit_15_dagger_408.in1k,72.970,27.030,91.090,8.910,28.50,408,1.000,bicubic,-10.870,-5.688,-52
caformer_s18.sail_in1k,72.960,27.040,91.010,8.990,26.34,224,1.000,bicubic,-10.694,-5.508,-25
regnety_320.tv2_in1k,72.940,27.060,90.760,9.240,145.05,224,0.965,bicubic,-10.222,-5.654,+21
ecaresnet101d.miil_in1k,72.930,27.070,91.180,8.820,44.57,288,0.950,bicubic,-10.054,-5.362,+43
regnetv_040.ra3_in1k,72.930,27.070,91.120,8.880,20.64,288,1.000,bicubic,-10.260,-5.538,+16
tf_efficientnet_b4.ap_in1k,72.900,27.100,90.980,9.020,19.34,380,0.922,bicubic,-10.350,-5.416,+9
maxvit_tiny_tf_224.in1k,72.900,27.100,90.830,9.170,30.92,224,0.950,bicubic,-10.502,-5.760,-7
coatnet_1_rw_224.sw_in1k,72.900,27.100,90.790,9.210,41.72,224,0.950,bicubic,-10.696,-5.592,-25
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,72.870,27.130,91.550,8.450,236.34,384,1.000,bicubic,-10.966,-5.576,-58
swinv2_cr_small_ns_224.sw_in1k,72.850,27.150,90.820,9.180,49.70,224,0.900,bicubic,-10.648,-5.664,-20
regnety_040.ra3_in1k,72.790,27.210,90.750,9.250,20.65,288,1.000,bicubic,-10.254,-5.752,+29
regnety_032.ra_in1k,72.780,27.220,90.950,9.050,19.44,288,1.000,bicubic,-9.946,-5.466,+54
convnextv2_tiny.fcmae_ft_in1k,72.770,27.230,91.180,8.820,28.64,288,1.000,bicubic,-10.694,-5.538,-20
xception65p.ra3_in1k,72.760,27.240,90.910,9.090,39.82,299,0.940,bicubic,-10.366,-5.572,+15
swin_s3_small_224.ms_in1k,72.720,27.280,90.560,9.440,49.74,224,0.900,bicubic,-11.036,-5.892,-53
resnetv2_101.a1h_in1k,72.690,27.310,90.670,9.330,44.54,288,1.000,bicubic,-10.310,-5.784,+28
efficientformer_l7.snap_dist_in1k,72.660,27.340,90.800,9.200,82.23,224,0.950,bicubic,-10.722,-5.736,-12
xcit_small_12_p8_224.fb_in1k,72.630,27.370,90.670,9.330,26.21,224,1.000,bicubic,-10.704,-5.812,-7
nfnet_l0.ra2_in1k,72.610,27.390,90.990,9.010,35.07,288,1.000,bicubic,-10.140,-5.526,+44
resnext101_64x4d.c1_in1k,72.610,27.390,90.830,9.170,83.46,288,1.000,bicubic,-10.546,-5.544,+3
focalnet_small_lrf.ms_in1k,72.610,27.390,90.720,9.280,50.34,224,0.900,bicubic,-10.884,-5.860,-28
pnasnet5large.tf_in1k,72.610,27.390,90.500,9.500,86.06,331,0.911,bicubic,-10.172,-5.540,+39
xception65.ra3_in1k,72.600,27.400,90.840,9.160,39.92,299,0.940,bicubic,-10.580,-5.752,-1
cs3sedarknet_x.c2ns_in1k,72.580,27.420,91.060,8.940,35.40,288,1.000,bicubic,-10.078,-5.290,+52
resnest101e.in1k,72.580,27.420,90.810,9.190,48.28,256,0.875,bilinear,-10.304,-5.512,+27
twins_pcpvt_large.in1k,72.580,27.420,90.700,9.300,60.99,224,0.900,bicubic,-10.550,-5.904,+2
gc_efficientnetv2_rw_t.agc_in1k,72.570,27.430,90.830,9.170,13.68,288,1.000,bicubic,-9.886,-5.466,+82
tf_efficientnet_b5.in1k,72.560,27.440,91.090,8.910,30.39,456,0.934,bicubic,-10.616,-5.446,-6
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,72.560,27.440,90.840,9.160,25.03,224,0.875,bilinear,-9.612,-5.384,+119
tresnet_xl.miil_in1k_448,72.560,27.440,90.310,9.690,78.44,448,0.875,bilinear,-10.498,-5.862,+9
twins_svt_base.in1k,72.550,27.450,90.450,9.550,56.07,224,0.900,bicubic,-10.570,-5.964,-1
deit_base_patch16_384.fb_in1k,72.550,27.450,90.260,9.740,86.86,384,1.000,bicubic,-10.554,-6.108,+3
resnetv2_50x3_bit.goog_in21k_ft_in1k,72.520,27.480,91.760,8.240,217.32,448,1.000,bilinear,-11.500,-5.366,-98
xcit_small_12_p16_224.fb_dist_in1k,72.520,27.480,91.130,8.870,26.25,224,1.000,bicubic,-10.808,-5.286,-22
rexnet_300.nav_in1k,72.520,27.480,90.610,9.390,34.71,224,0.875,bicubic,-10.254,-5.628,+28
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,72.510,27.490,90.880,9.120,88.22,224,0.900,bicubic,-10.786,-5.648,-23
deit3_medium_patch16_224.fb_in1k,72.510,27.490,90.810,9.190,38.85,224,0.900,bicubic,-10.576,-5.484,-1
convformer_s18.sail_in1k,72.510,27.490,90.510,9.490,26.77,224,1.000,bicubic,-10.476,-5.740,+10
convnext_tiny.fb_in22k_ft_in1k_384,72.470,27.530,91.540,8.460,28.59,384,1.000,bicubic,-11.618,-5.604,-114
regnety_080_tv.tv2_in1k,72.460,27.540,90.540,9.460,39.38,224,0.965,bicubic,-10.134,-5.708,+47
xcit_tiny_24_p8_224.fb_dist_in1k,72.440,27.560,90.920,9.080,12.11,224,1.000,bicubic,-10.126,-5.138,+55
sequencer2d_m.in1k,72.430,27.570,90.710,9.290,38.31,224,0.875,bicubic,-10.382,-5.564,+14
resnet101d.ra2_in1k,72.430,27.570,90.650,9.350,44.57,320,1.000,bicubic,-10.590,-5.802,0
regnetx_320.tv2_in1k,72.420,27.580,90.300,9.700,107.81,224,0.965,bicubic,-10.390,-5.908,+14
maxxvit_rmlp_nano_rw_256.sw_in1k,72.370,27.630,90.750,9.250,16.78,256,0.950,bicubic,-10.672,-5.600,-4
nest_small_jx.goog_in1k,72.350,27.650,90.690,9.310,38.35,224,0.875,bicubic,-10.774,-5.630,-17
maxvit_rmlp_nano_rw_256.sw_in1k,72.350,27.650,90.430,9.570,15.50,256,0.950,bicubic,-10.604,-5.836,+2
efficientvit_b2.r288_in1k,72.330,27.670,90.930,9.070,24.33,288,1.000,bicubic,-10.770,-5.374,-13
resnext101_32x8d.tv2_in1k,72.330,27.670,90.270,9.730,88.79,224,0.965,bilinear,-10.502,-5.962,+6
tf_efficientnet_b4.aa_in1k,72.300,27.700,90.590,9.410,19.34,380,0.922,bicubic,-10.718,-5.710,-7
tf_efficientnet_b2.ns_jft_in1k,72.280,27.720,91.110,8.890,9.11,260,0.890,bicubic,-10.098,-5.144,+65
hrnet_w18_ssld.paddle_in1k,72.270,27.730,90.810,9.190,21.30,288,1.000,bilinear,-9.778,-5.440,+108
maxvit_nano_rw_256.sw_in1k,72.270,27.730,90.570,9.430,15.45,256,0.950,bicubic,-10.658,-5.650,-3
tresnet_m.miil_in21k_ft_in1k,72.270,27.730,90.230,9.770,31.39,224,0.875,bilinear,-10.800,-5.880,-16
swin_base_patch4_window7_224.ms_in1k,72.260,27.740,90.810,9.190,87.77,224,0.900,bicubic,-11.346,-5.642,-77
resnext101_64x4d.tv_in1k,72.260,27.740,90.550,9.450,83.46,224,0.875,bilinear,-10.732,-5.694,-10
resnetv2_50x1_bit.goog_distilled_in1k,72.240,27.760,90.980,9.020,25.55,224,0.875,bicubic,-10.584,-5.538,-2
nasnetalarge.tf_in1k,72.240,27.760,90.460,9.540,88.75,331,0.911,bicubic,-10.386,-5.582,+24
efficientnetv2_rw_t.ra2_in1k,72.230,27.770,90.420,9.580,13.65,288,1.000,bicubic,-10.120,-5.772,+62
crossvit_18_240.in1k,72.230,27.770,90.270,9.730,43.27,240,0.875,bicubic,-10.170,-5.790,+54
regnetz_c16_evos.ch_in1k,72.220,27.780,91.210,8.790,13.49,320,0.950,bicubic,-10.416,-5.264,+18
cait_xxs36_384.fb_dist_in1k,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.014,-5.304,+80
twins_pcpvt_base.in1k,72.190,27.810,90.500,9.500,43.83,224,0.900,bicubic,-10.524,-5.846,+3
crossvit_18_dagger_240.in1k,72.190,27.810,90.090,9.910,44.27,240,0.875,bicubic,-10.328,-5.978,+38
regnetz_c16.ra3_in1k,72.180,27.820,91.120,8.880,13.46,320,1.000,bicubic,-10.452,-5.198,+15
regnety_320.seer_ft_in1k,72.170,27.830,90.870,9.130,145.05,384,1.000,bicubic,-11.158,-5.838,-55
maxxvitv2_nano_rw_256.sw_in1k,72.170,27.830,90.470,9.530,23.70,256,0.950,bicubic,-10.940,-5.854,-33
repvit_m1_5.dist_450e_in1k,72.160,27.840,90.370,9.630,14.64,224,0.950,bicubic,-10.352,-5.742,+34
resnet101.a1h_in1k,72.100,27.900,90.820,9.180,44.55,288,1.000,bicubic,-10.678,-5.490,-9
regnety_160.tv2_in1k,72.100,27.900,90.230,9.770,83.59,224,0.965,bicubic,-10.546,-5.984,+8
inception_next_tiny.sail_in1k,72.090,27.910,90.100,9.900,28.06,224,0.875,bicubic,-10.388,-5.922,+36
xcit_tiny_24_p16_384.fb_dist_in1k,72.070,27.930,90.580,9.420,12.12,384,1.000,bicubic,-10.500,-5.696,+23
tiny_vit_11m_224.dist_in22k_ft_in1k,72.050,27.950,91.450,8.550,11.00,224,0.950,bicubic,-11.178,-5.180,-56
cs3edgenet_x.c2_in1k,72.050,27.950,90.370,9.630,47.82,288,1.000,bicubic,-10.658,-6.000,-5
vit_relpos_medium_patch16_cls_224.sw_in1k,72.020,27.980,90.300,9.700,38.76,224,0.900,bicubic,-10.552,-5.768,+19
davit_tiny.msft_in1k,72.010,27.990,90.070,9.930,28.36,224,0.950,bicubic,-10.686,-6.204,-5
convnext_tiny_hnf.a2h_in1k,72.000,28.000,89.770,10.230,28.59,288,1.000,bicubic,-10.584,-6.238,+13
mobilevitv2_200.cvnets_in22k_ft_in1k_384,71.990,28.010,90.650,9.350,18.45,384,1.000,bicubic,-11.410,-5.932,-77
efficientformer_l3.snap_dist_in1k,71.980,28.020,90.270,9.730,31.41,224,0.950,bicubic,-10.568,-5.980,+18
convnext_tiny.fb_in1k,71.980,28.020,90.220,9.780,28.59,288,1.000,bicubic,-10.718,-6.412,-9
vit_relpos_base_patch16_clsgap_224.sw_in1k,71.970,28.030,90.250,9.750,86.43,224,0.900,bicubic,-10.790,-5.922,-18
sequencer2d_s.in1k,71.940,28.060,90.500,9.500,27.65,224,0.875,bicubic,-10.400,-5.528,+41
resnet152.a2_in1k,71.940,28.060,89.420,10.580,60.19,288,1.000,bicubic,-10.668,-6.708,+2
dm_nfnet_f0.dm_in1k,71.910,28.090,90.760,9.240,71.49,256,0.900,bicubic,-11.576,-5.808,-92
rexnetr_200.sw_in12k_ft_in1k,71.900,28.100,91.260,8.740,16.52,288,1.000,bicubic,-11.238,-5.376,-60
convnext_nano.in12k_ft_in1k,71.900,28.100,91.000,9.000,15.59,288,1.000,bicubic,-10.962,-5.556,-31
swinv2_cr_small_224.sw_in1k,71.900,28.100,90.270,9.730,49.70,224,0.900,bicubic,-11.236,-5.838,-60
convnextv2_nano.fcmae_ft_in22k_in1k,71.890,28.110,90.890,9.110,15.62,288,1.000,bicubic,-10.774,-5.630,-13
fastvit_sa24.apple_in1k,71.890,28.110,90.630,9.370,21.55,256,0.900,bicubic,-10.788,-5.642,-16
repvit_m1_5.dist_300e_in1k,71.850,28.150,90.330,9.670,14.64,224,0.950,bicubic,-10.526,-5.700,+27
resnet101.a1_in1k,71.850,28.150,89.150,10.850,44.55,288,1.000,bicubic,-10.472,-6.482,+36
eca_nfnet_l0.ra2_in1k,71.840,28.160,91.110,8.890,24.14,288,1.000,bicubic,-10.738,-5.382,0
mobilevitv2_175.cvnets_in22k_ft_in1k_384,71.840,28.160,90.770,9.230,14.25,384,1.000,bicubic,-11.098,-5.656,-44
vit_relpos_base_patch16_224.sw_in1k,71.820,28.180,90.250,9.750,86.43,224,0.900,bicubic,-10.676,-5.888,+10
regnetx_160.tv2_in1k,71.800,28.200,90.050,9.950,54.28,224,0.965,bicubic,-10.766,-6.122,+2
seresnext50_32x4d.racm_in1k,71.800,28.200,90.030,9.970,27.56,288,0.950,bicubic,-10.396,-6.118,+48
swin_small_patch4_window7_224.ms_in1k,71.770,28.230,90.260,9.740,49.61,224,0.900,bicubic,-11.438,-6.056,-77
coat_small.in1k,71.750,28.250,90.410,9.590,21.69,224,0.900,bicubic,-10.612,-5.798,+20
flexivit_small.1200ep_in1k,71.750,28.250,90.270,9.730,22.06,240,0.950,bicubic,-10.776,-5.856,0
mvitv2_tiny.fb_in1k,71.740,28.260,90.310,9.690,24.17,224,0.900,bicubic,-10.670,-5.842,+11
flexivit_small.300ep_in1k,71.730,28.270,89.980,10.020,22.06,240,0.950,bicubic,-10.448,-6.058,+45
efficientvit_b2.r256_in1k,71.720,28.280,90.350,9.650,24.33,256,1.000,bicubic,-10.970,-5.744,-30
flexivit_small.600ep_in1k,71.720,28.280,90.150,9.850,22.06,240,0.950,bicubic,-10.642,-5.934,+16
pit_b_224.in1k,71.720,28.280,89.250,10.750,73.76,224,0.900,bicubic,-10.718,-6.464,+6
xcit_large_24_p16_224.fb_in1k,71.710,28.290,89.170,10.830,189.10,224,1.000,bicubic,-11.192,-6.714,-54
pvt_v2_b2_li.in1k,71.700,28.300,90.010,9.990,22.55,224,0.900,bicubic,-10.494,-6.082,+39
resnet50.fb_swsl_ig1b_ft_in1k,71.690,28.310,90.500,9.500,25.56,224,0.875,bilinear,-9.482,-5.486,+136
ecaresnet101d_pruned.miil_in1k,71.680,28.320,90.430,9.570,24.88,288,0.950,bicubic,-10.318,-5.730,+56
coatnet_bn_0_rw_224.sw_in1k,71.670,28.330,90.380,9.620,27.44,224,0.950,bicubic,-10.730,-5.806,+3
gcvit_xtiny.in1k,71.670,28.330,90.250,9.750,19.98,224,0.875,bicubic,-10.284,-5.716,+60
tresnet_xl.miil_in1k,71.650,28.350,89.630,10.370,78.44,224,0.875,bilinear,-10.424,-6.298,+46
resnet61q.ra2_in1k,71.630,28.370,90.280,9.720,36.85,288,1.000,bicubic,-10.894,-5.850,-12
tresnet_l.miil_in1k_448,71.630,28.370,90.030,9.970,55.99,448,0.875,bilinear,-10.646,-5.948,+22
poolformerv2_m48.sail_in1k,71.620,28.380,89.800,10.200,73.35,224,1.000,bicubic,-10.998,-6.272,-32
convnextv2_nano.fcmae_ft_in22k_in1k_384,71.590,28.410,90.760,9.240,15.62,384,1.000,bicubic,-11.784,-5.984,-109
xcit_tiny_12_p8_384.fb_dist_in1k,71.590,28.410,90.700,9.300,6.71,384,1.000,bicubic,-10.798,-5.520,-2
swinv2_tiny_window16_256.ms_in1k,71.590,28.410,90.330,9.670,28.35,256,0.900,bicubic,-11.214,-5.906,-57
convit_base.fb_in1k,71.580,28.420,90.120,9.880,86.54,224,0.875,bicubic,-10.710,-5.816,+13
coatnet_0_rw_224.sw_in1k,71.570,28.430,89.400,10.600,27.44,224,0.950,bicubic,-10.820,-6.436,-5
resnetv2_50d_evos.ah_in1k,71.560,28.440,90.090,9.910,25.59,288,1.000,bicubic,-10.442,-5.810,+43
fbnetv3_g.ra2_in1k,71.530,28.470,90.370,9.630,16.62,288,0.950,bilinear,-10.510,-5.690,+40
crossvit_15_dagger_240.in1k,71.520,28.480,89.850,10.150,28.21,240,0.875,bicubic,-10.810,-6.106,+4
poolformer_m48.sail_in1k,71.520,28.480,89.770,10.230,73.47,224,0.950,bicubic,-10.962,-6.196,-17
resnet152.tv2_in1k,71.510,28.490,89.970,10.030,60.19,224,0.965,bilinear,-10.776,-6.034,+8
resnetaa50d.sw_in12k_ft_in1k,71.500,28.500,90.320,9.680,25.58,288,1.000,bicubic,-11.100,-6.178,-40
mobilevitv2_150.cvnets_in22k_ft_in1k_384,71.490,28.510,90.420,9.580,10.59,384,1.000,bicubic,-11.096,-5.894,-37
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,71.480,28.520,90.470,9.530,88.79,224,0.875,bilinear,-10.126,-5.570,+68
wide_resnet50_2.racm_in1k,71.480,28.520,90.220,9.780,68.88,288,0.950,bicubic,-10.800,-5.844,+6
efficientnet_b3.ra2_in1k,71.470,28.530,90.060,9.940,12.23,320,1.000,bicubic,-10.776,-6.058,+7
pvt_v2_b2.in1k,71.450,28.550,90.060,9.940,25.36,224,0.900,bicubic,-10.634,-5.896,+26
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,71.420,28.580,90.530,9.470,194.03,224,0.875,bilinear,-10.418,-5.562,+47
resnet51q.ra2_in1k,71.410,28.590,90.180,9.820,35.70,288,1.000,bilinear,-10.950,-6.006,-12
efficientvit_b2.r224_in1k,71.380,28.620,89.710,10.290,24.33,224,0.950,bicubic,-10.768,-5.996,+17
wide_resnet101_2.tv2_in1k,71.360,28.640,89.790,10.210,126.89,224,0.965,bilinear,-11.142,-6.226,-31
xcit_tiny_24_p8_224.fb_in1k,71.350,28.650,90.230,9.770,12.11,224,1.000,bicubic,-10.542,-5.740,+37
vit_relpos_medium_patch16_224.sw_in1k,71.350,28.650,89.960,10.040,38.75,224,0.900,bicubic,-11.112,-6.122,-28
resnet152.a1_in1k,71.350,28.650,89.310,10.690,60.19,288,1.000,bicubic,-11.382,-6.410,-70
tf_efficientnetv2_b3.in21k_ft_in1k,71.340,28.660,90.760,9.240,14.36,300,0.900,bicubic,-11.330,-5.866,-64
vit_base_patch16_224.orig_in21k_ft_in1k,71.320,28.680,90.480,9.520,86.57,224,0.900,bicubic,-10.470,-5.646,+43
tf_efficientnet_b4.in1k,71.320,28.680,90.110,9.890,19.34,380,0.922,bicubic,-11.288,-5.642,-56
mixer_b16_224.miil_in21k_ft_in1k,71.300,28.700,89.650,10.350,59.88,224,0.875,bilinear,-11.006,-6.070,-11
pit_s_distilled_224.in1k,71.260,28.740,89.640,10.360,24.04,224,0.900,bicubic,-10.554,-6.090,+39
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,71.250,28.750,90.480,9.520,236.34,224,0.875,bicubic,-11.626,-6.102,-90
ecaresnet50t.ra2_in1k,71.240,28.760,90.450,9.550,25.57,320,0.950,bicubic,-11.112,-5.690,-23
convmixer_1536_20.in1k,71.230,28.770,89.450,10.550,51.63,224,0.960,bicubic,-10.132,-6.164,+81
deit_base_patch16_224.fb_in1k,71.220,28.780,89.190,10.810,86.57,224,0.900,bicubic,-10.772,-6.546,+19
xcit_small_12_p16_224.fb_in1k,71.200,28.800,89.750,10.250,26.25,224,1.000,bicubic,-10.770,-6.062,+21
resnext50_32x4d.a1h_in1k,71.190,28.810,89.690,10.310,25.03,288,1.000,bicubic,-10.824,-6.244,+13
ecaresnet50t.a1_in1k,71.190,28.810,89.580,10.420,25.57,288,1.000,bicubic,-10.938,-6.062,+5
vit_relpos_medium_patch16_rpn_224.sw_in1k,71.170,28.830,90.080,9.920,38.73,224,0.900,bicubic,-11.140,-5.892,-22
crossvit_base_240.in1k,71.170,28.830,89.830,10.170,105.03,240,0.875,bicubic,-11.044,-6.004,-9
vit_base_patch32_clip_224.laion2b_ft_in1k,71.160,28.840,90.210,9.790,88.22,224,0.900,bicubic,-11.422,-5.990,-61
swin_s3_tiny_224.ms_in1k,71.150,28.850,89.710,10.290,28.33,224,0.900,bicubic,-10.994,-6.244,-2
cs3sedarknet_l.c2ns_in1k,71.110,28.890,90.350,9.650,21.91,288,0.950,bicubic,-10.674,-5.614,+30
efficientformerv2_s2.snap_dist_in1k,71.110,28.890,90.140,9.860,12.71,224,0.950,bicubic,-11.056,-5.770,-7
halo2botnet50ts_256.a1h_in1k,71.110,28.890,89.600,10.400,22.64,256,0.950,bicubic,-10.950,-6.034,+2
cs3darknet_x.c2ns_in1k,71.090,28.910,90.150,9.850,35.05,288,1.000,bicubic,-11.132,-6.080,-18
mobilevitv2_200.cvnets_in22k_ft_in1k,71.090,28.910,89.700,10.300,18.45,256,0.888,bicubic,-11.242,-6.242,-33
ecaresnet50d.miil_in1k,71.080,28.920,90.240,9.760,25.58,288,0.950,bicubic,-10.570,-5.642,+32
focalnet_tiny_lrf.ms_in1k,71.060,28.940,89.570,10.430,28.65,224,0.900,bicubic,-11.094,-6.378,-11
convnextv2_nano.fcmae_ft_in1k,71.050,28.950,90.100,9.900,15.62,288,1.000,bicubic,-11.436,-6.126,-56
focalnet_tiny_srf.ms_in1k,71.050,28.950,89.600,10.400,28.43,224,0.900,bicubic,-11.088,-6.368,-10
xcit_tiny_12_p8_224.fb_dist_in1k,71.040,28.960,89.890,10.110,6.71,224,1.000,bicubic,-10.172,-5.712,+77
xcit_small_24_p16_224.fb_in1k,71.040,28.960,89.680,10.320,47.67,224,1.000,bicubic,-11.536,-6.332,-70
tresnet_m.miil_in1k_448,71.020,28.980,88.680,11.320,31.39,448,0.875,bilinear,-10.690,-6.894,+21
resnetv2_101x1_bit.goog_in21k_ft_in1k,71.010,28.990,91.080,8.920,44.54,448,1.000,bilinear,-11.332,-5.440,-43
visformer_small.in1k,71.010,28.990,89.440,10.560,40.22,224,0.900,bicubic,-11.096,-6.438,-13
repvit_m3.dist_in1k,70.990,29.010,89.630,10.370,10.68,224,0.950,bicubic,-10.512,-5.938,+39
xcit_medium_24_p16_224.fb_in1k,70.990,29.010,89.530,10.470,84.40,224,1.000,bicubic,-11.650,-6.452,-93
resnet101.a2_in1k,70.990,29.010,89.160,10.840,44.55,288,1.000,bicubic,-11.246,-6.570,-32
lamhalobotnet50ts_256.a1h_in1k,70.990,29.010,89.040,10.960,22.57,256,0.950,bicubic,-10.562,-6.452,+32
edgenext_small.usi_in1k,70.980,29.020,89.880,10.120,5.59,320,1.000,bicubic,-10.584,-5.832,+27
resnetv2_50d_gn.ah_in1k,70.960,29.040,89.830,10.170,25.57,288,1.000,bicubic,-10.998,-6.098,-5
tnt_s_patch16_224,70.960,29.040,89.610,10.390,23.76,224,0.900,bicubic,-10.576,-6.080,+27
convnext_nano.d1h_in1k,70.960,29.040,89.430,10.570,15.59,288,1.000,bicubic,-10.522,-6.228,+38
tiny_vit_11m_224.in1k,70.940,29.060,89.840,10.160,11.00,224,0.950,bicubic,-10.552,-6.022,+31
resnest50d_4s2x40d.in1k,70.940,29.060,89.720,10.280,30.42,224,0.875,bicubic,-10.180,-5.840,+72
coatnet_nano_rw_224.sw_in1k,70.940,29.060,89.700,10.300,15.14,224,0.900,bicubic,-10.756,-5.946,+11
vit_srelpos_medium_patch16_224.sw_in1k,70.920,29.080,89.940,10.060,38.74,224,0.900,bicubic,-11.320,-6.002,-43
tf_efficientnet_b3.ap_in1k,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.900,-6.196,+1
vit_small_patch16_224.augreg_in21k_ft_in1k,70.910,29.090,90.170,9.830,22.05,224,0.900,bicubic,-10.476,-5.966,+42
coatnext_nano_rw_224.sw_in1k,70.890,29.110,90.250,9.750,14.70,224,0.900,bicubic,-11.052,-5.666,-12
coatnet_rmlp_nano_rw_224.sw_in1k,70.890,29.110,89.920,10.080,15.15,224,0.900,bicubic,-11.160,-5.958,-23
vit_base_patch16_rpn_224.sw_in1k,70.890,29.110,89.770,10.230,86.54,224,0.900,bicubic,-11.312,-6.226,-41
vit_large_patch32_384.orig_in21k_ft_in1k,70.870,29.130,90.570,9.430,306.63,384,1.000,bicubic,-10.640,-5.520,+22
nest_tiny_jx.goog_in1k,70.860,29.140,89.940,10.060,17.06,224,0.875,bicubic,-10.566,-5.678,+32
resnetrs101.tf_in1k,70.860,29.140,89.830,10.170,63.62,288,0.940,bicubic,-11.424,-6.184,-54
rexnet_200.nav_in1k,70.860,29.140,89.700,10.300,16.37,224,0.875,bicubic,-10.776,-5.966,+5
poolformerv2_m36.sail_in1k,70.850,29.150,89.330,10.670,56.08,224,1.000,bicubic,-11.366,-6.594,-49
tf_efficientnet_b1.ns_jft_in1k,70.840,29.160,90.120,9.880,7.79,240,0.882,bicubic,-10.548,-5.618,+31
regnety_032.tv2_in1k,70.840,29.160,89.850,10.150,19.44,224,0.965,bicubic,-10.916,-5.994,-5
wide_resnet50_2.tv2_in1k,70.840,29.160,89.270,10.730,68.88,224,0.965,bilinear,-10.766,-6.490,+4
tresnet_l.miil_in1k,70.830,29.170,89.600,10.400,55.99,224,0.875,bilinear,-10.650,-6.024,+16
poolformer_m36.sail_in1k,70.830,29.170,89.510,10.490,56.17,224,0.950,bicubic,-11.272,-6.188,-39
tf_efficientnetv2_b3.in1k,70.830,29.170,89.510,10.490,14.36,300,0.904,bicubic,-11.142,-6.292,-28
fastvit_sa12.apple_dist_in1k,70.830,29.170,89.240,10.760,11.58,256,0.900,bicubic,-11.024,-6.470,-16
levit_384.fb_dist_in1k,70.810,29.190,89.320,10.680,39.13,224,0.900,bicubic,-11.786,-6.698,-111
levit_conv_384.fb_dist_in1k,70.800,29.200,89.320,10.680,39.13,224,0.900,bicubic,-11.790,-6.696,-110
coat_lite_small.in1k,70.780,29.220,89.580,10.420,19.84,224,0.900,bicubic,-11.532,-6.270,-71
ecaresnetlight.miil_in1k,70.750,29.250,89.880,10.120,30.16,288,0.950,bicubic,-10.658,-5.936,+19
deit3_small_patch16_224.fb_in1k,70.750,29.250,89.450,10.550,22.06,224,0.900,bicubic,-10.620,-6.006,+25
regnetx_080.tv2_in1k,70.730,29.270,89.330,10.670,39.57,224,0.965,bicubic,-10.810,-6.212,-1
swinv2_cr_tiny_ns_224.sw_in1k,70.700,29.300,89.380,10.620,28.33,224,0.900,bicubic,-11.102,-6.438,-21
seresnet50.ra2_in1k,70.690,29.310,89.870,10.130,28.09,288,0.950,bicubic,-10.594,-5.782,+28
vit_base_patch32_clip_224.openai_ft_in1k,70.690,29.310,89.830,10.170,88.22,224,0.900,bicubic,-11.240,-6.136,-33
resnet50d.ra2_in1k,70.690,29.310,89.310,10.690,25.58,288,0.950,bicubic,-10.666,-6.428,+23
vit_relpos_small_patch16_224.sw_in1k,70.680,29.320,90.000,10.000,21.98,224,0.900,bicubic,-10.782,-5.820,+7
mobilevitv2_175.cvnets_in22k_ft_in1k,70.670,29.330,89.700,10.300,14.25,256,0.888,bicubic,-11.268,-6.090,-36
resnet101.tv2_in1k,70.630,29.370,89.390,10.610,44.55,224,0.965,bilinear,-11.258,-6.378,-34
tf_efficientnet_b3.aa_in1k,70.620,29.380,89.440,10.560,12.23,300,0.904,bicubic,-11.020,-6.282,-20
resnet50_gn.a1h_in1k,70.620,29.380,89.390,10.610,25.56,288,0.950,bicubic,-10.596,-5.994,+27
crossvit_small_240.in1k,70.620,29.380,89.360,10.640,26.86,240,0.875,bicubic,-10.398,-6.096,+51
cait_xxs24_384.fb_dist_in1k,70.600,29.400,89.730,10.270,12.03,384,1.000,bicubic,-10.372,-5.910,+51
senet154.gluon_in1k,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.626,-6.438,+22
convit_small.fb_in1k,70.580,29.420,89.590,10.410,27.78,224,0.875,bicubic,-10.840,-6.154,+4
convnext_nano_ols.d1h_in1k,70.570,29.430,89.100,10.900,15.65,288,1.000,bicubic,-11.030,-6.536,-19
twins_pcpvt_small.in1k,70.570,29.430,89.070,10.930,24.11,224,0.900,bicubic,-10.522,-6.578,+37
regnetz_b16.ra3_in1k,70.550,29.450,89.400,10.600,9.72,288,1.000,bicubic,-10.178,-6.118,+75
resnet50.c2_in1k,70.540,29.460,89.170,10.830,25.56,288,1.000,bicubic,-10.330,-6.364,+60
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,70.530,29.470,89.770,10.230,44.18,224,0.875,bilinear,-10.394,-5.964,+50
resnetv2_50.a1h_in1k,70.530,29.470,89.110,10.890,25.55,288,1.000,bicubic,-10.868,-6.616,+1
vit_small_r26_s32_224.augreg_in21k_ft_in1k,70.520,29.480,90.110,9.890,36.43,224,0.900,bicubic,-11.344,-5.912,-46
swinv2_tiny_window8_256.ms_in1k,70.510,29.490,89.500,10.500,28.35,256,0.900,bicubic,-11.310,-6.494,-44
deit_small_distilled_patch16_224.fb_in1k,70.500,29.500,89.460,10.540,22.44,224,0.900,bicubic,-10.716,-6.164,+15
legacy_senet154.in1k,70.500,29.500,88.990,11.010,115.09,224,0.875,bilinear,-10.812,-6.570,+6
repvit_m1_1.dist_450e_in1k,70.490,29.510,89.140,10.860,8.80,224,0.950,bicubic,-10.822,-6.396,+3
halonet50ts.a1h_in1k,70.480,29.520,89.340,10.660,22.73,256,0.940,bicubic,-11.182,-6.270,-38
tf_efficientnet_lite4.in1k,70.450,29.550,89.130,10.870,13.01,380,0.920,bilinear,-11.080,-6.534,-24
resnetaa50.a1h_in1k,70.440,29.560,89.990,10.010,25.56,288,1.000,bicubic,-11.174,-5.812,-36
crossvit_15_240.in1k,70.440,29.560,89.530,10.470,27.53,240,0.875,bicubic,-11.096,-6.206,-26
poolformerv2_s36.sail_in1k,70.430,29.570,89.630,10.370,30.79,224,1.000,bicubic,-11.136,-6.060,-34
twins_svt_small.in1k,70.430,29.570,89.360,10.640,24.06,224,0.900,bicubic,-11.246,-6.298,-45
ecaresnet50t.a2_in1k,70.430,29.570,89.020,10.980,25.57,288,1.000,bicubic,-11.228,-6.530,-41
resnest50d_1s4x24d.in1k,70.420,29.580,89.220,10.780,25.68,224,0.875,bicubic,-10.568,-6.106,+28
resnest50d.in1k,70.420,29.580,88.760,11.240,27.48,224,0.875,bilinear,-10.540,-6.622,+33
seresnext101_64x4d.gluon_in1k,70.410,29.590,89.360,10.640,88.23,224,0.875,bicubic,-10.484,-5.936,+37
gernet_l.idstcv_in1k,70.410,29.590,88.980,11.020,31.08,256,0.875,bilinear,-10.944,-6.550,-8
gcresnext50ts.ch_in1k,70.400,29.600,89.420,10.580,15.67,288,1.000,bicubic,-10.830,-6.122,-3
cs3darknet_l.c2ns_in1k,70.350,29.650,89.750,10.250,21.16,288,0.950,bicubic,-10.546,-5.912,+34
resnet152s.gluon_in1k,70.310,29.690,88.870,11.130,60.32,224,0.875,bicubic,-10.698,-6.546,+22
vit_srelpos_small_patch16_224.sw_in1k,70.290,29.710,89.540,10.460,21.97,224,0.900,bicubic,-10.802,-6.030,+14
repvgg_b3.rvgg_in1k,70.230,29.770,88.740,11.260,123.09,224,0.875,bilinear,-10.276,-6.514,+69
coat_mini.in1k,70.200,29.800,89.460,10.540,10.34,224,0.900,bicubic,-11.070,-5.922,-9
xception41p.ra3_in1k,70.200,29.800,89.100,10.900,26.91,299,0.940,bicubic,-11.772,-6.684,-78
sebotnet33ts_256.a1h_in1k,70.160,29.840,88.820,11.180,13.70,256,0.940,bicubic,-11.008,-6.348,-1
efficientnet_el.ra_in1k,70.140,29.860,89.310,10.690,10.59,300,0.904,bicubic,-11.172,-6.180,-16
inception_resnet_v2.tf_in1k,70.130,29.870,88.690,11.310,55.84,299,0.897,bicubic,-10.328,-6.500,+71
resnet50.d_in1k,70.120,29.880,88.740,11.260,25.56,288,1.000,bicubic,-10.852,-6.690,+17
resnet50d.a1_in1k,70.120,29.880,88.350,11.650,25.58,288,1.000,bicubic,-11.330,-6.868,-33
poolformer_s36.sail_in1k,70.100,29.900,89.130,10.870,30.86,224,0.900,bicubic,-11.330,-6.314,-34
resmlp_36_224.fb_distilled_in1k,70.100,29.900,89.090,10.910,44.69,224,0.875,bicubic,-11.048,-6.388,-5
haloregnetz_b.ra3_in1k,70.100,29.900,88.900,11.100,11.68,224,0.940,bicubic,-10.946,-6.300,+9
ecaresnet50d_pruned.miil_in1k,70.090,29.910,89.510,10.490,19.94,288,0.950,bicubic,-10.700,-6.060,+34
resnet50.c1_in1k,70.070,29.930,89.000,11.000,25.56,288,1.000,bicubic,-10.842,-6.552,+18
gcresnet50t.ra2_in1k,70.050,29.950,89.520,10.480,25.90,288,1.000,bicubic,-11.406,-6.198,-40
sehalonet33ts.ra2_in1k,70.050,29.950,88.740,11.260,13.69,256,0.940,bicubic,-10.908,-6.532,+12
seresnext101_32x4d.gluon_in1k,70.020,29.980,88.940,11.060,48.96,224,0.875,bicubic,-10.872,-6.356,+17
regnety_320.pycls_in1k,70.020,29.980,88.900,11.100,145.05,224,0.875,bicubic,-10.790,-6.338,+28
seresnet50.a2_in1k,70.000,30.000,88.710,11.290,28.09,288,1.000,bicubic,-11.106,-6.512,-8
levit_256.fb_dist_in1k,69.970,30.030,89.240,10.760,18.89,224,0.900,bicubic,-11.554,-6.254,-55
levit_conv_256.fb_dist_in1k,69.960,30.040,89.250,10.750,18.89,224,0.900,bicubic,-11.562,-6.240,-55
resnet152d.gluon_in1k,69.940,30.060,88.500,11.500,60.21,224,0.875,bicubic,-10.536,-6.702,+53
fastvit_s12.apple_dist_in1k,69.910,30.090,88.930,11.070,9.47,256,0.900,bicubic,-11.160,-6.354,-5
maxvit_rmlp_pico_rw_256.sw_in1k,69.890,30.110,89.250,10.750,7.52,256,0.950,bicubic,-10.624,-5.964,+45
pit_s_224.in1k,69.880,30.120,88.940,11.060,23.46,224,0.900,bicubic,-11.206,-6.390,-8
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,69.850,30.150,90.410,9.590,28.29,224,0.900,bicubic,-11.118,-5.604,0
resnext50_32x4d.ra_in1k,69.830,30.170,88.220,11.780,25.03,288,0.950,bicubic,-10.868,-7.172,+29
regnety_016.tv2_in1k,69.810,30.190,89.360,10.640,11.20,224,0.965,bicubic,-10.856,-5.970,+31
seresnet50.a1_in1k,69.810,30.190,88.550,11.450,28.09,288,1.000,bicubic,-11.292,-6.778,-16
mobilevitv2_150.cvnets_in22k_ft_in1k,69.800,30.200,89.180,10.820,10.59,256,0.888,bicubic,-11.688,-6.488,-60
resnet50.a1_in1k,69.780,30.220,88.350,11.650,25.56,288,1.000,bicubic,-11.434,-6.752,-31
mobilevitv2_200.cvnets_in1k,69.750,30.250,88.620,11.380,18.45,256,0.888,bicubic,-11.384,-6.742,-24
resnext50_32x4d.a2_in1k,69.750,30.250,88.200,11.800,25.03,288,1.000,bicubic,-11.554,-6.896,-41
resnet50.tv2_in1k,69.740,30.260,88.600,11.400,25.56,224,0.965,bilinear,-11.108,-6.834,+8
ese_vovnet39b.ra_in1k,69.730,30.270,89.550,10.450,24.57,288,0.950,bicubic,-10.620,-5.816,+53
tiny_vit_5m_224.dist_in22k_ft_in1k,69.730,30.270,89.440,10.560,5.39,224,0.950,bicubic,-11.146,-6.224,0
lambda_resnet50ts.a1h_in1k,69.730,30.270,88.830,11.170,21.54,256,0.950,bicubic,-11.428,-6.268,-30
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,69.710,30.290,89.420,10.580,25.03,224,0.875,bilinear,-10.624,-5.980,+53
xcit_tiny_24_p16_224.fb_dist_in1k,69.710,30.290,88.720,11.280,12.12,224,1.000,bicubic,-10.744,-6.498,+42
fastvit_sa12.apple_in1k,69.700,30.300,88.950,11.050,11.58,256,0.900,bicubic,-11.144,-6.390,+3
xcit_tiny_12_p16_384.fb_dist_in1k,69.690,30.310,89.020,10.980,6.72,384,1.000,bicubic,-11.248,-6.394,-12
resmlp_24_224.fb_distilled_in1k,69.660,30.340,89.070,10.930,30.02,224,0.875,bicubic,-11.096,-6.154,+8
resnext101_64x4d.gluon_in1k,69.660,30.340,88.260,11.740,83.46,224,0.875,bicubic,-10.940,-6.732,+22
tresnet_m.miil_in1k,69.650,30.350,88.000,12.000,31.39,224,0.875,bilinear,-11.148,-6.856,+2
resnext50d_32x4d.bt_in1k,69.640,30.360,89.250,10.750,25.05,288,0.950,bicubic,-11.024,-6.170,+15
regnetx_032.tv2_in1k,69.620,30.380,89.360,10.640,15.30,224,0.965,bicubic,-11.306,-5.918,-16
efficientnet_b3_pruned.in1k,69.580,30.420,88.960,11.040,9.86,300,0.904,bicubic,-11.272,-6.284,-6
fastvit_t12.apple_dist_in1k,69.580,30.420,88.410,11.590,7.55,256,0.900,bicubic,-10.772,-6.632,+39
convnextv2_pico.fcmae_ft_in1k,69.570,30.430,89.230,10.770,9.07,288,0.950,bicubic,-11.516,-6.250,-33
eva02_tiny_patch14_336.mim_in22k_ft_in1k,69.560,30.440,89.320,10.680,5.76,336,1.000,bicubic,-11.070,-6.206,+12
repvit_m1_1.dist_300e_in1k,69.560,30.440,88.820,11.180,8.80,224,0.950,bicubic,-11.266,-6.350,-7
nf_resnet50.ra2_in1k,69.550,30.450,88.730,11.270,25.56,288,0.940,bicubic,-11.090,-6.604,+9
gernet_m.idstcv_in1k,69.540,30.460,88.690,11.310,21.14,224,0.875,bilinear,-11.196,-6.500,-1
inception_resnet_v2.tf_ens_adv_in1k,69.540,30.460,88.490,11.510,55.84,299,0.897,bicubic,-10.438,-6.458,+61
repvgg_b3g4.rvgg_in1k,69.530,30.470,88.450,11.550,83.83,224,0.875,bilinear,-10.686,-6.642,+50
gcresnet33ts.ra2_in1k,69.510,30.490,89.110,10.890,19.88,288,1.000,bicubic,-11.090,-6.212,+7
efficientnet_el_pruned.in1k,69.510,30.490,88.940,11.060,10.59,300,0.904,bicubic,-10.788,-6.282,+40
efficientnet_b2.ra_in1k,69.490,30.510,88.690,11.310,9.11,288,1.000,bicubic,-11.120,-6.624,+5
resnext50_32x4d.a1_in1k,69.490,30.510,88.340,11.660,25.03,288,1.000,bicubic,-11.976,-6.834,-86
swin_tiny_patch4_window7_224.ms_in1k,69.470,30.530,89.060,10.940,28.29,224,0.900,bicubic,-11.906,-6.484,-77
regnetx_320.pycls_in1k,69.470,30.530,88.270,11.730,107.81,224,0.875,bicubic,-10.776,-6.752,+41
vit_base_patch32_224.augreg_in21k_ft_in1k,69.450,30.550,89.440,10.560,88.22,224,0.900,bicubic,-11.266,-6.126,-9
res2net101d.in1k,69.450,30.550,88.710,11.290,45.23,224,0.875,bilinear,-11.768,-6.640,-65
gcvit_xxtiny.in1k,69.440,30.560,88.840,11.160,12.00,224,0.875,bicubic,-10.286,-6.240,+72
cspresnext50.ra_in1k,69.430,30.570,88.600,11.400,20.57,256,0.887,bilinear,-11.124,-6.726,+1
rexnet_150.nav_in1k,69.410,30.590,88.980,11.020,9.73,224,0.875,bicubic,-10.914,-6.010,+25
efficientvit_b1.r288_in1k,69.410,30.590,88.680,11.320,9.10,288,1.000,bicubic,-10.914,-6.496,+27
resnext50_32x4d.tv2_in1k,69.400,30.600,88.440,11.560,25.03,224,0.965,bilinear,-11.782,-6.900,-66
eca_resnet33ts.ra2_in1k,69.380,30.620,89.210,10.790,19.68,288,1.000,bicubic,-11.292,-6.154,-12
seresnet33ts.ra2_in1k,69.380,30.620,89.190,10.810,19.78,288,1.000,bicubic,-11.404,-6.172,-23
convmixer_768_32.in1k,69.380,30.620,88.880,11.120,21.11,224,0.960,bicubic,-10.788,-6.194,+39
inception_v4.tf_in1k,69.370,30.630,88.780,11.220,42.68,299,0.875,bicubic,-10.786,-6.190,+38
xception71.tf_in1k,69.360,30.640,88.270,11.730,42.34,299,0.903,bicubic,-10.514,-6.658,+49
darknet53.c2ns_in1k,69.350,30.650,88.780,11.220,41.61,288,1.000,bicubic,-11.182,-6.652,-6
cs3darknet_focus_l.c2ns_in1k,69.340,30.660,89.420,10.580,21.15,288,0.950,bicubic,-11.536,-6.262,-39
legacy_seresnext101_32x4d.in1k,69.340,30.660,88.050,11.950,48.96,224,0.875,bilinear,-10.892,-6.970,+28
vit_small_patch16_384.augreg_in1k,69.320,30.680,89.000,11.000,22.20,384,1.000,bicubic,-11.796,-6.574,-67
repvit_m1_0.dist_450e_in1k,69.310,30.690,88.700,11.300,7.30,224,0.950,bicubic,-11.124,-6.218,+4
mobilevitv2_175.cvnets_in1k,69.290,30.710,88.960,11.040,14.25,256,0.888,bicubic,-11.570,-6.296,-39
efficientformer_l1.snap_dist_in1k,69.280,30.720,88.560,11.440,12.29,224,0.950,bicubic,-11.218,-6.428,-8
vit_small_patch32_384.augreg_in21k_ft_in1k,69.270,30.730,89.820,10.180,22.92,384,1.000,bicubic,-11.216,-5.780,-8
convnext_pico_ols.d1_in1k,69.240,30.760,88.840,11.160,9.06,288,1.000,bicubic,-11.222,-6.412,-6
repvit_m2.dist_in1k,69.230,30.770,88.740,11.260,8.80,224,0.950,bicubic,-11.230,-6.428,-6
edgenext_small_rw.sw_in1k,69.210,30.790,88.740,11.260,7.83,320,1.000,bicubic,-11.248,-6.568,-5
vit_base_patch16_384.augreg_in1k,69.190,30.810,88.370,11.630,86.86,384,1.000,bicubic,-11.912,-6.750,-73
resnet50.b2k_in1k,69.180,30.820,88.660,11.340,25.56,288,1.000,bicubic,-11.274,-6.658,-6
resnet152c.gluon_in1k,69.140,30.860,87.890,12.110,60.21,224,0.875,bicubic,-10.772,-6.956,+33
resnet50.b1k_in1k,69.100,30.900,88.740,11.260,25.56,288,1.000,bicubic,-11.606,-6.692,-34
resnet50.a1h_in1k,69.100,30.900,88.510,11.490,25.56,224,1.000,bicubic,-11.578,-6.796,-31
tf_efficientnetv2_b2.in1k,69.100,30.900,88.230,11.770,10.10,260,0.890,bicubic,-11.096,-6.812,+16
mixnet_xl.ra_in1k,69.090,30.910,88.310,11.690,11.90,224,0.875,bicubic,-11.392,-6.626,-17
resnetblur50.bt_in1k,69.070,30.930,88.450,11.550,25.56,288,0.950,bicubic,-11.164,-6.784,+11
repvgg_b2g4.rvgg_in1k,69.010,30.990,88.360,11.640,61.76,224,0.875,bilinear,-10.372,-6.316,+69
regnety_160.pycls_in1k,69.010,30.990,88.270,11.730,83.59,224,0.875,bicubic,-11.288,-6.694,+4
resnet101d.gluon_in1k,69.010,30.990,88.080,11.920,44.57,224,0.875,bicubic,-11.416,-6.944,-12
xception65.tf_in1k,68.950,31.050,88.470,11.530,39.92,299,0.903,bicubic,-10.606,-6.188,+54
resnext101_32x4d.gluon_in1k,68.940,31.060,88.340,11.660,44.18,224,0.875,bicubic,-11.400,-6.590,-7
tf_efficientnet_b2.ap_in1k,68.930,31.070,88.330,11.670,9.11,260,0.890,bicubic,-11.380,-6.696,-5
repvit_m1_0.dist_300e_in1k,68.930,31.070,88.100,11.900,7.30,224,0.950,bicubic,-11.196,-6.644,+13
poolformerv2_s24.sail_in1k,68.920,31.080,88.670,11.330,21.34,224,1.000,bicubic,-11.828,-6.640,-50
cspdarknet53.ra_in1k,68.920,31.080,88.600,11.400,27.64,256,0.887,bilinear,-11.148,-6.478,+13
convnext_pico.d1_in1k,68.900,31.100,88.480,11.520,9.05,288,0.950,bicubic,-11.516,-6.568,-18
resnet50d.a2_in1k,68.900,31.100,87.980,12.020,25.58,288,1.000,bicubic,-12.264,-7.100,-98
mobilevitv2_150.cvnets_in1k,68.890,31.110,88.080,11.920,10.59,256,0.888,bicubic,-11.480,-6.994,-18
regnety_120.pycls_in1k,68.860,31.140,88.330,11.670,51.82,224,0.875,bicubic,-11.520,-6.796,-20
resnet152.a3_in1k,68.820,31.180,87.760,12.240,60.19,224,0.950,bicubic,-11.726,-7.240,-39
resnet152.gluon_in1k,68.820,31.180,87.700,12.300,60.19,224,0.875,bicubic,-10.876,-7.030,+32
poolformer_s24.sail_in1k,68.780,31.220,88.170,11.830,21.39,224,0.900,bicubic,-11.514,-6.904,-12
dpn107.mx_in1k,68.780,31.220,88.130,11.870,86.92,224,0.875,bicubic,-11.390,-6.812,-1
gmlp_s16_224.ra3_in1k,68.780,31.220,88.070,11.930,19.42,224,0.875,bicubic,-10.864,-6.552,+36
dpn131.mx_in1k,68.770,31.230,87.570,12.430,79.25,224,0.875,bicubic,-11.044,-7.130,+19
darknetaa53.c2ns_in1k,68.760,31.240,88.700,11.300,36.02,288,1.000,bilinear,-11.746,-6.622,-42
tf_efficientnet_b2.aa_in1k,68.760,31.240,87.950,12.050,9.11,260,0.890,bicubic,-11.324,-6.956,-1
deit_small_patch16_224.fb_in1k,68.730,31.270,88.200,11.800,22.05,224,0.900,bicubic,-11.118,-6.844,+12
regnety_080.pycls_in1k,68.710,31.290,87.970,12.030,39.18,224,0.875,bicubic,-11.158,-6.862,+8
resnet101s.gluon_in1k,68.710,31.290,87.900,12.100,44.67,224,0.875,bicubic,-11.594,-7.252,-21
seresnext50_32x4d.gluon_in1k,68.660,31.340,88.360,11.640,27.56,224,0.875,bicubic,-11.264,-6.464,+1
resnet50.ram_in1k,68.630,31.370,88.330,11.670,25.56,288,0.950,bicubic,-11.346,-6.722,-3
hrnet_w64.ms_in1k,68.630,31.370,88.070,11.930,128.06,224,0.875,bilinear,-10.846,-6.582,+38
xcit_tiny_12_p8_224.fb_in1k,68.580,31.420,88.720,11.280,6.71,224,1.000,bicubic,-11.108,-6.334,+21
resnet50.a2_in1k,68.570,31.430,88.000,12.000,25.56,288,1.000,bicubic,-12.202,-6.988,-72
tf_efficientnet_b3.in1k,68.530,31.470,88.700,11.300,12.23,300,0.904,bicubic,-12.344,-6.600,-84
dpn98.mx_in1k,68.520,31.480,87.610,12.390,61.57,224,0.875,bicubic,-11.150,-7.044,+21
regnetx_160.pycls_in1k,68.510,31.490,88.460,11.540,54.28,224,0.875,bicubic,-11.356,-6.368,0
fastvit_s12.apple_in1k,68.490,31.510,87.850,12.150,9.47,256,0.900,bicubic,-11.452,-6.944,-8
rexnet_130.nav_in1k,68.460,31.540,88.040,11.960,7.56,224,0.875,bicubic,-11.046,-6.638,+27
cspresnet50.ra_in1k,68.450,31.550,87.960,12.040,21.62,256,0.887,bilinear,-11.132,-6.750,+22
tf_efficientnet_el.in1k,68.440,31.560,88.210,11.790,10.59,300,0.904,bicubic,-11.808,-6.910,-29
regnety_064.pycls_in1k,68.440,31.560,88.060,11.940,30.58,224,0.875,bicubic,-11.276,-6.706,+10
xcit_tiny_24_p16_224.fb_in1k,68.420,31.580,88.290,11.710,12.12,224,1.000,bicubic,-11.028,-6.588,+28
cait_xxs36_224.fb_dist_in1k,68.410,31.590,88.650,11.350,17.30,224,1.000,bicubic,-11.336,-6.224,+3
skresnext50_32x4d.ra_in1k,68.400,31.600,87.570,12.430,27.48,224,0.875,bicubic,-11.764,-7.070,-23
resnet50.fb_ssl_yfcc100m_ft_in1k,68.380,31.620,88.560,11.440,25.56,224,0.875,bilinear,-10.850,-6.266,+47
efficientvit_b1.r256_in1k,68.370,31.630,87.910,12.090,9.10,256,1.000,bicubic,-11.364,-6.870,+1
dla102x2.in1k,68.350,31.650,87.890,12.110,41.28,224,0.875,bilinear,-11.096,-6.742,+24
fbnetv3_d.ra2_in1k,68.330,31.670,88.420,11.580,10.31,256,0.950,bilinear,-11.352,-6.524,+6
efficientnet_b2_pruned.in1k,68.300,31.700,88.100,11.900,8.31,260,0.890,bicubic,-11.620,-6.752,-18
res2net50d.in1k,68.300,31.700,88.100,11.900,25.72,224,0.875,bilinear,-11.954,-6.936,-39
resmlp_big_24_224.fb_in1k,68.300,31.700,87.540,12.460,129.14,224,0.875,bicubic,-12.736,-7.478,-119
vit_base_patch16_224.sam_in1k,68.290,31.710,87.730,12.270,86.57,224,0.900,bicubic,-11.948,-7.026,-39
resnext50_32x4d.gluon_in1k,68.290,31.710,87.330,12.670,25.03,224,0.875,bicubic,-11.070,-7.100,+26
ecaresnet26t.ra2_in1k,68.260,31.740,88.810,11.190,16.01,320,0.950,bicubic,-11.590,-6.280,-17
efficientformerv2_s1.snap_dist_in1k,68.240,31.760,88.330,11.670,6.19,224,0.950,bicubic,-11.452,-6.386,-3
tf_efficientnet_lite3.in1k,68.220,31.780,87.730,12.270,8.20,300,0.904,bilinear,-11.586,-7.184,-13
resnet101.a3_in1k,68.220,31.780,87.640,12.360,44.55,224,0.950,bicubic,-11.594,-6.974,-13
pit_xs_distilled_224.in1k,68.190,31.810,87.730,12.270,11.00,224,0.900,bicubic,-10.990,-6.636,+38
resnet50.ra_in1k,68.150,31.850,87.900,12.100,25.56,288,0.950,bicubic,-11.686,-7.066,-18
fbnetv3_b.ra2_in1k,68.140,31.860,87.920,12.080,8.60,256,0.950,bilinear,-11.006,-6.824,+39
tiny_vit_5m_224.in1k,68.140,31.860,87.840,12.160,5.39,224,0.950,bicubic,-11.030,-6.954,+36
regnetx_120.pycls_in1k,68.130,31.870,87.610,12.390,46.11,224,0.875,bicubic,-11.458,-7.132,-3
mobileone_s4.apple_in1k,68.130,31.870,87.110,12.890,14.95,224,0.900,bilinear,-11.296,-7.370,+11
resmlp_36_224.fb_in1k,68.090,31.910,88.180,11.820,44.69,224,0.875,bicubic,-11.682,-6.704,-19
resnet50.bt_in1k,68.070,31.930,87.810,12.190,25.56,288,0.950,bicubic,-11.570,-7.082,-8
dpn68b.ra_in1k,68.070,31.930,87.380,12.620,12.61,288,1.000,bicubic,-11.290,-7.056,+12
regnetx_016.tv2_in1k,68.050,31.950,88.230,11.770,9.19,224,0.965,bicubic,-11.386,-6.538,+5
resnetrs50.tf_in1k,68.040,31.960,87.730,12.270,35.69,224,0.910,bicubic,-11.854,-7.244,-35
dpn92.mx_in1k,67.980,32.020,87.600,12.400,37.67,224,0.875,bicubic,-12.058,-7.260,-43
nf_regnet_b1.ra2_in1k,67.970,32.030,88.220,11.780,10.22,288,0.900,bicubic,-11.338,-6.520,+12
repvit_m0_9.dist_450e_in1k,67.920,32.080,87.890,12.110,5.49,224,0.950,bicubic,-11.146,-6.490,+34
fastvit_t12.apple_in1k,67.920,32.080,87.550,12.450,7.55,256,0.900,bicubic,-11.344,-7.012,+17
resnet50d.gluon_in1k,67.910,32.090,87.120,12.880,25.58,224,0.875,bicubic,-11.168,-7.346,+30
resnetv2_50x1_bit.goog_in21k_ft_in1k,67.900,32.100,89.270,10.730,25.55,448,1.000,bilinear,-12.442,-6.412,-75
levit_192.fb_dist_in1k,67.900,32.100,87.900,12.100,10.95,224,0.900,bicubic,-11.938,-6.884,-34
levit_conv_192.fb_dist_in1k,67.900,32.100,87.890,12.110,10.95,224,0.900,bicubic,-11.938,-6.888,-36
tf_efficientnetv2_b1.in1k,67.890,32.110,87.810,12.190,8.14,240,0.882,bicubic,-11.570,-6.912,-9
regnetx_080.pycls_in1k,67.890,32.110,87.000,13.000,39.57,224,0.875,bicubic,-11.308,-7.554,+18
legacy_seresnext50_32x4d.in1k,67.860,32.140,87.620,12.380,27.56,224,0.875,bilinear,-11.216,-6.812,+25
efficientnet_em.ra2_in1k,67.850,32.150,88.130,11.870,6.90,240,0.882,bicubic,-11.394,-6.664,+11
resnext101_32x8d.tv_in1k,67.840,32.160,87.490,12.510,88.79,224,0.875,bilinear,-11.470,-7.030,0
resmlp_24_224.fb_in1k,67.820,32.180,87.610,12.390,30.02,224,0.875,bicubic,-11.554,-6.936,-6
lambda_resnet26t.c1_in1k,67.800,32.200,87.790,12.210,10.96,256,0.940,bicubic,-11.288,-6.800,+19
ecaresnet50t.a3_in1k,67.790,32.210,87.560,12.440,25.57,224,0.950,bicubic,-11.762,-7.134,-21
hrnet_w48.ms_in1k,67.760,32.240,87.410,12.590,77.47,224,0.875,bilinear,-11.546,-7.106,-3
efficientvit_b1.r224_in1k,67.760,32.240,87.340,12.660,9.10,224,0.950,bicubic,-11.492,-6.964,+5
hrnet_w44.ms_in1k,67.750,32.250,87.540,12.460,67.06,224,0.875,bilinear,-11.144,-6.824,+28
resnet33ts.ra2_in1k,67.730,32.270,88.100,11.900,19.68,288,1.000,bicubic,-11.996,-6.728,-39
coat_lite_mini.in1k,67.710,32.290,87.710,12.290,11.01,224,0.900,bicubic,-11.392,-6.898,+12
tf_efficientnet_b0.ns_jft_in1k,67.700,32.300,88.070,11.930,5.29,224,0.875,bicubic,-10.968,-6.302,+42
resnext50_32x4d.a3_in1k,67.700,32.300,86.930,13.070,25.03,224,0.950,bicubic,-11.568,-7.376,-3
legacy_xception.tf_in1k,67.690,32.310,87.560,12.440,22.86,299,0.897,bicubic,-11.350,-6.822,+15
eca_botnext26ts_256.c1_in1k,67.680,32.320,87.080,12.920,10.59,256,0.950,bicubic,-11.588,-7.526,-7
regnetx_064.pycls_in1k,67.670,32.330,87.520,12.480,26.21,224,0.875,bicubic,-11.396,-6.940,+11
resnet32ts.ra2_in1k,67.650,32.350,87.580,12.420,17.96,288,1.000,bicubic,-11.738,-7.072,-22
convnext_femto_ols.d1_in1k,67.650,32.350,87.390,12.610,5.23,288,0.950,bicubic,-11.274,-7.136,+18
halonet26t.a1h_in1k,67.620,32.380,87.250,12.750,12.48,256,0.950,bicubic,-11.486,-7.056,+3
inception_v3.gluon_in1k,67.590,32.410,87.450,12.550,23.83,299,0.875,bicubic,-11.212,-6.926,+24
hrnet_w40.ms_in1k,67.580,32.420,87.140,12.860,57.56,224,0.875,bilinear,-11.352,-7.324,+13
regnety_040.pycls_in1k,67.570,32.430,87.490,12.510,20.65,224,0.875,bicubic,-11.650,-7.166,-7
resnet101c.gluon_in1k,67.560,32.440,87.160,12.840,44.57,224,0.875,bicubic,-11.978,-7.424,-37
legacy_seresnet152.in1k,67.550,32.450,87.380,12.620,66.82,224,0.875,bilinear,-11.110,-6.990,+33
dla169.in1k,67.540,32.460,87.570,12.430,53.39,224,0.875,bilinear,-11.168,-6.774,+28
tf_efficientnet_b1.ap_in1k,67.520,32.480,87.750,12.250,7.79,240,0.882,bicubic,-11.756,-6.562,-20
efficientnet_b1.ft_in1k,67.520,32.480,87.470,12.530,7.79,256,1.000,bicubic,-11.280,-6.872,+19
mobilevitv2_125.cvnets_in1k,67.480,32.520,87.570,12.430,7.48,256,0.888,bicubic,-12.200,-7.288,-52
tf_efficientnet_cc_b1_8e.in1k,67.480,32.520,87.300,12.700,39.72,240,0.882,bicubic,-11.822,-7.074,-24
eca_halonext26ts.c1_in1k,67.480,32.520,87.240,12.760,10.76,256,0.940,bicubic,-12.006,-7.360,-40
resnet101.gluon_in1k,67.470,32.530,87.230,12.770,44.55,224,0.875,bicubic,-11.840,-7.292,-29
res2net101_26w_4s.in1k,67.460,32.540,87.010,12.990,45.21,224,0.875,bilinear,-11.740,-7.426,-16
res2net50_26w_8s.in1k,67.440,32.560,87.250,12.750,48.40,224,0.875,bilinear,-11.502,-7.044,0
repvit_m0_9.dist_300e_in1k,67.430,32.570,87.230,12.770,5.49,224,0.950,bicubic,-11.228,-6.886,+24
regnety_008_tv.tv2_in1k,67.410,32.590,88.030,11.970,6.43,224,0.965,bicubic,-11.256,-6.360,+21
resnet34d.ra2_in1k,67.400,32.600,87.960,12.040,21.82,288,0.950,bicubic,-11.036,-6.384,+36
tf_efficientnet_b2.in1k,67.400,32.600,87.580,12.420,9.11,260,0.890,bicubic,-12.208,-7.134,-57
regnety_032.pycls_in1k,67.400,32.600,87.260,12.740,19.44,224,0.875,bicubic,-11.476,-7.148,+2
convnextv2_femto.fcmae_ft_in1k,67.350,32.650,87.580,12.420,5.23,288,0.950,bicubic,-11.988,-6.980,-38
cait_xxs24_224.fb_dist_in1k,67.340,32.660,87.520,12.480,11.96,224,1.000,bicubic,-11.044,-6.796,+39
xception41.tf_in1k,67.260,32.740,87.190,12.810,26.97,299,0.903,bicubic,-11.244,-7.086,+23
coat_tiny.in1k,67.240,32.760,87.310,12.690,5.50,224,0.900,bicubic,-11.186,-6.738,+32
repghostnet_200.in1k,67.240,32.760,87.290,12.710,9.80,224,0.875,bicubic,-11.566,-7.040,-1
regnetx_032.pycls_in1k,67.240,32.760,86.990,13.010,15.30,224,0.875,bicubic,-10.928,-7.092,+50
resnest26d.gluon_in1k,67.220,32.780,87.180,12.820,17.07,224,0.875,bilinear,-11.262,-7.114,+23
convnext_femto.d1_in1k,67.190,32.810,87.480,12.520,5.22,288,0.950,bicubic,-11.526,-6.950,+5
repvgg_b2.rvgg_in1k,67.160,32.840,87.320,12.680,89.02,224,0.875,bilinear,-11.632,-7.100,-1
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,67.160,32.840,86.480,13.520,119.42,256,0.900,bicubic,-12.324,-7.658,-59
botnet26t_256.c1_in1k,67.140,32.860,87.510,12.490,12.49,256,0.950,bicubic,-12.118,-7.022,-38
legacy_seresnet101.in1k,67.110,32.890,87.050,12.950,49.33,224,0.875,bilinear,-11.276,-7.212,+28
resnet50s.gluon_in1k,67.110,32.890,86.860,13.140,25.68,224,0.875,bicubic,-11.604,-7.382,+1
dla60x.in1k,67.070,32.930,87.200,12.800,17.35,224,0.875,bilinear,-11.166,-6.826,+37
visformer_tiny.in1k,67.050,32.950,87.060,12.940,10.32,224,0.900,bicubic,-11.110,-7.106,+41
dla60_res2net.in1k,67.030,32.970,87.140,12.860,20.85,224,0.875,bilinear,-11.434,-7.058,+16
resnet34.a1_in1k,67.030,32.970,86.280,13.720,21.80,288,1.000,bicubic,-10.888,-7.484,+57
xcit_tiny_12_p16_224.fb_dist_in1k,67.020,32.980,87.400,12.600,6.72,224,1.000,bicubic,-11.554,-6.798,+3
dla102x.in1k,67.000,33.000,86.790,13.210,26.31,224,0.875,bilinear,-11.512,-7.446,+6
resnet152.tv_in1k,66.990,33.010,87.560,12.440,60.19,224,0.875,bilinear,-11.332,-6.486,+25
lambda_resnet26rpt_256.c1_in1k,66.960,33.040,87.130,12.870,10.99,256,0.940,bicubic,-12.004,-7.306,-27
mixnet_l.ft_in1k,66.960,33.040,86.940,13.060,7.33,224,0.875,bicubic,-12.006,-7.242,-29
pit_xs_224.in1k,66.920,33.080,87.280,12.720,10.62,224,0.900,bicubic,-11.256,-6.882,+31
repvgg_b1.rvgg_in1k,66.920,33.080,86.780,13.220,57.42,224,0.875,bilinear,-11.448,-7.316,+18
resnet50d.a3_in1k,66.920,33.080,86.540,13.460,25.58,224,0.950,bicubic,-11.800,-7.692,-13
pvt_v2_b1.in1k,66.910,33.090,87.410,12.590,14.01,224,0.900,bicubic,-11.794,-7.092,-10
res2net50_26w_6s.in1k,66.910,33.090,86.880,13.120,37.05,224,0.875,bilinear,-11.658,-7.242,-5
tf_efficientnet_b1.aa_in1k,66.900,33.100,87.020,12.980,7.79,240,0.882,bicubic,-11.928,-7.180,-25
xcit_nano_12_p8_384.fb_dist_in1k,66.870,33.130,87.110,12.890,3.05,384,1.000,bicubic,-10.950,-6.930,+51
efficientnet_es.ra_in1k,66.860,33.140,86.710,13.290,5.44,224,0.875,bicubic,-11.198,-7.216,+32
mobilevit_s.cvnets_in1k,66.850,33.150,87.070,12.930,5.58,256,0.900,bicubic,-11.462,-7.078,+15
regnetx_040.pycls_in1k,66.820,33.180,86.740,13.260,22.12,224,0.875,bicubic,-11.672,-7.502,-4
hrnet_w32.ms_in1k,66.780,33.220,87.290,12.710,41.23,224,0.875,bilinear,-11.662,-6.900,0
resnet50.am_in1k,66.780,33.220,86.740,13.260,25.56,224,0.875,bicubic,-12.222,-7.658,-42
tf_mixnet_l.in1k,66.780,33.220,86.480,13.520,7.33,224,0.875,bicubic,-11.996,-7.522,-26
seresnext26t_32x4d.bt_in1k,66.770,33.230,86.720,13.280,16.81,288,0.950,bicubic,-11.974,-7.592,-25
hrnet_w18.ms_aug_in1k,66.760,33.240,87.480,12.520,21.30,224,0.950,bilinear,-11.362,-6.574,+22
repvit_m1.dist_in1k,66.760,33.240,87.180,12.820,5.49,224,0.950,bicubic,-11.778,-6.890,-15
hrnet_w30.ms_in1k,66.760,33.240,86.790,13.210,37.71,224,0.875,bilinear,-11.436,-7.432,+14
selecsls60b.in1k,66.750,33.250,86.540,13.460,32.77,224,0.875,bicubic,-11.662,-7.628,-1
vit_small_patch16_224.augreg_in1k,66.690,33.310,86.720,13.280,22.05,224,0.900,bicubic,-12.158,-7.568,-41
tf_efficientnetv2_b0.in1k,66.690,33.310,86.700,13.300,7.14,224,0.875,bicubic,-11.668,-7.314,+2
wide_resnet101_2.tv_in1k,66.680,33.320,87.030,12.970,126.89,224,0.875,bilinear,-12.162,-7.252,-42
seresnext26d_32x4d.bt_in1k,66.680,33.320,86.830,13.170,16.81,288,0.950,bicubic,-12.134,-7.410,-39
dla60_res2next.in1k,66.660,33.340,87.020,12.980,17.03,224,0.875,bilinear,-11.780,-7.124,-11
wide_resnet50_2.tv_in1k,66.650,33.350,86.800,13.200,68.88,224,0.875,bilinear,-11.826,-7.288,-15
inception_v3.tf_adv_in1k,66.630,33.370,86.580,13.420,23.83,299,0.875,bicubic,-10.962,-7.150,+42
mobilevitv2_100.cvnets_in1k,66.610,33.390,87.020,12.980,4.90,256,0.888,bicubic,-11.470,-7.150,+13
vit_tiny_patch16_384.augreg_in21k_ft_in1k,66.590,33.410,87.250,12.750,5.79,384,1.000,bicubic,-11.834,-7.292,-12
cs3darknet_m.c2ns_in1k,66.580,33.420,87.180,12.820,9.31,288,0.950,bicubic,-11.054,-6.836,+37
levit_128.fb_dist_in1k,66.560,33.440,86.740,13.260,9.21,224,0.900,bicubic,-11.930,-7.272,-22
levit_conv_128.fb_dist_in1k,66.550,33.450,86.730,13.270,9.21,224,0.900,bicubic,-11.944,-7.278,-25
tf_efficientnet_b1.in1k,66.540,33.460,86.700,13.300,7.79,240,0.882,bicubic,-12.022,-7.394,-31
resnet50c.gluon_in1k,66.540,33.460,86.170,13.830,25.58,224,0.875,bicubic,-11.466,-7.822,+13
dla102.in1k,66.530,33.470,86.910,13.090,33.27,224,0.875,bilinear,-11.494,-7.024,+10
hrnet_w18_small_v2.gluon_in1k,66.510,33.490,86.500,13.500,15.60,224,0.875,bicubic,-11.680,-7.402,-3
mobileone_s3.apple_in1k,66.500,33.500,86.370,13.630,10.17,224,0.900,bilinear,-11.492,-7.544,+12
vit_base_patch16_224.augreg_in1k,66.480,33.520,86.260,13.740,86.57,224,0.900,bicubic,-12.674,-7.830,-76
vit_base_patch32_384.augreg_in1k,66.430,33.570,86.960,13.040,88.30,384,1.000,bicubic,-12.326,-7.266,-50
gmixer_24_224.ra3_in1k,66.430,33.570,86.160,13.840,24.72,224,0.875,bicubic,-11.596,-7.508,+5
inception_v3.tf_in1k,66.420,33.580,86.680,13.320,23.83,299,0.875,bicubic,-11.436,-7.186,+17
bat_resnext26ts.ch_in1k,66.390,33.610,86.860,13.140,10.73,256,0.900,bicubic,-11.862,-7.238,-14
hardcorenas_f.miil_green_in1k,66.360,33.640,86.190,13.810,8.20,224,0.875,bilinear,-11.736,-7.612,-3
seresnext26ts.ch_in1k,66.320,33.680,86.700,13.300,10.39,288,1.000,bicubic,-11.950,-7.392,-17
coat_lite_tiny.in1k,66.290,33.710,86.960,13.040,5.72,224,0.900,bicubic,-11.230,-6.962,+27
eca_resnext26ts.ch_in1k,66.270,33.730,86.410,13.590,10.30,288,1.000,bicubic,-11.730,-7.516,+2
legacy_seresnet50.in1k,66.250,33.750,86.300,13.700,28.09,224,0.875,bilinear,-11.394,-7.458,+18
efficientnet_b0.ra_in1k,66.250,33.750,85.970,14.030,5.29,224,0.875,bicubic,-11.444,-7.562,+17
cs3darknet_focus_m.c2ns_in1k,66.240,33.760,87.080,12.920,9.30,288,0.950,bicubic,-11.044,-6.886,+39
selecsls60.in1k,66.220,33.780,86.330,13.670,30.67,224,0.875,bicubic,-11.768,-7.500,0
tf_efficientnet_cc_b0_8e.in1k,66.220,33.780,86.230,13.770,24.01,224,0.875,bicubic,-11.684,-7.432,+4
res2net50_26w_4s.in1k,66.150,33.850,86.610,13.390,25.70,224,0.875,bilinear,-11.800,-7.242,0
tf_efficientnet_em.in1k,66.150,33.850,86.360,13.640,6.90,240,0.882,bicubic,-11.976,-7.688,-16
resnext50_32x4d.tv_in1k,66.150,33.850,86.040,13.960,25.03,224,0.875,bilinear,-11.472,-7.656,+15
densenetblur121d.ra_in1k,66.140,33.860,86.600,13.400,8.00,288,0.950,bicubic,-11.182,-7.188,+28
resmlp_12_224.fb_distilled_in1k,66.120,33.880,86.620,13.380,15.35,224,0.875,bicubic,-11.834,-6.940,-5
inception_v3.tv_in1k,66.110,33.890,86.320,13.680,23.83,299,0.875,bicubic,-11.324,-7.154,+22
resnet50.a3_in1k,66.110,33.890,85.820,14.180,25.56,224,0.950,bicubic,-11.938,-7.960,-15
ghostnetv2_160.in1k,66.080,33.920,86.730,13.270,12.39,224,0.875,bicubic,-11.752,-7.210,0
resnet26t.ra2_in1k,66.080,33.920,86.670,13.330,16.01,320,1.000,bicubic,-12.248,-7.454,-37
regnety_016.pycls_in1k,66.080,33.920,86.370,13.630,11.20,224,0.875,bicubic,-11.788,-7.348,-4
efficientnet_b1_pruned.in1k,66.070,33.930,86.550,13.450,6.33,240,0.882,bicubic,-12.170,-7.284,-32
gcresnext26ts.ch_in1k,66.040,33.960,86.750,13.250,10.48,288,1.000,bicubic,-12.374,-7.286,-45
resnet50.gluon_in1k,66.030,33.970,86.270,13.730,25.56,224,0.875,bicubic,-11.552,-7.450,+7
rexnet_100.nav_in1k,66.020,33.980,86.490,13.510,4.80,224,0.875,bicubic,-11.836,-7.150,-8
tinynet_a.in1k,66.020,33.980,85.780,14.220,6.19,192,0.875,bicubic,-11.628,-7.760,-1
res2net50_14w_8s.in1k,66.000,34.000,86.230,13.770,25.06,224,0.875,bilinear,-12.158,-7.616,-30
poolformerv2_s12.sail_in1k,65.890,34.110,86.510,13.490,11.89,224,1.000,bicubic,-12.112,-7.354,-21
resnet34.a2_in1k,65.870,34.130,86.140,13.860,21.80,288,1.000,bicubic,-11.288,-7.134,+26
densenet161.tv_in1k,65.850,34.150,86.460,13.540,28.68,224,0.875,bicubic,-11.508,-7.182,+12
res2next50.in1k,65.850,34.150,85.830,14.170,24.67,224,0.875,bilinear,-12.392,-8.062,-42
convnextv2_atto.fcmae_ft_in1k,65.840,34.160,86.170,13.830,3.71,288,0.950,bicubic,-11.920,-7.556,-10
hardcorenas_e.miil_green_in1k,65.830,34.170,85.970,14.030,8.07,224,0.875,bilinear,-11.960,-7.730,-12
repvgg_b1g4.rvgg_in1k,65.820,34.180,86.110,13.890,39.97,224,0.875,bilinear,-11.768,-7.726,-4
regnetx_008.tv2_in1k,65.810,34.190,86.210,13.790,7.26,224,0.965,bicubic,-11.496,-7.454,+10
xcit_tiny_12_p16_224.fb_in1k,65.790,34.210,86.220,13.780,6.72,224,1.000,bicubic,-11.350,-7.496,+20
ese_vovnet19b_dw.ra_in1k,65.770,34.230,86.470,13.530,6.54,288,0.950,bicubic,-11.974,-7.314,-14
mobilenetv3_large_100.miil_in21k_ft_in1k,65.770,34.230,85.180,14.820,5.48,224,0.875,bilinear,-12.150,-7.734,-25
skresnet34.ra_in1k,65.740,34.260,85.950,14.050,22.28,224,0.875,bicubic,-11.170,-7.194,+29
resnet101.tv_in1k,65.710,34.290,85.990,14.010,44.55,224,0.875,bilinear,-11.670,-7.556,+1
convnext_tiny.fb_in22k_ft_in1k,65.650,34.350,86.620,13.380,28.59,288,1.000,bicubic,-13.248,-8.054,-103
hardcorenas_d.miil_green_in1k,65.650,34.350,85.450,14.550,7.50,224,0.875,bilinear,-11.784,-8.040,-4
poolformer_s12.sail_in1k,65.630,34.370,86.210,13.790,11.92,224,0.900,bicubic,-11.610,-7.322,+7
dpn68b.mx_in1k,65.620,34.380,85.950,14.050,12.61,224,0.875,bicubic,-11.898,-7.902,-11
selecsls42b.in1k,65.620,34.380,85.840,14.160,32.46,224,0.875,bicubic,-11.550,-7.552,+9
convnext_atto_ols.a2_in1k,65.600,34.400,86.260,13.740,3.70,288,0.950,bicubic,-11.616,-7.416,+5
fastvit_t8.apple_dist_in1k,65.550,34.450,86.160,13.840,4.03,256,0.900,bicubic,-11.626,-7.138,+6
tf_efficientnet_b0.ap_in1k,65.500,34.500,85.570,14.430,5.29,224,0.875,bicubic,-11.590,-7.692,+10
mobileone_s2.apple_in1k,65.440,34.560,85.950,14.050,7.88,224,0.900,bilinear,-12.076,-7.718,-15
tf_efficientnet_lite2.in1k,65.400,34.600,86.020,13.980,6.09,260,0.890,bicubic,-12.062,-7.732,-14
convmixer_1024_20_ks9_p14.in1k,65.390,34.610,85.610,14.390,24.38,224,0.960,bicubic,-11.546,-7.740,+15
res2net50_48w_2s.in1k,65.350,34.650,85.950,14.050,25.29,224,0.875,bilinear,-12.164,-7.600,-17
resnet26d.bt_in1k,65.340,34.660,86.000,14.000,16.01,288,0.950,bicubic,-12.068,-7.638,-13
densenet201.tv_in1k,65.270,34.730,85.670,14.330,20.01,224,0.875,bicubic,-12.016,-7.810,-7
dla60.in1k,65.220,34.780,85.760,14.240,22.04,224,0.875,bilinear,-11.826,-7.558,+6
seresnet50.a3_in1k,65.190,34.810,85.300,14.700,28.09,224,0.950,bicubic,-11.836,-7.772,+6
crossvit_9_dagger_240.in1k,65.170,34.830,86.570,13.430,8.78,240,0.875,bicubic,-11.808,-7.048,+7
gernet_s.idstcv_in1k,65.150,34.850,85.530,14.470,8.17,224,0.875,bilinear,-11.760,-7.786,+11
tf_efficientnet_cc_b0_4e.in1k,65.150,34.850,85.140,14.860,13.31,224,0.875,bicubic,-12.152,-8.196,-13
mobilenetv2_120d.ra_in1k,65.060,34.940,85.990,14.010,5.83,224,0.875,bicubic,-12.248,-7.512,-16
legacy_seresnext26_32x4d.in1k,65.040,34.960,85.630,14.370,16.79,224,0.875,bicubic,-12.068,-7.684,-4
resnet34.bt_in1k,64.940,35.060,86.210,13.790,21.80,288,0.950,bicubic,-11.540,-7.144,+21
convnext_atto.d2_in1k,64.920,35.080,86.230,13.770,3.70,288,0.950,bicubic,-12.088,-7.472,0
hrnet_w18.ms_in1k,64.920,35.080,85.730,14.270,21.30,224,0.875,bilinear,-11.832,-7.714,+8
resnext26ts.ra2_in1k,64.900,35.100,85.710,14.290,10.30,288,1.000,bicubic,-12.278,-7.754,-13
efficientvit_m5.r224_in1k,64.890,35.110,85.390,14.610,12.47,224,0.875,bicubic,-12.168,-7.794,-6
hardcorenas_c.miil_green_in1k,64.870,35.130,85.250,14.750,5.52,224,0.875,bilinear,-12.196,-7.912,-8
repghostnet_150.in1k,64.830,35.170,85.880,14.120,6.58,224,0.875,bicubic,-12.630,-7.630,-31
efficientformerv2_s0.snap_dist_in1k,64.800,35.200,85.650,14.350,3.60,224,0.950,bicubic,-11.314,-7.208,+23
densenet169.tv_in1k,64.790,35.210,85.250,14.750,14.15,224,0.875,bicubic,-11.110,-7.778,+27
fastvit_t8.apple_in1k,64.730,35.270,85.680,14.320,4.03,256,0.900,bicubic,-11.444,-7.372,+19
ghostnetv2_130.in1k,64.720,35.280,85.420,14.580,8.96,224,0.875,bicubic,-12.036,-7.942,-1
mixnet_m.ft_in1k,64.670,35.330,85.460,14.540,5.01,224,0.875,bicubic,-12.590,-7.958,-24
xcit_nano_12_p8_224.fb_dist_in1k,64.610,35.390,85.990,14.010,3.05,224,1.000,bicubic,-11.722,-7.108,+14
levit_128s.fb_dist_in1k,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.936,-8.142,+4
levit_conv_128s.fb_dist_in1k,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.930,-8.136,+4
repvgg_a2.rvgg_in1k,64.470,35.530,85.140,14.860,28.21,224,0.875,bilinear,-11.988,-7.862,+7
xcit_nano_12_p16_384.fb_dist_in1k,64.420,35.580,85.300,14.700,3.05,384,1.000,bicubic,-11.038,-7.398,+31
hardcorenas_b.miil_green_in1k,64.390,35.610,84.880,15.120,5.18,224,0.875,bilinear,-12.158,-7.882,-2
regnetx_016.pycls_in1k,64.380,35.620,85.470,14.530,9.19,224,0.875,bicubic,-12.544,-7.946,-13
tf_efficientnet_lite1.in1k,64.370,35.630,85.460,14.540,5.42,240,0.882,bicubic,-12.274,-7.764,-7
resmlp_12_224.fb_in1k,64.350,35.650,85.590,14.410,15.35,224,0.875,bicubic,-12.298,-7.588,-9
tf_efficientnet_b0.aa_in1k,64.300,35.700,85.280,14.720,5.29,224,0.875,bicubic,-12.544,-7.938,-13
tf_mixnet_m.in1k,64.260,35.740,85.100,14.900,5.01,224,0.875,bicubic,-12.694,-8.054,-19
densenet121.ra_in1k,64.250,35.750,85.820,14.180,7.98,288,0.950,bicubic,-12.250,-7.548,-3
resnet26.bt_in1k,64.200,35.800,85.210,14.790,16.00,288,0.950,bicubic,-12.166,-7.970,0
tf_efficientnet_es.in1k,64.200,35.800,84.740,15.260,5.44,224,0.875,bicubic,-12.398,-8.462,-11
regnety_008.pycls_in1k,64.140,35.860,85.240,14.760,6.26,224,0.875,bicubic,-12.162,-7.822,+1
dpn68.mx_in1k,64.120,35.880,85.080,14.920,12.61,224,0.875,bicubic,-12.226,-7.928,-2
vit_small_patch32_224.augreg_in21k_ft_in1k,64.090,35.910,85.550,14.450,22.88,224,0.900,bicubic,-11.904,-7.250,+3
mobilenetv2_140.ra_in1k,64.070,35.930,85.030,14.970,6.11,224,0.875,bicubic,-12.446,-7.958,-10
repghostnet_130.in1k,63.960,36.040,84.840,15.160,5.48,224,0.875,bicubic,-12.416,-8.052,-7
hardcorenas_a.miil_green_in1k,63.720,36.280,84.410,15.590,5.26,224,0.875,bilinear,-12.218,-8.098,+3
resnest14d.gluon_in1k,63.620,36.380,84.230,15.770,10.61,224,0.875,bilinear,-11.888,-8.278,+12
regnety_004.tv2_in1k,63.600,36.400,84.860,15.140,4.34,224,0.965,bicubic,-11.994,-7.840,+9
mobilevitv2_075.cvnets_in1k,63.590,36.410,84.950,15.050,2.87,256,0.888,bicubic,-12.018,-7.794,+7
tf_mixnet_s.in1k,63.580,36.420,84.260,15.740,4.13,224,0.875,bicubic,-12.072,-8.380,+4
tf_efficientnet_b0.in1k,63.520,36.480,84.870,15.130,5.29,224,0.875,bicubic,-13.010,-8.138,-20
mixnet_s.ft_in1k,63.390,36.610,84.720,15.280,4.13,224,0.875,bicubic,-12.604,-8.550,-5
mobilenetv3_large_100.ra_in1k,63.380,36.620,84.080,15.920,5.48,224,0.875,bicubic,-12.386,-8.458,-1
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,63.340,36.660,85.270,14.730,6.36,384,1.000,bicubic,-12.620,-7.992,-6
resnet50.tv_in1k,63.330,36.670,84.670,15.330,25.56,224,0.875,bilinear,-12.798,-8.188,-11
efficientnet_es_pruned.in1k,63.310,36.690,84.950,15.050,5.44,224,0.875,bicubic,-11.696,-7.494,+16
mixer_b16_224.goog_in21k_ft_in1k,63.290,36.710,83.340,16.660,59.88,224,0.875,bicubic,-13.312,-8.884,-29
mobilenetv3_rw.rmsp_in1k,63.240,36.760,84.520,15.480,5.48,224,0.875,bicubic,-12.380,-8.184,-4
efficientnet_lite0.ra_in1k,63.240,36.760,84.410,15.590,4.65,224,0.875,bicubic,-12.242,-8.110,+2
mobileone_s1.apple_in1k,63.200,36.800,84.210,15.790,4.83,224,0.900,bilinear,-12.586,-8.582,-9
semnasnet_100.rmsp_in1k,63.160,36.840,84.540,15.460,3.89,224,0.875,bicubic,-12.290,-8.058,+2
vit_tiny_patch16_224.augreg_in21k_ft_in1k,63.130,36.870,84.870,15.130,5.72,224,0.900,bicubic,-12.332,-7.974,-1
regnety_006.pycls_in1k,63.090,36.910,84.240,15.760,6.06,224,0.875,bicubic,-12.178,-8.286,+1
pit_ti_distilled_224.in1k,63.020,36.980,83.810,16.190,5.10,224,0.900,bicubic,-11.236,-8.142,+22
densenet121.tv_in1k,62.940,37.060,84.250,15.750,7.98,224,0.875,bicubic,-11.824,-7.904,+12
mobilevit_xs.cvnets_in1k,62.930,37.070,84.830,15.170,2.32,256,0.900,bicubic,-11.704,-7.518,+14
ghostnetv2_100.in1k,62.900,37.100,84.090,15.910,6.16,224,0.875,bicubic,-12.266,-8.264,-2
legacy_seresnet34.in1k,62.890,37.110,84.230,15.770,21.96,224,0.875,bilinear,-11.912,-7.896,+8
hrnet_w18_small_v2.ms_in1k,62.830,37.170,83.970,16.030,15.60,224,0.875,bilinear,-12.280,-8.446,-1
edgenext_x_small.in1k,62.810,37.190,84.670,15.330,2.34,288,1.000,bicubic,-12.878,-8.096,-17
mobilenetv2_110d.ra_in1k,62.810,37.190,84.480,15.520,4.52,224,0.875,bicubic,-12.244,-7.704,-1
deit_tiny_distilled_patch16_224.fb_in1k,62.790,37.210,83.930,16.070,5.91,224,0.900,bicubic,-11.714,-7.960,+11
resnet18.fb_swsl_ig1b_ft_in1k,62.750,37.250,84.300,15.700,11.69,224,0.875,bilinear,-10.538,-7.430,+30
tinynet_b.in1k,62.720,37.280,84.220,15.780,3.73,188,0.875,bicubic,-12.258,-7.966,-1
repvgg_b0.rvgg_in1k,62.710,37.290,83.880,16.120,15.82,224,0.875,bilinear,-12.434,-8.536,-9
tf_efficientnet_lite0.in1k,62.580,37.420,84.230,15.770,4.65,224,0.875,bicubic,-12.252,-7.940,-1
xcit_nano_12_p8_224.fb_in1k,62.560,37.440,84.210,15.790,3.05,224,1.000,bicubic,-11.350,-7.958,+17
resnet34.gluon_in1k,62.540,37.460,84.000,16.000,21.80,224,0.875,bicubic,-12.040,-7.982,+4
regnetx_008.pycls_in1k,62.490,37.510,84.020,15.980,7.26,224,0.875,bicubic,-12.538,-8.318,-9
dla34.in1k,62.490,37.510,83.930,16.070,15.74,224,0.875,bilinear,-12.150,-8.136,0
fbnetc_100.rmsp_in1k,62.450,37.550,83.370,16.630,5.57,224,0.875,bilinear,-12.680,-9.018,-14
tf_mobilenetv3_large_100.in1k,62.440,37.560,83.950,16.050,5.48,224,0.875,bilinear,-13.076,-8.644,-24
crossvit_9_240.in1k,62.250,37.750,84.240,15.760,8.55,240,0.875,bicubic,-11.710,-7.722,+8
repghostnet_111.in1k,62.250,37.750,83.880,16.120,4.54,224,0.875,bicubic,-12.806,-8.312,-15
crossvit_tiny_240.in1k,62.080,37.920,83.610,16.390,7.01,240,0.875,bicubic,-11.260,-8.298,+16
regnetx_004_tv.tv2_in1k,62.060,37.940,83.770,16.230,5.50,224,0.965,bicubic,-12.540,-8.400,-5
resnet18d.ra2_in1k,62.000,38.000,83.790,16.210,11.71,288,0.950,bicubic,-11.794,-8.048,+9
repvgg_a1.rvgg_in1k,61.970,38.030,83.040,16.960,14.09,224,0.875,bilinear,-12.492,-8.816,-4
efficientvit_m4.r224_in1k,61.950,38.050,83.580,16.420,8.80,224,0.875,bicubic,-12.418,-8.400,-4
mnasnet_100.rmsp_in1k,61.920,38.080,83.700,16.300,4.38,224,0.875,bicubic,-12.732,-8.422,-12
vgg19_bn.tv_in1k,61.870,38.130,83.450,16.550,143.68,224,0.875,bilinear,-12.346,-8.394,-4
regnety_004.pycls_in1k,61.840,38.160,83.410,16.590,4.34,224,0.875,bicubic,-12.186,-8.338,-2
convit_tiny.fb_in1k,61.590,38.410,84.090,15.910,5.71,224,0.875,bicubic,-11.522,-7.622,+12
resnet18.a1_in1k,61.580,38.420,82.470,17.530,11.69,288,1.000,bicubic,-11.578,-8.556,+10
resnet34.a3_in1k,61.490,38.510,82.610,17.390,21.80,224,0.950,bicubic,-11.480,-8.496,+13
resnet18.fb_ssl_yfcc100m_ft_in1k,61.480,38.520,83.330,16.670,11.69,224,0.875,bilinear,-11.118,-8.086,+15
repghostnet_100.in1k,61.380,38.620,82.750,17.250,4.07,224,0.875,bicubic,-12.826,-8.792,-9
regnetx_006.pycls_in1k,61.360,38.640,83.450,16.550,6.20,224,0.875,bicubic,-12.508,-8.228,-3
hrnet_w18_small.gluon_in1k,61.290,38.710,82.280,17.720,13.19,224,0.875,bicubic,-12.630,-8.914,-6
ghostnet_100.in1k,61.250,38.750,82.300,17.700,5.18,224,0.875,bicubic,-12.708,-9.232,-8
spnasnet_100.rmsp_in1k,61.240,38.760,82.760,17.240,4.42,224,0.875,bilinear,-12.854,-9.060,-12
resnet34.tv_in1k,61.200,38.800,82.740,17.260,21.80,224,0.875,bilinear,-12.106,-8.680,0
vit_base_patch32_224.augreg_in1k,61.040,38.960,82.730,17.270,88.22,224,0.900,bicubic,-13.854,-9.048,-29
efficientvit_m3.r224_in1k,61.010,38.990,83.200,16.800,6.90,224,0.875,bicubic,-12.364,-8.148,-5
pit_ti_224.in1k,60.960,39.040,83.850,16.150,4.85,224,0.900,bicubic,-11.950,-7.554,+5
skresnet18.ra_in1k,60.850,39.150,82.850,17.150,11.96,224,0.875,bicubic,-12.184,-8.322,0
vgg16_bn.tv_in1k,60.770,39.230,82.960,17.040,138.37,224,0.875,bilinear,-12.600,-8.554,-7
semnasnet_075.rmsp_in1k,60.720,39.280,82.540,17.460,2.91,224,0.875,bicubic,-12.284,-8.600,-1
tf_mobilenetv3_large_075.in1k,60.380,39.620,81.970,18.030,3.99,224,0.875,bilinear,-13.050,-9.382,-11
xcit_nano_12_p16_224.fb_dist_in1k,60.220,39.780,82.490,17.510,3.05,224,1.000,bicubic,-12.090,-8.370,+7
resnet18.a2_in1k,60.200,39.800,81.840,18.160,11.69,288,1.000,bicubic,-12.172,-8.756,+4
mobilenetv2_100.ra_in1k,60.150,39.850,82.190,17.810,3.50,224,0.875,bicubic,-12.818,-8.826,-3
vit_base_patch32_224.sam_in1k,59.990,40.010,81.220,18.780,88.22,224,0.900,bicubic,-13.704,-9.794,-16
deit_tiny_patch16_224.fb_in1k,59.800,40.200,82.660,17.340,5.72,224,0.900,bicubic,-12.370,-8.456,+7
legacy_seresnet18.in1k,59.790,40.210,81.680,18.320,11.78,224,0.875,bicubic,-11.970,-8.652,+11
repvgg_a0.rvgg_in1k,59.760,40.240,81.250,18.750,9.11,224,0.875,bilinear,-12.648,-9.242,-4
vgg19.tv_in1k,59.710,40.290,81.460,18.540,143.67,224,0.875,bilinear,-12.668,-9.414,-3
edgenext_xx_small.in1k,59.410,40.590,81.840,18.160,1.33,288,1.000,bicubic,-12.468,-8.712,+6
regnetx_004.pycls_in1k,59.380,40.620,81.740,18.260,5.16,224,0.875,bicubic,-13.022,-9.086,-6
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,59.080,40.920,81.760,18.240,6.34,224,0.900,bicubic,-12.718,-9.064,+4
tf_mobilenetv3_large_minimal_100.in1k,59.080,40.920,81.130,18.870,3.92,224,0.875,bilinear,-13.184,-9.510,-2
repghostnet_080.in1k,59.060,40.940,81.160,18.840,3.28,224,0.875,bicubic,-13.152,-9.324,-2
hrnet_w18_small.ms_in1k,58.960,41.040,81.340,18.660,13.19,224,0.875,bilinear,-13.376,-9.340,-7
vgg13_bn.tv_in1k,58.960,41.040,81.090,18.910,133.05,224,0.875,bilinear,-12.628,-9.288,+4
lcnet_100.ra2_in1k,58.920,41.080,81.200,18.800,2.95,224,0.875,bicubic,-13.182,-9.154,-3
vgg16.tv_in1k,58.840,41.160,81.660,18.340,138.36,224,0.875,bilinear,-12.752,-8.724,+1
pvt_v2_b0.in1k,58.770,41.230,82.120,17.880,3.67,224,0.900,bicubic,-11.890,-8.076,+7
mobileone_s0.apple_in1k,58.580,41.420,80.080,19.920,5.29,224,0.875,bilinear,-12.822,-9.762,+1
efficientvit_m2.r224_in1k,58.420,41.580,81.360,18.640,4.19,224,0.875,bicubic,-12.394,-8.782,+4
resnet18.gluon_in1k,58.340,41.660,80.970,19.030,11.69,224,0.875,bicubic,-12.494,-8.786,+2
xcit_nano_12_p16_224.fb_in1k,58.330,41.670,80.920,19.080,3.05,224,1.000,bicubic,-11.632,-8.842,+7
tinynet_c.in1k,58.200,41.800,80.280,19.720,2.46,184,0.875,bicubic,-13.042,-9.452,-1
resnet14t.c3_in1k,58.190,41.810,80.300,19.700,10.08,224,0.950,bicubic,-14.064,-10.006,-14
efficientvit_b0.r224_in1k,58.050,41.950,79.800,20.200,3.41,224,0.950,bicubic,-13.348,-9.628,-4
mobilevitv2_050.cvnets_in1k,57.700,42.300,80.880,19.120,1.37,256,0.888,bicubic,-12.448,-9.038,+2
vgg11_bn.tv_in1k,57.410,42.590,80.010,19.990,132.87,224,0.875,bilinear,-12.972,-9.798,-1
resnet18.tv_in1k,57.180,42.820,80.190,19.810,11.69,224,0.875,bilinear,-12.580,-8.880,+3
mobilevit_xxs.cvnets_in1k,57.170,42.830,79.760,20.240,1.27,256,0.900,bicubic,-11.748,-9.186,+4
vgg13.tv_in1k,57.140,42.860,79.550,20.450,133.05,224,0.875,bilinear,-12.792,-9.700,0
regnety_002.pycls_in1k,57.010,42.990,79.880,20.120,3.16,224,0.875,bicubic,-13.270,-9.650,-4
mixer_l16_224.goog_in21k_ft_in1k,56.660,43.340,76.010,23.990,208.20,224,0.875,bicubic,-15.394,-11.664,-18
repghostnet_058.in1k,56.110,43.890,78.510,21.490,2.55,224,0.875,bicubic,-12.804,-9.910,+1
regnetx_002.pycls_in1k,56.070,43.930,79.210,20.790,2.68,224,0.875,bicubic,-12.682,-9.332,+2
resnet18.a3_in1k,56.000,44.000,78.970,21.030,11.69,224,0.950,bicubic,-12.252,-9.202,+4
dla60x_c.in1k,55.990,44.010,78.970,21.030,1.32,224,0.875,bilinear,-11.922,-9.462,+5
vgg11.tv_in1k,55.820,44.180,78.830,21.170,132.86,224,0.875,bilinear,-13.202,-9.794,-5
resnet10t.c3_in1k,55.560,44.440,78.440,21.560,5.44,224,0.950,bicubic,-12.804,-9.596,-1
efficientvit_m1.r224_in1k,55.470,44.530,79.150,20.850,2.98,224,0.875,bicubic,-12.836,-9.520,-1
lcnet_075.ra2_in1k,55.440,44.560,78.350,21.650,2.36,224,0.875,bicubic,-13.342,-10.010,-5
mobilenetv3_small_100.lamb_in1k,54.680,45.320,77.770,22.230,2.54,224,0.875,bicubic,-12.978,-9.866,+1
tf_mobilenetv3_small_100.in1k,54.470,45.530,77.070,22.930,2.54,224,0.875,bilinear,-13.452,-10.602,-2
repghostnet_050.in1k,53.550,46.450,76.740,23.260,2.31,224,0.875,bicubic,-13.416,-10.180,+1
tinynet_d.in1k,53.430,46.570,76.320,23.680,2.34,152,0.875,bicubic,-13.542,-10.746,-1
mnasnet_small.lamb_in1k,53.270,46.730,75.910,24.090,2.03,224,0.875,bicubic,-12.926,-10.594,0
dla46x_c.in1k,53.040,46.960,76.840,23.160,1.07,224,0.875,bilinear,-12.952,-10.134,0
mobilenetv2_050.lamb_in1k,52.840,47.160,75.450,24.550,1.97,224,0.875,bicubic,-13.108,-10.634,0
dla46_c.in1k,52.200,47.800,75.670,24.330,1.30,224,0.875,bilinear,-12.672,-10.628,+2
tf_mobilenetv3_small_075.in1k,52.160,47.840,75.450,24.550,2.04,224,0.875,bilinear,-13.566,-10.682,-1
mobilenetv3_small_075.lamb_in1k,51.890,48.110,74.740,25.260,2.04,224,0.875,bicubic,-13.346,-10.706,-1
efficientvit_m0.r224_in1k,50.750,49.250,74.470,25.530,2.35,224,0.875,bicubic,-12.520,-10.706,0
lcnet_050.ra2_in1k,49.980,50.020,73.440,26.560,1.88,224,0.875,bicubic,-13.158,-10.942,0
tf_mobilenetv3_small_minimal_100.in1k,49.530,50.470,73.020,26.980,2.04,224,0.875,bilinear,-13.364,-11.218,0
tinynet_e.in1k,46.730,53.270,70.360,29.640,2.04,106,0.875,bicubic,-13.136,-11.402,0
mobilenetv3_small_050.lamb_in1k,44.850,55.150,67.690,32.310,1.59,224,0.875,bicubic,-13.066,-12.490,0
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/model_metadata-in1k.csv | model,pretrain
adv_inception_v3,in1k-adv
bat_resnext26ts,in1k
beit_base_patch16_224,in21k-selfsl
beit_base_patch16_384,in21k-selfsl
beit_large_patch16_224,in21k-selfsl
beit_large_patch16_384,in21k-selfsl
beit_large_patch16_512,in21k-selfsl
botnet26t_256,in1k
cait_m36_384,in1k-dist
cait_m48_448,in1k-dist
cait_s24_224,in1k-dist
cait_s24_384,in1k-dist
cait_s36_384,in1k-dist
cait_xs24_384,in1k-dist
cait_xxs24_224,in1k-dist
cait_xxs24_384,in1k-dist
cait_xxs36_224,in1k-dist
cait_xxs36_384,in1k-dist
coat_lite_mini,in1k
coat_lite_small,in1k
coat_lite_tiny,in1k
coat_mini,in1k
coat_tiny,in1k
convit_base,in1k
convit_small,in1k
convit_tiny,in1k
convmixer_1024_20_ks9_p14,in1k
convmixer_1536_20,in1k
convmixer_768_32,in1k
crossvit_15_240,in1k
crossvit_15_dagger_240,in1k
crossvit_15_dagger_408,in1k
crossvit_18_240,in1k
crossvit_18_dagger_240,in1k
crossvit_18_dagger_408,in1k
crossvit_9_240,in1k
crossvit_9_dagger_240,in1k
crossvit_base_240,in1k
crossvit_small_240,in1k
crossvit_tiny_240,in1k
cspdarknet53,in1k
cspresnet50,in1k
cspresnext50,in1k
deit_base_distilled_patch16_224,in1k-dist
deit_base_distilled_patch16_384,in1k-dist
deit_base_patch16_224,in1k
deit_base_patch16_384,in1k
deit_small_distilled_patch16_224,in1k-dist
deit_small_patch16_224,in1k
deit_tiny_distilled_patch16_224,in1k-dist
deit_tiny_patch16_224,in1k
densenet121,in1k
densenet161,in1k
densenet169,in1k
densenet201,in1k
densenetblur121d,in1k
dla102,in1k
dla102x,in1k
dla102x2,in1k
dla169,in1k
dla34,in1k
dla46_c,in1k
dla46x_c,in1k
dla60,in1k
dla60_res2net,in1k
dla60_res2next,in1k
dla60x,in1k
dla60x_c,in1k
dm_nfnet_f0,in1k
dm_nfnet_f1,in1k
dm_nfnet_f2,in1k
dm_nfnet_f3,in1k
dm_nfnet_f4,in1k
dm_nfnet_f5,in1k
dm_nfnet_f6,in1k
dpn107,in1k
dpn131,in1k
dpn68,in1k
dpn68b,in1k
dpn92,in1k
dpn98,in1k
eca_botnext26ts_256,in1k
eca_halonext26ts,in1k
eca_nfnet_l0,in1k
eca_nfnet_l1,in1k
eca_nfnet_l2,in1k
eca_resnet33ts,in1k
eca_resnext26ts,in1k
ecaresnet101d,in1k
ecaresnet101d_pruned,in1k
ecaresnet269d,in1k
ecaresnet26t,in1k
ecaresnet50d,in1k
ecaresnet50d_pruned,in1k
ecaresnet50t,in1k
ecaresnetlight,in1k
efficientnet_b0,in1k
efficientnet_b1,in1k
efficientnet_b1_pruned,in1k
efficientnet_b2,in1k
efficientnet_b2_pruned,in1k
efficientnet_b3,in1k
efficientnet_b3_pruned,in1k
efficientnet_b4,in1k
efficientnet_el,in1k
efficientnet_el_pruned,in1k
efficientnet_em,in1k
efficientnet_es,in1k
efficientnet_es_pruned,in1k
efficientnet_lite0,in1k
efficientnetv2_rw_m,in1k
efficientnetv2_rw_s,in1k
efficientnetv2_rw_t,in1k
ens_adv_inception_resnet_v2,in1k-adv
ese_vovnet19b_dw,in1k
ese_vovnet39b,in1k
fbnetc_100,in1k
gc_efficientnetv2_rw_t,in1k
gcresnet33ts,in1k
gcresnet50t,in1k
gcresnext26ts,in1k
gcresnext50ts,in1k
gernet_l,in1k
gernet_m,in1k
gernet_s,in1k
ghostnet_100,in1k
gluon_inception_v3,in1k
gluon_resnet101_v1b,in1k
gluon_resnet101_v1c,in1k
gluon_resnet101_v1d,in1k
gluon_resnet101_v1s,in1k
gluon_resnet152_v1b,in1k
gluon_resnet152_v1c,in1k
gluon_resnet152_v1d,in1k
gluon_resnet152_v1s,in1k
gluon_resnet18_v1b,in1k
gluon_resnet34_v1b,in1k
gluon_resnet50_v1b,in1k
gluon_resnet50_v1c,in1k
gluon_resnet50_v1d,in1k
gluon_resnet50_v1s,in1k
gluon_resnext101_32x4d,in1k
gluon_resnext101_64x4d,in1k
gluon_resnext50_32x4d,in1k
gluon_senet154,in1k
gluon_seresnext101_32x4d,in1k
gluon_seresnext101_64x4d,in1k
gluon_seresnext50_32x4d,in1k
gluon_xception65,in1k
gmixer_24_224,in1k
gmlp_s16_224,in1k
halo2botnet50ts_256,in1k
halonet26t,in1k
halonet50ts,in1k
haloregnetz_b,in1k
hardcorenas_a,in1k
hardcorenas_b,in1k
hardcorenas_c,in1k
hardcorenas_d,in1k
hardcorenas_e,in1k
hardcorenas_f,in1k
hrnet_w18,in1k
hrnet_w18_small,in1k
hrnet_w18_small_v2,in1k
hrnet_w30,in1k
hrnet_w32,in1k
hrnet_w40,in1k
hrnet_w44,in1k
hrnet_w48,in1k
hrnet_w64,in1k
ig_resnext101_32x16d,ig1b-wsl
ig_resnext101_32x32d,ig1b-wsl
ig_resnext101_32x48d,ig1b-wsl
ig_resnext101_32x8d,ig1b-wsl
inception_resnet_v2,in1k
inception_v3,in1k
inception_v4,in1k
jx_nest_base,in1k
jx_nest_small,in1k
jx_nest_tiny,in1k
lambda_resnet26rpt_256,in1k
lambda_resnet26t,in1k
lambda_resnet50ts,in1k
lamhalobotnet50ts_256,in1k
legacy_senet154,in1k
legacy_seresnet101,in1k
legacy_seresnet152,in1k
legacy_seresnet18,in1k
legacy_seresnet34,in1k
legacy_seresnet50,in1k
legacy_seresnext101_32x4d,in1k
legacy_seresnext26_32x4d,in1k
legacy_seresnext50_32x4d,in1k
levit_128,in1k-dist
levit_128s,in1k-dist
levit_192,in1k-dist
levit_256,in1k-dist
levit_384,in1k-dist
mixer_b16_224,in1k
mixer_b16_224_miil,in21k
mixer_l16_224,in1k
mixnet_l,in1k
mixnet_m,in1k
mixnet_s,in1k
mixnet_xl,in1k
mnasnet_100,in1k
mobilenetv2_100,in1k
mobilenetv2_110d,in1k
mobilenetv2_120d,in1k
mobilenetv2_140,in1k
mobilenetv3_large_100,in1k
mobilenetv3_large_100_miil,in21k
mobilenetv3_rw,in1k
nasnetalarge,in1k
nf_regnet_b1,in1k
nf_resnet50,in1k
nfnet_l0,in1k
pit_b_224,in1k
pit_b_distilled_224,in1k-dist
pit_s_224,in1k
pit_s_distilled_224,in1k-dist
pit_ti_224,in1k
pit_ti_distilled_224,in1k-dist
pit_xs_224,in1k
pit_xs_distilled_224,in1k-dist
pnasnet5large,in1k
regnetx_002,in1k
regnetx_004,in1k
regnetx_006,in1k
regnetx_008,in1k
regnetx_016,in1k
regnetx_032,in1k
regnetx_040,in1k
regnetx_064,in1k
regnetx_080,in1k
regnetx_120,in1k
regnetx_160,in1k
regnetx_320,in1k
regnety_002,in1k
regnety_004,in1k
regnety_006,in1k
regnety_008,in1k
regnety_016,in1k
regnety_032,in1k
regnety_040,in1k
regnety_064,in1k
regnety_080,in1k
regnety_120,in1k
regnety_160,in1k
regnety_320,in1k
regnetz_b,in1k
regnetz_c,in1k
regnetz_d,in1k
repvgg_a2,in1k
repvgg_b0,in1k
repvgg_b1,in1k
repvgg_b1g4,in1k
repvgg_b2,in1k
repvgg_b2g4,in1k
repvgg_b3,in1k
repvgg_b3g4,in1k
res2net101_26w_4s,in1k
res2net50_14w_8s,in1k
res2net50_26w_4s,in1k
res2net50_26w_6s,in1k
res2net50_26w_8s,in1k
res2net50_48w_2s,in1k
res2next50,in1k
resmlp_12_224,in1k
resmlp_12_distilled_224,in1k-dist
resmlp_24_224,in1k
resmlp_24_distilled_224,in1k-dist
resmlp_36_224,in1k
resmlp_36_distilled_224,in1k-dist
resmlp_big_24_224,in1k
resmlp_big_24_224_in22ft1k,in21k
resmlp_big_24_distilled_224,in1k-dist
resnest101e,in1k
resnest14d,in1k
resnest200e,in1k
resnest269e,in1k
resnest26d,in1k
resnest50d,in1k
resnest50d_1s4x24d,in1k
resnest50d_4s2x40d,in1k
resnet101d,in1k
resnet152d,in1k
resnet18,in1k
resnet18d,in1k
resnet200d,in1k
resnet26,in1k
resnet26d,in1k
resnet26t,in1k
resnet32ts,in1k
resnet33ts,in1k
resnet34,in1k
resnet34d,in1k
resnet50,in1k
resnet50d,in1k
resnet51q,in1k
resnet61q,in1k
resnetblur50,in1k
resnetrs101,in1k
resnetrs152,in1k
resnetrs200,in1k
resnetrs270,in1k
resnetrs350,in1k
resnetrs420,in1k
resnetrs50,in1k
resnetv2_101,in1k
resnetv2_101x1_bitm,in21k
resnetv2_101x3_bitm,in21k
resnetv2_152x2_bit_teacher,in21k
resnetv2_152x2_bit_teacher_384,in21k
resnetv2_152x2_bitm,in21k
resnetv2_152x4_bitm,in21k
resnetv2_50,in1k
resnetv2_50x1_bit_distilled,in1k-dist
resnetv2_50x1_bitm,in21k
resnetv2_50x3_bitm,in21k
resnext101_32x8d,in1k
resnext26ts,in1k
resnext50_32x4d,in1k
resnext50d_32x4d,in1k
rexnet_100,in1k
rexnet_130,in1k
rexnet_150,in1k
rexnet_200,in1k
sehalonet33ts,in1k
selecsls42b,in1k
selecsls60,in1k
selecsls60b,in1k
semnasnet_100,in1k
seresnet152d,in1k
seresnet33ts,in1k
seresnet50,in1k
seresnext26d_32x4d,in1k
seresnext26t_32x4d,in1k
seresnext26ts,in1k
seresnext50_32x4d,in1k
skresnet18,in1k
skresnet34,in1k
skresnext50_32x4d,in1k
spnasnet_100,in1k
ssl_resnet18,yfc-semisl
ssl_resnet50,yfc-semisl
ssl_resnext101_32x16d,yfc-semisl
ssl_resnext101_32x4d,yfc-semisl
ssl_resnext101_32x8d,yfc-semisl
ssl_resnext50_32x4d,yfc-semisl
swin_base_patch4_window12_384,in21k
swin_base_patch4_window7_224,in21k
swin_large_patch4_window12_384,in21k
swin_large_patch4_window7_224,in21k
swin_small_patch4_window7_224,in1k
swin_tiny_patch4_window7_224,in1k
swsl_resnet18,ig1b-swsl
swsl_resnet50,ig1b-swsl
swsl_resnext101_32x16d,ig1b-swsl
swsl_resnext101_32x4d,ig1b-swsl
swsl_resnext101_32x8d,ig1b-swsl
swsl_resnext50_32x4d,ig1b-swsl
tf_efficientnet_b0,in1k
tf_efficientnet_b0_ap,in1k-ap
tf_efficientnet_b0_ns,jft300m-ns
tf_efficientnet_b1,in1k
tf_efficientnet_b1_ap,in1k-ap
tf_efficientnet_b1_ns,jft300m-ns
tf_efficientnet_b2,in1k
tf_efficientnet_b2_ap,in1k-ap
tf_efficientnet_b2_ns,jft300m-ns
tf_efficientnet_b3,in1k
tf_efficientnet_b3_ap,in1k-ap
tf_efficientnet_b3_ns,jft300m-ns
tf_efficientnet_b4,in1k
tf_efficientnet_b4_ap,in1k-ap
tf_efficientnet_b4_ns,jft300m-ns
tf_efficientnet_b5,in1k
tf_efficientnet_b5_ap,in1k-ap
tf_efficientnet_b5_ns,jft300m-ns
tf_efficientnet_b6,in1k
tf_efficientnet_b6_ap,in1k-ap
tf_efficientnet_b6_ns,jft300m-ns
tf_efficientnet_b7,in1k
tf_efficientnet_b7_ap,in1k-ap
tf_efficientnet_b7_ns,jft300m-ns
tf_efficientnet_b8,in1k
tf_efficientnet_b8_ap,in1k-ap
tf_efficientnet_cc_b0_4e,in1k
tf_efficientnet_cc_b0_8e,in1k
tf_efficientnet_cc_b1_8e,in1k
tf_efficientnet_el,in1k
tf_efficientnet_em,in1k
tf_efficientnet_es,in1k
tf_efficientnet_l2_ns,jft300m-ns
tf_efficientnet_l2_ns_475,jft300m-ns
tf_efficientnet_lite0,in1k
tf_efficientnet_lite1,in1k
tf_efficientnet_lite2,in1k
tf_efficientnet_lite3,in1k
tf_efficientnet_lite4,in1k
tf_efficientnetv2_b0,in1k
tf_efficientnetv2_b1,in1k
tf_efficientnetv2_b2,in1k
tf_efficientnetv2_b3,in1k
tf_efficientnetv2_l,in1k
tf_efficientnetv2_l_in21ft1k,in21k
tf_efficientnetv2_m,in1k
tf_efficientnetv2_m_in21ft1k,in21k
tf_efficientnetv2_s,in1k
tf_efficientnetv2_s_in21ft1k,in21k
tf_efficientnetv2_xl_in21ft1k,in21k
tf_inception_v3,in1k
tf_mixnet_l,in1k
tf_mixnet_m,in1k
tf_mixnet_s,in1k
tf_mobilenetv3_large_075,in1k
tf_mobilenetv3_large_100,in1k
tf_mobilenetv3_large_minimal_100,in1k
tf_mobilenetv3_small_075,in1k
tf_mobilenetv3_small_100,in1k
tf_mobilenetv3_small_minimal_100,in1k
tnt_s_patch16_224,in1k
tresnet_l,in1k
tresnet_l_448,in1k
tresnet_m,in21k
tresnet_m_448,in1k
tresnet_xl,in1k
tresnet_xl_448,in1k
tv_densenet121,in1k
tv_resnet101,in1k
tv_resnet152,in1k
tv_resnet34,in1k
tv_resnet50,in1k
tv_resnext50_32x4d,in1k
twins_pcpvt_base,in1k
twins_pcpvt_large,in1k
twins_pcpvt_small,in1k
twins_svt_base,in1k
twins_svt_large,in1k
twins_svt_small,in1k
vgg11,in1k
vgg11_bn,in1k
vgg13,in1k
vgg13_bn,in1k
vgg16,in1k
vgg16_bn,in1k
vgg19,in1k
vgg19_bn,in1k
visformer_small,in1k
vit_base_patch16_224,in21k
vit_base_patch16_224_miil,in21k
vit_base_patch16_384,in21k
vit_base_patch16_224_sam,in1k
vit_base_patch32_224,in21k
vit_base_patch32_384,in21k
vit_base_patch32_224_sam,in1k
vit_base_r50_s16_384,in21k
vit_large_patch16_224,in21k
vit_large_patch16_384,in21k
vit_large_patch32_384,in21k
vit_large_r50_s32_224,in21k
vit_large_r50_s32_384,in21k
vit_small_patch16_224,in21k
vit_small_patch16_384,in21k
vit_small_patch32_224,in21k
vit_small_patch32_384,in21k
vit_small_r26_s32_224,in21k
vit_small_r26_s32_384,in21k
vit_tiny_patch16_224,in21k
vit_tiny_patch16_384,in21k
vit_tiny_r_s16_p8_224,in21k
vit_tiny_r_s16_p8_384,in21k
wide_resnet101_2,in1k
wide_resnet50_2,in1k
xception,in1k
xception41,in1k
xception65,in1k
xception71,in1k
xcit_large_24_p16_224,in1k
xcit_large_24_p16_224_dist,in1k-dist
xcit_large_24_p16_384_dist,in1k-dist
xcit_large_24_p8_224,in1k
xcit_large_24_p8_224_dist,in1k-dist
xcit_large_24_p8_384_dist,in1k-dist
xcit_medium_24_p16_224,in1k
xcit_medium_24_p16_224_dist,in1k-dist
xcit_medium_24_p16_384_dist,in1k-dist
xcit_medium_24_p8_224,in1k
xcit_medium_24_p8_224_dist,in1k-dist
xcit_medium_24_p8_384_dist,in1k-dist
xcit_nano_12_p16_224,in1k
xcit_nano_12_p16_224_dist,in1k-dist
xcit_nano_12_p16_384_dist,in1k-dist
xcit_nano_12_p8_224,in1k
xcit_nano_12_p8_224_dist,in1k-dist
xcit_nano_12_p8_384_dist,in1k-dist
xcit_small_12_p16_224,in1k
xcit_small_12_p16_224_dist,in1k-dist
xcit_small_12_p16_384_dist,in1k-dist
xcit_small_12_p8_224,in1k
xcit_small_12_p8_224_dist,in1k-dist
xcit_small_12_p8_384_dist,in1k-dist
xcit_small_24_p16_224,in1k
xcit_small_24_p16_224_dist,in1k-dist
xcit_small_24_p16_384_dist,in1k-dist
xcit_small_24_p8_224,in1k
xcit_small_24_p8_224_dist,in1k-dist
xcit_small_24_p8_384_dist,in1k-dist
xcit_tiny_12_p16_224,in1k
xcit_tiny_12_p16_224_dist,in1k-dist
xcit_tiny_12_p16_384_dist,in1k-dist
xcit_tiny_12_p8_224,in1k
xcit_tiny_12_p8_224_dist,in1k-dist
xcit_tiny_12_p8_384_dist,in1k-dist
xcit_tiny_24_p16_224,in1k
xcit_tiny_24_p16_224_dist,in1k-dist
xcit_tiny_24_p16_384_dist,in1k-dist
xcit_tiny_24_p8_224,in1k
xcit_tiny_24_p8_224_dist,in1k-dist
xcit_tiny_24_p8_384_dist,in1k-dist
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt210-cu121-rtx3090.csv | model,infer_img_size,infer_batch_size,infer_samples_per_sec,infer_step_time,infer_gmacs,infer_macts,param_count
tinynet_e,106,1024.0,75290.96,13.591,0.03,0.69,2.04
mobilenetv3_small_050,224,1024.0,56785.93,18.023,0.03,0.92,1.59
efficientvit_m0,224,1024.0,50656.23,20.205,0.08,0.91,2.35
lcnet_035,224,1024.0,48853.22,20.951,0.03,1.04,1.64
lcnet_050,224,1024.0,42147.98,24.285,0.05,1.26,1.88
mobilenetv3_small_075,224,1024.0,42002.46,24.369,0.05,1.3,2.04
mobilenetv3_small_100,224,1024.0,38516.23,26.573,0.06,1.42,2.54
tinynet_d,152,1024.0,37989.71,26.944,0.05,1.42,2.34
efficientvit_m1,224,1024.0,37486.44,27.306,0.17,1.33,2.98
tf_mobilenetv3_small_minimal_100,224,1024.0,33948.13,30.153,0.06,1.41,2.04
efficientvit_m2,224,1024.0,33551.67,30.51,0.2,1.47,4.19
tf_mobilenetv3_small_075,224,1024.0,33262.15,30.775,0.05,1.3,2.04
tf_mobilenetv3_small_100,224,1024.0,31002.71,33.019,0.06,1.42,2.54
lcnet_075,224,1024.0,30664.19,33.384,0.1,1.99,2.36
efficientvit_m3,224,1024.0,29423.78,34.792,0.27,1.62,6.9
efficientvit_m4,224,1024.0,27882.1,36.716,0.3,1.7,8.8
mnasnet_small,224,1024.0,25015.02,40.925,0.07,2.16,2.03
regnetx_002,224,1024.0,24564.71,41.67,0.2,2.16,2.68
lcnet_100,224,1024.0,24268.72,42.183,0.16,2.52,2.95
levit_128s,224,1024.0,22705.11,45.089,0.31,1.88,7.78
regnety_002,224,1024.0,22248.91,46.012,0.2,2.17,3.16
resnet10t,176,1024.0,22236.3,46.04,0.7,1.51,5.44
mobilenetv2_035,224,1024.0,22055.42,46.418,0.07,2.86,1.68
levit_conv_128s,224,1024.0,21863.15,46.826,0.31,1.88,7.78
ghostnet_050,224,1024.0,20782.95,49.261,0.05,1.77,2.59
mnasnet_050,224,1024.0,20672.17,49.525,0.11,3.07,2.22
repghostnet_050,224,1024.0,20617.05,49.657,0.05,2.02,2.31
efficientvit_m5,224,1024.0,19010.14,53.856,0.53,2.41,12.47
tinynet_c,184,1024.0,18737.07,54.641,0.11,2.87,2.46
efficientvit_b0,224,1024.0,18023.56,56.804,0.1,2.87,3.41
semnasnet_050,224,1024.0,17573.38,58.26,0.11,3.44,2.08
mobilenetv2_050,224,1024.0,17491.5,58.532,0.1,3.64,1.97
regnetx_004,224,1024.0,17164.74,59.647,0.4,3.14,5.16
repghostnet_058,224,1024.0,16947.81,60.41,0.07,2.59,2.55
regnetx_004_tv,224,1024.0,16485.73,62.101,0.42,3.17,5.5
vit_small_patch32_224,224,1024.0,16428.86,62.319,1.12,2.09,22.88
cs3darknet_focus_s,256,1024.0,16333.25,62.684,0.69,2.7,3.27
lcnet_150,224,1024.0,15841.02,64.632,0.34,3.79,4.5
gernet_s,224,1024.0,15617.62,65.556,0.75,2.65,8.17
cs3darknet_s,256,1024.0,15597.89,65.64,0.72,2.97,3.28
levit_128,224,1024.0,15372.6,66.601,0.41,2.71,9.21
vit_tiny_r_s16_p8_224,224,1024.0,15191.19,67.397,0.43,1.85,6.34
levit_conv_128,224,1024.0,14904.31,68.695,0.41,2.71,9.21
mobilenetv3_large_075,224,1024.0,14843.63,68.964,0.16,4.0,3.99
pit_ti_distilled_224,224,1024.0,14746.15,69.432,0.51,2.77,5.1
pit_ti_224,224,1024.0,14700.08,69.649,0.5,2.75,4.85
mixer_s32_224,224,1024.0,14362.24,71.288,1.0,2.28,19.1
resnet10t,224,1024.0,14254.88,71.825,1.1,2.43,5.44
repghostnet_080,224,1024.0,13967.84,73.293,0.1,3.22,3.28
tf_efficientnetv2_b0,192,1024.0,13629.52,75.121,0.54,3.51,7.14
mobilenetv3_rw,224,1024.0,13582.75,75.38,0.23,4.41,5.48
levit_192,224,1024.0,13511.34,75.778,0.66,3.2,10.95
mnasnet_075,224,1024.0,13417.36,76.309,0.23,4.77,3.17
mobilenetv3_large_100,224,1024.0,13322.79,76.851,0.23,4.41,5.48
hardcorenas_a,224,1024.0,13314.34,76.899,0.23,4.38,5.26
levit_conv_192,224,1024.0,12952.02,79.05,0.66,3.2,10.95
regnety_004,224,1024.0,12651.55,80.929,0.41,3.89,4.34
tf_mobilenetv3_large_075,224,1024.0,12636.69,81.023,0.16,4.0,3.99
nf_regnet_b0,192,1024.0,12264.41,83.481,0.37,3.15,8.76
tinynet_b,188,1024.0,12262.56,83.495,0.21,4.44,3.73
tf_mobilenetv3_large_minimal_100,224,1024.0,12182.74,84.043,0.22,4.4,3.92
hardcorenas_b,224,1024.0,12118.5,84.488,0.26,5.09,5.18
hardcorenas_c,224,1024.0,12088.28,84.699,0.28,5.01,5.52
resnet14t,176,1024.0,11843.82,86.448,1.07,3.61,10.08
mnasnet_100,224,1024.0,11686.43,87.612,0.33,5.46,4.38
regnety_006,224,1024.0,11675.48,87.69,0.61,4.33,6.06
ese_vovnet19b_slim_dw,224,1024.0,11663.91,87.781,0.4,5.28,1.9
repghostnet_100,224,1024.0,11508.79,88.956,0.15,3.98,4.07
tf_mobilenetv3_large_100,224,1024.0,11443.62,89.472,0.23,4.41,5.48
vit_tiny_patch16_224,224,1024.0,11342.82,90.267,1.08,4.12,5.72
hardcorenas_d,224,1024.0,11329.99,90.369,0.3,4.93,7.5
deit_tiny_distilled_patch16_224,224,1024.0,11311.9,90.514,1.09,4.15,5.91
deit_tiny_patch16_224,224,1024.0,11286.31,90.719,1.08,4.12,5.72
semnasnet_075,224,1024.0,11132.28,91.974,0.23,5.54,2.91
resnet18,224,1024.0,11101.69,92.228,1.82,2.48,11.69
ghostnet_100,224,1024.0,11039.87,92.744,0.15,3.55,5.18
mobilenetv2_075,224,1024.0,10984.87,93.208,0.22,5.86,2.64
spnasnet_100,224,1024.0,10557.11,96.986,0.35,6.03,4.42
tf_efficientnetv2_b1,192,1024.0,10473.04,97.765,0.76,4.59,8.14
regnetx_008,224,1024.0,10422.45,98.23,0.81,5.15,7.26
seresnet18,224,1024.0,10416.31,98.297,1.82,2.49,11.78
tf_efficientnetv2_b0,224,1024.0,10174.51,100.633,0.73,4.77,7.14
legacy_seresnet18,224,1024.0,10133.12,101.044,1.82,2.49,11.78
repghostnet_111,224,1024.0,10094.28,101.428,0.18,4.38,4.54
hardcorenas_f,224,1024.0,10012.95,102.257,0.35,5.57,8.2
tinynet_a,192,1024.0,9946.05,102.945,0.35,5.41,6.19
dla46_c,224,1024.0,9943.77,102.967,0.58,4.5,1.3
hardcorenas_e,224,1024.0,9851.75,103.931,0.35,5.65,8.07
semnasnet_100,224,1024.0,9823.16,104.233,0.32,6.23,3.89
levit_256,224,1024.0,9811.76,104.354,1.13,4.23,18.89
repvgg_a0,224,1024.0,9709.7,105.449,1.52,3.59,9.11
mobilenetv2_100,224,1024.0,9654.78,106.051,0.31,6.68,3.5
regnety_008,224,1024.0,9643.2,106.178,0.81,5.25,6.26
fbnetc_100,224,1024.0,9552.51,107.186,0.4,6.51,5.57
efficientnet_lite0,224,1024.0,9466.4,108.161,0.4,6.74,4.65
levit_conv_256,224,1024.0,9461.49,108.218,1.13,4.23,18.89
resnet18d,224,1024.0,9458.4,108.253,2.06,3.29,11.71
pit_xs_224,224,1024.0,9332.33,109.714,1.1,4.12,10.62
ese_vovnet19b_slim,224,1024.0,9277.16,110.369,1.69,3.52,3.17
regnety_008_tv,224,1024.0,9213.78,111.127,0.84,5.42,6.43
pit_xs_distilled_224,224,1024.0,9203.86,111.241,1.11,4.15,11.0
convnext_atto,224,1024.0,9104.06,112.467,0.55,3.81,3.7
repghostnet_130,224,1024.0,8873.05,115.395,0.25,5.24,5.48
ghostnet_130,224,1024.0,8870.81,115.424,0.24,4.6,7.36
convnext_atto_ols,224,1024.0,8829.55,115.964,0.58,4.11,3.7
regnetz_005,224,1024.0,8796.44,116.392,0.52,5.86,7.12
xcit_nano_12_p16_224,224,1024.0,8604.96,118.991,0.56,4.17,3.05
levit_256d,224,1024.0,8322.97,123.022,1.4,4.93,26.21
regnetx_006,224,1024.0,8320.1,123.064,0.61,3.98,6.2
tf_efficientnet_lite0,224,1024.0,8163.21,125.431,0.4,6.74,4.65
fbnetv3_b,224,1024.0,8152.31,125.598,0.42,6.97,8.6
efficientnet_b0,224,1024.0,8085.72,126.633,0.4,6.75,5.29
levit_conv_256d,224,1024.0,8055.13,127.113,1.4,4.93,26.21
edgenext_xx_small,256,1024.0,8014.51,127.757,0.26,3.33,1.33
mnasnet_140,224,1024.0,7984.3,128.241,0.6,7.71,7.12
convnext_femto,224,1024.0,7977.79,128.346,0.79,4.57,5.22
tf_efficientnetv2_b2,208,1024.0,7861.13,130.251,1.06,6.0,10.1
mobilevit_xxs,256,1024.0,7827.79,130.801,0.34,5.74,1.27
repghostnet_150,224,1024.0,7766.69,131.835,0.32,6.0,6.58
convnext_femto_ols,224,1024.0,7757.32,131.994,0.82,4.87,5.23
rexnetr_100,224,1024.0,7545.9,135.692,0.43,7.72,4.88
repvit_m1,224,1024.0,7543.44,135.728,0.83,7.45,5.49
resnet14t,224,1024.0,7466.4,137.137,1.69,5.8,10.08
mobilenetv2_110d,224,1024.0,7331.32,139.66,0.45,8.71,4.52
hrnet_w18_small,224,1024.0,7298.3,140.296,1.61,5.72,13.19
cs3darknet_focus_m,256,1024.0,7202.61,142.16,1.98,4.89,9.3
repvit_m0_9,224,1024.0,7165.5,142.888,0.83,7.45,5.49
crossvit_tiny_240,240,1024.0,7123.68,143.735,1.3,5.67,7.01
efficientvit_b1,224,1024.0,7109.59,144.02,0.53,7.25,9.1
tf_efficientnet_b0,224,1024.0,7104.21,144.129,0.4,6.75,5.29
crossvit_9_240,240,1024.0,7025.32,145.747,1.55,5.59,8.55
nf_regnet_b0,256,1024.0,6992.1,146.441,0.64,5.58,8.76
repvgg_a1,224,1024.0,6942.64,147.483,2.64,4.74,14.09
mobilevitv2_050,256,1024.0,6935.55,147.628,0.48,8.04,1.37
cs3darknet_m,256,1024.0,6929.59,147.762,2.08,5.28,9.31
efficientnet_b1_pruned,240,1024.0,6922.7,147.909,0.4,6.21,6.33
gernet_m,224,1024.0,6840.64,149.682,3.02,5.24,21.14
fbnetv3_d,224,1024.0,6784.35,150.925,0.52,8.5,10.31
semnasnet_140,224,1024.0,6771.35,151.215,0.6,8.87,6.11
crossvit_9_dagger_240,240,1024.0,6704.51,152.722,1.68,6.03,8.78
tf_efficientnetv2_b1,240,1024.0,6611.54,154.87,1.21,7.34,8.14
mobilenetv2_140,224,1024.0,6588.7,155.407,0.6,9.57,6.11
resnet34,224,1024.0,6504.25,157.425,3.67,3.74,21.8
ese_vovnet19b_dw,224,1024.0,6406.95,159.816,1.34,8.25,6.54
selecsls42,224,1024.0,6366.41,160.834,2.94,4.62,30.35
resnet18,288,1024.0,6354.7,161.13,3.01,4.11,11.69
selecsls42b,224,1024.0,6344.62,161.386,2.98,4.62,32.46
efficientnet_b0_g16_evos,224,1024.0,6342.4,161.442,1.01,7.42,8.11
edgenext_xx_small,288,1024.0,6334.97,161.631,0.33,4.21,1.33
efficientnet_lite1,240,1024.0,6268.15,163.355,0.62,10.14,5.42
pvt_v2_b0,224,1024.0,6254.52,163.711,0.53,7.01,3.67
visformer_tiny,224,1024.0,6218.29,164.665,1.27,5.72,10.32
convnext_pico,224,1024.0,6208.02,164.938,1.37,6.1,9.05
fbnetv3_b,256,1024.0,6192.25,165.357,0.55,9.1,8.6
efficientnet_es_pruned,224,1024.0,6175.39,165.809,1.81,8.73,5.44
efficientnet_es,224,1024.0,6170.12,165.95,1.81,8.73,5.44
rexnet_100,224,1024.0,6170.05,165.953,0.41,7.44,4.8
ghostnetv2_100,224,1024.0,6155.62,166.342,0.18,4.55,6.16
seresnet34,224,1024.0,6069.09,168.714,3.67,3.74,21.96
convnext_pico_ols,224,1024.0,6043.01,169.442,1.43,6.5,9.06
seresnet18,288,1024.0,5998.94,170.686,3.01,4.11,11.78
dla46x_c,224,1024.0,5992.19,170.877,0.54,5.66,1.07
dla34,224,1024.0,5954.72,171.952,3.07,5.02,15.74
repghostnet_200,224,1024.0,5934.75,172.524,0.54,7.96,9.8
resnet26,224,1024.0,5916.33,173.07,2.36,7.35,16.0
levit_384,224,1024.0,5897.4,173.625,2.36,6.26,39.13
resnet34d,224,1024.0,5884.13,174.017,3.91,4.54,21.82
cs3darknet_focus_m,288,1024.0,5878.89,174.173,2.51,6.19,9.3
legacy_seresnet34,224,1024.0,5873.4,174.335,3.67,3.74,21.96
repvit_m2,224,1024.0,5866.53,174.53,1.36,9.43,8.8
vit_base_patch32_224,224,1024.0,5866.04,174.553,4.37,4.19,88.22
vit_base_patch32_clip_224,224,1024.0,5864.79,174.59,4.37,4.19,88.22
repvit_m1_0,224,1024.0,5862.26,174.66,1.13,8.69,7.3
tf_efficientnet_es,224,1024.0,5831.76,175.58,1.81,8.73,5.44
rexnetr_130,224,1024.0,5827.09,175.72,0.68,9.81,7.61
resnetrs50,160,1024.0,5819.33,175.954,2.29,6.2,35.69
dla60x_c,224,1024.0,5709.85,179.326,0.59,6.01,1.32
vit_small_patch32_384,384,1024.0,5700.23,179.631,3.26,6.07,22.92
levit_conv_384,224,1024.0,5694.64,179.807,2.36,6.26,39.13
tiny_vit_5m_224,224,1024.0,5681.84,180.212,1.18,9.32,12.08
efficientnet_b1,224,1024.0,5671.54,180.54,0.59,9.36,7.79
cs3darknet_m,288,1024.0,5670.5,180.573,2.63,6.69,9.31
resnetblur18,224,1024.0,5631.98,181.808,2.34,3.39,11.69
tf_efficientnet_lite1,240,1024.0,5588.09,183.236,0.62,10.14,5.42
repvit_m1_1,224,1024.0,5584.25,183.355,1.36,9.43,8.8
mixnet_s,224,1024.0,5566.85,183.931,0.25,6.25,4.13
convnext_atto,288,1024.0,5556.64,184.274,0.91,6.3,3.7
darknet17,256,1024.0,5525.94,185.298,3.26,7.18,14.3
pit_s_224,224,1024.0,5520.06,185.491,2.42,6.18,23.46
resnet18d,288,1024.0,5497.35,186.262,3.41,5.43,11.71
selecsls60,224,1024.0,5496.69,186.283,3.59,5.52,30.67
pit_s_distilled_224,224,1024.0,5494.69,186.349,2.45,6.22,24.04
xcit_tiny_12_p16_224,224,1024.0,5472.11,187.12,1.24,6.29,6.72
selecsls60b,224,1024.0,5466.97,187.296,3.63,5.52,32.77
skresnet18,224,1024.0,5432.07,188.499,1.82,3.24,11.96
convnext_atto_ols,288,1024.0,5378.78,190.367,0.96,6.8,3.7
resmlp_12_224,224,1024.0,5371.14,190.637,3.01,5.5,15.35
regnetz_005,288,1024.0,5353.96,191.249,0.86,9.68,7.12
mobilenetv2_120d,224,1024.0,5347.39,191.484,0.69,11.97,5.83
convnextv2_atto,224,1024.0,5293.77,193.425,0.55,3.81,3.71
repvgg_b0,224,1024.0,5265.8,194.451,3.41,6.15,15.82
mixer_b32_224,224,1024.0,5245.72,195.191,3.24,6.29,60.29
vit_tiny_r_s16_p8_384,384,1024.0,5235.72,195.568,1.25,5.39,6.36
nf_regnet_b1,256,1024.0,5226.46,195.915,0.82,7.27,10.22
nf_regnet_b2,240,1024.0,5223.53,196.02,0.97,7.23,14.31
vit_base_patch32_clip_quickgelu_224,224,1024.0,5220.87,196.124,4.37,4.19,87.85
resnetaa34d,224,1024.0,5205.31,196.711,4.43,5.07,21.82
resnet26d,224,1024.0,5169.81,198.062,2.6,8.15,16.01
tf_mixnet_s,224,1024.0,5128.65,199.652,0.25,6.25,4.13
rexnetr_150,224,1024.0,5105.32,200.564,0.89,11.13,9.78
gmixer_12_224,224,1024.0,5083.79,201.414,2.67,7.26,12.7
fbnetv3_d,256,1024.0,5047.63,202.856,0.68,11.1,10.31
edgenext_x_small,256,1024.0,5018.94,204.014,0.54,5.93,2.34
mixer_s16_224,224,1024.0,5009.58,204.393,3.79,5.97,18.53
regnetz_b16,224,1024.0,5008.24,204.437,1.45,9.95,9.72
gmlp_ti16_224,224,1024.0,4999.44,204.811,1.34,7.55,5.87
darknet21,256,1024.0,4956.17,206.601,3.93,7.47,20.86
eva02_tiny_patch14_224,224,1024.0,4940.45,207.258,1.4,6.17,5.5
ghostnetv2_130,224,1024.0,4896.55,209.116,0.28,5.9,8.96
convnext_femto,288,1024.0,4844.52,211.362,1.3,7.56,5.22
nf_resnet26,224,1024.0,4822.21,212.339,2.41,7.35,16.0
efficientnet_lite2,260,1024.0,4817.66,212.541,0.89,12.9,6.09
tf_efficientnetv2_b2,260,1024.0,4797.27,213.444,1.72,9.84,10.1
efficientnet_cc_b0_8e,224,1024.0,4749.51,215.591,0.42,9.42,24.01
sedarknet21,256,1024.0,4747.46,215.684,3.93,7.47,20.95
efficientnet_cc_b0_4e,224,1024.0,4720.11,216.933,0.41,9.42,13.31
efficientnet_b2_pruned,260,1024.0,4716.64,217.093,0.73,9.13,8.31
convnext_femto_ols,288,1024.0,4709.5,217.422,1.35,8.06,5.23
resnext26ts,256,1024.0,4668.94,219.311,2.43,10.52,10.3
tiny_vit_11m_224,224,1024.0,4649.32,220.237,1.9,10.73,20.35
ecaresnet50d_pruned,224,1024.0,4636.78,220.832,2.53,6.43,19.94
deit_small_patch16_224,224,1024.0,4620.93,221.59,4.25,8.25,22.05
efficientformer_l1,224,1024.0,4616.64,221.795,1.3,5.53,12.29
vit_small_patch16_224,224,1024.0,4614.32,221.907,4.25,8.25,22.05
dpn48b,224,1024.0,4588.67,223.146,1.69,8.92,9.13
deit_small_distilled_patch16_224,224,1024.0,4587.3,223.214,4.27,8.29,22.44
vit_base_patch32_clip_256,256,1024.0,4547.51,225.168,5.68,5.44,87.86
convnextv2_femto,224,1024.0,4545.73,225.256,0.79,4.57,5.23
mobilevitv2_075,256,1024.0,4537.95,225.638,1.05,12.06,2.87
eca_resnext26ts,256,1024.0,4521.18,226.479,2.43,10.52,10.3
seresnext26ts,256,1024.0,4517.43,226.666,2.43,10.52,10.39
efficientnetv2_rw_t,224,1024.0,4511.98,226.94,1.93,9.94,13.65
legacy_seresnext26_32x4d,224,1024.0,4489.21,228.092,2.49,9.39,16.79
gernet_l,256,1024.0,4474.96,228.817,4.57,8.0,31.08
gcresnext26ts,256,1024.0,4472.11,228.964,2.43,10.53,10.48
rexnet_130,224,1024.0,4453.51,229.92,0.68,9.71,7.56
tf_efficientnet_b1,240,1024.0,4442.45,230.492,0.71,10.88,7.79
tf_efficientnet_cc_b0_8e,224,1024.0,4391.83,233.15,0.42,9.42,24.01
convnext_nano,224,1024.0,4389.78,233.258,2.46,8.37,15.59
gc_efficientnetv2_rw_t,224,1024.0,4373.41,234.132,1.94,9.97,13.68
tf_efficientnet_cc_b0_4e,224,1024.0,4373.37,234.134,0.41,9.42,13.31
tf_efficientnetv2_b3,240,1024.0,4372.06,234.204,1.93,9.95,14.36
tf_efficientnet_lite2,260,1024.0,4324.79,236.764,0.89,12.9,6.09
efficientnet_b1,256,1024.0,4298.75,238.198,0.77,12.22,7.79
deit3_small_patch16_224,224,1024.0,4270.38,239.779,4.25,8.25,22.06
cs3darknet_focus_l,256,1024.0,4230.07,242.066,4.66,8.03,21.15
nf_regnet_b1,288,1024.0,4135.98,247.568,1.02,9.2,10.22
convnext_nano_ols,224,1024.0,4118.16,248.644,2.65,9.38,15.65
nf_seresnet26,224,1024.0,4112.79,248.966,2.41,7.36,17.4
nf_ecaresnet26,224,1024.0,4107.39,249.292,2.41,7.36,16.0
efficientnet_b2,256,1024.0,4105.27,249.424,0.89,12.81,9.11
cs3darknet_l,256,1024.0,4101.41,249.66,4.86,8.55,21.16
nf_regnet_b2,272,1024.0,4097.18,249.913,1.22,9.27,14.31
ecaresnext50t_32x4d,224,1024.0,4074.12,251.332,2.7,10.09,15.41
ecaresnext26t_32x4d,224,1024.0,4072.14,251.454,2.7,10.09,15.41
seresnext26t_32x4d,224,1024.0,4061.05,252.141,2.7,10.09,16.81
repvgg_a2,224,1024.0,4049.32,252.867,5.7,6.26,28.21
poolformer_s12,224,1024.0,4047.55,252.981,1.82,5.53,11.92
seresnext26d_32x4d,224,1024.0,4037.54,253.609,2.73,10.19,16.81
regnetx_016,224,1024.0,4025.84,254.342,1.62,7.93,9.19
resnet26t,256,1024.0,4021.85,254.598,3.35,10.52,16.01
flexivit_small,240,1024.0,4011.8,255.236,4.88,9.46,22.06
edgenext_x_small,288,1024.0,3990.87,256.573,0.68,7.5,2.34
rexnet_150,224,1024.0,3983.48,257.051,0.9,11.21,9.73
vit_relpos_small_patch16_rpn_224,224,1024.0,3975.32,257.575,4.24,9.38,21.97
repvit_m3,224,1024.0,3966.18,258.164,1.89,13.94,10.68
vit_relpos_small_patch16_224,224,1024.0,3948.05,259.358,4.24,9.38,21.98
vit_srelpos_small_patch16_224,224,1024.0,3937.22,260.07,4.23,8.49,21.97
mobileone_s1,224,1024.0,3931.71,260.434,0.86,9.67,4.83
resnetv2_50,224,1024.0,3890.29,263.208,4.11,11.11,25.55
eca_botnext26ts_256,256,1024.0,3883.93,263.639,2.46,11.6,10.59
cs3sedarknet_l,256,1024.0,3835.91,266.94,4.86,8.56,21.91
ghostnetv2_160,224,1024.0,3826.79,267.576,0.42,7.23,12.39
resnet34,288,1024.0,3820.15,268.041,6.07,6.18,21.8
edgenext_small,256,1024.0,3794.31,269.865,1.26,9.07,5.59
dpn68,224,1024.0,3788.79,270.258,2.35,10.47,12.61
ese_vovnet19b_dw,288,1024.0,3782.88,270.682,2.22,13.63,6.54
fbnetv3_g,240,1024.0,3779.41,270.931,1.28,14.87,16.62
convnext_pico,288,1024.0,3777.8,271.046,2.27,10.08,9.05
ecaresnetlight,224,1024.0,3759.77,272.346,4.11,8.42,30.16
eca_halonext26ts,256,1024.0,3745.07,273.414,2.44,11.46,10.76
dpn68b,224,1024.0,3719.51,275.293,2.35,10.47,12.61
mixnet_m,224,1024.0,3687.37,277.689,0.36,8.19,5.01
resnet50,224,1024.0,3687.18,277.708,4.11,11.11,25.56
efficientnet_em,240,1024.0,3685.78,277.814,3.04,14.34,6.9
convnext_pico_ols,288,1024.0,3673.49,278.743,2.37,10.74,9.06
resnet32ts,256,1024.0,3641.96,281.156,4.63,11.58,17.96
bat_resnext26ts,256,1024.0,3638.35,281.435,2.53,12.51,10.73
efficientnet_b3_pruned,300,1024.0,3633.29,281.827,1.04,11.86,9.86
botnet26t_256,256,1024.0,3632.31,281.904,3.32,11.98,12.49
hrnet_w18_small_v2,224,1024.0,3631.33,281.979,2.62,9.65,15.6
ecaresnet101d_pruned,224,1024.0,3611.37,283.538,3.48,7.69,24.88
ecaresnet26t,256,1024.0,3599.02,284.511,3.35,10.53,16.01
regnetv_040,224,1024.0,3598.04,284.583,4.0,12.29,20.64
seresnet34,288,1024.0,3583.61,285.735,6.07,6.18,21.96
resnetv2_50t,224,1024.0,3573.26,286.561,4.32,11.82,25.57
pvt_v2_b1,224,1024.0,3571.19,286.726,2.04,14.01,14.01
regnety_016,224,1024.0,3567.37,287.031,1.63,8.04,11.2
resnext26ts,288,1024.0,3565.74,287.167,3.07,13.31,10.3
regnety_040,224,1024.0,3565.62,287.173,4.0,12.29,20.65
resnet33ts,256,1024.0,3563.66,287.335,4.76,11.66,19.68
resnetv2_50d,224,1024.0,3553.44,288.159,4.35,11.92,25.57
tf_efficientnet_em,240,1024.0,3544.42,288.894,3.04,14.34,6.9
halonet26t,256,1024.0,3541.55,289.129,3.19,11.69,12.48
dla60,224,1024.0,3527.55,290.275,4.26,10.16,22.04
tf_mixnet_m,224,1024.0,3524.0,290.567,0.36,8.19,5.01
resnet50c,224,1024.0,3521.04,290.812,4.35,11.92,25.58
edgenext_small_rw,256,1024.0,3501.76,292.411,1.58,9.51,7.83
resnet34d,288,1024.0,3491.3,293.29,6.47,7.51,21.82
convnextv2_pico,224,1024.0,3480.58,294.194,1.37,6.1,9.07
vit_small_resnet26d_224,224,1024.0,3476.26,294.557,5.04,10.65,63.61
convit_tiny,224,1024.0,3460.49,295.901,1.26,7.94,5.71
tresnet_m,224,1024.0,3457.69,296.14,5.75,7.31,31.39
resnet26,288,1024.0,3457.48,296.158,3.9,12.15,16.0
seresnext26ts,288,1024.0,3455.43,296.333,3.07,13.32,10.39
vit_relpos_base_patch32_plus_rpn_256,256,1024.0,3447.98,296.974,7.59,6.63,119.42
seresnet33ts,256,1024.0,3444.98,297.233,4.76,11.66,19.78
eca_resnext26ts,288,1024.0,3443.01,297.404,3.07,13.32,10.3
eca_resnet33ts,256,1024.0,3442.23,297.471,4.76,11.66,19.68
tf_efficientnet_b2,260,1024.0,3440.99,297.578,1.02,13.83,9.11
gcresnet33ts,256,1024.0,3424.64,298.998,4.76,11.68,19.88
gcresnext26ts,288,1024.0,3414.23,299.91,3.07,13.33,10.48
resnet50t,224,1024.0,3401.57,301.026,4.32,11.82,25.57
vovnet39a,224,1024.0,3395.56,301.56,7.09,6.73,22.6
resnet50d,224,1024.0,3380.59,302.894,4.35,11.92,25.58
efficientvit_b2,224,1024.0,3359.89,304.76,1.6,14.62,24.33
resnest14d,224,1024.0,3357.89,304.943,2.76,7.33,10.61
vit_base_patch32_plus_256,256,1024.0,3354.04,305.293,7.7,6.35,119.48
efficientnet_b0_gn,224,1024.0,3353.74,305.319,0.42,6.75,5.29
cs3darknet_focus_l,288,1024.0,3340.22,306.556,5.9,10.16,21.15
selecsls84,224,1024.0,3335.07,307.029,5.9,7.57,50.95
vit_tiny_patch16_384,384,1024.0,3332.37,307.277,3.16,12.08,5.79
legacy_seresnet50,224,1024.0,3325.14,307.946,3.88,10.6,28.09
coatnet_nano_cc_224,224,1024.0,3301.24,310.176,2.13,13.1,13.76
fastvit_t8,256,1024.0,3298.88,310.398,0.7,8.63,4.03
resnetblur18,288,1024.0,3292.39,311.01,3.87,5.6,11.69
repvit_m1_5,224,1024.0,3281.4,312.05,2.31,15.7,14.64
ese_vovnet39b,224,1024.0,3276.58,312.51,7.09,6.74,24.57
levit_512,224,1024.0,3274.29,312.728,5.64,10.22,95.17
haloregnetz_b,224,1024.0,3272.82,312.869,1.97,11.94,11.68
mobilevit_xs,256,1024.0,3272.76,312.87,0.93,13.62,2.32
coat_lite_tiny,224,1024.0,3257.39,314.352,1.6,11.65,5.72
coatnext_nano_rw_224,224,1024.0,3256.31,314.455,2.36,10.68,14.7
eca_vovnet39b,224,1024.0,3252.14,314.859,7.09,6.74,22.6
efficientnet_b2,288,1024.0,3249.31,315.132,1.12,16.2,9.11
resnetaa50,224,1024.0,3245.58,315.495,5.15,11.64,25.56
coatnet_nano_rw_224,224,1024.0,3238.25,316.209,2.29,13.29,15.14
cs3darknet_l,288,1024.0,3236.81,316.35,6.16,10.83,21.16
convnextv2_atto,288,1024.0,3226.1,317.401,0.91,6.3,3.71
mobileone_s2,224,1024.0,3211.19,318.869,1.34,11.55,7.88
seresnet50,224,1024.0,3200.07,319.981,4.11,11.13,28.09
nf_regnet_b3,288,1024.0,3185.16,321.477,1.67,11.84,18.59
crossvit_small_240,240,1024.0,3184.9,321.506,5.09,11.34,26.86
res2net50_48w_2s,224,1024.0,3168.87,323.132,4.18,11.72,25.29
resnetaa34d,288,1024.0,3155.87,324.463,7.33,8.38,21.82
vit_small_r26_s32_224,224,1024.0,3124.44,327.727,3.54,9.44,36.43
dla60x,224,1024.0,3106.99,329.567,3.54,13.8,17.35
efficientnet_b0_g8_gn,224,1024.0,3104.31,329.853,0.66,6.75,6.56
resnext50_32x4d,224,1024.0,3099.2,330.397,4.26,14.4,25.03
levit_conv_512,224,1024.0,3078.02,332.67,5.64,10.22,95.17
skresnet34,224,1024.0,3073.03,333.21,3.67,5.13,22.28
coat_lite_mini,224,1024.0,3058.66,334.777,2.0,12.25,11.01
resnet26d,288,1024.0,3053.73,335.317,4.29,13.48,16.01
mobileone_s0,224,1024.0,3053.01,335.391,1.09,15.48,5.29
levit_512d,224,1024.0,3045.04,336.274,5.85,11.3,92.5
cs3sedarknet_l,288,1024.0,3026.08,338.38,6.16,10.83,21.91
resnetaa50d,224,1024.0,3022.22,338.813,5.39,12.44,25.58
convnext_tiny,224,1024.0,3015.62,339.555,4.47,13.44,28.59
eca_nfnet_l0,224,1024.0,3011.21,340.052,4.35,10.47,24.14
xcit_nano_12_p16_384,384,1024.0,3011.18,340.055,1.64,12.14,3.05
nfnet_l0,224,1024.0,3000.78,341.23,4.36,10.47,35.07
resnetrs50,224,1024.0,2989.89,342.477,4.48,12.14,35.69
efficientnet_cc_b1_8e,240,1024.0,2988.69,342.615,0.75,15.44,39.72
regnetz_b16,288,1024.0,2987.05,342.79,2.39,16.43,9.72
seresnet50t,224,1024.0,2984.21,343.128,4.32,11.83,28.1
ecaresnet50d,224,1024.0,2975.54,344.128,4.35,11.93,25.58
regnetz_c16,256,1024.0,2971.35,344.607,2.51,16.57,13.46
densenet121,224,1024.0,2967.84,345.021,2.87,6.9,7.98
crossvit_15_240,240,1024.0,2967.06,345.11,5.17,12.01,27.53
resnet50s,224,1024.0,2958.0,346.169,5.47,13.52,25.68
rexnetr_200,224,1024.0,2955.32,346.483,1.59,15.11,16.52
mixnet_l,224,1024.0,2926.26,349.918,0.58,10.84,7.33
xcit_tiny_24_p16_224,224,1024.0,2925.33,350.035,2.34,11.82,12.12
levit_conv_512d,224,1024.0,2899.99,353.091,5.85,11.3,92.5
gcresnext50ts,256,1024.0,2897.54,353.393,3.75,15.46,15.67
lambda_resnet26rpt_256,256,1024.0,2887.51,354.621,3.16,11.87,10.99
resnext50d_32x4d,224,1024.0,2876.86,355.933,4.5,15.2,25.05
resnet32ts,288,1024.0,2868.64,356.953,5.86,14.65,17.96
crossvit_15_dagger_240,240,1024.0,2848.99,359.413,5.5,12.68,28.21
tiny_vit_21m_224,224,1024.0,2842.09,360.287,4.08,15.96,33.22
vit_base_resnet26d_224,224,1024.0,2837.87,360.821,6.93,12.34,101.4
tf_efficientnet_cc_b1_8e,240,1024.0,2835.77,361.09,0.75,15.44,39.72
cspresnet50,256,1024.0,2834.55,361.245,4.54,11.5,21.62
mobilevitv2_100,256,1024.0,2833.62,361.358,1.84,16.08,4.9
resnet33ts,288,1024.0,2829.43,361.9,6.02,14.75,19.68
vovnet57a,224,1024.0,2821.83,362.874,8.95,7.52,36.64
deit3_medium_patch16_224,224,1024.0,2805.09,365.038,7.53,10.99,38.85
inception_next_tiny,224,1024.0,2798.9,365.847,4.19,11.98,28.06
tf_mixnet_l,224,1024.0,2798.14,365.947,0.58,10.84,7.33
res2next50,224,1024.0,2797.04,366.091,4.2,13.71,24.67
dla60_res2next,224,1024.0,2795.54,366.285,3.49,13.17,17.03
coatnet_pico_rw_224,224,1024.0,2793.27,366.584,1.96,12.91,10.85
convnext_tiny_hnf,224,1024.0,2770.64,369.577,4.47,13.44,28.59
gcresnet50t,256,1024.0,2767.9,369.943,5.42,14.67,25.9
convnextv2_femto,288,1024.0,2762.62,370.652,1.3,7.56,5.23
tf_efficientnetv2_b3,300,1024.0,2757.15,371.387,3.04,15.74,14.36
legacy_seresnext50_32x4d,224,1024.0,2750.41,372.297,4.26,14.42,27.56
ecaresnet50d_pruned,288,1024.0,2749.78,372.383,4.19,10.61,19.94
res2net50_26w_4s,224,1024.0,2749.69,372.394,4.28,12.61,25.7
seresnext50_32x4d,224,1024.0,2749.17,372.464,4.26,14.42,27.56
vgg11_bn,224,1024.0,2746.28,372.857,7.62,7.44,132.87
resmlp_24_224,224,1024.0,2745.97,372.9,5.96,10.91,30.02
resnetv2_50x1_bit,224,1024.0,2742.41,373.383,4.23,11.11,25.55
eca_resnet33ts,288,1024.0,2737.24,374.089,6.02,14.76,19.68
efficientnetv2_rw_t,288,1024.0,2736.91,374.133,3.19,16.42,13.65
seresnet33ts,288,1024.0,2734.83,374.417,6.02,14.76,19.78
nfnet_f0,192,1024.0,2731.03,374.934,7.21,10.16,71.49
res2net50_14w_8s,224,1024.0,2724.75,375.804,4.21,13.28,25.06
visformer_small,224,1024.0,2720.95,376.328,4.88,11.43,40.22
ese_vovnet57b,224,1024.0,2711.8,377.598,8.95,7.52,38.61
gcresnet33ts,288,1024.0,2705.39,378.493,6.02,14.78,19.88
cspresnet50d,256,1024.0,2702.61,378.881,4.86,12.55,21.64
twins_svt_small,224,1024.0,2696.15,379.788,2.82,10.7,24.06
efficientvit_l1,224,1024.0,2692.51,380.303,5.27,15.85,52.65
resnetblur50,224,1024.0,2689.65,380.707,5.16,12.02,25.56
seresnetaa50d,224,1024.0,2682.26,381.757,5.4,12.46,28.11
fbnetv3_g,288,1024.0,2673.23,383.046,1.77,21.09,16.62
cspresnet50w,256,1024.0,2671.97,383.228,5.04,12.19,28.12
dla60_res2net,224,1024.0,2669.84,383.53,4.15,12.34,20.85
convnext_nano,288,1024.0,2669.05,383.645,4.06,13.84,15.59
gc_efficientnetv2_rw_t,288,1024.0,2659.37,385.042,3.2,16.45,13.68
gcvit_xxtiny,224,1024.0,2658.4,385.182,2.14,15.36,12.0
poolformerv2_s12,224,1024.0,2624.04,390.223,1.83,5.53,11.89
vit_relpos_medium_patch16_rpn_224,224,1024.0,2618.88,390.989,7.5,12.13,38.73
mobileone_s3,224,1024.0,2616.83,391.296,1.94,13.85,10.17
davit_tiny,224,1024.0,2612.7,391.92,4.47,17.08,28.36
vit_relpos_medium_patch16_224,224,1024.0,2603.89,393.246,7.5,12.13,38.75
resnet51q,256,1024.0,2602.52,393.454,6.38,16.55,35.7
gmixer_24_224,224,1024.0,2594.59,394.657,5.28,14.45,24.72
maxvit_pico_rw_256,256,768.0,2593.58,296.105,1.68,18.77,7.46
vit_srelpos_medium_patch16_224,224,1024.0,2591.17,395.176,7.49,11.32,38.74
vit_relpos_medium_patch16_cls_224,224,1024.0,2587.16,395.789,7.55,13.3,38.76
maxvit_rmlp_pico_rw_256,256,768.0,2587.02,296.857,1.69,21.32,7.52
nf_regnet_b3,320,1024.0,2582.41,396.514,2.05,14.61,18.59
res2net50d,224,1024.0,2577.65,397.25,4.52,13.41,25.72
cs3darknet_focus_x,256,1024.0,2569.33,398.536,8.03,10.69,35.02
densenetblur121d,224,1024.0,2559.52,400.063,3.11,7.9,8.0
inception_v3,299,1024.0,2546.29,402.143,5.73,8.97,23.83
coatnet_0_rw_224,224,1024.0,2545.57,402.256,4.23,15.1,27.44
repvgg_b1g4,224,1024.0,2545.06,402.332,8.15,10.64,39.97
regnetx_032,224,1024.0,2534.07,404.077,3.2,11.37,15.3
twins_pcpvt_small,224,1024.0,2533.92,404.104,3.68,15.51,24.11
resnetblur50d,224,1024.0,2528.9,404.909,5.4,12.82,25.58
rexnet_200,224,1024.0,2519.88,406.358,1.56,14.91,16.37
resnetrs101,192,1024.0,2505.12,408.751,6.04,12.7,63.62
resnet26t,320,1024.0,2502.87,409.119,5.24,16.44,16.01
nf_ecaresnet50,224,1024.0,2502.03,409.253,4.21,11.13,25.56
convnext_nano_ols,288,1024.0,2497.73,409.961,4.38,15.5,15.65
convnextv2_nano,224,1024.0,2497.72,409.963,2.46,8.37,15.62
nf_seresnet50,224,1024.0,2494.79,410.425,4.21,11.13,28.09
regnety_032,224,1024.0,2483.68,412.275,3.2,11.26,19.44
vit_medium_patch16_gap_240,240,1024.0,2477.36,413.332,8.6,12.57,44.4
cs3darknet_x,256,1024.0,2475.51,413.641,8.38,11.35,35.05
densenet169,224,1024.0,2463.83,415.603,3.4,7.3,14.15
xcit_small_12_p16_224,224,1024.0,2460.07,416.237,4.82,12.57,26.25
cspresnext50,256,1024.0,2452.36,417.546,4.05,15.86,20.57
mobilevit_s,256,1024.0,2447.35,418.395,1.86,17.03,5.58
darknet53,256,1024.0,2439.82,419.693,9.31,12.39,41.61
darknetaa53,256,1024.0,2432.07,421.03,7.97,12.39,36.02
edgenext_small,320,1024.0,2429.25,421.516,1.97,14.16,5.59
seresnext26t_32x4d,288,1024.0,2412.74,424.404,4.46,16.68,16.81
sehalonet33ts,256,1024.0,2403.77,425.986,3.55,14.7,13.69
seresnext26d_32x4d,288,1024.0,2391.16,428.231,4.51,16.85,16.81
resnet61q,256,1024.0,2368.17,432.39,7.8,17.01,36.85
fastvit_t12,256,1024.0,2356.34,434.562,1.42,12.42,7.55
vit_base_r26_s32_224,224,1024.0,2354.84,434.838,6.76,11.54,101.38
focalnet_tiny_srf,224,1024.0,2353.35,435.113,4.42,16.32,28.43
resnetv2_101,224,1024.0,2342.24,437.176,7.83,16.23,44.54
cs3sedarknet_x,256,1024.0,2329.01,439.66,8.38,11.35,35.4
nf_resnet50,256,1024.0,2318.52,441.645,5.46,14.52,25.56
xcit_nano_12_p8_224,224,1024.0,2310.67,443.15,2.16,15.71,3.05
resnest26d,224,1024.0,2309.28,443.418,3.64,9.97,17.07
coatnet_rmlp_nano_rw_224,224,1024.0,2308.34,443.598,2.51,18.21,15.15
resnetv2_50,288,1024.0,2302.9,444.644,6.79,18.37,25.55
ecaresnet50t,256,1024.0,2299.59,445.285,5.64,15.45,25.57
gmlp_s16_224,224,1024.0,2291.16,446.925,4.42,15.1,19.42
efficientnet_lite3,300,1024.0,2290.17,447.117,1.65,21.85,8.2
dm_nfnet_f0,192,1024.0,2271.28,450.836,7.21,10.16,71.49
resnet101,224,1024.0,2263.99,452.287,7.83,16.23,44.55
ecaresnet26t,320,1024.0,2258.47,453.393,5.24,16.44,16.01
edgenext_base,256,1024.0,2256.96,453.695,3.85,15.58,18.51
efficientnetv2_s,288,1024.0,2251.36,454.825,4.75,20.13,21.46
skresnet50,224,1024.0,2250.82,454.933,4.11,12.5,25.8
dla102,224,1024.0,2248.24,455.455,7.19,14.18,33.27
edgenext_small_rw,320,1024.0,2240.98,456.929,2.46,14.85,7.83
ecaresnetlight,288,1024.0,2235.21,458.11,6.79,13.91,30.16
dpn68b,288,1024.0,2234.13,458.331,3.89,17.3,12.61
gcresnext50ts,288,1024.0,2232.45,458.676,4.75,19.57,15.67
fastvit_s12,256,1024.0,2229.72,459.239,1.82,13.67,9.47
fastvit_sa12,256,1024.0,2225.03,460.206,1.96,13.83,11.58
focalnet_tiny_lrf,224,1024.0,2222.33,460.766,4.49,17.76,28.65
resnetv2_101d,224,1024.0,2216.51,461.976,8.07,17.04,44.56
resnet101c,224,1024.0,2202.12,464.995,8.08,17.04,44.57
vit_base_resnet50d_224,224,1024.0,2199.36,465.578,8.68,16.1,110.97
regnetv_040,288,1024.0,2190.89,467.375,6.6,20.3,20.64
vit_medium_patch16_gap_256,256,1024.0,2190.03,467.563,9.78,14.29,38.86
resnet50,288,1024.0,2185.5,468.532,6.8,18.37,25.56
gcresnet50t,288,1024.0,2180.99,469.5,6.86,18.57,25.9
regnety_040,288,1024.0,2169.28,472.031,6.61,20.3,20.65
vgg13,224,1024.0,2159.6,474.15,11.31,12.25,133.05
eva02_small_patch14_224,224,1024.0,2151.59,475.915,5.53,12.34,21.62
vit_medium_patch16_reg4_gap_256,256,1024.0,2149.02,476.485,9.93,14.51,38.87
efficientnetv2_rw_s,288,1024.0,2146.83,476.971,4.91,21.41,23.94
ecaresnet101d_pruned,288,1024.0,2141.83,478.084,5.75,12.71,24.88
mobilevitv2_125,256,1024.0,2139.71,478.555,2.86,20.1,7.48
vit_medium_patch16_reg4_256,256,1024.0,2136.17,479.352,9.97,14.56,38.87
skresnet50d,224,1024.0,2134.1,479.815,4.36,13.31,25.82
pvt_v2_b2,224,1024.0,2119.72,483.066,3.9,24.96,25.36
hrnet_w18_ssld,224,1024.0,2114.47,484.27,4.32,16.31,21.3
convnextv2_pico,288,1024.0,2113.62,484.464,2.27,10.08,9.07
eva02_tiny_patch14_336,336,1024.0,2113.11,484.582,3.14,13.85,5.76
efficientvit_l2,224,1024.0,2109.14,485.494,6.97,19.58,63.71
hrnet_w18,224,1024.0,2100.77,487.428,4.32,16.31,21.3
regnetx_040,224,1024.0,2099.85,487.636,3.99,12.2,22.12
tf_efficientnet_lite3,300,1024.0,2090.5,489.823,1.65,21.85,8.2
wide_resnet50_2,224,1024.0,2081.66,491.904,11.43,14.4,68.88
resnet51q,288,1024.0,2069.71,494.744,8.07,20.94,35.7
poolformer_s24,224,1024.0,2067.46,495.278,3.41,10.68,21.39
sebotnet33ts_256,256,512.0,2066.45,247.758,3.89,17.46,13.7
efficientformer_l3,224,1024.0,2064.62,495.963,3.93,12.01,31.41
resnest50d_1s4x24d,224,1024.0,2057.55,497.667,4.43,13.57,25.68
gcvit_xtiny,224,1024.0,2053.45,498.662,2.93,20.26,19.98
cspdarknet53,256,1024.0,2048.51,499.863,6.57,16.81,27.64
crossvit_18_240,240,1024.0,2029.53,504.539,8.21,16.14,43.27
mixnet_xl,224,1024.0,2029.05,504.653,0.93,14.57,11.9
vit_base_patch32_384,384,1024.0,2028.15,504.881,12.67,12.14,88.3
efficientnet_b3,288,1024.0,2027.72,504.989,1.63,21.49,12.23
vit_base_patch32_clip_384,384,1024.0,2026.31,505.34,12.67,12.14,88.3
resnet50t,288,1024.0,2024.16,505.879,7.14,19.53,25.57
dla102x,224,1024.0,2023.35,506.08,5.89,19.42,26.31
legacy_seresnet101,224,1024.0,2012.58,508.788,7.61,15.74,49.33
resnet50d,288,1024.0,2012.14,508.9,7.19,19.7,25.58
cs3edgenet_x,256,1024.0,2002.36,511.384,11.53,12.92,47.82
resnetaa101d,224,1024.0,1994.67,513.346,9.12,17.56,44.57
repvgg_b1,224,1024.0,1994.42,513.418,13.16,10.64,57.42
res2net50_26w_6s,224,1024.0,1979.48,517.295,6.33,15.28,37.05
regnetz_d32,256,1024.0,1978.14,517.642,5.98,23.74,27.58
cs3sedarknet_xdw,256,1024.0,1970.5,519.653,5.97,17.18,21.6
resnetaa50,288,1024.0,1968.61,520.152,8.52,19.24,25.56
seresnet101,224,1024.0,1966.15,520.803,7.84,16.27,49.33
resnet101s,224,1024.0,1964.56,521.226,9.19,18.64,44.67
cs3darknet_x,288,1024.0,1958.87,522.739,10.6,14.36,35.05
crossvit_18_dagger_240,240,1024.0,1955.55,523.625,8.65,16.91,44.27
swin_tiny_patch4_window7_224,224,1024.0,1951.67,524.668,4.51,17.06,28.29
tresnet_v2_l,224,1024.0,1947.69,525.738,8.85,16.34,46.17
ese_vovnet39b,288,1024.0,1941.03,527.543,11.71,11.13,24.57
regnetz_d8,256,1024.0,1940.13,527.785,3.97,23.74,23.37
tf_efficientnetv2_s,300,1024.0,1939.51,527.958,5.35,22.73,21.46
regnetz_c16,320,1024.0,1933.29,529.65,3.92,25.88,13.46
coatnet_bn_0_rw_224,224,1024.0,1926.49,531.525,4.48,18.41,27.44
darknet53,288,1024.0,1924.44,532.092,11.78,15.68,41.61
resnext101_32x4d,224,1024.0,1923.83,532.261,8.01,21.23,44.18
coatnet_rmlp_0_rw_224,224,1024.0,1920.22,533.259,4.52,21.26,27.45
xcit_tiny_12_p16_384,384,1024.0,1917.57,533.997,3.64,18.25,6.72
darknetaa53,288,1024.0,1915.93,534.454,10.08,15.68,36.02
mobileone_s4,224,1024.0,1915.84,534.474,3.04,17.74,14.95
maxxvit_rmlp_nano_rw_256,256,768.0,1913.61,401.326,4.17,21.53,16.78
nest_tiny,224,1024.0,1909.31,536.303,5.24,14.75,17.06
regnetz_040,256,1024.0,1906.99,536.946,4.06,24.19,27.12
nf_regnet_b4,320,1024.0,1906.99,536.957,3.29,19.88,30.21
seresnet50,288,1024.0,1902.22,538.306,6.8,18.39,28.09
pvt_v2_b2_li,224,1024.0,1897.86,539.539,3.77,25.04,22.55
regnetz_040_h,256,1024.0,1896.27,539.981,4.12,24.29,28.94
densenet201,224,1024.0,1895.14,540.319,4.34,7.85,20.01
halonet50ts,256,1024.0,1887.53,542.495,5.3,19.2,22.73
nest_tiny_jx,224,1024.0,1885.06,543.199,5.24,14.75,17.06
vgg13_bn,224,1024.0,1884.94,543.241,11.33,12.25,133.05
regnetx_080,224,1024.0,1883.47,543.661,8.02,14.06,39.57
vit_large_patch32_224,224,1024.0,1882.39,543.977,15.27,11.11,305.51
ecaresnet101d,224,1024.0,1880.92,544.404,8.08,17.07,44.57
resnet61q,288,1024.0,1874.14,546.373,9.87,21.52,36.85
nf_resnet101,224,1024.0,1864.42,549.218,8.01,16.23,44.55
cs3se_edgenet_x,256,1024.0,1859.86,550.568,11.53,12.94,50.72
repvit_m2_3,224,1024.0,1852.95,552.61,4.57,26.21,23.69
resmlp_36_224,224,1024.0,1843.66,555.406,8.91,16.33,44.69
cs3sedarknet_x,288,1024.0,1843.16,555.556,10.6,14.37,35.4
resnext50_32x4d,288,1024.0,1841.23,556.139,7.04,23.81,25.03
convnext_small,224,1024.0,1838.66,556.915,8.71,21.56,50.22
convnext_tiny,288,1024.0,1835.18,557.972,7.39,22.21,28.59
resnetv2_50d_gn,224,1024.0,1829.29,559.767,4.38,11.92,25.57
resnetaa50d,288,1024.0,1827.2,560.408,8.92,20.57,25.58
pit_b_224,224,1024.0,1823.77,561.458,10.56,16.6,73.76
eca_nfnet_l0,288,1024.0,1822.69,561.796,7.12,17.29,24.14
nfnet_l0,288,1024.0,1817.7,563.332,7.13,17.29,35.07
sequencer2d_s,224,1024.0,1816.41,563.738,4.96,11.31,27.65
pit_b_distilled_224,224,1024.0,1810.4,565.6,10.63,16.67,74.79
nf_resnet50,288,1024.0,1794.38,570.655,6.88,18.37,25.56
twins_pcpvt_base,224,1024.0,1790.37,571.935,6.46,21.35,43.83
rexnetr_200,288,768.0,1782.92,430.745,2.62,24.96,16.52
seresnet50t,288,1024.0,1780.59,575.079,7.14,19.55,28.1
cait_xxs24_224,224,1024.0,1779.24,575.513,2.53,20.29,11.96
swin_s3_tiny_224,224,1024.0,1777.31,576.139,4.64,19.13,28.33
resnet50_gn,224,1024.0,1776.88,576.279,4.14,11.11,25.56
ecaresnet50d,288,1024.0,1775.84,576.616,7.19,19.72,25.58
resnetblur101d,224,1024.0,1765.86,579.878,9.12,17.94,44.57
densenet121,288,1024.0,1761.12,581.437,4.74,11.41,7.98
coat_lite_small,224,1024.0,1760.12,581.767,3.96,22.09,19.84
mixer_b16_224,224,1024.0,1758.48,582.299,12.62,14.53,59.88
mobilevitv2_150,256,768.0,1748.31,439.266,4.09,24.11,10.59
efficientvit_b3,224,1024.0,1742.56,587.628,3.99,26.9,48.65
rexnetr_300,224,1024.0,1736.82,589.571,3.39,22.16,34.81
vgg16,224,1024.0,1730.88,591.595,15.47,13.56,138.36
maxxvitv2_nano_rw_256,256,768.0,1724.32,445.384,6.12,19.66,23.7
res2net101_26w_4s,224,1024.0,1723.01,594.296,8.1,18.45,45.21
resnext50d_32x4d,288,1024.0,1717.01,596.374,7.44,25.13,25.05
maxvit_nano_rw_256,256,768.0,1709.05,449.363,4.26,25.76,15.45
legacy_seresnext101_32x4d,224,1024.0,1707.02,599.865,8.02,21.26,48.96
seresnext101_32x4d,224,1024.0,1706.74,599.963,8.02,21.26,48.96
maxvit_rmlp_nano_rw_256,256,768.0,1705.93,450.183,4.28,27.4,15.5
resnetv2_50d_frn,224,1024.0,1703.71,601.028,4.33,11.92,25.59
mobilevitv2_175,256,512.0,1701.95,300.817,5.54,28.13,14.25
tf_efficientnet_b3,300,1024.0,1694.25,604.385,1.87,23.83,12.23
convnext_tiny_hnf,288,1024.0,1681.52,608.96,7.39,22.21,28.59
ese_vovnet39b_evos,224,1024.0,1671.22,612.716,7.07,6.74,24.58
res2net50_26w_8s,224,1024.0,1656.9,618.009,8.37,17.95,48.4
resnet101d,256,1024.0,1654.59,618.871,10.55,22.25,44.57
tresnet_l,224,1024.0,1652.13,619.794,10.9,11.9,55.99
res2net101d,224,1024.0,1652.09,619.808,8.35,19.25,45.23
mixer_l32_224,224,1024.0,1651.22,620.129,11.27,19.86,206.94
regnetz_b16_evos,224,1024.0,1648.87,621.016,1.43,9.95,9.74
botnet50ts_256,256,512.0,1645.51,311.14,5.54,22.23,22.74
efficientnet_b3,320,1024.0,1641.76,623.708,2.01,26.52,12.23
seresnext50_32x4d,288,1024.0,1638.34,625.012,7.04,23.82,27.56
coatnet_0_224,224,512.0,1634.58,313.22,4.43,21.14,25.04
swinv2_cr_tiny_224,224,1024.0,1629.27,628.491,4.66,28.45,28.33
inception_next_small,224,1024.0,1628.58,628.755,8.36,19.27,49.37
resnetv2_152,224,1024.0,1628.46,628.801,11.55,22.56,60.19
regnetx_064,224,1024.0,1628.2,628.898,6.49,16.37,26.21
hrnet_w32,224,1024.0,1627.55,629.157,8.97,22.02,41.23
convnextv2_tiny,224,1024.0,1627.26,629.266,4.47,13.44,28.64
seresnetaa50d,288,1024.0,1622.33,631.178,8.92,20.59,28.11
davit_small,224,1024.0,1614.32,634.313,8.69,27.54,49.75
regnety_040_sgn,224,1024.0,1612.57,634.996,4.03,12.29,20.65
legacy_xception,299,768.0,1604.43,478.663,8.4,35.83,22.86
swinv2_cr_tiny_ns_224,224,1024.0,1600.49,639.793,4.66,28.45,28.33
resnetblur50,288,1024.0,1598.7,640.511,8.52,19.87,25.56
efficientnet_el,300,1024.0,1595.26,641.889,8.0,30.7,10.59
efficientnet_el_pruned,300,1024.0,1592.53,642.988,8.0,30.7,10.59
resnet152,224,1024.0,1589.58,644.183,11.56,22.56,60.19
deit_base_patch16_224,224,1024.0,1581.19,647.603,16.87,16.49,86.57
cs3edgenet_x,288,1024.0,1577.26,649.216,14.59,16.36,47.82
deit_base_distilled_patch16_224,224,1024.0,1575.74,649.842,16.95,16.58,87.34
vit_base_patch16_224,224,1024.0,1574.94,650.173,16.87,16.49,86.57
vit_base_patch16_224_miil,224,1024.0,1574.63,650.301,16.88,16.5,94.4
vit_base_patch16_clip_224,224,1024.0,1574.46,650.371,16.87,16.49,86.57
vit_base_patch16_siglip_224,224,1024.0,1571.54,651.577,17.02,16.71,92.88
resnetv2_152d,224,1024.0,1564.52,654.501,11.8,23.36,60.2
vit_base_patch16_gap_224,224,1024.0,1563.13,655.085,16.78,16.41,86.57
halo2botnet50ts_256,256,1024.0,1562.09,655.52,5.02,21.78,22.64
resnet152c,224,1024.0,1558.11,657.195,11.8,23.36,60.21
ese_vovnet99b,224,1024.0,1554.99,658.512,16.51,11.27,63.2
vit_small_resnet50d_s16_224,224,1024.0,1551.97,659.792,13.0,21.12,57.53
nf_seresnet101,224,1024.0,1549.92,660.662,8.02,16.27,49.33
nf_ecaresnet101,224,1024.0,1549.88,660.683,8.01,16.27,44.55
tf_efficientnet_el,300,1024.0,1543.58,663.384,8.0,30.7,10.59
coatnet_rmlp_1_rw_224,224,1024.0,1542.97,663.643,7.44,28.08,41.69
nfnet_f0,256,1024.0,1541.8,664.144,12.62,18.05,71.49
vgg16_bn,224,1024.0,1533.25,667.85,15.5,13.56,138.37
resnest50d,224,1024.0,1530.42,669.084,5.4,14.36,27.48
caformer_s18,224,1024.0,1528.28,670.023,3.9,15.18,26.34
pvt_v2_b3,224,1024.0,1527.57,670.328,6.71,33.8,45.24
densenetblur121d,288,1024.0,1521.38,673.062,5.14,13.06,8.0
maxvit_tiny_rw_224,224,768.0,1520.98,504.928,4.93,28.54,29.06
mvitv2_tiny,224,1024.0,1518.09,674.509,4.7,21.16,24.17
vit_base_patch16_rpn_224,224,1024.0,1516.7,675.134,16.78,16.41,86.54
convnextv2_nano,288,768.0,1514.74,507.006,4.06,13.84,15.62
regnety_032,288,1024.0,1514.59,676.077,5.29,18.61,19.44
rexnet_300,224,1024.0,1508.74,678.701,3.44,22.4,34.71
resnetblur50d,288,1024.0,1506.45,679.732,8.92,21.19,25.58
deit3_base_patch16_224,224,1024.0,1497.14,683.959,16.87,16.49,86.59
convit_small,224,1024.0,1494.54,685.148,5.76,17.87,27.78
vit_base_patch32_clip_448,448,1024.0,1493.83,685.476,17.21,16.49,88.34
dla169,224,1024.0,1487.25,688.504,11.6,20.2,53.39
skresnext50_32x4d,224,1024.0,1470.99,696.12,4.5,17.18,27.48
xcit_tiny_12_p8_224,224,1024.0,1465.13,698.903,4.81,23.6,6.71
vit_small_patch16_36x1_224,224,1024.0,1460.65,701.044,12.63,24.59,64.67
ecaresnet50t,320,1024.0,1451.46,705.484,8.82,24.13,25.57
beitv2_base_patch16_224,224,1024.0,1448.02,707.161,16.87,16.49,86.53
vgg19,224,1024.0,1441.93,710.149,19.63,14.86,143.67
beit_base_patch16_224,224,1024.0,1440.48,710.862,16.87,16.49,86.53
hrnet_w30,224,1024.0,1436.17,712.996,8.15,21.21,37.71
edgenext_base,320,1024.0,1435.98,713.087,6.01,24.32,18.51
resnet152s,224,1024.0,1434.4,713.876,12.92,24.96,60.32
convformer_s18,224,1024.0,1427.19,717.481,3.96,15.82,26.77
resnetv2_50d_evos,224,1024.0,1426.57,717.793,4.33,11.92,25.59
focalnet_small_srf,224,1024.0,1426.35,717.904,8.62,26.26,49.89
sequencer2d_m,224,1024.0,1413.9,724.228,6.55,14.26,38.31
vit_relpos_base_patch16_rpn_224,224,1024.0,1408.36,727.069,16.8,17.63,86.41
volo_d1_224,224,1024.0,1407.83,727.348,6.94,24.43,26.63
regnety_080,224,1024.0,1407.5,727.512,8.0,17.97,39.18
vit_small_patch16_18x2_224,224,1024.0,1407.09,727.729,12.63,24.59,64.67
gcvit_tiny,224,1024.0,1405.32,728.65,4.79,29.82,28.22
dpn92,224,1024.0,1404.08,729.292,6.54,18.21,37.67
vit_relpos_base_patch16_224,224,1024.0,1402.98,729.864,16.8,17.63,86.43
resnetv2_101,288,1024.0,1402.28,730.227,12.94,26.83,44.54
regnetx_160,224,1024.0,1400.84,730.974,15.99,25.52,54.28
dla102x2,224,1024.0,1395.12,733.975,9.34,29.91,41.28
legacy_seresnet152,224,1024.0,1394.86,734.109,11.33,22.08,66.82
vit_relpos_base_patch16_clsgap_224,224,1024.0,1394.83,734.131,16.88,17.72,86.43
vit_relpos_base_patch16_cls_224,224,1024.0,1392.12,735.556,16.88,17.72,86.43
vit_small_patch16_384,384,1024.0,1390.73,736.291,12.45,24.15,22.2
poolformer_s36,224,1024.0,1388.46,737.493,5.0,15.82,30.86
vit_base_patch16_clip_quickgelu_224,224,1024.0,1388.13,737.672,16.87,16.49,86.19
densenet161,224,1024.0,1384.23,739.75,7.79,11.06,28.68
flexivit_base,240,1024.0,1380.45,741.777,19.35,18.92,86.59
efficientformerv2_s0,224,1024.0,1377.72,743.244,0.41,5.3,3.6
seresnet152,224,1024.0,1371.27,746.737,11.57,22.61,66.82
poolformerv2_s24,224,1024.0,1356.43,754.905,3.42,10.68,21.34
resnet101,288,1024.0,1354.29,756.102,12.95,26.83,44.55
focalnet_small_lrf,224,1024.0,1339.63,764.378,8.74,28.61,50.34
inception_v4,299,1024.0,1338.22,765.183,12.28,15.09,42.68
repvgg_b2,224,1024.0,1336.97,765.895,20.45,12.9,89.02
nf_regnet_b4,384,1024.0,1327.28,771.488,4.7,28.61,30.21
repvgg_b2g4,224,1024.0,1323.55,773.658,12.63,12.9,61.76
eca_nfnet_l1,256,1024.0,1319.97,775.763,9.62,22.04,41.41
fastvit_sa24,256,1024.0,1310.4,781.428,3.79,23.92,21.55
xcit_small_24_p16_224,224,1024.0,1307.21,783.335,9.1,23.63,47.67
twins_pcpvt_large,224,1024.0,1303.57,785.524,9.53,30.21,60.99
vit_base_patch16_xp_224,224,1024.0,1302.82,785.975,16.85,16.49,86.51
maxvit_tiny_tf_224,224,768.0,1301.05,590.28,5.42,31.21,30.92
deit3_small_patch16_384,384,1024.0,1298.34,788.686,12.45,24.15,22.21
coatnet_rmlp_1_rw2_224,224,1024.0,1296.36,789.892,7.71,32.74,41.72
coatnet_1_rw_224,224,1024.0,1295.8,790.234,7.63,27.22,41.72
regnety_080_tv,224,1024.0,1291.63,792.778,8.51,19.73,39.38
vgg19_bn,224,1024.0,1290.82,793.286,19.66,14.86,143.68
mixnet_xxl,224,768.0,1286.88,596.774,2.04,23.43,23.96
dm_nfnet_f0,256,1024.0,1286.75,795.79,12.62,18.05,71.49
efficientnet_b4,320,768.0,1280.17,599.91,3.13,34.76,19.34
hrnet_w18_ssld,288,1024.0,1279.49,800.308,7.14,26.96,21.3
maxxvit_rmlp_tiny_rw_256,256,768.0,1274.84,602.417,6.36,32.69,29.64
efficientformerv2_s1,224,1024.0,1271.59,805.28,0.67,7.66,6.19
convnext_base,224,1024.0,1268.86,807.011,15.38,28.75,88.59
mobilevitv2_200,256,512.0,1268.57,403.59,7.22,32.15,18.45
regnetz_d32,320,1024.0,1265.97,808.844,9.33,37.08,27.58
efficientnetv2_s,384,1024.0,1265.12,809.401,8.44,35.77,21.46
twins_svt_base,224,1024.0,1261.93,811.442,8.36,20.42,56.07
wide_resnet50_2,288,1024.0,1242.89,823.878,18.89,23.81,68.88
regnetz_d8,320,1024.0,1242.36,824.221,6.19,37.08,23.37
regnetz_040,320,512.0,1238.82,413.274,6.35,37.78,27.12
regnetz_040_h,320,512.0,1231.07,415.879,6.43,37.94,28.94
nest_small,224,1024.0,1230.37,832.252,9.41,22.88,38.35
tf_efficientnetv2_s,384,1024.0,1224.58,836.191,8.44,35.77,21.46
nest_small_jx,224,1024.0,1220.76,838.798,9.41,22.88,38.35
maxvit_tiny_rw_256,256,768.0,1213.37,632.937,6.44,37.27,29.07
maxvit_rmlp_tiny_rw_256,256,768.0,1210.44,634.468,6.47,39.84,29.15
vit_base_patch16_siglip_256,256,1024.0,1208.23,847.511,22.23,21.83,92.93
efficientnetv2_rw_s,384,1024.0,1208.22,847.514,8.72,38.03,23.94
resnetaa101d,288,1024.0,1207.75,847.844,15.07,29.03,44.57
swin_small_patch4_window7_224,224,1024.0,1206.81,848.507,8.77,27.47,49.61
dpn98,224,1024.0,1206.02,849.061,11.73,25.2,61.57
swinv2_tiny_window8_256,256,1024.0,1197.34,855.217,5.96,24.57,28.35
cs3se_edgenet_x,320,1024.0,1196.49,855.827,18.01,20.21,50.72
resnext101_64x4d,224,1024.0,1196.17,856.053,15.52,31.21,83.46
cait_xxs36_224,224,1024.0,1193.04,858.302,3.77,30.34,17.3
resnext101_32x8d,224,1024.0,1188.06,861.896,16.48,31.21,88.79
seresnet101,288,1024.0,1178.9,868.597,12.95,26.87,49.33
resnet152d,256,1024.0,1177.58,869.569,15.41,30.51,60.21
wide_resnet101_2,224,1024.0,1172.43,873.387,22.8,21.23,126.89
crossvit_base_240,240,1024.0,1171.25,874.269,20.13,22.67,105.03
resnet200,224,1024.0,1159.72,882.961,15.07,32.19,64.67
inception_resnet_v2,299,1024.0,1156.1,885.722,13.18,25.06,55.84
rexnetr_300,288,512.0,1153.3,443.932,5.59,36.61,34.81
resnetrs101,288,1024.0,1142.76,896.066,13.56,28.53,63.62
davit_base,224,1024.0,1141.57,896.996,15.36,36.72,87.95
tresnet_xl,224,1024.0,1136.08,901.333,15.2,15.34,78.44
coat_tiny,224,1024.0,1135.01,902.184,4.35,27.2,5.5
tnt_s_patch16_224,224,1024.0,1134.91,902.262,5.24,24.37,23.76
mvitv2_small,224,1024.0,1131.08,905.308,7.0,28.08,34.87
ecaresnet101d,288,1024.0,1130.54,905.749,13.35,28.19,44.57
vit_base_patch16_reg8_gap_256,256,1024.0,1124.62,910.517,22.6,22.09,86.62
maxvit_tiny_pm_256,256,768.0,1121.86,684.565,6.31,40.82,30.09
hrnet_w40,224,1024.0,1119.9,914.356,12.75,25.29,57.56
convnext_small,288,1024.0,1119.4,914.761,14.39,35.65,50.22
nfnet_f1,224,1024.0,1117.42,916.384,17.87,22.94,132.63
efficientnet_lite4,380,768.0,1117.23,687.403,4.04,45.66,13.01
pvt_v2_b4,224,1024.0,1107.81,924.328,9.83,48.14,62.56
seresnext101_64x4d,224,1024.0,1107.71,924.416,15.53,31.25,88.23
seresnext101_32x8d,224,1024.0,1101.53,929.602,16.48,31.25,93.57
resnetv2_50d_gn,288,1024.0,1100.54,930.437,7.24,19.7,25.57
coatnet_1_224,224,512.0,1098.68,466.003,8.28,31.3,42.23
repvgg_b3g4,224,1024.0,1097.61,932.923,17.89,15.1,83.83
samvit_base_patch16_224,224,1024.0,1097.38,933.118,16.83,17.2,86.46
eva02_base_patch16_clip_224,224,1024.0,1094.75,935.361,16.9,18.91,86.26
mvitv2_small_cls,224,1024.0,1086.56,942.407,7.04,28.17,34.87
vit_large_r50_s32_224,224,1024.0,1082.13,946.268,19.45,22.22,328.99
inception_next_base,224,1024.0,1079.66,948.435,14.85,25.69,86.67
resnet50_gn,288,1024.0,1076.3,951.4,6.85,18.37,25.56
pvt_v2_b5,224,1024.0,1073.94,953.474,11.39,44.23,81.96
seresnext101d_32x8d,224,1024.0,1071.41,955.74,16.72,32.05,93.59
efficientnetv2_m,320,1024.0,1070.2,956.818,11.01,39.97,54.14
vit_small_r26_s32_384,384,1024.0,1066.07,960.526,10.24,27.67,36.47
resnetblur101d,288,1024.0,1059.66,966.334,15.07,29.65,44.57
resnet101d,320,1024.0,1045.1,979.801,16.48,34.77,44.57
regnetz_e8,256,1024.0,1042.94,981.82,9.91,40.94,57.7
tf_efficientnet_lite4,380,768.0,1038.99,739.169,4.04,45.66,13.01
xception41p,299,768.0,1034.81,742.157,9.25,39.86,26.91
repvgg_b3,224,1024.0,1031.23,992.974,29.16,15.1,123.09
xcit_tiny_24_p16_384,384,1024.0,1026.84,997.227,6.87,34.29,12.12
resnetrs152,256,1024.0,1024.28,999.711,15.59,30.83,86.62
seresnet152d,256,1024.0,1022.13,1001.814,15.42,30.56,66.84
swinv2_cr_small_224,224,1024.0,1005.65,1018.232,9.07,50.27,49.7
vit_base_patch16_plus_240,240,1024.0,1004.91,1018.982,26.31,22.07,117.56
regnetz_b16_evos,288,768.0,997.65,769.796,2.36,16.43,9.74
focalnet_base_srf,224,1024.0,995.12,1029.007,15.28,35.01,88.15
swinv2_cr_small_ns_224,224,1024.0,993.65,1030.528,9.08,50.27,49.7
convnextv2_small,224,1024.0,992.07,1032.17,8.71,21.56,50.32
convnextv2_tiny,288,768.0,989.58,776.074,7.39,22.21,28.64
vit_small_patch8_224,224,1024.0,985.02,1039.56,16.76,32.86,21.67
regnety_040_sgn,288,1024.0,979.5,1045.407,6.67,20.3,20.65
regnetz_c16_evos,256,768.0,978.11,785.174,2.48,16.57,13.49
vit_base_r50_s16_224,224,1024.0,971.42,1054.108,20.94,27.88,97.89
hrnet_w44,224,1024.0,967.41,1058.48,14.94,26.92,67.06
efficientformer_l7,224,1024.0,966.26,1059.742,10.17,24.45,82.23
hrnet_w48_ssld,224,1024.0,963.59,1062.678,17.34,28.56,77.47
hrnet_w48,224,1024.0,962.72,1063.645,17.34,28.56,77.47
poolformer_m36,224,1024.0,959.97,1066.674,8.8,22.02,56.17
resnet152,288,1024.0,955.06,1072.17,19.11,37.28,60.19
cait_s24_224,224,1024.0,951.69,1075.97,9.35,40.58,46.92
tiny_vit_21m_384,384,512.0,946.04,541.193,11.94,46.84,21.23
focalnet_base_lrf,224,1024.0,946.02,1082.418,15.43,38.13,88.75
dm_nfnet_f1,224,1024.0,943.8,1084.958,17.87,22.94,132.63
efficientnet_b3_gn,288,512.0,943.58,542.602,1.74,23.35,11.73
efficientnetv2_rw_m,320,1024.0,934.42,1095.856,12.72,47.14,53.24
vit_relpos_base_patch16_plus_240,240,1024.0,933.99,1096.357,26.21,23.41,117.38
gmlp_b16_224,224,1024.0,931.13,1099.724,15.78,30.21,73.08
fastvit_sa36,256,1024.0,928.53,1102.809,5.62,34.02,31.53
xception41,299,768.0,927.7,827.842,9.28,39.86,26.97
eva02_small_patch14_336,336,1024.0,926.94,1104.696,12.41,27.7,22.13
maxvit_rmlp_small_rw_224,224,768.0,923.72,831.408,10.48,42.44,64.9
sequencer2d_l,224,1024.0,917.56,1115.991,9.74,22.12,54.3
poolformerv2_s36,224,1024.0,914.51,1119.704,5.01,15.82,30.79
xcit_medium_24_p16_224,224,1024.0,901.57,1135.786,16.13,31.71,84.4
coat_mini,224,1024.0,900.78,1136.787,6.82,33.68,10.34
coat_lite_medium,224,1024.0,898.48,1139.693,9.81,40.06,44.57
swin_s3_small_224,224,768.0,882.63,870.118,9.43,37.84,49.74
efficientnet_b3_g8_gn,288,512.0,882.63,580.072,2.59,23.35,14.25
dpn131,224,1024.0,878.67,1165.389,16.09,32.97,79.25
levit_384_s8,224,512.0,874.93,585.181,9.98,35.86,39.12
efficientnet_b4,384,512.0,874.47,585.489,4.51,50.04,19.34
vit_medium_patch16_gap_384,384,1024.0,873.17,1172.722,22.01,32.15,39.03
nest_base,224,1024.0,871.22,1175.339,16.71,30.51,67.72
nf_regnet_b5,384,1024.0,867.94,1179.793,7.95,42.9,49.74
resnet200d,256,1024.0,866.43,1181.848,20.0,43.09,64.69
maxvit_small_tf_224,224,512.0,864.97,591.915,11.39,46.31,68.93
nest_base_jx,224,1024.0,863.51,1185.835,16.71,30.51,67.72
xcit_small_12_p16_384,384,1024.0,860.6,1189.852,14.14,36.5,26.25
resnetv2_50d_evos,288,1024.0,857.98,1193.488,7.15,19.7,25.59
swin_base_patch4_window7_224,224,1024.0,857.23,1194.527,15.47,36.63,87.77
gcvit_small,224,1024.0,850.2,1204.416,8.57,41.61,51.09
crossvit_15_dagger_408,408,1024.0,849.94,1204.779,16.07,37.0,28.5
eca_nfnet_l1,320,1024.0,845.79,1210.693,14.92,34.42,41.41
tf_efficientnet_b4,380,512.0,836.31,612.204,4.49,49.49,19.34
regnety_080,288,1024.0,834.08,1227.682,13.22,29.69,39.18
levit_conv_384_s8,224,512.0,831.47,615.767,9.98,35.86,39.12
twins_svt_large,224,1024.0,829.67,1234.208,14.84,27.23,99.27
seresnet152,288,1024.0,826.68,1238.676,19.11,37.34,66.82
xception65p,299,768.0,826.46,929.251,13.91,52.48,39.82
eva02_base_patch14_224,224,1024.0,822.18,1245.459,22.0,24.67,85.76
caformer_s36,224,1024.0,811.28,1262.182,7.55,29.29,39.3
maxxvit_rmlp_small_rw_256,256,768.0,805.75,953.134,14.21,47.76,66.01
coatnet_2_rw_224,224,512.0,802.77,637.783,14.55,39.37,73.87
swinv2_base_window12_192,192,1024.0,801.77,1277.157,11.9,39.72,109.28
mvitv2_base,224,1024.0,789.29,1297.348,10.16,40.5,51.47
densenet264d,224,1024.0,784.72,1304.914,13.57,14.0,72.74
resnest50d_4s2x40d,224,1024.0,782.94,1307.879,4.4,17.94,30.42
swinv2_tiny_window16_256,256,512.0,779.51,656.811,6.68,39.02,28.35
volo_d2_224,224,1024.0,778.59,1315.191,14.34,41.34,58.68
dpn107,224,1024.0,773.9,1323.149,18.38,33.46,86.92
xcit_tiny_24_p8_224,224,1024.0,770.47,1329.042,9.21,45.38,12.11
convnext_base,288,1024.0,769.28,1331.103,25.43,47.53,88.59
coatnet_rmlp_2_rw_224,224,512.0,762.93,671.09,14.64,44.94,73.88
mvitv2_base_cls,224,1024.0,760.58,1346.32,10.23,40.65,65.44
convit_base,224,1024.0,757.3,1352.149,17.52,31.77,86.54
convformer_s36,224,1024.0,757.3,1352.161,7.67,30.5,40.01
coatnet_2_224,224,384.0,753.79,509.418,15.94,42.41,74.68
hrnet_w64,224,1024.0,748.82,1367.478,28.97,35.09,128.06
resnet152d,320,1024.0,747.67,1369.57,24.08,47.67,60.21
ecaresnet200d,256,1024.0,744.16,1376.037,20.0,43.15,64.69
seresnet200d,256,1024.0,743.64,1376.992,20.01,43.15,71.86
resnetrs200,256,1024.0,743.56,1377.137,20.18,43.42,93.21
swinv2_small_window8_256,256,1024.0,740.78,1382.313,11.58,40.14,49.73
xception65,299,768.0,738.05,1040.572,13.96,52.48,39.92
fastvit_ma36,256,1024.0,734.46,1394.207,7.85,40.39,44.07
swinv2_cr_small_ns_256,256,1024.0,733.6,1395.843,12.07,76.21,49.7
senet154,224,1024.0,731.81,1399.262,20.77,38.69,115.09
maxvit_rmlp_small_rw_256,256,768.0,731.54,1049.835,13.69,55.48,64.9
legacy_senet154,224,1024.0,730.99,1400.828,20.77,38.69,115.09
tf_efficientnetv2_m,384,1024.0,728.54,1405.529,15.85,57.52,54.14
xcit_nano_12_p8_384,384,1024.0,723.54,1415.249,6.34,46.06,3.05
poolformer_m48,224,1024.0,722.45,1417.374,11.59,29.17,73.47
tnt_b_patch16_224,224,1024.0,722.04,1418.187,14.09,39.01,65.41
efficientvit_l3,224,1024.0,720.55,1421.127,27.62,39.16,246.04
swinv2_cr_base_224,224,1024.0,719.69,1422.825,15.86,59.66,87.88
efficientnet_b3_g8_gn,320,512.0,718.69,712.395,3.2,28.83,14.25
resnest101e,256,1024.0,718.12,1425.925,13.38,28.66,48.28
swin_s3_base_224,224,1024.0,717.57,1427.034,13.69,48.26,71.13
resnext101_64x4d,288,1024.0,717.4,1427.37,25.66,51.59,83.46
swinv2_cr_base_ns_224,224,1024.0,713.5,1435.162,15.86,59.66,87.88
convnextv2_base,224,768.0,711.23,1079.807,15.38,28.75,88.72
resnet200,288,1024.0,697.53,1468.023,24.91,53.21,64.67
efficientnet_b3_gn,320,512.0,695.5,736.148,2.14,28.83,11.73
coat_small,224,1024.0,694.03,1475.431,12.61,44.25,21.69
convnext_large,224,1024.0,690.43,1483.117,34.4,43.13,197.77
regnetz_e8,320,1024.0,670.8,1526.503,15.46,63.94,57.7
efficientformerv2_s2,224,1024.0,670.26,1527.748,1.27,11.77,12.71
seresnext101_32x8d,288,1024.0,656.14,1560.626,27.24,51.63,93.57
resnetrs152,320,1024.0,655.8,1561.431,24.34,48.14,86.62
xcit_small_12_p8_224,224,1024.0,655.5,1562.148,18.69,47.19,26.21
maxxvitv2_rmlp_base_rw_224,224,768.0,651.85,1178.173,23.88,54.39,116.09
seresnet152d,320,1024.0,649.85,1575.74,24.09,47.72,66.84
vit_large_patch32_384,384,1024.0,647.57,1581.281,44.28,32.22,306.63
poolformerv2_m36,224,1024.0,646.73,1583.338,8.81,22.02,56.08
resnext101_32x16d,224,1024.0,641.29,1596.767,36.27,51.18,194.03
seresnext101d_32x8d,288,1024.0,639.61,1600.97,27.64,52.95,93.59
regnetz_d8_evos,256,1024.0,638.02,1604.938,4.5,24.92,23.46
davit_large,224,1024.0,634.07,1614.963,34.37,55.08,196.81
efficientnetv2_m,416,1024.0,633.12,1617.367,18.6,67.5,54.14
regnety_064,224,1024.0,632.1,1619.968,6.39,16.41,30.58
regnetv_064,224,1024.0,629.87,1625.704,6.39,16.41,30.58
regnetz_c16_evos,320,512.0,622.61,822.333,3.86,25.88,13.49
gcvit_base,224,1024.0,620.94,1649.111,14.87,55.48,90.32
nf_regnet_b5,456,512.0,602.97,849.111,11.7,61.95,49.74
seresnextaa101d_32x8d,288,1024.0,601.98,1701.035,28.51,56.44,93.59
xception71,299,768.0,600.76,1278.366,18.09,69.92,42.34
eca_nfnet_l2,320,1024.0,593.89,1724.216,20.95,47.43,56.72
nfnet_f2,256,1024.0,593.31,1725.904,33.76,41.85,193.78
crossvit_18_dagger_408,408,1024.0,585.92,1747.666,25.31,49.38,44.61
hrnet_w48_ssld,288,1024.0,585.32,1749.444,28.66,47.21,77.47
ecaresnet200d,288,1024.0,584.36,1752.321,25.31,54.59,64.69
seresnet200d,288,1024.0,583.25,1755.672,25.32,54.6,71.86
caformer_m36,224,1024.0,582.88,1756.773,12.75,40.61,56.2
levit_512_s8,224,256.0,582.77,439.271,21.82,52.28,74.05
maxvit_rmlp_base_rw_224,224,768.0,582.44,1318.589,22.63,79.3,116.14
seresnet269d,256,1024.0,581.62,1760.578,26.59,53.6,113.67
convmixer_768_32,224,1024.0,580.09,1765.235,19.55,25.95,21.11
resnetrs270,256,1024.0,565.62,1810.398,27.06,55.84,129.86
mixer_l16_224,224,1024.0,553.36,1850.484,44.6,41.69,208.2
levit_conv_512_s8,224,256.0,552.47,463.363,21.82,52.28,74.05
efficientnetv2_rw_m,416,1024.0,552.47,1853.491,21.49,79.62,53.24
resnet200d,320,1024.0,551.74,1855.93,31.25,67.33,64.69
nfnet_f1,320,1024.0,548.82,1865.795,35.97,46.77,132.63
convformer_m36,224,1024.0,548.78,1865.947,12.89,42.05,57.05
volo_d3_224,224,1024.0,541.9,1889.619,20.78,60.09,86.33
swinv2_base_window8_256,256,1024.0,530.42,1930.519,20.37,52.59,87.92
maxvit_base_tf_224,224,512.0,517.72,988.937,23.52,81.67,119.47
xcit_large_24_p16_224,224,1024.0,511.16,2003.26,35.86,47.26,189.1
convmixer_1024_20_ks9_p14,224,1024.0,510.74,2004.929,5.55,5.51,24.38
dm_nfnet_f2,256,1024.0,503.11,2035.325,33.76,41.85,193.78
swin_large_patch4_window7_224,224,768.0,494.53,1552.967,34.53,54.94,196.53
vit_base_patch16_18x2_224,224,1024.0,494.1,2072.443,50.37,49.17,256.73
deit_base_patch16_384,384,1024.0,493.77,2073.808,49.4,48.3,86.86
vit_base_patch16_384,384,1024.0,493.5,2074.946,49.4,48.3,86.86
deit_base_distilled_patch16_384,384,1024.0,493.31,2075.754,49.49,48.39,87.63
vit_base_patch16_clip_384,384,1024.0,492.52,2079.081,49.41,48.3,86.86
eva_large_patch14_196,196,1024.0,491.4,2083.813,59.66,43.77,304.14
vit_base_patch16_siglip_384,384,1024.0,490.82,2086.272,50.0,49.11,93.18
vit_large_patch16_224,224,1024.0,489.19,2093.231,59.7,43.77,304.33
halonet_h1,256,256.0,487.96,524.621,3.0,51.17,8.1
tiny_vit_21m_512,512,256.0,487.73,524.868,21.23,83.26,21.27
seresnextaa101d_32x8d,320,768.0,487.6,1575.053,35.19,69.67,93.59
swinv2_large_window12_192,192,768.0,487.6,1575.036,26.17,56.53,228.77
swinv2_small_window16_256,256,512.0,487.58,1050.071,12.82,66.29,49.73
poolformerv2_m48,224,1024.0,487.33,2101.208,11.59,29.17,73.35
resnetrs200,320,1024.0,476.69,2148.152,31.51,67.81,93.21
xcit_tiny_12_p8_384,384,1024.0,472.87,2165.479,14.12,69.12,6.71
vit_small_patch14_dinov2,518,1024.0,470.72,2175.374,29.46,57.34,22.06
deit3_base_patch16_384,384,1024.0,469.96,2178.883,49.4,48.3,86.88
vit_small_patch14_reg4_dinov2,518,1024.0,469.28,2182.048,29.55,57.51,22.06
deit3_large_patch16_224,224,1024.0,468.18,2187.162,59.7,43.77,304.37
tf_efficientnetv2_m,480,1024.0,466.8,2193.627,24.76,89.84,54.14
dm_nfnet_f1,320,1024.0,463.74,2208.099,35.97,46.77,132.63
xcit_small_24_p16_384,384,1024.0,458.11,2235.247,26.72,68.57,47.67
seresnet269d,288,1024.0,457.25,2239.451,33.65,67.81,113.67
beit_large_patch16_224,224,1024.0,453.95,2255.726,59.7,43.77,304.43
beitv2_large_patch16_224,224,1024.0,453.79,2256.515,59.7,43.77,304.43
regnetx_120,224,1024.0,452.56,2262.648,12.13,21.37,46.11
efficientnet_b5,448,512.0,444.06,1152.996,9.59,93.56,30.39
regnety_120,224,1024.0,444.03,2306.127,12.14,21.38,51.82
efficientformerv2_l,224,1024.0,441.81,2317.703,2.59,18.54,26.32
coatnet_3_rw_224,224,384.0,441.21,870.327,32.63,59.07,181.81
resnetv2_152x2_bit,224,1024.0,439.95,2327.532,46.95,45.11,236.34
convnext_xlarge,224,768.0,438.91,1749.766,60.98,57.5,350.2
coatnet_rmlp_3_rw_224,224,256.0,438.69,583.549,32.75,64.7,165.15
coatnet_3_224,224,256.0,431.52,593.24,35.72,63.61,166.97
convnextv2_base,288,512.0,430.66,1188.858,25.43,47.53,88.72
flexivit_large,240,1024.0,427.93,2392.897,68.48,50.22,304.36
convnextv2_large,224,512.0,424.61,1205.798,34.4,43.13,197.96
swinv2_cr_large_224,224,768.0,424.12,1810.813,35.1,78.42,196.68
swinv2_cr_tiny_384,384,256.0,420.98,608.099,15.34,161.01,28.33
caformer_b36,224,768.0,420.2,1827.698,22.5,54.14,98.75
maxvit_tiny_tf_384,384,256.0,419.78,609.84,16.0,94.22,30.98
convnext_large,288,768.0,417.93,1837.619,56.87,71.29,197.77
regnety_160,224,1024.0,417.09,2455.096,15.96,23.04,83.59
eca_nfnet_l2,384,1024.0,412.81,2480.539,30.05,68.28,56.72
maxxvitv2_rmlp_large_rw_224,224,768.0,411.22,1867.582,43.69,75.4,215.42
efficientnetv2_l,384,1024.0,409.83,2498.611,36.1,101.16,118.52
davit_huge,224,768.0,407.6,1884.205,60.93,73.44,348.92
tf_efficientnetv2_l,384,1024.0,405.08,2527.906,36.1,101.16,118.52
regnety_320,224,1024.0,403.27,2539.241,32.34,30.26,145.05
regnetz_d8_evos,320,768.0,403.13,1905.094,7.03,38.92,23.46
beit_base_patch16_384,384,1024.0,402.61,2543.386,49.4,48.3,86.74
convformer_b36,224,768.0,397.77,1930.749,22.69,56.06,99.88
tf_efficientnet_b5,456,384.0,394.74,972.77,10.46,98.86,30.39
eca_nfnet_l3,352,1024.0,378.23,2707.314,32.57,73.12,72.04
vit_large_patch16_siglip_256,256,1024.0,375.52,2726.866,78.12,57.42,315.96
ecaresnet269d,320,1024.0,372.48,2749.133,41.53,83.69,102.09
vit_large_r50_s32_384,384,1024.0,369.32,2772.633,56.4,64.88,329.09
maxvit_large_tf_224,224,384.0,359.98,1066.726,42.99,109.57,211.79
vit_large_patch14_224,224,1024.0,359.62,2847.449,77.83,57.11,304.2
vit_large_patch14_clip_224,224,1024.0,359.62,2847.409,77.83,57.11,304.2
swinv2_base_window16_256,256,384.0,359.2,1069.042,22.02,84.71,87.92
swinv2_base_window12to16_192to256,256,384.0,359.01,1069.609,22.02,84.71,87.92
nasnetalarge,331,384.0,356.97,1075.708,23.89,90.56,88.75
resnetrs350,288,1024.0,356.46,2872.642,43.67,87.09,163.96
vit_base_patch8_224,224,1024.0,351.76,2911.045,66.87,65.71,86.58
volo_d4_224,224,1024.0,343.2,2983.708,44.34,80.22,192.96
xcit_small_24_p8_224,224,1024.0,342.74,2987.714,35.81,90.77,47.63
volo_d1_384,384,512.0,340.3,1504.541,22.75,108.55,26.78
convnext_large_mlp,320,512.0,338.23,1513.736,70.21,88.02,200.13
repvgg_d2se,320,1024.0,335.87,3048.766,74.57,46.82,133.33
vit_large_patch14_clip_quickgelu_224,224,1024.0,324.37,3156.896,77.83,57.11,303.97
vit_base_r50_s16_384,384,1024.0,315.28,3247.919,61.29,81.77,98.95
nfnet_f2,352,1024.0,313.79,3263.314,63.22,79.06,193.78
xcit_medium_24_p16_384,384,1024.0,313.38,3267.626,47.39,91.63,84.4
vit_large_patch14_xp_224,224,1024.0,311.53,3287.018,77.77,57.11,304.06
ecaresnet269d,352,1024.0,307.84,3326.422,50.25,101.25,102.09
coat_lite_medium_384,384,512.0,301.48,1698.273,28.73,116.7,44.57
regnety_064,288,1024.0,298.91,3425.709,10.56,27.11,30.58
resnetrs270,352,1024.0,298.81,3426.892,51.13,105.48,129.86
regnetv_064,288,1024.0,298.12,3434.809,10.55,27.11,30.58
resnext101_32x32d,224,512.0,296.06,1729.362,87.29,91.12,468.53
nfnet_f3,320,1024.0,290.3,3527.352,68.77,83.93,254.92
efficientnetv2_xl,384,1024.0,290.02,3530.821,52.81,139.2,208.12
tf_efficientnetv2_xl,384,1024.0,287.47,3562.138,52.81,139.2,208.12
cait_xxs24_384,384,1024.0,284.02,3605.396,9.63,122.65,12.03
maxvit_small_tf_384,384,192.0,274.58,699.228,33.58,139.86,69.02
coatnet_4_224,224,256.0,274.31,933.246,60.81,98.85,275.43
convnext_xlarge,288,512.0,265.38,1929.279,100.8,95.05,350.2
dm_nfnet_f2,352,1024.0,265.36,3858.944,63.22,79.06,193.78
vit_base_patch16_siglip_512,512,512.0,263.16,1945.545,88.89,87.3,93.52
vit_so400m_patch14_siglip_224,224,1024.0,262.63,3898.968,106.18,70.45,427.68
efficientnetv2_l,480,512.0,261.08,1961.059,56.4,157.99,118.52
swinv2_cr_small_384,384,256.0,258.97,988.525,29.7,298.03,49.7
convnextv2_large,288,384.0,257.89,1488.981,56.87,71.29,197.96
tf_efficientnetv2_l,480,512.0,257.78,1986.206,56.4,157.99,118.52
eva02_large_patch14_224,224,1024.0,256.9,3985.935,77.9,65.52,303.27
eva02_large_patch14_clip_224,224,1024.0,253.93,4032.531,77.93,65.52,304.11
regnety_120,288,768.0,253.81,3025.924,20.06,35.34,51.82
xcit_tiny_24_p8_384,384,1024.0,248.2,4125.63,27.05,132.94,12.11
coatnet_rmlp_2_rw_384,384,192.0,247.61,775.41,43.04,132.57,73.88
dm_nfnet_f3,320,1024.0,247.07,4144.617,68.77,83.93,254.92
resnetrs420,320,1024.0,244.54,4187.355,64.2,126.56,191.89
mvitv2_large,224,512.0,243.6,2101.832,43.87,112.02,217.99
mvitv2_large_cls,224,512.0,241.75,2117.866,42.17,111.69,234.58
resmlp_big_24_224,224,1024.0,241.59,4238.519,100.23,87.31,129.14
regnety_160,288,768.0,237.71,3230.76,26.37,38.07,83.59
xcit_medium_24_p8_224,224,768.0,234.01,3281.941,63.52,121.22,84.32
eca_nfnet_l3,448,512.0,233.43,2193.322,52.55,118.4,72.04
volo_d5_224,224,1024.0,228.8,4475.542,72.4,118.11,295.46
swin_base_patch4_window12_384,384,256.0,227.46,1125.454,47.19,134.78,87.9
xcit_small_12_p8_384,384,384.0,223.23,1720.206,54.92,138.25,26.21
swinv2_large_window12to16_192to256,256,256.0,219.08,1168.537,47.81,121.53,196.74
maxxvitv2_rmlp_base_rw_384,384,384.0,217.17,1768.16,70.18,160.22,116.09
efficientnet_b6,528,256.0,205.22,1247.45,19.4,167.39,43.04
regnetx_320,224,768.0,200.5,3830.333,31.81,36.3,107.81
resnetrs350,384,1024.0,199.92,5122.143,77.59,154.74,163.96
cait_xs24_384,384,768.0,198.76,3863.971,19.28,183.98,26.67
maxvit_xlarge_tf_224,224,256.0,198.54,1289.412,96.49,164.37,506.99
tf_efficientnet_b6,528,192.0,198.54,967.028,19.4,167.39,43.04
focalnet_huge_fl3,224,512.0,191.39,2675.182,118.26,104.8,745.28
volo_d2_384,384,384.0,190.85,2012.066,46.17,184.51,58.87
cait_xxs36_384,384,1024.0,189.78,5395.721,14.35,183.7,17.37
eva02_base_patch14_448,448,512.0,189.58,2700.759,87.74,98.4,87.12
vit_huge_patch14_gap_224,224,1024.0,186.27,5497.294,161.36,94.7,630.76
swinv2_cr_base_384,384,256.0,185.05,1383.395,50.57,333.68,87.88
swinv2_cr_huge_224,224,384.0,182.04,2109.357,115.97,121.08,657.83
maxvit_rmlp_base_rw_384,384,384.0,179.65,2137.52,66.51,233.79,116.14
vit_huge_patch14_224,224,1024.0,179.6,5701.574,161.99,95.07,630.76
vit_huge_patch14_clip_224,224,1024.0,179.43,5706.842,161.99,95.07,632.05
xcit_large_24_p16_384,384,1024.0,177.48,5769.692,105.34,137.15,189.1
vit_base_patch14_dinov2,518,512.0,176.68,2897.828,117.11,114.68,86.58
vit_base_patch14_reg4_dinov2,518,512.0,175.98,2909.337,117.45,115.02,86.58
deit3_huge_patch14_224,224,1024.0,173.53,5900.889,161.99,95.07,632.13
nfnet_f3,416,768.0,171.77,4471.127,115.58,141.78,254.92
maxvit_tiny_tf_512,512,128.0,170.91,748.92,28.66,172.66,31.05
seresnextaa201d_32x8d,384,512.0,170.35,3005.583,101.11,199.72,149.39
maxvit_base_tf_384,384,192.0,166.63,1152.259,69.34,247.75,119.65
vit_huge_patch14_clip_quickgelu_224,224,1024.0,165.5,6187.275,161.99,95.07,632.08
efficientnetv2_xl,512,512.0,163.45,3132.529,93.85,247.32,208.12
nfnet_f4,384,768.0,163.26,4704.17,122.14,147.57,316.07
tf_efficientnetv2_xl,512,512.0,161.63,3167.699,93.85,247.32,208.12
vit_huge_patch14_xp_224,224,1024.0,159.72,6411.21,161.88,95.07,631.8
eva_large_patch14_336,336,768.0,155.72,4931.845,174.74,128.21,304.53
vit_large_patch14_clip_336,336,768.0,155.28,4945.947,174.74,128.21,304.53
vit_large_patch16_384,384,768.0,155.12,4950.906,174.85,128.21,304.72
vit_large_patch16_siglip_384,384,768.0,154.94,4956.619,175.76,129.18,316.28
convnext_xxlarge,256,384.0,153.59,2500.071,198.09,124.45,846.47
vit_giant_patch16_gap_224,224,1024.0,153.47,6672.363,198.14,103.64,1011.37
cait_s24_384,384,512.0,153.12,3343.821,32.17,245.3,47.06
davit_giant,224,384.0,152.05,2525.491,192.34,138.2,1406.47
deit3_large_patch16_384,384,1024.0,148.73,6884.872,174.85,128.21,304.76
coatnet_5_224,224,192.0,147.83,1298.762,142.72,143.69,687.47
dm_nfnet_f3,416,512.0,146.0,3506.787,115.58,141.78,254.92
resnetrs420,416,768.0,144.59,5311.727,108.45,213.79,191.89
vit_large_patch14_clip_quickgelu_336,336,768.0,141.12,5441.998,174.74,128.21,304.29
dm_nfnet_f4,384,768.0,139.13,5519.969,122.14,147.57,316.07
swin_large_patch4_window12_384,384,128.0,135.95,941.498,104.08,202.16,196.74
xcit_large_24_p8_224,224,512.0,131.73,3886.696,141.22,181.53,188.93
beit_large_patch16_384,384,768.0,129.79,5917.023,174.84,128.21,305.0
efficientnet_b7,600,192.0,128.05,1499.407,38.33,289.94,66.35
tf_efficientnet_b7,600,192.0,124.56,1541.433,38.33,289.94,66.35
focalnet_huge_fl4,224,512.0,123.26,4153.862,118.9,113.34,686.46
eva_giant_patch14_clip_224,224,1024.0,116.99,8753.07,259.74,135.89,1012.59
eva_giant_patch14_224,224,1024.0,116.91,8758.747,259.74,135.89,1012.56
nfnet_f5,416,768.0,116.91,6569.029,170.71,204.56,377.21
xcit_small_24_p8_384,384,384.0,116.73,3289.571,105.23,265.87,47.63
maxvit_large_tf_384,384,128.0,116.56,1098.144,126.61,332.3,212.03
vit_giant_patch14_224,224,1024.0,114.32,8957.604,259.74,135.89,1012.61
vit_giant_patch14_clip_224,224,1024.0,114.12,8973.257,259.74,135.89,1012.65
swinv2_cr_large_384,384,192.0,113.51,1691.47,108.96,404.96,196.68
eva02_large_patch14_clip_336,336,768.0,110.42,6955.361,174.97,147.1,304.43
mvitv2_huge_cls,224,384.0,105.54,3638.368,120.67,243.63,694.8
maxvit_small_tf_512,512,96.0,104.89,915.238,60.02,256.36,69.13
cait_s36_384,384,512.0,102.28,5005.663,47.99,367.39,68.37
dm_nfnet_f5,416,512.0,99.59,5141.209,170.71,204.56,377.21
swinv2_base_window12to24_192to384,384,96.0,96.5,994.841,55.25,280.36,87.92
focalnet_large_fl3,384,256.0,93.78,2729.925,105.06,168.04,239.13
nfnet_f4,512,512.0,91.69,5583.92,216.26,262.26,316.07
focalnet_large_fl4,384,256.0,90.64,2824.324,105.2,181.78,239.32
nfnet_f6,448,512.0,86.88,5893.345,229.7,273.62,438.36
efficientnet_b8,672,128.0,85.75,1492.768,63.48,442.89,87.41
tf_efficientnet_b8,672,128.0,83.71,1529.068,63.48,442.89,87.41
volo_d3_448,448,128.0,81.1,1578.235,96.33,446.83,86.63
vit_so400m_patch14_siglip_384,384,512.0,80.75,6340.618,302.34,200.62,428.23
xcit_medium_24_p8_384,384,256.0,80.25,3189.919,186.67,354.69,84.32
dm_nfnet_f4,512,384.0,78.23,4908.575,216.26,262.26,316.07
vit_huge_patch14_clip_336,336,512.0,75.44,6786.84,363.7,213.44,632.46
dm_nfnet_f6,448,512.0,74.17,6903.248,229.7,273.62,438.36
maxvit_base_tf_512,512,96.0,72.37,1326.47,123.93,456.26,119.88
nfnet_f5,544,384.0,68.39,5614.643,290.97,349.71,377.21
nfnet_f7,480,512.0,66.61,7686.561,300.08,355.86,499.5
vit_gigantic_patch14_224,224,512.0,66.24,7729.406,473.4,204.12,1844.44
vit_gigantic_patch14_clip_224,224,512.0,66.15,7739.524,473.41,204.12,1844.91
focalnet_xlarge_fl3,384,192.0,65.92,2912.463,185.61,223.99,408.79
maxvit_xlarge_tf_384,384,96.0,64.9,1479.208,283.86,498.45,475.32
focalnet_xlarge_fl4,384,192.0,63.63,3017.361,185.79,242.31,409.03
beit_large_patch16_512,512,256.0,61.48,4163.85,310.6,227.76,305.67
volo_d4_448,448,192.0,60.99,3147.895,197.13,527.35,193.41
regnety_640,384,192.0,60.97,3149.012,188.47,124.83,281.38
convnextv2_huge,384,96.0,60.92,1575.922,337.96,232.35,660.29
swinv2_large_window12to24_192to384,384,48.0,60.75,790.151,116.15,407.83,196.74
eva02_large_patch14_448,448,512.0,59.67,8581.221,310.69,261.32,305.08
dm_nfnet_f5,544,384.0,58.35,6580.773,290.97,349.71,377.21
vit_huge_patch14_clip_378,378,512.0,58.14,8806.389,460.13,270.04,632.68
convmixer_1536_20,224,1024.0,56.99,17967.01,48.68,33.03,51.63
vit_large_patch14_dinov2,518,384.0,56.83,6757.154,414.89,304.42,304.37
vit_large_patch14_reg4_dinov2,518,384.0,56.64,6779.944,416.1,305.31,304.37
maxvit_large_tf_512,512,64.0,54.68,1170.494,225.96,611.85,212.33
tf_efficientnet_l2,475,96.0,54.05,1776.14,172.11,609.89,480.31
vit_huge_patch14_clip_quickgelu_378,378,384.0,53.95,7117.573,460.13,270.04,632.68
vit_huge_patch16_gap_448,448,512.0,52.86,9685.108,494.35,290.02,631.67
nfnet_f6,576,384.0,52.55,7307.184,378.69,452.2,438.36
swinv2_cr_giant_224,224,192.0,52.45,3660.551,483.85,309.15,2598.76
eva_giant_patch14_336,336,512.0,49.65,10312.606,583.14,305.1,1013.01
swinv2_cr_huge_384,384,96.0,49.62,1934.539,352.04,583.18,657.94
xcit_large_24_p8_384,384,192.0,45.19,4249.177,415.0,531.74,188.93
dm_nfnet_f6,576,256.0,44.83,5710.109,378.69,452.2,438.36
volo_d5_448,448,192.0,42.49,4518.905,315.06,737.92,295.91
nfnet_f7,608,256.0,41.52,6165.283,480.39,570.85,499.5
cait_m36_384,384,256.0,33.1,7733.448,173.11,734.79,271.22
resnetv2_152x4_bit,480,96.0,32.12,2989.13,844.84,414.26,936.53
maxvit_xlarge_tf_512,512,48.0,30.41,1578.222,505.95,917.77,475.77
regnety_2560,384,128.0,30.25,4231.43,747.83,296.49,1282.6
volo_d5_512,512,128.0,29.54,4332.489,425.09,1105.37,296.09
samvit_base_patch16,1024,16.0,23.81,671.88,371.55,403.08,89.67
regnety_1280,384,128.0,22.93,5583.053,374.99,210.2,644.81
efficientnet_l2,800,48.0,19.03,2521.932,479.12,1707.39,480.31
vit_giant_patch14_dinov2,518,192.0,17.15,11193.542,1553.56,871.89,1136.48
vit_giant_patch14_reg4_dinov2,518,192.0,17.12,11212.072,1558.09,874.43,1136.48
swinv2_cr_giant_384,384,32.0,15.04,2127.877,1450.71,1394.86,2598.76
eva_giant_patch14_560,560,192.0,15.03,12771.913,1618.04,846.56,1014.45
cait_m48_448,448,128.0,13.96,9172.063,329.4,1708.21,356.46
samvit_large_patch16,1024,12.0,10.64,1127.934,1317.08,1055.58,308.28
samvit_huge_patch16,1024,8.0,6.61,1210.638,2741.59,1727.57,637.03
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt111-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,9380.97,53.881,512,106,2.04
mobilenetv3_small_050,7276.68,69.643,512,224,1.59
tf_mobilenetv3_small_minimal_100,6334.14,80.291,512,224,2.04
mobilenetv3_small_075,5920.21,85.765,512,224,2.04
lcnet_035,5760.61,88.397,512,224,1.64
mobilenetv3_small_100,5583.48,90.99,512,224,2.54
tf_mobilenetv3_small_075,5569.37,91.204,512,224,2.04
levit_128s,5426.95,93.402,512,224,7.78
lcnet_050,5425.23,93.895,512,224,1.88
tf_mobilenetv3_small_100,5275.47,96.328,512,224,2.54
tinynet_d,4879.29,104.179,512,152,2.34
mixer_s32_224,4491.84,113.42,512,224,19.1
vit_small_patch32_224,4321.32,117.658,512,224,22.88
lcnet_075,4147.31,122.971,512,224,2.36
levit_128,3971.6,127.764,512,224,9.21
vit_tiny_r_s16_p8_224,3957.53,128.516,512,224,6.34
lcnet_100,3524.02,144.807,512,224,2.95
mnasnet_small,3488.23,145.878,512,224,2.03
levit_192,3436.74,147.842,512,224,10.95
regnetx_002,3423.6,148.87,512,224,2.68
regnety_002,3178.35,160.128,512,224,3.16
mobilenetv2_035,3069.55,166.033,512,224,1.68
gernet_s,2949.06,172.893,512,224,8.17
mnasnet_050,2821.34,180.673,512,224,2.22
ssl_resnet18,2720.94,187.823,512,224,11.69
swsl_resnet18,2720.55,187.842,512,224,11.69
gluon_resnet18_v1b,2718.42,187.998,512,224,11.69
resnet18,2711.83,188.459,512,224,11.69
semnasnet_050,2700.91,188.67,512,224,2.08
tinynet_c,2654.89,191.851,512,184,2.46
mobilenetv2_050,2650.81,192.34,512,224,1.97
levit_256,2620.65,194.218,512,224,18.89
lcnet_150,2613.69,195.4,512,224,4.5
regnetx_004,2532.79,201.109,512,224,5.16
seresnet18,2519.64,202.716,512,224,11.78
legacy_seresnet18,2461.74,207.479,512,224,11.78
ese_vovnet19b_slim_dw,2456.97,207.908,512,224,1.9
mobilenetv3_large_075,2342.92,217.677,512,224,3.99
tf_mobilenetv3_large_minimal_100,2311.18,220.824,512,224,3.92
vit_tiny_patch16_224,2249.49,226.757,512,224,5.72
levit_256d,2248.95,226.128,512,224,26.21
deit_tiny_patch16_224,2244.74,227.248,512,224,5.72
tf_mobilenetv3_large_075,2222.25,229.522,512,224,3.99
deit_tiny_distilled_patch16_224,2207.23,231.107,512,224,5.91
ghostnet_050,2193.43,232.046,512,224,2.59
mnasnet_075,2163.05,235.897,512,224,3.17
mobilenetv3_rw,2147.03,237.6,512,224,5.48
mobilenetv3_large_100_miil,2135.44,238.882,512,224,5.48
mobilenetv3_large_100,2134.66,238.979,512,224,5.48
resnet18d,2086.64,244.99,512,224,11.71
pit_ti_distilled_224,2085.77,244.59,512,224,5.1
pit_ti_224,2083.57,244.847,512,224,4.85
hardcorenas_a,2050.08,249.015,512,224,5.26
regnetx_006,2048.56,249.103,512,224,6.2
tf_mobilenetv3_large_100,2046.24,249.335,512,224,5.48
ese_vovnet19b_slim,1997.73,255.915,512,224,3.17
mnasnet_100,1996.94,255.609,512,224,4.38
mnasnet_b1,1993.53,256.025,512,224,4.38
xcit_nano_12_p16_224_dist,1946.62,261.255,512,224,3.05
xcit_nano_12_p16_224,1946.1,261.308,512,224,3.05
semnasnet_075,1927.77,264.67,512,224,2.91
hardcorenas_b,1912.19,266.758,512,224,5.18
mobilenetv2_075,1888.29,270.368,512,224,2.64
hardcorenas_c,1885.12,270.645,512,224,5.52
tf_efficientnetv2_b0,1880.38,271.08,512,224,7.14
tinynet_b,1857.15,274.614,512,188,3.73
regnety_004,1855.83,274.756,512,224,4.34
resnetblur18,1846.1,276.978,512,224,11.69
regnety_006,1808.76,282.043,512,224,6.06
hardcorenas_d,1808.52,281.873,512,224,7.5
mnasnet_a1,1796.84,284.044,512,224,3.89
semnasnet_100,1784.41,286.019,512,224,3.89
spnasnet_100,1782.41,286.288,512,224,4.42
skresnet18,1776.65,287.57,512,224,11.96
mobilenetv2_100,1761.25,289.902,512,224,3.5
mixer_b32_224,1711.23,298.43,512,224,60.29
regnetx_008,1669.43,305.887,512,224,7.26
levit_384,1662.81,306.81,512,224,39.13
vit_base_patch32_224,1658.09,307.969,512,224,88.22
vit_base_patch32_224_sam,1657.82,308.015,512,224,88.22
efficientnet_lite0,1636.56,312.086,512,224,4.65
visformer_tiny,1629.51,313.524,512,224,10.32
gluon_resnet34_v1b,1626.25,314.261,512,224,21.8
resnet34,1624.32,314.625,512,224,21.8
tv_resnet34,1622.42,314.985,512,224,21.8
ghostnet_100,1610.26,316.618,512,224,5.18
hardcorenas_f,1606.37,317.568,512,224,8.2
pit_xs_distilled_224,1596.23,319.885,512,224,11.0
pit_xs_224,1595.39,320.023,512,224,10.62
hardcorenas_e,1595.11,319.841,512,224,8.07
tinynet_a,1592.8,320.187,512,192,6.19
resmlp_12_distilled_224,1562.43,326.888,512,224,15.35
resmlp_12_224,1562.2,326.933,512,224,15.35
regnety_008,1550.03,329.315,512,224,6.26
tf_efficientnet_lite0,1547.08,330.18,512,224,4.65
mixer_s16_224,1538.34,332.27,512,224,18.53
fbnetc_100,1532.35,333.142,512,224,5.57
seresnet34,1494.24,341.776,512,224,21.96
mnasnet_140,1493.96,341.916,512,224,7.12
nf_regnet_b0,1481.15,344.453,512,256,8.76
legacy_seresnet34,1458.06,350.271,512,224,21.96
gmixer_12_224,1449.53,352.403,512,224,12.7
gernet_m,1442.79,354.129,512,224,21.14
ese_vovnet19b_dw,1440.24,354.984,512,224,6.54
vit_small_patch32_384,1423.05,358.931,512,384,22.92
nf_resnet26,1422.6,359.386,512,224,16.0
efficientnet_b0,1413.79,270.572,384,224,5.29
dla46_c,1408.04,362.893,512,224,1.3
rexnetr_100,1385.71,275.935,384,224,4.88
mobilenetv2_110d,1384.57,276.313,384,224,4.52
rexnet_100,1381.06,276.903,384,224,4.8
vit_tiny_r_s16_p8_384,1371.38,279.143,384,384,6.36
ghostnet_130,1367.64,372.982,512,224,7.36
resnet34d,1367.17,373.876,512,224,21.82
tf_efficientnet_b0,1353.9,282.522,384,224,5.29
tf_efficientnet_b0_ap,1353.18,282.683,384,224,5.29
tf_efficientnet_b0_ns,1352.63,282.744,384,224,5.29
selecsls42,1349.71,378.706,512,224,30.35
selecsls42b,1347.34,379.337,512,224,32.46
gmlp_ti16_224,1340.35,284.915,384,224,5.87
regnetz_005,1326.54,384.587,512,224,7.12
crossvit_tiny_240,1322.74,385.447,512,240,7.01
resnet26,1321.4,386.974,512,224,16.0
semnasnet_140,1315.96,388.179,512,224,6.11
xcit_tiny_12_p16_224,1309.91,389.1,512,224,6.72
xcit_tiny_12_p16_224_dist,1303.81,390.894,512,224,6.72
hrnet_w18_small,1302.26,391.765,512,224,13.19
efficientnet_b1_pruned,1277.21,399.368,512,240,6.33
mobilevit_xxs,1270.89,301.046,384,256,1.27
mobilenetv2_140,1250.7,306.236,384,224,6.11
poolformer_s12,1245.33,410.466,512,224,11.92
crossvit_9_240,1236.49,309.127,384,240,8.55
nf_seresnet26,1207.14,423.49,512,224,17.4
tf_efficientnetv2_b1,1198.02,319.027,384,240,8.14
repvgg_b0,1184.29,431.28,512,224,15.82
crossvit_9_dagger_240,1177.9,324.572,384,240,8.78
selecsls60,1152.93,443.193,512,224,30.67
mixnet_s,1150.64,443.717,512,224,4.13
selecsls60b,1149.12,444.678,512,224,32.77
efficientnet_es,1142.77,447.293,512,224,5.44
efficientnet_es_pruned,1141.22,447.892,512,224,5.44
nf_ecaresnet26,1137.4,449.596,512,224,16.0
resnet26d,1125.5,454.383,512,224,16.01
tf_efficientnet_es,1120.47,456.18,512,224,5.44
efficientnet_lite1,1109.69,229.723,256,240,5.42
rexnetr_130,1097.15,232.191,256,224,7.61
tf_mixnet_s,1090.03,468.412,512,224,4.13
convit_tiny,1087.94,469.614,512,224,5.71
convnext_nano_hnf,1075.51,356.223,384,224,15.59
dla34,1072.51,476.787,512,224,15.74
tf_efficientnet_lite1,1061.0,240.288,256,240,5.42
dla46x_c,1058.5,482.972,512,224,1.07
rexnet_130,1051.04,242.43,256,224,7.56
regnetx_016,1042.11,490.415,512,224,9.19
mobilenetv2_120d,1036.53,245.787,256,224,5.83
skresnet34,1033.13,494.425,512,224,22.28
vit_small_patch16_224,1032.86,370.943,384,224,22.05
deit_small_patch16_224,1031.61,371.386,384,224,22.05
deit_small_distilled_patch16_224,1011.7,378.721,384,224,22.44
dla60x_c,1011.12,505.401,512,224,1.32
ecaresnet50d_pruned,1009.57,506.231,512,224,19.94
gernet_l,1007.43,507.303,512,256,31.08
efficientnet_b0_g16_evos,992.51,385.814,384,224,8.11
rexnetr_150,973.93,261.684,256,224,9.78
vit_base2_patch32_256,970.35,526.818,512,256,119.46
pit_s_distilled_224,963.9,264.693,256,224,24.04
pit_s_224,963.25,264.885,256,224,23.46
fbnetv3_b,936.5,408.4,384,256,8.6
rexnet_150,934.54,272.774,256,224,9.73
repvgg_a2,931.84,548.616,512,224,28.21
legacy_seresnext26_32x4d,930.69,411.955,384,224,16.79
regnety_016,921.99,553.558,512,224,11.2
resnest14d,909.12,562.716,512,224,10.61
efficientnet_cc_b0_4e,892.75,428.931,384,224,13.31
efficientnet_cc_b0_8e,889.3,430.599,384,224,24.01
coat_lite_tiny,885.61,432.738,384,224,5.72
nf_regnet_b1,883.44,578.138,512,288,10.22
resnetv2_50,883.05,579.001,512,224,25.55
resnext26ts,882.89,434.405,384,256,10.3
resnet26t,880.13,581.227,512,256,16.01
tf_efficientnetv2_b2,877.18,290.276,256,260,10.1
fbnetv3_d,875.63,290.586,256,256,10.31
nf_regnet_b2,875.24,583.366,512,272,14.31
tf_efficientnet_cc_b0_4e,872.5,438.913,384,224,13.31
tf_efficientnet_cc_b0_8e,867.49,441.45,384,224,24.01
efficientnet_b0_gn,860.35,296.448,256,224,5.29
eca_resnext26ts,852.81,299.643,256,256,10.3
seresnext26ts,848.06,301.17,256,256,10.39
botnet26t_256,835.32,459.139,384,256,12.49
gluon_resnet50_v1b,835.19,458.946,384,224,25.56
twins_svt_small,835.02,458.264,384,224,24.06
swsl_resnet50,834.38,459.391,384,224,25.56
resnet50,833.17,460.051,384,224,25.56
tf_efficientnet_b1_ap,832.71,305.903,256,240,7.79
tf_efficientnet_b1,832.37,306.044,256,240,7.79
tf_efficientnet_b1_ns,831.62,306.366,256,240,7.79
tv_resnet50,831.54,460.947,384,224,25.56
efficientnet_b2_pruned,831.45,306.38,256,260,8.31
seresnext26tn_32x4d,831.09,461.371,384,224,16.81
seresnext26d_32x4d,830.66,461.598,384,224,16.81
gcresnext26ts,830.6,307.372,256,256,10.48
seresnext26t_32x4d,830.1,461.943,384,224,16.81
ssl_resnet50,828.96,462.397,384,224,25.56
coat_lite_mini,825.96,464.063,384,224,11.01
vgg11,818.58,625.301,512,224,132.86
halonet26t,813.08,471.69,384,256,12.48
vit_small_resnet26d_224,812.32,471.613,384,224,63.61
eca_botnext26ts_256,804.88,317.468,256,256,10.59
efficientnet_lite2,800.14,318.978,256,260,6.09
efficientnet_b1,797.8,319.366,256,256,7.79
resnetv2_50t,793.9,644.111,512,224,25.57
ecaresnext26t_32x4d,792.87,483.752,384,224,15.41
ecaresnext50t_32x4d,790.88,484.99,384,224,15.41
resnetv2_50d,790.5,646.873,512,224,25.57
tresnet_m,787.27,647.568,512,224,31.39
convnext_tiny,782.73,326.109,256,224,28.59
eca_halonext26ts,781.32,327.03,256,256,10.76
vovnet39a,779.14,656.53,512,224,22.6
ecaresnet101d_pruned,778.75,655.702,512,224,24.88
mixnet_m,778.63,491.619,384,224,5.01
cspresnet50,774.32,495.068,384,256,21.62
tf_efficientnet_lite2,771.04,331.043,256,260,6.09
gluon_resnet50_v1c,769.5,498.184,384,224,25.58
ecaresnetlight,767.59,666.098,512,224,30.16
resmlp_24_224,766.31,332.575,256,224,30.02
resmlp_24_distilled_224,766.21,332.58,256,224,30.02
cspresnext50,765.28,500.93,384,224,20.57
resnet50t,753.13,509.003,384,224,25.57
resnet50d,752.95,509.12,384,224,25.58
gluon_resnet50_v1d,752.94,509.122,384,224,25.58
legacy_seresnet50,751.28,510.001,384,224,28.09
mobilevit_xs,751.23,339.638,256,256,2.32
tf_mixnet_m,748.19,511.684,384,224,5.01
ese_vovnet39b,748.06,683.786,512,224,24.57
resnet32ts,744.11,343.453,256,256,17.96
visformer_small,743.29,515.928,384,224,40.22
dpn68b,737.5,519.461,384,224,12.61
selecsls84,735.99,694.378,512,224,50.95
resnet33ts,732.63,348.809,256,256,19.68
nf_seresnet50,732.06,523.375,384,224,28.09
rexnetr_200,724.64,263.786,192,224,16.52
lambda_resnet26t,724.61,529.342,384,256,10.96
seresnet50,722.32,530.5,384,224,28.09
dpn68,721.48,531.095,384,224,12.61
res2net50_48w_2s,717.64,534.245,384,224,25.29
gmixer_24_224,717.31,355.331,256,224,24.72
eca_resnet33ts,712.11,358.824,256,256,19.68
seresnet33ts,709.27,360.12,256,256,19.78
bat_resnext26ts,708.45,360.137,256,256,10.73
rexnet_200,703.57,271.754,192,224,16.37
resnetblur50,702.19,546.026,384,224,25.56
cspresnet50d,701.76,546.305,384,256,21.64
vgg11_bn,698.67,549.368,384,224,132.87
twins_pcpvt_small,698.21,364.986,256,224,24.11
eca_vovnet39b,697.82,733.074,512,224,22.6
efficientnet_em,694.64,551.807,384,240,6.9
dla60,693.8,552.499,384,224,22.04
tf_efficientnet_em,692.96,368.419,256,240,6.9
cspresnet50w,692.76,553.433,384,256,28.12
resnest26d,688.5,556.976,384,224,17.07
gcresnet33ts,687.02,371.584,256,256,19.88
tv_densenet121,686.65,371.007,256,224,7.98
densenet121,686.51,371.091,256,224,7.98
vit_small_r26_s32_224,685.79,371.994,256,224,36.43
convnext_tiny_hnf,683.74,373.449,256,224,28.59
xcit_nano_12_p16_384_dist,682.7,373.202,256,384,3.05
xcit_tiny_24_p16_224_dist,681.26,372.348,256,224,12.12
lambda_resnet26rpt_256,679.63,281.896,192,256,10.99
xcit_tiny_24_p16_224,679.38,373.443,256,224,12.12
vit_base_resnet26d_224,679.21,563.967,384,224,101.4
resnetaa50d,678.8,564.84,384,224,25.58
nf_ecaresnet50,673.73,568.984,384,224,25.56
hrnet_w18_small_v2,672.52,758.874,512,224,15.6
gluon_resnet50_v1s,671.98,570.574,384,224,25.68
efficientnet_b0_g8_gn,661.67,385.796,256,224,6.56
seresnet50t,659.6,581.03,384,224,28.1
efficientnet_b3_pruned,657.34,387.781,256,300,9.86
haloregnetz_b,656.48,388.419,256,224,11.68
tv_resnext50_32x4d,651.27,588.791,384,224,25.03
swsl_resnext50_32x4d,651.11,588.925,384,224,25.03
gluon_resnext50_32x4d,650.97,589.059,384,224,25.03
ssl_resnext50_32x4d,650.9,589.136,384,224,25.03
resnext50_32x4d,649.99,589.957,384,224,25.03
resnetrs50,648.71,590.781,384,224,35.69
densenet121d,646.33,394.275,256,224,8.0
regnetx_032,643.77,595.296,384,224,15.3
resnetblur50d,642.77,397.429,256,224,25.58
res2net50_26w_4s,636.71,601.86,384,224,25.7
swin_tiny_patch4_window7_224,635.13,402.044,256,224,28.29
skresnet50,634.96,603.369,384,224,25.8
poolformer_s24,634.53,402.132,256,224,21.39
gmlp_s16_224,632.58,301.939,192,224,19.42
ese_vovnet57b,632.35,606.334,384,224,38.61
crossvit_small_240,625.73,407.457,256,240,26.86
xcit_small_12_p16_224_dist,625.08,407.723,256,224,26.25
xcit_small_12_p16_224,624.67,407.992,256,224,26.25
densenetblur121d,617.78,412.569,256,224,8.0
ecaresnet50d,612.94,625.548,384,224,25.58
tf_efficientnet_b2_ns,610.27,313.114,192,260,9.11
tf_efficientnet_b2_ap,610.1,313.168,192,260,9.11
tf_efficientnet_b2,609.89,313.274,192,260,9.11
mixnet_l,603.6,634.707,384,224,7.33
gluon_inception_v3,601.71,636.771,384,299,23.83
inception_v3,601.07,637.475,384,299,23.83
adv_inception_v3,601.02,637.509,384,299,23.83
tf_inception_v3,600.65,637.912,384,299,23.83
sehalonet33ts,599.96,425.883,256,256,13.69
seresnetaa50d,599.53,425.823,256,224,28.11
resnext50d_32x4d,598.58,426.825,256,224,25.05
mobilevit_s,596.24,320.895,192,256,5.58
resnetv2_50x1_bit_distilled,588.8,325.252,192,224,25.55
gcresnet50t,583.98,436.858,256,256,25.9
skresnet50d,583.54,437.238,256,224,25.82
swin_s3_tiny_224,576.57,442.971,256,224,28.33
seresnext50_32x4d,576.37,443.036,256,224,27.56
gluon_seresnext50_32x4d,576.3,443.051,256,224,27.56
tf_mixnet_l,576.22,442.743,256,224,7.33
legacy_seresnext50_32x4d,575.52,443.681,256,224,27.56
res2next50,575.07,443.835,256,224,24.67
repvgg_b1g4,572.25,893.649,512,224,39.97
cspresnext50_iabn,572.06,669.169,384,256,20.57
convnext_tiny_hnfd,570.41,447.777,256,224,28.63
res2net50_14w_8s,569.05,447.639,256,224,25.06
resnest50d_1s4x24d,568.93,448.655,256,224,25.68
cait_xxs24_224,568.71,447.7,256,224,11.96
crossvit_15_240,567.12,336.74,192,240,27.53
semobilevit_s,566.94,337.375,192,256,5.74
densenet169,563.82,451.519,256,224,14.15
efficientnet_b2,561.37,340.497,192,288,9.11
efficientnet_cc_b1_8e,559.34,455.785,256,240,39.72
efficientnet_b2a,558.58,342.255,192,288,9.11
darknet53,556.85,458.909,256,256,41.61
dla60_res2net,555.54,459.405,256,224,20.85
dla60x,553.4,461.598,256,224,17.35
mixer_b16_224,552.46,462.598,256,224,59.88
nf_resnet101,550.61,695.793,384,224,44.55
tf_efficientnet_cc_b1_8e,549.81,463.949,256,240,39.72
regnetx_040,549.5,697.703,384,224,22.12
mixer_b16_224_miil,549.25,465.267,256,224,59.88
crossvit_15_dagger_240,547.66,348.734,192,240,28.21
nf_regnet_b3,547.65,465.691,256,320,18.59
vovnet57a,547.59,934.12,512,224,36.64
resnetv2_101,544.49,468.639,256,224,44.54
xcit_nano_12_p8_224,542.16,470.425,256,224,3.05
xcit_nano_12_p8_224_dist,541.55,470.928,256,224,3.05
tf_efficientnetv2_b3,541.54,352.751,192,300,14.36
sebotnet33ts_256,540.04,236.225,128,256,13.7
gcresnext50ts,538.97,354.583,192,256,15.67
nf_resnet50,536.27,715.168,384,288,25.56
resnet50_gn,532.97,359.41,192,224,25.56
vit_base_r26_s32_224,532.84,359.064,192,224,101.38
vit_base_patch32_384,531.15,481.156,256,384,88.3
efficientnetv2_rw_t,527.04,362.193,192,288,13.65
resnet101,526.31,484.87,256,224,44.55
gluon_resnet101_v1b,525.74,485.353,256,224,44.55
tv_resnet101,523.69,487.267,256,224,44.55
vit_large_patch32_224,515.11,495.389,256,224,306.54
vit_base_resnet50d_224,514.44,495.989,256,224,110.97
mixer_l32_224,507.93,376.544,192,224,206.94
resnetv2_101d,506.46,503.899,256,224,44.56
swin_v2_cr_tiny_224,505.66,378.391,192,224,28.33
vit_tiny_patch16_384,505.36,252.456,128,384,5.79
resmlp_36_224,505.0,378.005,192,224,44.69
resmlp_36_distilled_224,504.83,378.088,192,224,44.69
swin_v2_cr_tiny_ns_224,503.02,380.384,192,224,28.33
repvgg_b1,501.28,1020.314,512,224,57.42
dla60_res2next,500.08,510.484,256,224,17.03
gluon_resnet101_v1c,498.53,511.943,256,224,44.57
wide_resnet50_2,491.97,779.7,384,224,68.88
gluon_resnet101_v1d,491.71,519.004,256,224,44.57
resnest50d,484.85,526.671,256,224,27.48
vgg13,482.75,795.25,384,224,133.05
cspdarknet53,481.82,530.306,256,256,27.64
gc_efficientnetv2_rw_t,480.93,396.503,192,288,13.68
convnext_small,478.98,399.137,192,224,50.22
efficientnet_lite3,478.82,266.239,128,300,8.2
ecaresnet26t,478.49,534.465,256,320,16.01
nest_tiny,477.21,267.341,128,224,17.06
regnetz_b16,476.53,401.488,192,288,9.72
dla102,475.31,537.042,256,224,33.27
cspdarknet53_iabn,474.61,806.628,384,256,27.64
res2net50_26w_6s,474.4,537.903,256,224,37.05
jx_nest_tiny,469.53,271.701,128,224,17.06
twins_pcpvt_base,465.48,409.697,192,224,43.83
vgg13_bn,465.35,549.842,256,224,133.05
halonet50ts,463.11,413.595,192,256,22.73
regnetx_080,462.29,829.538,384,224,39.57
lambda_resnet50ts,461.63,414.909,192,256,21.54
coat_lite_small,461.47,414.609,192,224,19.84
legacy_seresnet101,460.47,553.804,256,224,49.33
vgg16,459.25,557.204,256,224,138.36
tf_efficientnet_lite3,458.84,277.849,128,300,8.2
resnetaa101d,458.35,556.916,256,224,44.57
gluon_resnet101_v1s,456.37,559.352,256,224,44.67
xcit_tiny_12_p16_384_dist,451.6,423.4,192,384,6.72
seresnet101,447.82,569.466,256,224,49.33
densenet201,447.49,426.067,192,224,20.01
mixnet_xl,447.09,570.692,256,224,11.9
nf_seresnet101,444.89,573.162,256,224,49.33
convit_small,441.84,433.517,192,224,27.78
resnetblur101d,441.53,578.26,256,224,44.57
nfnet_l0,426.47,599.103,256,288,35.07
skresnext50_32x4d,424.37,601.881,256,224,27.48
poolformer_s36,424.29,450.68,192,224,30.86
botnet50ts_256,421.6,302.649,128,256,22.74
halo2botnet50ts_256,418.8,457.379,192,256,22.64
gluon_resnext101_32x4d,418.26,610.512,256,224,44.18
resnext101_32x4d,417.6,611.486,256,224,44.18
ssl_resnext101_32x4d,417.52,611.578,256,224,44.18
swsl_resnext101_32x4d,417.45,611.654,256,224,44.18
fbnetv3_g,415.62,305.885,128,288,16.62
twins_svt_base,413.84,461.835,192,224,56.07
ese_vovnet39b_evos,413.01,308.979,128,224,24.58
res2net101_26w_4s,405.25,629.29,256,224,45.21
eca_nfnet_l0,404.73,631.533,256,288,24.14
tresnet_l,403.54,1265.42,512,224,55.99
lamhalobotnet50ts_256,403.43,474.897,192,256,22.57
volo_d1_224,400.45,478.074,192,224,26.63
crossvit_18_240,400.32,317.735,128,240,43.27
resnet51q,397.98,481.556,192,288,35.7
nf_ecaresnet101,396.22,644.266,256,224,44.55
dla102x,392.86,487.139,192,224,26.31
vit_base_patch16_224_miil,392.0,489.026,192,224,86.54
swin_small_patch4_window7_224,391.85,488.136,192,224,49.61
regnety_032,391.81,651.958,256,288,19.44
regnetx_064,390.82,654.137,256,224,26.21
res2net50_26w_8s,389.26,655.478,256,224,48.4
vgg16_bn,388.47,658.66,256,224,138.37
deit_base_patch16_224,386.56,495.833,192,224,86.57
crossvit_18_dagger_240,386.08,329.466,128,240,44.27
vit_base_patch16_224,385.82,496.783,192,224,86.57
resnest50d_4s2x40d,385.78,662.242,256,224,30.42
xception,384.95,331.72,128,299,22.86
vit_base_patch16_224_sam,384.94,497.961,192,224,86.57
ecaresnet101d,381.61,669.061,256,224,44.57
deit_base_distilled_patch16_224,379.86,504.583,192,224,87.34
resnetv2_152,379.0,673.236,256,224,60.19
repvgg_b2g4,377.95,1353.629,512,224,61.76
ese_vovnet99b,373.94,683.067,256,224,63.2
vit_small_resnet50d_s16_224,373.61,512.677,192,224,57.53
cait_xxs36_224,371.71,512.777,192,224,17.3
resnet152,371.52,514.561,192,224,60.19
tv_resnet152,371.15,515.007,192,224,60.19
gluon_resnet152_v1b,368.93,518.118,192,224,60.19
gluon_seresnext101_32x4d,368.19,519.228,192,224,48.96
seresnext101_32x4d,367.5,520.242,192,224,48.96
nfnet_f0,366.97,696.402,256,256,71.49
legacy_seresnext101_32x4d,366.06,522.375,192,224,48.96
tf_efficientnet_b3_ap,364.58,349.402,128,300,12.23
tf_efficientnet_b3_ns,364.49,349.53,128,300,12.23
tf_efficientnet_b3,364.34,349.633,128,300,12.23
resnetv2_152d,361.2,529.264,192,224,60.2
resnet61q,358.48,356.024,128,288,36.85
resnetv2_50d_frn,357.45,356.923,128,224,25.59
ese_vovnet99b_iabn,357.31,1071.637,384,224,63.2
gluon_resnet152_v1c,355.88,537.206,192,224,60.21
hrnet_w18,355.73,714.824,256,224,21.3
efficientnet_b3,355.08,358.849,128,320,12.23
efficientnet_b3a,354.83,359.115,128,320,12.23
regnety_040,354.78,539.617,192,288,20.65
beit_base_patch16_224,353.55,541.973,192,224,86.53
gluon_resnet152_v1d,353.52,540.757,192,224,60.21
regnety_040s_gn,353.11,360.919,128,224,20.65
vgg19,352.0,1090.659,384,224,143.67
xcit_tiny_12_p8_224,351.32,362.59,128,224,6.71
xcit_tiny_12_p8_224_dist,351.06,362.838,128,224,6.71
xception41p,343.38,371.913,128,299,26.91
regnetv_040,342.33,372.402,128,288,20.64
tnt_s_patch16_224,339.73,563.196,192,224,23.76
repvgg_b2,339.2,1508.352,512,224,89.02
vgg19_bn,336.08,761.352,256,224,143.68
gluon_resnet152_v1s,334.98,570.781,192,224,60.32
densenet161,332.41,382.595,128,224,28.68
dm_nfnet_f0,331.82,770.266,256,256,71.49
dla169,331.14,577.405,192,224,53.39
resnetv2_50d_gn,330.91,385.985,128,288,25.57
convnext_base_in22ft1k,328.04,388.471,128,224,88.59
convnext_tiny_in22ft1k,327.7,388.845,128,224,88.59
convnext_small_in22ft1k,326.36,390.508,128,224,88.59
convnext_base,325.78,391.109,128,224,88.59
xcit_small_24_p16_224_dist,322.26,393.856,128,224,47.67
xcit_small_24_p16_224,321.22,395.027,128,224,47.67
repvgg_b3g4,321.08,1194.912,384,224,83.83
pit_b_224,319.89,399.203,128,224,73.76
twins_pcpvt_large,319.03,397.109,128,224,60.99
pit_b_distilled_224,318.55,400.853,128,224,74.79
legacy_seresnet152,315.59,605.069,192,224,66.82
dpn92,314.54,812.398,256,224,37.67
inception_v4,312.24,612.734,192,299,42.68
regnetx_120,309.13,827.171,256,224,46.11
hrnet_w32,308.88,823.922,256,224,41.23
convmixer_1024_20_ks9_p14,308.12,830.025,256,224,24.38
ecaresnet50t,306.63,416.493,128,320,25.57
seresnet152,305.84,415.318,128,224,66.82
coat_tiny,303.51,419.779,128,224,5.5
vit_small_patch16_36x1_224,303.33,419.341,128,224,64.67
regnetz_c16,302.72,421.37,128,320,13.46
vit_small_patch16_18x2_224,302.0,421.199,128,224,64.67
hrnet_w30,301.17,845.104,256,224,37.71
swin_v2_cr_small_224,300.16,424.024,128,224,49.7
nest_small,296.43,322.186,96,224,38.35
tresnet_xl,295.35,1296.648,384,224,78.44
jx_nest_small,294.56,324.278,96,224,38.35
efficientnet_el,291.47,438.031,128,300,10.59
xception41,291.46,437.936,128,299,26.97
efficientnet_el_pruned,291.16,438.564,128,300,10.59
regnety_120,291.14,658.173,192,224,51.82
cait_s24_224,290.55,438.065,128,224,46.92
nf_regnet_b4,288.27,441.948,128,384,30.21
twins_svt_large,288.14,442.123,128,224,99.27
wide_resnet101_2,287.9,665.343,192,224,126.89
mixnet_xxl,287.84,442.746,128,224,23.96
tf_efficientnet_el,286.59,445.507,128,300,10.59
poolformer_m36,284.22,448.517,128,224,56.17
swin_s3_small_224,277.99,343.436,96,224,49.74
repvgg_b3,276.27,1388.871,384,224,123.09
swin_base_patch4_window7_224,273.38,466.273,128,224,87.77
gmlp_b16_224,266.77,358.289,96,224,73.08
gluon_resnext101_64x4d,266.55,478.624,128,224,83.46
dla102x2,265.99,479.601,128,224,41.28
resnet200,264.41,481.093,128,224,64.67
resnetv2_50d_evob,261.14,366.354,96,224,25.59
regnetx_160,260.8,735.124,192,224,54.28
xception65p,258.86,493.161,128,299,39.82
inception_resnet_v2,255.7,747.606,192,299,55.84
ens_adv_inception_resnet_v2,255.53,748.108,192,299,55.84
resnetrs101,253.22,503.169,128,288,63.62
resnext101_32x8d,252.1,506.144,128,224,88.79
ig_resnext101_32x8d,251.82,506.789,128,224,88.79
ssl_resnext101_32x8d,251.44,507.525,128,224,88.79
swsl_resnext101_32x8d,251.43,507.504,128,224,88.79
crossvit_base_240,250.95,380.881,96,240,105.03
dpn98,249.4,511.692,128,224,61.57
efficientnet_lite4,247.4,257.372,64,380,13.01
coat_mini,246.33,517.632,128,224,10.34
efficientnetv2_s,245.94,388.167,96,384,21.46
gluon_seresnext101_64x4d,243.95,522.441,128,224,88.23
tf_efficientnetv2_s_in21ft1k,243.54,391.981,96,384,21.46
tf_efficientnetv2_s,242.89,393.02,96,384,21.46
resnet101d,240.52,530.628,128,320,44.57
resnest101e,240.15,530.415,128,256,48.28
tf_efficientnet_lite4,239.36,265.97,64,380,13.01
vit_small_patch16_384,235.44,270.99,64,384,22.2
xcit_tiny_24_p16_384_dist,233.56,407.54,96,384,12.12
efficientnetv2_rw_s,232.48,273.03,64,384,23.94
regnety_064,232.21,549.529,128,288,30.58
xcit_medium_24_p16_224_dist,231.18,550.33,128,224,84.4
xcit_medium_24_p16_224,231.11,550.432,128,224,84.4
vit_large_r50_s32_224,229.5,415.85,96,224,328.99
regnetv_064,225.87,564.974,128,288,30.58
vit_small_r26_s32_384,225.43,282.622,64,384,36.47
convit_base,225.0,567.909,128,224,86.54
gluon_xception65,223.32,427.945,96,299,39.92
xception65,222.42,429.727,96,299,39.92
tnt_b_patch16_224,222.32,573.844,128,224,65.41
volo_d2_224,221.58,431.459,96,224,58.68
regnety_080,221.2,577.488,128,288,39.18
hrnet_w40,220.15,867.238,192,224,57.56
swin_s3_base_224,219.92,433.728,96,224,71.13
xcit_small_12_p16_384_dist,216.0,442.656,96,384,26.25
swin_v2_cr_base_224,214.24,445.643,96,224,87.88
hrnet_w48,213.16,895.867,192,224,77.47
resnetv2_50d_evos,211.1,226.199,48,288,25.59
nest_base,210.29,302.692,64,224,67.72
jx_nest_base,209.17,304.343,64,224,67.72
vit_base_r50_s16_224,208.73,458.333,96,224,98.66
tresnet_m_448,206.95,924.987,192,448,31.39
hrnet_w44,206.72,924.031,192,224,67.06
efficientnet_b4,203.38,312.633,64,384,19.34
regnetz_b16_evos,201.27,316.106,64,288,9.74
regnetz_040h,199.85,318.401,64,320,28.94
regnetz_040,199.66,318.691,64,320,27.12
efficientnet_b3_gn,199.47,319.133,64,320,11.73
densenet264,199.22,477.999,96,224,72.69
regnetz_d8,197.46,322.524,64,320,23.37
eca_nfnet_l1,189.9,672.191,128,320,41.41
tf_efficientnet_b4,188.81,336.904,64,380,19.34
tf_efficientnet_b4_ns,188.45,337.501,64,380,19.34
tf_efficientnet_b4_ap,188.4,337.642,64,380,19.34
poolformer_m48,187.63,509.206,96,224,73.47
dpn131,186.23,685.159,128,224,79.25
regnetz_d32,186.08,342.366,64,320,27.58
xcit_nano_12_p8_384_dist,183.21,347.477,64,384,3.05
convnext_large_in22ft1k,182.13,525.349,96,224,197.77
convnext_large,180.72,529.451,96,224,197.77
xcit_tiny_24_p8_224_dist,179.19,532.437,96,224,12.11
xcit_tiny_24_p8_224,179.14,532.524,96,224,12.11
dpn107,174.56,731.55,128,224,86.92
resnet152d,172.45,554.35,96,320,60.21
hrnet_w64,170.75,744.856,128,224,128.06
halonet_h1,170.55,373.795,64,256,8.1
xception71,168.15,378.514,64,299,42.34
mixer_l16_224,167.46,571.737,96,224,208.2
regnety_320,166.3,768.299,128,224,145.05
vit_large_patch32_384,165.23,579.454,96,384,306.63
densenet264d_iabn,165.07,1158.795,192,224,72.74
xcit_small_12_p8_224,164.33,387.646,64,224,26.21
xcit_small_12_p8_224_dist,164.16,388.107,64,224,26.21
efficientnet_b3_g8_gn,153.99,413.949,64,320,14.25
swin_large_patch4_window7_224,152.41,417.988,64,224,196.53
volo_d3_224,152.25,417.779,64,224,86.33
ecaresnet200d,150.97,632.522,96,256,64.69
seresnet200d,149.9,422.786,64,256,71.86
regnetx_320,146.61,871.937,128,224,107.81
seresnet152d,143.56,442.443,64,320,66.84
resnetv2_50x1_bitm,142.06,337.009,48,448,25.55
gluon_senet154,141.6,674.51,96,224,115.09
resnetrs152,141.55,448.821,64,320,86.62
senet154,141.16,676.729,96,224,115.09
legacy_senet154,139.88,683.0,96,224,115.09
regnety_160,136.04,704.372,96,288,83.59
xcit_large_24_p16_224_dist,130.76,486.175,64,224,189.1
xcit_large_24_p16_224,130.33,487.671,64,224,189.1
seresnext101_32x8d,126.87,502.294,64,288,93.57
nfnet_f1,125.34,763.802,96,320,132.63
resnet200d,124.61,510.654,64,320,64.69
regnetz_c16_evos,123.93,385.448,48,320,13.49
resnext101_64x4d,122.56,781.647,96,288,83.46
efficientnetv2_m,120.01,396.838,48,416,54.14
swin_v2_cr_large_224,119.95,397.782,48,224,196.68
xcit_tiny_12_p8_384_dist,119.32,400.563,48,384,6.71
vit_large_patch16_224,116.44,548.026,64,224,304.33
crossvit_15_dagger_408,116.23,273.461,32,408,28.5
seresnet269d,116.02,545.703,64,256,113.67
vit_base_patch16_18x2_224,115.21,552.796,64,224,256.73
convnext_xlarge_in22ft1k,113.61,561.618,64,224,350.2
convnext_base_384_in22ft1k,112.88,423.483,48,384,88.59
dm_nfnet_f1,112.71,565.567,64,320,132.63
convnext_tiny_384_in22ft1k,112.65,424.398,48,384,88.59
convnext_small_384_in22ft1k,112.59,424.604,48,384,88.59
nf_regnet_b5,112.22,567.655,64,456,49.74
swin_v2_cr_tiny_384,111.16,286.572,32,384,28.33
xcit_small_24_p16_384_dist,110.42,431.325,48,384,47.67
beit_large_patch16_224,107.44,593.678,64,224,304.43
regnetz_e8,103.46,461.955,48,320,57.7
efficientnetv2_rw_m,103.11,307.008,32,416,53.24
tresnet_l_448,101.52,1257.535,128,448,55.99
swsl_resnext101_32x16d,99.54,962.862,96,224,194.03
ig_resnext101_32x16d,99.3,965.129,96,224,194.03
ssl_resnext101_32x16d,99.25,965.706,96,224,194.03
volo_d1_384,98.94,322.06,32,384,26.78
resnetrs200,98.6,482.325,48,320,93.21
cait_xxs24_384,97.24,491.195,48,384,12.03
deit_base_patch16_384,95.92,332.748,32,384,86.86
vit_base_patch16_384,95.88,332.951,32,384,86.86
volo_d4_224,95.4,500.622,48,224,192.96
eca_nfnet_l2,94.36,675.671,64,384,56.72
deit_base_distilled_patch16_384,93.97,339.685,32,384,87.63
efficientnet_b5,90.42,351.458,32,456,30.39
tf_efficientnetv2_m,89.36,354.724,32,480,54.14
tf_efficientnetv2_m_in21ft1k,89.12,355.723,32,480,54.14
tf_efficientnet_b5,88.7,358.215,32,456,30.39
tf_efficientnet_b5_ap,88.68,358.368,32,456,30.39
tf_efficientnet_b5_ns,88.58,358.774,32,456,30.39
crossvit_18_dagger_408,87.86,362.177,32,408,44.61
resnetv2_101x1_bitm,87.29,364.971,32,448,44.54
convmixer_768_32,87.13,1100.537,96,224,21.11
resnetv2_152x2_bit_teacher,87.1,364.899,32,224,236.34
vit_large_patch14_224,84.6,565.739,48,224,304.2
regnetz_d8_evos,83.97,379.015,32,320,23.46
beit_base_patch16_384,82.88,385.038,32,384,86.74
xcit_small_24_p8_224_dist,82.62,383.85,32,224,47.63
xcit_small_24_p8_224,82.5,384.488,32,224,47.63
resnest200e,79.64,597.77,48,320,70.2
tresnet_xl_448,77.44,1236.244,96,448,78.44
xcit_medium_24_p16_384_dist,76.61,414.275,32,384,84.4
vit_large_r50_s32_384,76.27,417.132,32,384,329.09
swin_base_patch4_window12_384,73.39,434.11,32,384,87.9
nfnet_f2,71.5,668.121,48,352,193.78
resmlp_big_24_224_in22ft1k,68.15,468.085,32,224,129.14
resmlp_big_24_224,68.13,468.141,32,224,129.14
resmlp_big_24_distilled_224,68.08,468.542,32,224,129.14
pnasnet5large,66.87,474.686,32,331,86.06
swin_v2_cr_small_384,66.28,359.629,24,384,49.7
nasnetalarge,65.59,482.923,32,331,88.75
cait_xs24_384,65.34,487.167,32,384,26.67
dm_nfnet_f2,64.55,740.206,48,352,193.78
cait_xxs36_384,62.84,505.392,32,384,17.37
vit_base_patch8_224,62.83,381.142,24,224,86.58
convnext_large_384_in22ft1k,61.61,517.714,32,384,197.77
ecaresnet269d,61.52,515.695,32,352,102.09
volo_d5_224,61.49,517.153,32,224,295.46
xcit_tiny_24_p8_384_dist,60.46,525.849,32,384,12.11
xcit_medium_24_p8_224,59.24,536.775,32,224,84.32
xcit_medium_24_p8_224_dist,59.23,536.852,32,224,84.32
vit_base_resnet50_384,58.93,405.608,24,384,98.95
vit_base_r50_s16_384,58.91,405.68,24,384,98.95
resnetrs270,58.9,537.38,32,352,129.86
xcit_small_12_p8_384_dist,56.03,426.632,24,384,26.21
volo_d2_384,55.33,287.363,16,384,58.87
ig_resnext101_32x32d,52.68,605.908,32,224,468.53
eca_nfnet_l3,50.62,628.636,32,448,72.04
convmixer_1536_20,49.63,966.338,48,224,51.63
cait_s24_384,49.12,486.034,24,384,47.06
efficientnet_b6,48.5,327.112,16,528,43.04
tf_efficientnet_b6_ns,48.02,330.391,16,528,43.04
tf_efficientnet_b6_ap,47.83,331.608,16,528,43.04
tf_efficientnet_b6,47.78,331.966,16,528,43.04
efficientnetv2_l,47.17,334.927,16,480,118.52
swin_v2_cr_base_384,47.07,337.445,16,384,87.88
tf_efficientnetv2_l_in21ft1k,46.85,337.127,16,480,118.52
tf_efficientnetv2_l,46.53,339.329,16,480,118.52
swin_v2_cr_huge_224,46.2,343.813,16,224,657.83
xcit_large_24_p16_384_dist,44.95,530.527,24,384,189.1
swin_large_patch4_window12_384,41.24,386.07,16,384,196.74
vit_huge_patch14_224,39.65,401.44,16,224,632.05
resnetrs350,37.15,638.194,24,384,163.96
convnext_xlarge_384_in22ft1k,36.94,431.409,16,384,350.2
nfnet_f3,35.43,673.367,24,416,254.92
resnest269e,33.7,705.124,24,416,110.93
xcit_large_24_p8_224_dist,33.34,476.606,16,224,188.93
xcit_large_24_p8_224,33.33,476.71,16,224,188.93
dm_nfnet_f3,32.28,738.882,24,416,254.92
resnetv2_50x3_bitm,31.96,499.813,16,448,217.32
cait_s36_384,31.71,500.682,16,384,68.37
resnetv2_152x2_bit_teacher_384,31.41,506.876,16,384,236.34
tf_efficientnetv2_xl_in21ft1k,31.07,380.33,12,512,208.12
efficientnetv2_xl,30.57,386.62,12,512,208.12
efficientnet_b7,30.54,258.47,8,600,66.35
tf_efficientnet_b7,30.24,261.049,8,600,66.35
tf_efficientnet_b7_ap,30.18,261.545,8,600,66.35
tf_efficientnet_b7_ns,30.17,261.527,8,600,66.35
vit_large_patch16_384,29.1,410.723,12,384,304.72
xcit_small_24_p8_384_dist,28.47,558.499,16,384,47.63
swin_v2_cr_large_384,28.33,421.122,12,384,196.68
ig_resnext101_32x48d,27.82,573.581,16,224,828.41
resnetrs420,25.59,615.408,16,416,191.89
beit_large_patch16_384,24.96,478.806,12,384,305.0
volo_d3_448,23.79,333.801,8,448,86.63
vit_giant_patch14_224,22.41,354.342,8,224,1012.61
resnetv2_152x2_bitm,21.79,364.675,8,448,236.34
xcit_medium_24_p8_384_dist,19.42,408.574,8,384,84.32
nfnet_f4,18.89,630.122,12,512,316.07
resnetv2_101x3_bitm,17.61,452.554,8,448,387.93
dm_nfnet_f4,17.17,693.324,12,512,316.07
volo_d4_448,16.84,353.766,6,448,193.41
efficientnet_b8,14.4,412.697,6,672,87.41
tf_efficientnet_b8_ap,14.32,415.19,6,672,87.41
tf_efficientnet_b8,14.24,417.31,6,672,87.41
nfnet_f5,12.31,643.8,8,544,377.21
cait_m36_384,11.75,507.058,6,384,271.22
xcit_large_24_p8_384_dist,11.36,524.55,6,384,188.93
dm_nfnet_f5,11.21,706.654,8,544,377.21
volo_d5_448,11.01,359.686,4,448,295.91
swin_v2_cr_huge_384,10.89,364.734,4,384,657.94
tf_efficientnet_l2_ns_475,10.43,377.703,4,475,480.31
nfnet_f6,10.18,778.679,8,576,438.36
beit_large_patch16_512,9.37,425.057,4,512,305.67
dm_nfnet_f6,8.56,692.978,6,576,438.36
volo_d5_512,7.76,383.588,3,512,296.09
nfnet_f7,7.54,787.101,6,608,499.5
cait_m48_448,4.71,419.695,2,448,356.46
resnetv2_152x4_bitm,4.53,438.64,2,480,936.53
tf_efficientnet_l2_ns,2.96,331.907,1,800,480.31
efficientnet_l2,2.92,336.419,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt111-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,param_count
tinynet_e,68298.73,14.982,1024,106,2.04
mobilenetv3_small_050,48773.32,20.985,1024,224,1.59
lcnet_035,47045.94,21.755,1024,224,1.64
lcnet_050,41541.83,24.639,1024,224,1.88
mobilenetv3_small_075,37803.23,27.076,1024,224,2.04
mobilenetv3_small_100,34839.31,29.381,1024,224,2.54
tinynet_d,34615.54,29.571,1024,152,2.34
tf_mobilenetv3_small_minimal_100,31097.5,32.918,1024,224,2.04
tf_mobilenetv3_small_075,30498.6,33.564,1024,224,2.04
tf_mobilenetv3_small_100,28466.28,35.962,1024,224,2.54
lcnet_075,26999.4,37.915,1024,224,2.36
mnasnet_small,23228.74,44.072,1024,224,2.03
lcnet_100,22774.77,44.951,1024,224,2.95
levit_128s,21485.8,47.648,1024,224,7.78
mobilenetv2_035,20032.08,51.106,1024,224,1.68
ghostnet_050,18639.82,54.925,1024,224,2.59
mnasnet_050,18244.9,56.115,1024,224,2.22
regnetx_002,17821.98,57.446,1024,224,2.68
tinynet_c,17586.87,58.214,1024,184,2.46
regnety_002,16673.08,61.405,1024,224,3.16
mobilenetv2_050,16415.14,62.371,1024,224,1.97
semnasnet_050,16295.23,62.83,1024,224,2.08
lcnet_150,15040.68,68.071,1024,224,4.5
levit_128,14657.83,69.849,1024,224,9.21
regnetx_004,14440.03,70.903,1024,224,5.16
gernet_s,14051.59,72.863,1024,224,8.17
mobilenetv3_large_075,13658.47,74.961,1024,224,3.99
levit_192,12892.86,79.412,1024,224,10.95
mnasnet_075,12457.54,82.188,1024,224,3.17
mobilenetv3_rw,12442.0,82.291,1024,224,5.48
hardcorenas_a,12441.72,82.293,1024,224,5.26
mixer_s32_224,12325.03,83.072,1024,224,19.1
mobilenetv3_large_100,12253.83,83.554,1024,224,5.48
mobilenetv3_large_100_miil,12253.15,83.559,1024,224,5.48
vit_small_patch32_224,12098.59,84.625,1024,224,22.88
tf_mobilenetv3_large_075,11757.16,87.085,1024,224,3.99
tinynet_b,11714.84,87.399,1024,188,3.73
hardcorenas_b,11307.89,90.545,1024,224,5.18
hardcorenas_c,11295.65,90.643,1024,224,5.52
ese_vovnet19b_slim_dw,11295.18,90.646,1024,224,1.9
tf_mobilenetv3_large_minimal_100,11279.9,90.77,1024,224,3.92
mnasnet_b1,10903.65,93.902,1024,224,4.38
mnasnet_100,10903.28,93.906,1024,224,4.38
swsl_resnet18,10835.46,94.49,1024,224,11.69
ssl_resnet18,10829.31,94.547,1024,224,11.69
resnet18,10826.05,94.576,1024,224,11.69
gluon_resnet18_v1b,10791.4,94.879,1024,224,11.69
tf_mobilenetv3_large_100,10638.58,96.242,1024,224,5.48
hardcorenas_d,10551.44,97.037,1024,224,7.5
semnasnet_075,10519.79,97.329,1024,224,2.91
ghostnet_100,10434.77,98.122,1024,224,5.18
mobilenetv2_075,10372.86,98.708,1024,224,2.64
seresnet18,10183.7,100.541,1024,224,11.78
regnety_006,9982.58,102.567,1024,224,6.06
vit_tiny_r_s16_p8_224,9895.77,103.465,1024,224,6.34
spnasnet_100,9875.93,103.675,1024,224,4.42
legacy_seresnet18,9845.25,103.999,1024,224,11.78
regnety_004,9552.58,107.183,1024,224,4.34
levit_256,9434.24,108.53,1024,224,18.89
tinynet_a,9412.38,108.782,1024,192,6.19
hardcorenas_f,9390.96,109.029,1024,224,8.2
semnasnet_100,9334.36,109.69,1024,224,3.89
mnasnet_a1,9318.68,109.875,1024,224,3.89
mobilenetv2_100,9260.72,110.564,1024,224,3.5
hardcorenas_e,9255.53,110.624,1024,224,8.07
tf_efficientnetv2_b0,9250.71,110.683,1024,224,7.14
fbnetc_100,9032.97,113.35,1024,224,5.57
efficientnet_lite0,8999.14,113.778,1024,224,4.65
resnet18d,8913.81,114.867,1024,224,11.71
ese_vovnet19b_slim,8715.26,117.484,1024,224,3.17
regnetx_008,8458.52,121.05,1024,224,7.26
levit_256d,8024.27,127.602,1024,224,26.21
regnetx_006,7937.85,128.991,1024,224,6.2
regnety_008,7871.11,130.085,1024,224,6.26
tf_efficientnet_lite0,7813.92,131.036,1024,224,4.65
efficientnet_b0,7681.79,133.291,1024,224,5.29
ghostnet_130,7655.04,133.756,1024,224,7.36
mnasnet_140,7500.9,136.506,1024,224,7.12
xcit_nano_12_p16_224_dist,7406.15,138.252,1024,224,3.05
xcit_nano_12_p16_224,7390.69,138.541,1024,224,3.05
rexnetr_100,7266.82,140.903,1024,224,4.88
mobilenetv2_110d,7027.38,145.704,1024,224,4.52
tf_efficientnet_b0_ap,6815.39,150.235,1024,224,5.29
tf_efficientnet_b0,6815.06,150.243,1024,224,5.29
tf_efficientnet_b0_ns,6813.82,150.27,1024,224,5.29
regnetz_005,6657.81,153.793,1024,224,7.12
hrnet_w18_small,6626.19,154.526,1024,224,13.19
gernet_m,6452.0,158.699,1024,224,21.14
semnasnet_140,6407.4,159.804,1024,224,6.11
tv_resnet34,6289.18,162.807,1024,224,21.8
gluon_resnet34_v1b,6283.02,162.967,1024,224,21.8
resnet34,6262.0,163.515,1024,224,21.8
vit_tiny_patch16_224,6224.71,164.493,1024,224,5.72
mobilenetv2_140,6223.01,164.539,1024,224,6.11
deit_tiny_patch16_224,6218.5,164.657,1024,224,5.72
ese_vovnet19b_dw,6178.2,165.733,1024,224,6.54
deit_tiny_distilled_patch16_224,6117.9,167.365,1024,224,5.91
efficientnet_b1_pruned,6023.92,169.977,1024,240,6.33
efficientnet_lite1,5983.27,171.132,1024,240,5.42
tf_efficientnetv2_b1,5921.06,172.93,1024,240,8.14
selecsls42,5908.85,173.287,1024,224,30.35
fbnetv3_b,5903.05,173.458,1024,256,8.6
seresnet34,5898.43,173.594,1024,224,21.96
selecsls42b,5885.09,173.988,1024,224,32.46
rexnet_100,5871.63,174.387,1024,224,4.8
pit_ti_distilled_224,5837.73,175.397,1024,224,5.1
pit_ti_224,5807.14,176.321,1024,224,4.85
efficientnet_es,5711.75,179.268,1024,224,5.44
efficientnet_es_pruned,5710.76,179.298,1024,224,5.44
resnet26,5694.28,179.817,1024,224,16.0
legacy_seresnet34,5670.37,180.577,1024,224,21.96
levit_384,5643.3,181.443,1024,224,39.13
resnet34d,5571.88,183.768,1024,224,21.82
resnetblur18,5507.37,185.919,1024,224,11.69
rexnetr_130,5484.64,186.691,1024,224,7.61
nf_regnet_b0,5429.0,188.605,1024,256,8.76
tf_efficientnet_es,5424.57,188.76,1024,224,5.44
tf_efficientnet_lite1,5360.34,191.021,1024,240,5.42
skresnet18,5350.54,191.371,1024,224,11.96
selecsls60,5263.66,194.53,1024,224,30.67
selecsls60b,5251.8,194.969,1024,224,32.77
mobilenetv2_120d,5160.96,198.401,1024,224,5.83
mobilevit_xxs,5125.56,199.771,1024,256,1.27
repvgg_b0,5049.08,202.797,1024,224,15.82
resnet26d,4891.07,209.349,1024,224,16.01
fbnetv3_d,4812.34,212.774,1024,256,10.31
rexnetr_150,4791.0,213.722,1024,224,9.78
xcit_tiny_12_p16_224_dist,4778.77,214.269,1024,224,6.72
xcit_tiny_12_p16_224,4774.3,214.469,1024,224,6.72
visformer_tiny,4714.23,217.203,1024,224,10.32
nf_resnet26,4639.54,220.7,1024,224,16.0
efficientnet_lite2,4604.04,222.402,1024,260,6.09
pit_xs_224,4582.73,223.434,1024,224,10.62
pit_xs_distilled_224,4539.81,225.546,1024,224,11.0
resmlp_12_distilled_224,4519.92,226.541,1024,224,15.35
resmlp_12_224,4518.97,226.589,1024,224,15.35
tf_efficientnetv2_b2,4403.08,232.553,1024,260,10.1
vit_base_patch32_224_sam,4330.12,236.472,1024,224,88.22
vit_base_patch32_224,4316.0,237.246,1024,224,88.22
mixer_b32_224,4294.72,238.421,1024,224,60.29
tf_efficientnet_b1_ns,4267.56,239.936,1024,240,7.79
tf_efficientnet_b1_ap,4266.36,240.004,1024,240,7.79
tf_efficientnet_b1,4266.12,240.017,1024,240,7.79
efficientnet_b0_g16_evos,4260.91,240.312,1024,224,8.11
legacy_seresnext26_32x4d,4210.62,243.183,1024,224,16.79
mixer_s16_224,4210.38,243.197,1024,224,18.53
gernet_l,4176.68,245.159,1024,256,31.08
tf_efficientnet_lite2,4154.51,246.467,1024,260,6.09
gmixer_12_224,4127.0,248.109,1024,224,12.7
efficientnet_b1,4101.97,249.625,1024,256,7.79
resnext26ts,4091.38,250.27,1024,256,10.3
rexnet_130,4013.83,255.106,1024,224,7.56
seresnext26ts,3966.87,258.123,1024,256,10.39
nf_seresnet26,3954.94,258.905,1024,224,17.4
eca_resnext26ts,3953.54,258.996,1024,256,10.3
nf_ecaresnet26,3938.91,259.956,1024,224,16.0
repvgg_a2,3868.66,264.68,1024,224,28.21
gmlp_ti16_224,3865.39,264.903,1024,224,5.87
crossvit_tiny_240,3854.54,265.648,1024,240,7.01
efficientnet_b2_pruned,3821.39,267.953,1024,260,8.31
vit_small_patch32_384,3802.4,269.289,1024,384,22.92
resnet26t,3789.73,270.19,1024,256,16.01
regnetx_016,3776.49,271.139,1024,224,9.19
rexnet_150,3774.39,271.29,1024,224,9.73
seresnext26t_32x4d,3769.71,271.627,1024,224,16.81
seresnext26tn_32x4d,3769.68,271.629,1024,224,16.81
gcresnext26ts,3763.59,272.069,1024,256,10.48
seresnext26d_32x4d,3760.94,272.26,1024,224,16.81
ecaresnext50t_32x4d,3751.71,272.931,1024,224,15.41
ecaresnext26t_32x4d,3745.62,273.373,1024,224,15.41
ecaresnet50d_pruned,3714.28,275.68,1024,224,19.94
resnetv2_50,3703.17,276.505,1024,224,25.55
eca_botnext26ts_256,3685.99,277.797,1024,256,10.59
crossvit_9_240,3640.93,281.235,1024,240,8.55
ecaresnetlight,3588.5,285.344,1024,224,30.16
eca_halonext26ts,3573.18,286.568,1024,256,10.76
crossvit_9_dagger_240,3562.28,287.444,1024,240,8.78
poolformer_s12,3555.09,288.026,1024,224,11.92
swsl_resnet50,3536.6,289.53,1024,224,25.56
gluon_resnet50_v1b,3534.52,289.702,1024,224,25.56
tv_resnet50,3534.18,289.73,1024,224,25.56
resnet50,3528.73,290.177,1024,224,25.56
ssl_resnet50,3522.22,290.713,1024,224,25.56
rexnetr_200,3521.98,145.362,512,224,16.52
dla46_c,3494.33,293.033,1024,224,1.3
botnet26t_256,3447.34,297.026,1024,256,12.49
efficientnet_em,3434.61,298.129,1024,240,6.9
dpn68,3423.83,299.067,1024,224,12.61
resnet32ts,3414.56,299.879,1024,256,17.96
dpn68b,3412.63,300.049,1024,224,12.61
regnety_016,3408.85,300.382,1024,224,11.2
halonet26t,3379.19,303.017,1024,256,12.48
resnetv2_50t,3374.73,303.416,1024,224,25.57
resnetv2_50d,3370.34,303.812,1024,224,25.57
resnet33ts,3369.92,303.852,1024,256,19.68
nf_regnet_b1,3357.92,304.938,1024,288,10.22
gluon_resnet50_v1c,3339.61,306.611,1024,224,25.58
nf_regnet_b2,3329.81,307.512,1024,272,14.31
tf_efficientnet_b2_ap,3315.4,308.848,1024,260,9.11
tf_efficientnet_b2_ns,3314.91,308.894,1024,260,9.11
tf_efficientnet_em,3313.67,309.011,1024,240,6.9
tf_efficientnet_b2,3313.08,309.063,1024,260,9.11
seresnet33ts,3285.4,311.671,1024,256,19.78
eca_resnet33ts,3264.82,313.634,1024,256,19.68
resnet50t,3209.81,319.009,1024,224,25.57
bat_resnext26ts,3208.41,319.147,1024,256,10.73
legacy_seresnet50,3208.39,319.15,1024,224,28.09
gluon_resnet50_v1d,3207.43,319.246,1024,224,25.58
convnext_nano_hnf,3203.44,319.644,1024,224,15.59
resnet50d,3201.53,319.835,1024,224,25.58
convit_tiny,3182.1,321.788,1024,224,5.71
gcresnet33ts,3115.24,328.694,1024,256,19.88
vit_small_resnet26d_224,3114.54,328.764,1024,224,63.61
efficientnet_b2,3113.3,328.898,1024,288,9.11
efficientnet_b2a,3112.75,328.957,1024,288,9.11
efficientnet_b3_pruned,3098.33,330.489,1024,300,9.86
mobilevit_xs,3098.23,165.245,512,256,2.32
vovnet39a,3094.91,330.85,1024,224,22.6
seresnet50,3084.99,331.918,1024,224,28.09
haloregnetz_b,3050.82,335.635,1024,224,11.68
skresnet34,3016.36,339.47,1024,224,22.28
ese_vovnet39b,2991.04,342.343,1024,224,24.57
eca_vovnet39b,2984.95,343.042,1024,224,22.6
cspresnext50,2981.83,343.401,1024,224,20.57
selecsls84,2979.47,343.673,1024,224,50.95
res2net50_48w_2s,2961.27,345.783,1024,224,25.29
ssl_resnext50_32x4d,2883.39,355.125,1024,224,25.03
resnext50_32x4d,2879.89,355.557,1024,224,25.03
swsl_resnext50_32x4d,2879.83,355.563,1024,224,25.03
tv_resnext50_32x4d,2876.25,356.006,1024,224,25.03
gluon_resnext50_32x4d,2870.17,356.762,1024,224,25.03
resnetaa50d,2869.21,356.878,1024,224,25.58
tv_densenet121,2861.74,357.812,1024,224,7.98
densenet121,2859.4,358.103,1024,224,7.98
mixnet_s,2849.57,359.34,1024,224,4.13
seresnet50t,2844.85,359.936,1024,224,28.1
resnetrs50,2844.33,360.0,1024,224,35.69
deit_small_patch16_224,2839.86,360.568,1024,224,22.05
vit_small_patch16_224,2838.21,360.777,1024,224,22.05
ecaresnet101d_pruned,2833.47,361.381,1024,224,24.88
coat_lite_tiny,2832.3,361.531,1024,224,5.72
gluon_resnet50_v1s,2824.57,362.521,1024,224,25.68
pit_s_224,2821.07,362.969,1024,224,23.46
ecaresnet50d,2815.88,363.64,1024,224,25.58
rexnet_200,2812.74,182.018,512,224,16.37
pit_s_distilled_224,2802.25,365.406,1024,224,24.04
deit_small_distilled_patch16_224,2791.58,366.805,1024,224,22.44
dla34,2790.34,366.966,1024,224,15.74
dla46x_c,2773.06,369.253,1024,224,1.07
cspresnet50,2754.0,371.811,1024,256,21.62
hrnet_w18_small_v2,2736.24,374.217,1024,224,15.6
densenet121d,2731.76,374.837,1024,224,8.0
tf_mixnet_s,2728.68,375.26,1024,224,4.13
efficientnet_lite3,2718.45,188.332,512,300,8.2
regnetz_b16,2715.55,377.076,1024,288,9.72
dla60x_c,2712.17,377.543,1024,224,1.32
vit_tiny_r_s16_p8_384,2700.75,189.562,512,384,6.36
coat_lite_mini,2663.5,384.444,1024,224,11.01
resnext50d_32x4d,2662.22,384.629,1024,224,25.05
resnetblur50,2619.08,390.962,1024,224,25.56
cspresnet50d,2596.32,394.393,1024,256,21.64
vgg11_bn,2593.2,197.428,512,224,132.87
vit_base2_patch32_256,2576.99,397.35,1024,256,119.46
seresnetaa50d,2576.43,397.436,1024,224,28.11
cspresnet50w,2574.64,397.712,1024,256,28.12
seresnext50_32x4d,2574.54,397.729,1024,224,27.56
lambda_resnet26rpt_256,2573.59,397.876,1024,256,10.99
legacy_seresnext50_32x4d,2571.71,398.166,1024,224,27.56
xcit_nano_12_p16_384_dist,2569.25,398.547,1024,384,3.05
gluon_seresnext50_32x4d,2564.07,399.352,1024,224,27.56
xcit_tiny_24_p16_224_dist,2557.0,400.456,1024,224,12.12
xcit_tiny_24_p16_224,2556.28,400.571,1024,224,12.12
efficientnetv2_rw_t,2545.32,402.294,1024,288,13.65
vovnet57a,2538.22,403.418,1024,224,36.64
fbnetv3_g,2530.36,404.673,1024,288,16.62
gcresnet50t,2514.25,407.265,1024,256,25.9
res2net50_26w_4s,2505.47,408.693,1024,224,25.7
tf_efficientnetv2_b3,2496.85,410.104,1024,300,14.36
twins_svt_small,2488.33,411.508,1024,224,24.06
vit_base_resnet26d_224,2472.64,414.118,1024,224,101.4
ese_vovnet57b,2446.54,418.537,1024,224,38.61
densenetblur121d,2445.52,418.711,1024,224,8.0
tf_efficientnet_lite3,2442.63,209.598,512,300,8.2
resnetblur50d,2422.6,422.672,1024,224,25.58
mobilevit_s,2415.06,211.991,512,256,5.58
resnest14d,2409.35,424.998,1024,224,10.61
tf_inception_v3,2400.27,426.605,1024,299,23.83
gluon_inception_v3,2398.26,426.963,1024,299,23.83
nf_seresnet50,2397.62,427.078,1024,224,28.09
inception_v3,2397.12,427.166,1024,299,23.83
nf_ecaresnet50,2388.81,428.654,1024,224,25.56
adv_inception_v3,2388.59,428.689,1024,299,23.83
densenet169,2362.23,433.477,1024,224,14.15
sehalonet33ts,2337.63,219.014,512,256,13.69
resmlp_24_224,2312.09,442.876,1024,224,30.02
gc_efficientnetv2_rw_t,2311.11,443.065,1024,288,13.68
resmlp_24_distilled_224,2308.78,443.512,1024,224,30.02
convnext_tiny,2275.45,450.007,1024,224,28.59
gcresnext50ts,2263.71,452.341,1024,256,15.67
res2net50_14w_8s,2252.88,454.515,1024,224,25.06
semobilevit_s,2250.59,227.485,512,256,5.74
darknet53,2248.73,227.672,512,256,41.61
resnetv2_101,2235.55,458.038,1024,224,44.54
xcit_small_12_p16_224_dist,2216.94,461.885,1024,224,26.25
xcit_small_12_p16_224,2212.11,462.891,1024,224,26.25
skresnet50,2193.01,466.925,1024,224,25.8
resnet101,2170.93,471.673,1024,224,44.55
tv_resnet101,2164.75,473.02,1024,224,44.55
gluon_resnet101_v1b,2163.02,473.399,1024,224,44.55
res2next50,2156.68,474.791,1024,224,24.67
ecaresnet26t,2145.15,477.343,1024,320,16.01
nf_regnet_b3,2126.07,481.627,1024,320,18.59
gmixer_24_224,2110.93,485.081,1024,224,24.72
resnetv2_101d,2090.05,489.925,1024,224,44.56
gluon_resnet101_v1c,2088.96,490.184,1024,224,44.57
dla60,2087.19,490.597,1024,224,22.04
convnext_tiny_hnf,2073.34,493.877,1024,224,28.59
skresnet50d,2063.75,496.172,1024,224,25.82
twins_pcpvt_small,2048.61,499.837,1024,224,24.11
gluon_resnet101_v1d,2036.64,502.777,1024,224,44.57
vgg13,2019.99,506.92,1024,224,133.05
efficientnet_b0_gn,2017.66,253.748,512,224,5.29
wide_resnet50_2,2001.07,511.71,1024,224,68.88
sebotnet33ts_256,1999.77,192.009,384,256,13.7
xcit_nano_12_p8_224,1978.07,517.663,1024,224,3.05
xcit_nano_12_p8_224_dist,1977.13,517.91,1024,224,3.05
vit_base_resnet50d_224,1950.99,524.848,1024,224,110.97
repvgg_b1,1936.11,528.882,1024,224,57.42
legacy_seresnet101,1932.53,529.863,1024,224,49.33
dla60x,1896.77,539.852,1024,224,17.35
resnetaa101d,1893.61,540.751,1024,224,44.57
tf_efficientnet_b3_ns,1893.08,270.444,512,300,12.23
tf_efficientnet_b3_ap,1892.83,270.481,512,300,12.23
tf_efficientnet_b3,1892.52,270.524,512,300,12.23
seresnet101,1891.69,541.301,1024,224,49.33
gluon_resnet101_v1s,1874.89,546.152,1024,224,44.67
resnet51q,1869.52,547.722,1024,288,35.7
efficientnet_b3,1853.06,276.288,512,320,12.23
efficientnet_b3a,1852.72,276.339,512,320,12.23
crossvit_small_240,1844.12,555.265,1024,240,26.86
poolformer_s24,1843.54,555.44,1024,224,21.39
swin_tiny_patch4_window7_224,1831.44,559.111,1024,224,28.29
halonet50ts,1826.61,560.589,1024,256,22.73
densenet201,1818.74,563.014,1024,224,20.01
gmlp_s16_224,1810.78,565.489,1024,224,19.42
ssl_resnext101_32x4d,1796.49,569.986,1024,224,44.18
resnext101_32x4d,1794.8,570.526,1024,224,44.18
swsl_resnext101_32x4d,1794.2,570.714,1024,224,44.18
convnext_tiny_hnfd,1793.87,570.819,1024,224,28.63
gluon_resnext101_32x4d,1791.38,571.613,1024,224,44.18
cspdarknet53,1782.45,287.233,512,256,27.64
nf_resnet101,1778.39,575.79,1024,224,44.55
vit_small_r26_s32_224,1778.32,575.808,1024,224,36.43
ecaresnet101d,1778.2,575.85,1024,224,44.57
regnetz_c16,1772.58,288.833,512,320,13.46
res2net50_26w_6s,1761.45,581.325,1024,224,37.05
resnest26d,1761.21,581.405,1024,224,17.07
dla60_res2net,1720.09,595.302,1024,224,20.85
nf_resnet50,1719.27,595.589,1024,288,25.56
crossvit_15_240,1700.36,602.213,1024,240,27.53
resnetblur101d,1688.41,606.472,1024,224,44.57
resnet61q,1677.9,610.274,1024,288,36.85
swin_s3_tiny_224,1668.56,613.692,1024,224,28.33
xcit_tiny_12_p16_384_dist,1651.99,619.843,1024,384,6.72
crossvit_15_dagger_240,1651.71,619.951,1024,240,28.21
resnetv2_50d_frn,1650.73,620.316,1024,224,25.59
vgg13_bn,1642.03,311.797,512,224,133.05
efficientnet_b0_g8_gn,1637.47,312.665,512,224,6.56
vgg16,1632.03,627.428,1024,224,138.36
cait_xxs24_224,1630.9,627.86,1024,224,11.96
repvgg_b1g4,1614.79,634.126,1024,224,39.97
regnetx_032,1605.58,637.76,1024,224,15.3
seresnext101_32x4d,1600.47,639.797,1024,224,48.96
gluon_seresnext101_32x4d,1597.12,641.141,1024,224,48.96
legacy_seresnext101_32x4d,1596.46,641.406,1024,224,48.96
botnet50ts_256,1594.64,321.062,512,256,22.74
res2net101_26w_4s,1591.38,643.454,1024,224,45.21
regnetx_040,1586.28,645.521,1024,224,22.12
ese_vovnet39b_evos,1584.69,646.169,1024,224,24.58
resnetv2_50d_evob,1578.15,648.842,1024,224,25.59
resnetv2_50x1_bit_distilled,1562.05,655.535,1024,224,25.55
visformer_small,1557.14,657.604,1024,224,40.22
xception41p,1555.03,329.24,512,299,26.91
resnetv2_152,1552.32,659.642,1024,224,60.19
dla102,1552.08,659.747,1024,224,33.27
resmlp_36_224,1551.92,659.816,1024,224,44.69
dla60_res2next,1551.9,659.821,1024,224,17.03
resmlp_36_distilled_224,1551.61,659.949,1024,224,44.69
resnest50d_1s4x24d,1548.62,661.22,1024,224,25.68
xception,1546.37,331.084,512,299,22.86
hrnet_w32,1543.69,663.332,1024,224,41.23
halo2botnet50ts_256,1528.74,669.82,1024,256,22.64
tv_resnet152,1516.25,675.334,1024,224,60.19
coat_lite_small,1515.55,675.647,1024,224,19.84
mixer_b16_224,1515.3,675.76,1024,224,59.88
mixer_b16_224_miil,1514.28,676.214,1024,224,59.88
gluon_resnet152_v1b,1512.22,677.139,1024,224,60.19
resnet152,1507.31,679.341,1024,224,60.19
efficientnet_el,1505.49,340.076,512,300,10.59
efficientnet_el_pruned,1505.01,340.185,512,300,10.59
vit_large_patch32_224,1500.0,682.653,1024,224,306.54
swin_v2_cr_tiny_224,1497.15,683.946,1024,224,28.33
res2net50_26w_8s,1487.1,688.575,1024,224,48.4
nf_seresnet101,1486.78,688.723,1024,224,49.33
resnetv2_152d,1485.01,689.541,1024,224,60.2
nf_ecaresnet101,1477.89,692.868,1024,224,44.55
gluon_resnet152_v1c,1474.38,694.516,1024,224,60.21
swin_v2_cr_tiny_ns_224,1472.66,695.323,1024,224,28.33
tf_efficientnet_el,1464.69,349.551,512,300,10.59
vit_tiny_patch16_384,1460.68,701.028,1024,384,5.79
vit_base_r26_s32_224,1452.81,704.825,1024,224,101.38
gluon_resnet152_v1d,1447.4,707.462,1024,224,60.21
hrnet_w18,1432.55,714.792,1024,224,21.3
mixnet_m,1431.87,715.134,1024,224,5.01
mixer_l32_224,1426.61,717.775,1024,224,206.94
dla102x,1416.93,722.674,1024,224,26.31
tf_mixnet_m,1413.24,724.561,1024,224,5.01
convnext_small,1410.61,725.911,1024,224,50.22
twins_pcpvt_base,1405.32,728.645,1024,224,43.83
ecaresnet50t,1382.49,740.677,1024,320,25.57
nest_tiny,1378.24,371.475,512,224,17.06
vit_base_patch32_384,1377.87,743.165,1024,384,88.3
convit_small,1368.52,748.238,1024,224,27.78
regnety_032,1367.75,748.662,1024,288,19.44
vgg19,1367.17,748.977,1024,224,143.67
gluon_resnet152_v1s,1363.8,750.829,1024,224,60.32
vgg16_bn,1363.24,375.563,512,224,138.37
jx_nest_tiny,1354.35,378.027,512,224,17.06
ese_vovnet99b,1351.66,757.57,1024,224,63.2
xception41,1337.66,382.745,512,299,26.97
legacy_seresnet152,1335.99,766.462,1024,224,66.82
seresnet152,1317.5,777.213,1024,224,66.82
dpn92,1303.11,785.8,1024,224,37.67
efficientnet_lite4,1287.19,298.314,384,380,13.01
tresnet_m,1283.27,797.947,1024,224,31.39
inception_v4,1282.29,798.555,1024,299,42.68
densenet161,1276.9,801.927,1024,224,28.68
xcit_tiny_12_p8_224_dist,1263.32,810.55,1024,224,6.71
xcit_tiny_12_p8_224,1260.86,812.129,1024,224,6.71
regnetx_080,1259.62,812.931,1024,224,39.57
skresnext50_32x4d,1255.24,815.767,1024,224,27.48
vit_small_resnet50d_s16_224,1252.87,817.305,1024,224,57.53
twins_svt_base,1249.6,819.445,1024,224,56.07
poolformer_s36,1245.02,822.461,1024,224,30.86
repvgg_b2,1243.81,823.263,1024,224,89.02
volo_d1_224,1232.57,830.769,1024,224,26.63
hrnet_w30,1221.32,838.421,1024,224,37.71
crossvit_18_240,1205.55,849.387,1024,240,43.27
resnest50d,1199.06,853.99,1024,224,27.48
tf_efficientnet_lite4,1182.46,324.733,384,380,13.01
xcit_small_24_p16_224_dist,1182.35,866.056,1024,224,47.67
xcit_small_24_p16_224,1181.92,866.371,1024,224,47.67
crossvit_18_dagger_240,1172.17,873.578,1024,240,44.27
regnetx_064,1166.56,438.885,512,224,26.21
vgg19_bn,1163.98,439.857,512,224,143.68
nf_regnet_b4,1158.13,884.17,1024,384,30.21
efficientnetv2_s,1149.66,890.683,1024,384,21.46
regnetz_d8,1143.01,895.867,1024,320,23.37
resnet50_gn,1132.8,903.934,1024,224,25.56
dla169,1130.6,905.699,1024,224,53.39
wide_resnet101_2,1125.72,909.622,1024,224,126.89
swin_small_patch4_window7_224,1123.67,911.288,1024,224,49.61
tf_efficientnetv2_s,1123.66,911.292,1024,384,21.46
tf_efficientnetv2_s_in21ft1k,1121.7,912.889,1024,384,21.46
gluon_resnext101_64x4d,1118.73,915.313,1024,224,83.46
mixnet_l,1117.6,458.111,512,224,7.33
xception65p,1115.45,458.995,512,299,39.82
vit_base_patch16_224_miil,1108.89,923.433,1024,224,86.54
nfnet_l0,1101.58,929.561,1024,288,35.07
eca_nfnet_l0,1100.75,930.261,1024,288,24.14
tf_mixnet_l,1100.74,465.128,512,224,7.33
dpn98,1097.38,933.115,1024,224,61.57
resnet200,1096.34,934.004,1024,224,64.67
resnetrs101,1095.82,934.443,1024,288,63.62
cait_xxs36_224,1095.14,935.023,1024,224,17.3
efficientnetv2_rw_s,1089.64,939.747,1024,384,23.94
vit_base_patch16_224_sam,1073.62,953.767,1024,224,86.57
vit_base_patch16_224,1073.46,953.912,1024,224,86.57
deit_base_patch16_224,1071.76,955.424,1024,224,86.57
inception_resnet_v2,1061.78,964.405,1024,299,55.84
ens_adv_inception_resnet_v2,1058.32,967.554,1024,299,55.84
deit_base_distilled_patch16_224,1057.89,967.952,1024,224,87.34
tnt_s_patch16_224,1053.73,971.773,1024,224,23.76
dla102x2,1044.22,490.307,512,224,41.28
regnetz_040,1039.59,369.364,384,320,27.12
gluon_seresnext101_64x4d,1039.37,985.196,1024,224,88.23
regnetz_040h,1034.17,371.302,384,320,28.94
resnext101_32x8d,1033.81,990.5,1024,224,88.79
ssl_resnext101_32x8d,1033.7,990.597,1024,224,88.79
swsl_resnext101_32x8d,1033.38,990.91,1024,224,88.79
ig_resnext101_32x8d,1028.15,995.947,1024,224,88.79
regnetz_d32,1009.91,1013.934,1024,320,27.58
resnest50d_4s2x40d,1008.21,1015.647,1024,224,30.42
repvgg_b3,1001.81,1022.132,1024,224,123.09
twins_pcpvt_large,999.56,1024.437,1024,224,60.99
resnet101d,999.12,1024.886,1024,320,44.57
beit_base_patch16_224,993.76,1030.414,1024,224,86.53
convnext_base,982.94,1041.763,1024,224,88.59
convnext_base_in22ft1k,982.23,1042.511,1024,224,88.59
hrnet_w40,980.59,1044.254,1024,224,57.56
coat_tiny,977.86,1047.174,1024,224,5.5
efficientnet_b4,974.19,394.16,384,384,19.34
gluon_xception65,972.42,526.509,512,299,39.92
xception65,965.78,530.126,512,299,39.92
pit_b_224,946.3,541.042,512,224,73.76
regnetz_b16_evos,945.35,541.586,512,288,9.74
pit_b_distilled_224,942.16,543.418,512,224,74.79
tf_efficientnet_b4,925.87,414.732,384,380,19.34
tf_efficientnet_b4_ap,925.37,414.955,384,380,19.34
tf_efficientnet_b4_ns,925.29,414.99,384,380,19.34
vit_small_patch16_36x1_224,920.52,1112.401,1024,224,64.67
swin_v2_cr_small_224,915.78,1118.157,1024,224,49.7
vit_small_patch16_18x2_224,899.5,1138.389,1024,224,64.67
xcit_tiny_24_p16_384_dist,885.13,1156.874,1024,384,12.12
twins_svt_large,884.59,1157.58,1024,224,99.27
nest_small,880.02,581.79,512,224,38.35
hrnet_w48,878.91,1165.063,1024,224,77.47
cait_s24_224,871.81,1174.556,1024,224,46.92
jx_nest_small,870.35,588.255,512,224,38.35
poolformer_m36,860.43,1190.082,1024,224,56.17
regnety_040,849.33,602.817,512,288,20.65
regnetv_040,849.16,602.939,512,288,20.64
nfnet_f0,843.53,1213.933,1024,256,71.49
resnetv2_50d_evos,834.99,1226.346,1024,288,25.59
swin_s3_small_224,821.03,623.593,512,224,49.74
repvgg_b2g4,812.87,1259.724,1024,224,61.76
xcit_medium_24_p16_224_dist,811.19,1262.323,1024,224,84.4
xcit_medium_24_p16_224,810.62,1263.211,1024,224,84.4
dpn131,808.5,1266.522,1024,224,79.25
swin_base_patch4_window7_224,797.2,1284.488,1024,224,87.77
hrnet_w44,791.54,1293.66,1024,224,67.06
coat_mini,786.27,1302.337,1024,224,10.34
regnetx_120,777.95,658.124,512,224,46.11
gmlp_b16_224,772.78,1325.073,1024,224,73.08
dm_nfnet_f0,761.49,1344.713,1024,256,71.49
regnety_120,754.35,678.713,512,224,51.82
densenet264,750.52,1364.368,1024,224,72.69
mixnet_xl,748.42,684.091,512,224,11.9
crossvit_base_240,747.2,685.212,512,240,105.03
xcit_small_12_p16_384_dist,744.87,1374.713,1024,384,26.25
resnetv2_50d_gn,742.22,689.806,512,288,25.57
xception71,741.19,690.762,512,299,42.34
hrnet_w64,723.1,1416.105,1024,224,128.06
vit_large_r50_s32_224,721.71,1418.838,1024,224,328.99
regnety_040s_gn,714.09,716.982,512,224,20.65
seresnet200d,712.88,1436.411,1024,256,71.86
resnet152d,711.54,1439.114,1024,320,60.21
ecaresnet200d,707.4,1447.533,1024,256,64.69
dpn107,701.19,1460.351,1024,224,86.92
cspresnext50_iabn,693.03,1477.558,1024,256,20.57
senet154,692.07,1479.599,1024,224,115.09
legacy_senet154,691.5,1480.826,1024,224,115.09
gluon_senet154,689.56,1484.981,1024,224,115.09
convit_base,687.54,1489.363,1024,224,86.54
vit_small_patch16_384,683.58,748.981,512,384,22.2
volo_d2_224,677.6,1511.204,1024,224,58.68
tnt_b_patch16_224,675.63,1515.603,1024,224,65.41
xcit_nano_12_p8_384_dist,672.93,1521.688,1024,384,3.05
resnext101_64x4d,667.5,767.03,512,288,83.46
xcit_tiny_24_p8_224,665.57,1538.51,1024,224,12.11
swin_s3_base_224,665.44,1538.819,1024,224,71.13
xcit_tiny_24_p8_224_dist,665.24,1539.279,1024,224,12.11
swin_v2_cr_base_224,655.51,1562.118,1024,224,87.88
poolformer_m48,648.5,1579.004,1024,224,73.47
repvgg_b3g4,644.06,1589.905,1024,224,83.83
tresnet_l,638.84,1602.889,1024,224,55.99
resnetrs152,628.07,1630.372,1024,320,86.62
nest_base,626.36,817.402,512,224,67.72
seresnet152d,626.17,1635.31,1024,320,66.84
regnetx_160,623.38,821.322,512,224,54.28
jx_nest_base,620.6,824.996,512,224,67.72
regnetz_e8,607.41,842.906,512,320,57.7
ese_vovnet99b_iabn,606.83,1687.442,1024,224,63.2
regnetz_c16_evos,598.38,855.63,512,320,13.49
vit_base_r50_s16_224,595.98,1718.162,1024,224,98.66
resnest101e,584.85,875.416,512,256,48.28
vit_small_r26_s32_384,582.99,878.207,512,384,36.47
convmixer_768_32,574.69,1781.818,1024,224,21.11
seresnext101_32x8d,574.1,891.822,512,288,93.57
cspdarknet53_iabn,571.87,1790.614,1024,256,27.64
xcit_small_12_p8_224,568.57,1800.991,1024,224,26.21
xcit_small_12_p8_224_dist,567.71,1803.708,1024,224,26.21
seresnet269d,558.78,1832.551,1024,256,113.67
convnext_large_in22ft1k,544.26,940.706,512,224,197.77
convnext_large,544.24,940.754,512,224,197.77
regnety_080,536.38,954.53,512,288,39.18
resnet200d,524.48,1952.377,1024,320,64.69
mixnet_xxl,489.07,785.144,384,224,23.96
halonet_h1,488.76,523.759,256,256,8.1
eca_nfnet_l1,486.84,2103.345,1024,320,41.41
mixer_l16_224,485.71,2108.232,1024,224,208.2
efficientnetv2_m,483.16,2119.353,1024,416,54.14
vit_large_patch32_384,479.45,2135.754,1024,384,306.63
volo_d3_224,473.36,2163.252,1024,224,86.33
tresnet_xl,471.89,2169.959,1024,224,78.44
regnety_064,471.86,1085.046,512,288,30.58
regnetv_064,468.27,1093.376,512,288,30.58
efficientnet_b5,467.72,547.327,256,456,30.39
xcit_large_24_p16_224_dist,464.03,2206.748,1024,224,189.1
xcit_large_24_p16_224,463.92,2207.253,1024,224,189.1
efficientnet_b3_gn,459.87,417.496,192,320,11.73
swin_large_patch4_window7_224,457.64,1118.756,512,224,196.53
resnetrs200,456.26,2244.305,1024,320,93.21
tf_efficientnet_b5_ap,447.29,572.321,256,456,30.39
tf_efficientnet_b5,447.21,572.425,256,456,30.39
tf_efficientnet_b5_ns,446.89,572.83,256,456,30.39
xcit_tiny_12_p8_384_dist,429.76,2382.721,1024,384,6.71
regnety_320,415.34,1232.707,512,224,145.05
regnety_160,414.45,926.515,384,288,83.59
xcit_small_24_p16_384_dist,397.04,2579.039,1024,384,47.67
efficientnetv2_rw_m,393.18,1302.2,512,416,53.24
regnetz_d8_evos,387.65,1320.76,512,320,23.46
swin_v2_cr_large_224,386.59,1324.379,512,224,196.68
efficientnet_b3_g8_gn,385.43,498.136,192,320,14.25
swin_v2_cr_tiny_384,366.25,698.968,256,384,28.33
convnext_xlarge_in22ft1k,359.4,1424.579,512,224,350.2
tf_efficientnetv2_m,359.31,1424.935,512,480,54.14
tf_efficientnetv2_m_in21ft1k,358.34,1428.804,512,480,54.14
vit_large_patch16_224,354.58,2887.929,1024,224,304.33
crossvit_15_dagger_408,354.15,722.849,256,408,28.5
ssl_resnext101_32x16d,347.17,1474.787,512,224,194.03
swsl_resnext101_32x16d,346.95,1475.72,512,224,194.03
vit_base_patch16_18x2_224,346.41,2956.033,1024,224,256.73
ig_resnext101_32x16d,345.97,1479.87,512,224,194.03
convnext_base_384_in22ft1k,335.81,1143.488,384,384,88.59
beit_large_patch16_224,328.45,3117.61,1024,224,304.43
tresnet_m_448,321.84,3181.675,1024,448,31.39
volo_d1_384,310.06,1651.255,512,384,26.78
volo_d4_224,303.03,3379.23,1024,224,192.96
pnasnet5large,299.35,1282.762,384,331,86.06
xcit_small_24_p8_224,297.54,3441.571,1024,224,47.63
xcit_small_24_p8_224_dist,297.52,3441.791,1024,224,47.63
ecaresnet269d,293.49,3489.006,1024,352,102.09
nasnetalarge,291.8,1315.945,384,331,88.75
resnetrs270,287.51,3561.559,1024,352,129.86
nfnet_f1,286.44,3574.909,1024,320,132.63
resnetv2_152x2_bit_teacher,280.03,1828.366,512,224,236.34
xcit_medium_24_p16_384_dist,278.67,1837.25,512,384,84.4
vit_base_patch16_384,277.57,1383.439,384,384,86.86
deit_base_patch16_384,277.28,1384.877,384,384,86.86
deit_base_distilled_patch16_384,273.16,1405.736,384,384,87.63
cait_xxs24_384,268.69,3811.003,1024,384,12.03
efficientnet_b6,268.02,477.57,128,528,43.04
regnetx_320,262.23,1464.357,384,224,107.81
dm_nfnet_f1,261.81,3911.156,1024,320,132.63
vit_large_patch14_224,261.02,3923.008,1024,224,304.2
crossvit_18_dagger_408,259.11,740.985,192,408,44.61
tf_efficientnet_b6_ns,257.39,497.28,128,528,43.04
tf_efficientnet_b6_ap,257.15,497.757,128,528,43.04
tf_efficientnet_b6,257.08,497.876,128,528,43.04
beit_base_patch16_384,239.23,1605.166,384,384,86.74
vit_large_r50_s32_384,236.31,1624.94,384,384,329.09
eca_nfnet_l2,232.83,2198.978,512,384,56.72
xcit_tiny_24_p8_384_dist,226.12,4528.604,1024,384,12.11
swin_v2_cr_small_384,224.11,1142.266,256,384,49.7
swin_base_patch4_window12_384,212.75,902.462,192,384,87.9
resmlp_big_24_distilled_224,209.1,4897.073,1024,224,129.14
resmlp_big_24_224,208.19,4918.549,1024,224,129.14
resmlp_big_24_224_in22ft1k,208.06,4921.598,1024,224,129.14
xcit_medium_24_p8_224,208.01,4922.91,1024,224,84.32
xcit_medium_24_p8_224_dist,207.93,4924.607,1024,224,84.32
resnest200e,201.92,2535.671,512,320,70.2
volo_d5_224,199.55,5131.435,1024,224,295.46
xcit_small_12_p8_384_dist,193.71,2643.156,512,384,26.21
resnetrs350,188.78,2712.164,512,384,163.96
cait_xs24_384,188.52,2715.867,512,384,26.67
efficientnetv2_l,186.87,2739.852,512,480,118.52
convnext_large_384_in22ft1k,186.32,1373.958,256,384,197.77
tf_efficientnetv2_l,185.75,2756.36,512,480,118.52
tf_efficientnetv2_l_in21ft1k,185.69,2757.265,512,480,118.52
vit_base_patch8_224,182.98,1399.048,256,224,86.58
cait_xxs36_384,179.96,5689.985,1024,384,17.37
volo_d2_384,173.85,1472.545,256,384,58.87
vit_base_r50_s16_384,171.75,2235.786,384,384,98.95
vit_base_resnet50_384,171.74,2235.946,384,384,98.95
swin_v2_cr_huge_224,170.46,2252.707,384,224,657.83
densenet264d_iabn,163.84,6249.837,1024,224,72.74
efficientnet_b7,161.28,595.212,96,600,66.35
nfnet_f2,161.05,6358.083,1024,352,193.78
swin_v2_cr_base_384,160.58,1195.67,192,384,87.88
xcit_large_24_p16_384_dist,158.84,3223.264,512,384,189.1
tf_efficientnet_b7,155.96,615.536,96,600,66.35
tf_efficientnet_b7_ap,155.94,615.594,96,600,66.35
tf_efficientnet_b7_ns,155.94,615.59,96,600,66.35
tresnet_l_448,152.84,6699.593,1024,448,55.99
dm_nfnet_f2,147.62,3468.342,512,352,193.78
cait_s24_384,145.6,3516.436,512,384,47.06
efficientnetv2_xl,141.55,2712.868,384,512,208.12
vit_huge_patch14_224,141.3,7246.972,1024,224,632.05
tf_efficientnetv2_xl_in21ft1k,141.27,2718.106,384,512,208.12
resnetrs420,134.22,3814.641,512,416,191.89
swin_large_patch4_window12_384,125.78,1017.629,128,384,196.74
eca_nfnet_l3,122.95,4164.413,512,448,72.04
convnext_xlarge_384_in22ft1k,122.83,1563.128,192,384,350.2
xcit_large_24_p8_224,118.58,4317.779,512,224,188.93
xcit_large_24_p8_224_dist,118.55,4318.826,512,224,188.93
tresnet_xl_448,113.53,9019.968,1024,448,78.44
efficientnet_cc_b0_8e,109.15,9.151,1,224,24.01
efficientnet_cc_b0_4e,106.25,9.403,1,224,13.31
tf_efficientnet_cc_b0_4e,105.28,9.489,1,224,13.31
tf_efficientnet_cc_b0_8e,105.02,9.512,1,224,24.01
xcit_small_24_p8_384_dist,101.44,5047.074,512,384,47.63
efficientnet_b8,98.72,972.467,96,672,87.41
swin_v2_cr_large_384,98.1,1304.769,128,384,196.68
cait_s36_384,97.37,5258.401,512,384,68.37
resnetv2_152x2_bit_teacher_384,96.99,2639.495,256,384,236.34
tf_efficientnet_b8,96.21,997.82,96,672,87.41
tf_efficientnet_b8_ap,96.12,998.706,96,672,87.41
resnest269e,94.82,4049.739,384,416,110.93
vit_large_patch16_384,94.39,2712.048,256,384,304.72
resnetv2_50x3_bitm,92.92,1377.53,128,448,217.32
vit_giant_patch14_224,92.83,5515.228,512,224,1012.61
nfnet_f3,83.35,6142.681,512,416,254.92
convmixer_1024_20_ks9_p14,82.56,12403.804,1024,224,24.38
beit_large_patch16_384,82.25,3112.33,256,384,305.0
efficientnet_cc_b1_8e,77.14,12.953,1,240,39.72
dm_nfnet_f3,76.92,6655.946,512,416,254.92
volo_d3_448,75.82,2532.146,192,448,86.63
tf_efficientnet_cc_b1_8e,73.25,13.64,1,240,39.72
xcit_medium_24_p8_384_dist,70.98,3606.847,256,384,84.32
resnetv2_152x2_bitm,70.34,2729.533,192,448,236.34
resnetv2_101x3_bitm,57.82,2213.862,128,448,387.93
vit_gigantic_patch14_224,56.06,9133.289,512,224,1844.44
volo_d4_448,55.8,3441.115,192,448,193.41
swin_v2_cr_giant_224,49.27,2597.97,128,224,2598.76
nfnet_f4,44.93,8547.007,384,512,316.07
swin_v2_cr_huge_384,43.72,1463.762,64,384,657.94
dm_nfnet_f4,41.24,9312.061,384,512,316.07
xcit_large_24_p8_384_dist,40.33,4760.775,192,384,188.93
volo_d5_448,38.49,3325.731,128,448,295.91
beit_large_patch16_512,33.13,2897.67,96,512,305.67
nfnet_f5,33.1,11599.778,384,544,377.21
cait_m36_384,31.8,8050.244,256,384,271.22
dm_nfnet_f5,31.5,8127.503,256,544,377.21
volo_d5_512,26.9,4758.532,128,512,296.09
nfnet_f6,25.41,10073.116,256,576,438.36
dm_nfnet_f6,23.41,10936.755,256,576,438.36
nfnet_f7,20.63,9307.125,192,608,499.5
resnetv2_152x4_bitm,18.31,3496.111,64,480,936.53
swin_v2_cr_giant_384,13.76,2325.078,32,384,2598.76
cait_m48_448,13.51,9473.095,128,448,356.46
convmixer_1536_20,13.51,75823.541,1024,224,51.63
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-train-amp-nchw-pt112-cu113-rtx3090.csv | model,train_samples_per_sec,train_step_time,train_batch_size,train_img_size,param_count
tinynet_e,10001.12,50.423,512,106,2.04
mobilenetv3_small_050,7406.47,68.392,512,224,1.59
tf_mobilenetv3_small_minimal_100,6438.14,78.983,512,224,2.04
mobilenetv3_small_075,6186.83,82.006,512,224,2.04
tf_mobilenetv3_small_075,5783.46,87.782,512,224,2.04
mobilenetv3_small_100,5749.13,88.315,512,224,2.54
lcnet_035,5673.53,89.75,512,224,1.64
tf_mobilenetv3_small_100,5383.9,94.36,512,224,2.54
levit_128s,5298.88,95.701,512,224,7.78
lcnet_050,5280.37,96.452,512,224,1.88
tinynet_d,5161.83,98.416,512,152,2.34
mixer_s32_224,4696.33,108.475,512,224,19.1
resnet10t,4669.46,109.393,512,176,5.44
vit_small_patch32_224,4447.28,114.289,512,224,22.88
lcnet_075,4278.23,119.175,512,224,2.36
vit_tiny_r_s16_p8_224,4137.87,122.895,512,224,6.34
levit_128,3895.0,130.318,512,224,9.21
regnetx_002,3718.05,137.026,512,224,2.68
lcnet_100,3569.0,142.969,512,224,2.95
mnasnet_small,3450.28,147.453,512,224,2.03
regnety_002,3414.18,149.006,512,224,3.16
cs3darknet_focus_s,3251.91,156.949,512,256,3.27
mobilenetv2_035,3160.04,161.202,512,224,1.68
levit_192,3046.5,166.9,512,224,10.95
gernet_s,3034.31,168.028,512,224,8.17
tinynet_c,2919.98,174.314,512,184,2.46
mnasnet_050,2847.14,179.025,512,224,2.22
cs3darknet_s,2821.27,180.951,512,256,3.28
resnet18,2764.22,184.877,512,224,11.69
ssl_resnet18,2760.71,185.109,512,224,11.69
mobilenetv2_050,2751.58,185.257,512,224,1.97
swsl_resnet18,2742.47,186.338,512,224,11.69
semnasnet_050,2741.67,185.816,512,224,2.08
gluon_resnet18_v1b,2741.53,186.395,512,224,11.69
lcnet_150,2713.5,188.193,512,224,4.5
regnetx_004,2695.23,188.875,512,224,5.16
ese_vovnet19b_slim_dw,2588.37,197.313,512,224,1.9
seresnet18,2562.51,199.293,512,224,11.78
nf_regnet_b0,2561.76,198.646,512,192,8.76
legacy_seresnet18,2500.8,204.207,512,224,11.78
tf_efficientnetv2_b0,2483.22,204.949,512,192,7.14
levit_256,2482.39,205.091,512,224,18.89
mobilenetv3_large_075,2392.41,213.119,512,224,3.99
resnet14t,2385.69,214.281,512,176,10.08
tf_mobilenetv3_large_minimal_100,2347.68,217.368,512,224,3.92
vit_tiny_patch16_224,2293.54,222.408,512,224,5.72
regnetx_006,2293.09,222.433,512,224,6.2
deit_tiny_patch16_224,2290.53,222.68,512,224,5.72
tf_mobilenetv3_large_075,2259.6,225.688,512,224,3.99
deit_tiny_distilled_patch16_224,2253.36,226.358,512,224,5.91
edgenext_xx_small,2231.33,228.598,512,256,1.33
ghostnet_050,2189.91,232.414,512,224,2.59
mobilenetv3_rw,2184.31,233.512,512,224,5.48
mnasnet_075,2176.02,234.492,512,224,3.17
mobilenetv3_large_100,2167.29,235.344,512,224,5.48
mobilenetv3_large_100_miil,2165.63,235.504,512,224,5.48
levit_256d,2159.5,235.516,512,224,26.21
resnet18d,2129.12,240.084,512,224,11.71
hardcorenas_a,2118.32,240.968,512,224,5.26
regnety_004,2100.99,242.536,512,224,4.34
pit_ti_distilled_224,2086.5,244.504,512,224,5.1
pit_ti_224,2079.54,245.311,512,224,4.85
ese_vovnet19b_slim,2066.1,247.446,512,224,3.17
mnasnet_100,2053.84,248.477,512,224,4.38
tf_mobilenetv3_large_100,2053.63,248.437,512,224,5.48
mnasnet_b1,2053.54,248.485,512,224,4.38
semnasnet_075,2008.51,253.986,512,224,2.91
hardcorenas_b,2008.46,253.96,512,224,5.18
mobilenetv2_075,1983.69,257.32,512,224,2.64
hardcorenas_c,1977.37,257.94,512,224,5.52
xcit_nano_12_p16_224_dist,1970.62,258.036,512,224,3.05
xcit_nano_12_p16_224,1969.78,258.084,512,224,3.05
tinynet_b,1965.95,259.368,512,188,3.73
hardcorenas_d,1880.3,271.085,512,224,7.5
tf_efficientnetv2_b1,1876.23,271.395,512,192,8.14
resnetblur18,1872.21,273.11,512,224,11.69
spnasnet_100,1862.13,273.955,512,224,4.42
mnasnet_a1,1859.21,274.476,512,224,3.89
semnasnet_100,1857.75,274.693,512,224,3.89
mobilenetv2_100,1832.14,278.633,512,224,3.5
regnety_006,1809.24,281.912,512,224,6.06
visformer_tiny,1802.41,283.384,512,224,10.32
mixer_b32_224,1784.58,286.101,512,224,60.29
skresnet18,1730.13,295.275,512,224,11.96
tinynet_a,1710.13,298.117,512,192,6.19
vit_base_patch32_224_sam,1703.64,299.668,512,224,88.22
vit_base_patch32_224,1703.57,299.695,512,224,88.22
efficientnet_lite0,1674.68,304.971,512,224,4.65
cs3darknet_focus_m,1668.48,306.209,512,256,9.3
hardcorenas_e,1650.74,309.021,512,224,8.07
hardcorenas_f,1646.88,309.777,512,224,8.2
gluon_resnet34_v1b,1634.03,312.731,512,224,21.8
regnetx_008,1632.2,312.851,512,224,7.26
tv_resnet34,1630.02,313.513,512,224,21.8
resnet34,1622.41,314.992,512,224,21.8
ghostnet_100,1601.5,318.319,512,224,5.18
tf_efficientnet_lite0,1591.79,320.884,512,224,4.65
fbnetc_100,1567.77,325.605,512,224,5.57
pit_xs_distilled_224,1551.83,329.02,512,224,11.0
pit_xs_224,1549.02,329.642,512,224,10.62
mixer_s16_224,1543.23,331.197,512,224,18.53
dla46_c,1532.94,333.18,512,224,1.3
mnasnet_140,1525.17,334.879,512,224,7.12
seresnet34,1505.77,339.147,512,224,21.96
cs3darknet_m,1499.82,340.716,512,256,9.31
regnety_008,1498.63,340.596,512,224,6.26
levit_384,1491.26,342.207,512,224,39.13
edgenext_x_small,1481.71,344.446,512,256,2.34
ese_vovnet19b_dw,1466.46,348.623,512,224,6.54
legacy_seresnet34,1465.81,348.38,512,224,21.96
efficientnet_b0,1459.11,262.1,384,224,5.29
gernet_m,1456.76,350.74,512,224,21.14
vit_small_patch32_384,1448.56,352.604,512,384,22.92
regnetz_005,1448.06,352.165,512,224,7.12
rexnet_100,1447.81,264.049,384,224,4.8
rexnetr_100,1441.71,265.216,384,224,4.88
nf_resnet26,1422.76,359.346,512,224,16.0
hrnet_w18_small,1410.43,361.614,512,224,13.19
selecsls42,1405.04,363.736,512,224,30.35
selecsls42b,1401.22,364.735,512,224,32.46
mobilenetv2_110d,1400.15,273.199,384,224,4.52
tf_efficientnet_b0_ap,1398.67,273.43,384,224,5.29
mobilevitv2_050,1396.45,365.664,512,256,1.37
tf_efficientnet_b0_ns,1395.54,274.064,384,224,5.29
tf_efficientnet_b0,1395.32,274.114,384,224,5.29
tf_efficientnetv2_b2,1392.9,365.948,512,208,10.1
vit_tiny_r_s16_p8_384,1392.75,274.873,384,384,6.36
resnet34d,1379.64,370.514,512,224,21.82
ghostnet_130,1364.55,373.824,512,224,7.36
gmixer_12_224,1352.72,377.701,512,224,12.7
crossvit_tiny_240,1349.19,377.902,512,240,7.01
gmlp_ti16_224,1340.6,284.894,384,224,5.87
semnasnet_140,1340.57,380.992,512,224,6.11
dla46x_c,1338.33,381.81,512,224,1.07
xcit_tiny_12_p16_224,1323.84,384.926,512,224,6.72
xcit_tiny_12_p16_224_dist,1317.19,386.895,512,224,6.72
resnetrs50,1317.01,387.565,512,160,35.69
mobilevit_xxs,1316.84,290.489,384,256,1.27
resnet26,1312.7,389.566,512,224,16.0
efficientnet_b1_pruned,1301.95,391.798,512,240,6.33
mobilenetv2_140,1267.4,302.189,384,224,6.11
dla60x_c,1262.98,404.404,512,224,1.32
crossvit_9_240,1260.08,303.33,384,240,8.55
convnext_nano_hnf,1235.34,413.703,512,224,15.59
convnext_nano_ols,1234.94,413.902,512,224,15.6
poolformer_s12,1234.11,414.201,512,224,11.92
convnext_nano,1233.61,414.261,512,224,15.59
resmlp_12_distilled_224,1232.37,414.645,512,224,15.35
resmlp_12_224,1232.04,414.762,512,224,15.35
fbnetv3_b,1226.89,415.617,512,224,8.6
nf_regnet_b2,1219.45,418.235,512,240,14.31
repvgg_b0,1217.24,419.512,512,224,15.82
selecsls60b,1214.07,420.825,512,224,32.77
selecsls60,1211.7,421.663,512,224,30.67
nf_regnet_b1,1209.03,421.975,512,256,10.22
crossvit_9_dagger_240,1206.16,316.906,384,240,8.78
nf_seresnet26,1198.39,426.558,512,224,17.4
mixnet_s,1181.75,431.958,512,224,4.13
nf_ecaresnet26,1174.85,435.233,512,224,16.0
efficientnet_lite1,1171.46,217.556,256,240,5.42
darknet17,1164.06,439.537,512,256,14.3
efficientnet_es_pruned,1160.76,440.317,512,224,5.44
efficientnet_es,1160.37,440.47,512,224,5.44
regnetx_016,1139.3,448.473,512,224,9.19
fbnetv3_d,1138.14,335.598,384,224,10.31
tf_efficientnet_es,1136.29,449.83,512,224,5.44
rexnetr_130,1133.04,224.76,256,224,7.61
dla34,1132.96,451.315,512,224,15.74
resnet26d,1119.56,456.822,512,224,16.01
tf_mixnet_s,1118.11,456.605,512,224,4.13
tf_efficientnet_lite1,1110.94,229.444,256,240,5.42
edgenext_small,1109.37,460.388,512,256,5.59
convit_tiny,1095.04,466.531,512,224,5.71
rexnet_130,1094.78,232.699,256,224,7.56
mobilenetv2_120d,1078.49,236.158,256,224,5.83
darknet21,1073.87,476.43,512,256,20.86
ecaresnet50d_pruned,1067.01,478.899,512,224,19.94
deit_small_patch16_224,1053.64,363.563,384,224,22.05
vit_small_patch16_224,1052.92,363.872,384,224,22.05
deit_small_distilled_patch16_224,1032.61,370.971,384,224,22.44
sedarknet21,1031.46,495.893,512,256,20.95
gernet_l,1030.31,496.058,512,256,31.08
efficientnet_b1,1030.3,246.963,256,224,7.79
rexnetr_150,1022.06,249.288,256,224,9.78
repvgg_a2,1010.18,506.008,512,224,28.21
edgenext_small_rw,1009.52,506.183,512,256,7.83
skresnet34,1008.96,506.323,512,224,22.28
resnest14d,979.06,522.497,512,224,10.61
cs3darknet_focus_l,977.57,391.957,384,256,21.15
deit3_small_patch16_224,977.26,391.961,384,224,22.06
deit3_small_patch16_224_in21ft1k,976.5,392.276,384,224,22.06
rexnet_150,965.2,264.04,256,224,9.73
regnety_016,954.26,534.657,512,224,11.2
vit_base_patch32_plus_256,951.64,537.091,512,256,119.48
mobilevitv2_075,947.54,269.157,256,256,2.87
legacy_seresnext26_32x4d,946.21,405.17,384,224,16.79
pit_s_224,942.8,270.615,256,224,23.46
pit_s_distilled_224,939.97,271.455,256,224,24.04
vit_srelpos_small_patch16_224,922.29,415.451,384,224,21.97
vit_relpos_small_patch16_224,921.7,415.439,384,224,21.98
efficientnet_b0_g16_evos,909.42,421.149,384,224,8.11
resnext26ts,905.09,423.733,384,256,10.3
cs3darknet_l,902.18,282.891,256,256,21.16
coat_lite_tiny,893.97,428.624,384,224,5.72
resnet26t,890.89,574.188,512,256,16.01
efficientnet_b0_gn,881.54,289.263,256,224,5.29
resnetv2_50,880.1,580.976,512,224,25.55
efficientnet_b2_pruned,874.13,291.317,256,260,8.31
seresnext26ts,867.62,294.407,256,256,10.39
eca_resnext26ts,867.54,294.527,256,256,10.3
tf_efficientnet_b1,863.78,294.816,256,240,7.79
tf_efficientnet_b1_ap,863.54,294.906,256,240,7.79
tf_efficientnet_b1_ns,863.39,294.941,256,240,7.79
cs3sedarknet_l,861.38,444.523,384,256,21.91
tf_efficientnetv2_b3,855.0,297.539,256,240,14.36
efficientnet_lite2,852.1,299.402,256,260,6.09
twins_svt_small,851.73,449.18,384,224,24.06
gcresnext26ts,850.58,300.113,256,256,10.48
efficientnetv2_rw_t,850.16,298.981,256,224,13.65
botnet26t_256,849.43,451.465,384,256,12.49
ecaresnetlight,846.78,603.683,512,224,30.16
seresnext26t_32x4d,845.6,453.458,384,224,16.81
seresnext26tn_32x4d,845.31,453.612,384,224,16.81
seresnext26d_32x4d,844.96,453.775,384,224,16.81
coat_lite_mini,842.06,455.115,384,224,11.01
tf_efficientnet_cc_b0_8e,837.03,457.594,384,224,24.01
ecaresnet101d_pruned,837.02,609.921,512,224,24.88
ecaresnext26t_32x4d,835.25,459.196,384,224,15.41
ecaresnext50t_32x4d,834.39,459.653,384,224,15.41
cspresnet50,830.57,461.498,384,256,21.62
swsl_resnet50,829.79,616.192,512,224,25.56
ssl_resnet50,829.64,616.294,512,224,25.56
gluon_resnet50_v1b,829.63,616.32,512,224,25.56
visformer_small,828.8,462.625,384,224,40.22
tv_resnet50,826.55,618.618,512,224,25.56
resnet50,826.06,618.983,512,224,25.56
vgg11,825.98,619.706,512,224,132.86
halonet26t,824.96,464.902,384,256,12.48
vovnet39a,817.65,625.544,512,224,22.6
tf_efficientnet_lite2,816.76,312.458,256,260,6.09
convnext_tiny_hnf,815.48,312.97,256,224,28.59
convnext_tiny_hnfd,815.19,313.078,256,224,28.59
vit_small_resnet26d_224,813.66,470.891,384,224,63.61
convnext_tiny,813.16,313.859,256,224,28.59
efficientnet_cc_b0_8e,812.96,471.165,384,224,24.01
vit_relpos_base_patch32_plus_rpn_256,811.26,630.0,512,256,119.42
mixnet_m,810.2,630.361,512,224,5.01
efficientnet_cc_b0_4e,808.8,473.577,384,224,13.31
convnext_tiny_in22ft1k,808.5,315.666,256,224,28.59
efficientnet_b2a,800.27,318.401,256,256,9.11
efficientnet_b2,799.96,318.544,256,256,9.11
regnetz_b16,796.72,319.811,256,224,9.72
tresnet_m,792.8,643.233,512,224,31.39
mobilevit_xs,792.23,321.979,256,256,2.32
ecaresnet26t,791.55,484.557,384,256,16.01
gc_efficientnetv2_rw_t,791.43,320.691,256,224,13.68
resnetv2_50t,790.83,646.602,512,224,25.57
resnetv2_50d,790.11,647.216,512,224,25.57
regnetx_032,787.5,486.368,384,224,15.3
ese_vovnet39b,786.37,650.429,512,224,24.57
tf_efficientnet_cc_b0_4e,784.54,488.254,384,224,13.31
eca_botnext26ts_256,781.43,326.976,256,256,10.59
resnet32ts,781.02,327.191,256,256,17.96
tf_mixnet_m,777.93,492.059,384,224,5.01
resnet33ts,767.78,332.812,256,256,19.68
gluon_resnet50_v1c,763.32,502.196,384,224,25.58
eca_halonext26ts,763.09,334.836,256,256,10.76
rexnetr_200,762.4,250.686,192,224,16.52
dpn68b,756.57,506.252,384,224,12.61
lambda_resnet26t,751.39,510.429,384,256,10.96
vit_relpos_small_patch16_rpn_224,751.37,510.029,384,224,21.97
resnet50t,748.03,512.487,384,224,25.57
cspresnet50d,746.91,341.842,256,256,21.64
gluon_resnet50_v1d,746.5,513.543,384,224,25.58
resnet50d,744.99,514.575,384,224,25.58
legacy_seresnet50,744.88,514.356,384,224,28.09
cspresnet50w,744.04,343.166,256,256,28.12
eca_resnet33ts,743.27,343.735,256,256,19.68
efficientnet_b0_g8_gn,743.27,343.315,256,224,6.56
seresnet33ts,742.4,344.003,256,256,19.78
resnetaa50,741.95,516.711,384,224,25.56
selecsls84,740.99,689.736,512,224,50.95
dpn68,739.97,517.747,384,224,12.61
res2net50_48w_2s,738.06,519.427,384,224,25.29
vit_small_r26_s32_224,737.22,345.982,256,224,36.43
eca_vovnet39b,735.59,695.354,512,224,22.6
lambda_resnet26rpt_256,735.09,260.579,192,256,10.99
nf_regnet_b3,732.33,522.471,384,288,18.59
rexnet_200,731.68,261.239,192,224,16.37
densenet121,730.11,348.758,256,224,7.98
resnest26d,728.94,526.039,384,224,17.07
bat_resnext26ts,728.42,350.197,256,256,10.73
mobilevitv2_100,727.72,262.852,192,256,4.9
tv_densenet121,727.58,350.07,256,224,7.98
nf_seresnet50,727.17,526.884,384,224,28.09
gcresnet33ts,725.89,351.666,256,256,19.88
eca_nfnet_l0,723.69,706.434,512,224,24.14
nfnet_l0,719.96,532.162,384,224,35.07
seresnet50,714.65,536.208,384,224,28.09
twins_pcpvt_small,714.45,356.63,256,224,24.11
nf_ecaresnet50,713.69,537.063,384,224,25.56
dla60,709.61,540.13,384,224,22.04
efficientnet_em,708.33,360.423,256,240,6.9
hrnet_w18_small_v2,705.02,723.712,512,224,15.6
resnetblur50d,704.94,362.275,256,224,25.58
vgg11_bn,703.05,545.962,384,224,132.87
resnetblur50,698.61,548.824,384,224,25.56
regnety_032,696.58,549.77,384,224,19.44
nf_resnet50,696.17,550.716,384,256,25.56
efficientnet_b3_pruned,694.05,367.106,256,300,9.86
tf_efficientnet_em,690.66,369.697,256,240,6.9
skresnet50,685.67,371.92,256,224,25.8
xcit_tiny_24_p16_224,683.44,371.201,256,224,12.12
poolformer_s24,681.93,374.176,256,224,21.39
xcit_tiny_24_p16_224_dist,681.85,371.937,256,224,12.12
vit_base_resnet26d_224,680.96,562.594,384,224,101.4
vovnet57a,678.75,564.837,384,224,36.64
densenet121d,678.22,375.614,256,224,8.0
resnetaa50d,673.73,569.117,384,224,25.58
gluon_resnet50_v1s,669.16,573.001,384,224,25.68
gmixer_24_224,666.22,382.715,256,224,24.72
swsl_resnext50_32x4d,663.66,577.766,384,224,25.03
resnext50_32x4d,663.39,577.966,384,224,25.03
ssl_resnext50_32x4d,663.18,578.185,384,224,25.03
tv_resnext50_32x4d,662.37,578.888,384,224,25.03
gluon_resnext50_32x4d,662.06,579.185,384,224,25.03
haloregnetz_b,660.09,386.296,256,224,11.68
ese_vovnet57b,656.27,584.17,384,224,38.61
cspresnext50,656.07,389.365,256,256,20.57
seresnet50t,655.71,584.407,384,224,28.1
vit_relpos_medium_patch16_cls_224,654.69,389.857,256,224,38.76
seresnetaa50d,654.11,390.147,256,224,28.11
densenetblur121d,649.47,392.249,256,224,8.0
res2net50_26w_4s,648.76,590.62,384,224,25.7
fbnetv3_g,647.3,294.603,192,240,16.62
swin_tiny_patch4_window7_224,646.76,394.841,256,224,28.29
ecaresnet50d,643.9,595.437,384,224,25.58
regnety_040,640.15,598.298,384,224,20.65
gmlp_s16_224,638.67,299.017,192,224,19.42
crossvit_small_240,637.47,399.952,256,240,26.86
resnext50d_32x4d,635.21,402.121,256,224,25.05
nfnet_f0,634.03,806.334,512,192,71.49
vit_srelpos_medium_patch16_224,629.85,405.54,256,224,38.74
mobilevit_s,629.67,303.779,192,256,5.58
skresnet50d,628.92,405.574,256,224,25.82
vit_relpos_medium_patch16_224,628.2,406.369,256,224,38.75
resnest50d_1s4x24d,628.12,406.263,256,224,25.68
mixnet_l,627.47,406.445,256,224,7.33
tf_efficientnet_b2_ns,627.11,304.591,192,260,9.11
tf_efficientnet_b2_ap,626.79,304.757,192,260,9.11
tf_efficientnet_b2,626.11,305.153,192,260,9.11
regnetx_040,624.89,613.356,384,224,22.12
regnetv_040,622.71,409.581,256,224,20.64
darknetaa53,614.47,415.819,256,256,36.02
seresnext50_32x4d,613.62,416.021,256,224,27.56
gluon_seresnext50_32x4d,613.35,416.206,256,224,27.56
sehalonet33ts,613.13,416.664,256,256,13.69
legacy_seresnext50_32x4d,612.89,416.52,256,224,27.56
dla60x,612.79,416.731,256,224,17.35
gcresnet50t,611.79,626.12,384,256,25.9
xcit_nano_12_p16_384_dist,611.55,416.81,256,384,3.05
resmlp_24_224,609.69,418.351,256,224,30.02
resmlp_24_distilled_224,609.51,418.474,256,224,30.02
gcresnext50ts,606.82,314.923,192,256,15.67
tf_inception_v3,603.29,635.057,384,299,23.83
gluon_inception_v3,603.22,635.143,384,299,23.83
adv_inception_v3,603.01,635.347,384,299,23.83
inception_v3,602.27,636.205,384,299,23.83
tf_mixnet_l,600.24,424.956,256,224,7.33
dm_nfnet_f0,600.1,638.573,384,192,71.49
xcit_small_12_p16_224,598.44,425.955,256,224,26.25
xcit_small_12_p16_224_dist,598.22,426.013,256,224,26.25
semobilevit_s,597.07,320.258,192,256,5.74
densenet169,592.78,429.221,256,224,14.15
res2next50,591.98,431.144,256,224,24.67
resnetv2_101,590.74,431.806,256,224,44.54
darknet53,590.64,432.606,256,256,41.61
resnetv2_50x1_bit_distilled,587.0,326.262,192,224,25.55
res2net50_14w_8s,586.94,433.992,256,224,25.06
swin_s3_tiny_224,586.39,435.576,256,224,28.33
repvgg_b1g4,584.26,875.234,512,224,39.97
dla60_res2net,583.07,437.618,256,224,20.85
crossvit_15_240,576.62,331.16,192,240,27.53
cait_xxs24_224,576.52,441.46,256,224,11.96
cs3darknet_focus_x,569.86,448.292,256,256,35.02
resnet101,568.98,448.321,256,224,44.55
gluon_resnet101_v1b,568.4,448.834,256,224,44.55
tv_resnet101,566.24,450.547,256,224,44.55
resnetrs101,564.72,451.1,256,192,63.62
efficientnet_cc_b1_8e,564.18,452.061,256,240,39.72
crossvit_15_dagger_240,558.23,342.033,192,240,28.21
vit_base_resnet50d_224,557.61,457.501,256,224,110.97
mobilevitv2_125,557.38,343.473,192,256,7.48
xcit_nano_12_p8_224_dist,555.43,459.069,256,224,3.05
xcit_nano_12_p8_224,555.18,459.311,256,224,3.05
sebotnet33ts_256,554.49,230.012,128,256,13.7
resnet51q,551.31,463.504,256,256,35.7
resnetv2_101d,548.02,465.6,256,224,44.56
tf_efficientnet_cc_b1_8e,547.16,466.173,256,240,39.72
resnetv2_50d_gn,546.54,350.469,192,224,25.57
nf_resnet101,543.9,704.337,384,224,44.55
vit_base_patch32_384,542.76,470.804,256,384,88.3
gluon_resnet101_v1c,537.15,475.0,256,224,44.57
cspdarknet53,537.1,475.617,256,256,27.64
cs3darknet_x,534.86,477.64,256,256,35.05
vit_base_r26_s32_224,534.67,357.767,192,224,101.38
resnest50d,534.66,477.434,256,224,27.48
resnet50_gn,531.78,360.235,192,224,25.56
regnetz_c16,530.35,360.552,192,256,13.46
gluon_resnet101_v1d,528.59,482.76,256,224,44.57
mixer_b16_224,528.02,484.004,256,224,59.88
mixer_l32_224,527.33,362.504,192,224,206.94
mixer_b16_224_miil,526.58,485.347,256,224,59.88
vit_large_patch32_224,521.73,489.021,256,224,306.54
dla60_res2next,520.31,490.572,256,224,17.03
ecaresnet50t,516.29,494.896,256,256,25.57
cs3sedarknet_xdw,516.24,246.008,128,256,21.6
lambda_resnet50ts,515.16,371.658,192,256,21.54
vit_tiny_patch16_384,512.2,249.072,128,384,5.79
resnet61q,510.55,375.027,192,256,36.85
swinv2_cr_tiny_224,505.83,504.823,256,224,28.33
halonet50ts,503.76,380.122,192,256,22.73
repvgg_b1,503.57,1015.623,512,224,57.42
swinv2_cr_tiny_ns_224,502.5,508.144,256,224,28.33
cs3sedarknet_x,501.96,508.547,256,256,35.4
dla102,497.28,513.14,256,224,33.27
wide_resnet50_2,495.68,773.85,384,224,68.88
res2net50_26w_6s,493.57,516.914,256,224,37.05
resnetaa101d,490.51,520.338,256,224,44.57
convnext_small,489.81,390.224,192,224,50.22
convnext_small_in22ft1k,489.45,390.576,192,224,50.22
legacy_seresnet101,487.73,522.616,256,224,49.33
vit_relpos_medium_patch16_rpn_224,485.5,526.221,256,224,38.73
efficientnet_lite3,484.47,263.098,128,300,8.2
gluon_resnet101_v1s,483.47,527.891,256,224,44.67
seresnet101,480.65,530.38,256,224,49.33
cs3edgenet_x,477.59,535.019,256,256,47.82
nest_tiny,476.68,267.593,128,224,17.06
nf_seresnet101,474.46,537.213,256,224,49.33
mobilevitv2_150_in22ft1k,473.86,269.132,128,256,10.59
mobilevitv2_150,473.84,269.144,128,256,10.59
resnetblur101d,472.23,540.497,256,224,44.57
jx_nest_tiny,472.22,270.163,128,224,17.06
nf_ecaresnet101,469.53,543.375,256,224,44.55
vgg13_bn,468.37,546.3,256,224,133.05
twins_pcpvt_base,466.47,408.848,192,224,43.83
tf_efficientnet_lite3,465.4,273.895,128,300,8.2
vgg16,465.36,824.954,384,224,138.36
sequencer2d_s,462.43,412.819,192,224,27.65
mixnet_xl,460.21,554.308,256,224,11.9
coat_lite_small,457.06,418.568,192,224,19.84
efficientnet_b3a,456.95,278.403,128,288,12.23
efficientnet_b3,456.81,278.448,128,288,12.23
regnetx_080,454.56,843.629,384,224,39.57
regnetx_064,452.43,564.953,256,224,26.21
halo2botnet50ts_256,451.35,424.392,192,256,22.64
ecaresnet101d,447.44,570.33,256,224,44.57
densenet201,447.44,425.987,192,224,20.01
nf_regnet_b4,445.8,428.533,192,320,30.21
convit_small,443.63,431.737,192,224,27.78
efficientnetv2_s,433.27,293.157,128,288,21.46
skresnext50_32x4d,432.3,590.802,256,224,27.48
cs3se_edgenet_x,431.68,443.324,192,256,50.72
botnet50ts_256,428.8,297.529,128,256,22.74
ssl_resnext101_32x4d,427.28,447.74,192,224,44.18
resnext101_32x4d,427.18,447.921,192,224,44.18
swsl_resnext101_32x4d,427.16,447.915,192,224,44.18
gluon_resnext101_32x4d,427.13,447.97,192,224,44.18
poolformer_s36,425.0,449.906,192,224,30.86
ese_vovnet39b_evos,421.31,302.862,128,224,24.58
resnet101d,418.0,457.739,192,256,44.57
dla102x,417.16,458.658,192,224,26.31
res2net101_26w_4s,416.51,612.121,256,224,45.21
lamhalobotnet50ts_256,413.09,463.774,192,256,22.57
twins_svt_base,411.8,464.138,192,224,56.07
crossvit_18_240,406.79,312.611,128,240,43.27
tresnet_l,404.84,1261.389,512,224,55.99
efficientnetv2_rw_s,402.34,315.806,128,288,23.94
volo_d1_224,401.47,476.8,192,224,26.63
resmlp_36_224,401.06,476.505,192,224,44.69
res2net50_26w_8s,400.52,636.999,256,224,48.4
resmlp_36_distilled_224,400.14,477.557,192,224,44.69
swin_small_patch4_window7_224,399.67,478.499,192,224,49.61
resnest50d_4s2x40d,396.0,645.092,256,224,30.42
vit_base_patch16_224_miil,395.78,484.311,192,224,86.54
crossvit_18_dagger_240,394.08,322.72,128,240,44.27
deit_base_patch16_224,390.88,490.307,192,224,86.57
vit_base_patch16_224,390.86,490.391,192,224,86.57
vit_base_patch16_224_sam,390.67,490.608,192,224,86.57
mobilevitv2_175_in22ft1k,389.97,327.241,128,256,14.25
mobilevitv2_175,389.95,327.23,128,256,14.25
tf_efficientnetv2_s_in21ft1k,389.66,326.288,128,300,21.46
tf_efficientnetv2_s,389.1,326.713,128,300,21.46
vgg16_bn,388.69,658.276,256,224,138.37
regnety_064,388.46,657.256,256,224,30.58
regnety_080,385.84,662.273,256,224,39.18
deit_base_distilled_patch16_224,385.62,497.03,192,224,87.34
xception,385.56,331.194,128,299,22.86
regnety_040s_gn,384.97,330.927,128,224,20.65
repvgg_b2g4,379.42,1348.329,512,224,61.76
resnetv2_152,379.4,503.868,192,224,60.19
regnetz_d8,378.76,336.282,128,256,23.37
hrnet_w18,378.26,671.883,256,224,21.3
ese_vovnet99b,377.27,677.036,256,224,63.2
vit_small_resnet50d_s16_224,376.52,508.654,192,224,57.53
cait_xxs36_224,375.8,507.032,192,224,17.3
gluon_seresnext101_32x4d,375.02,509.78,192,224,48.96
regnetz_040,374.91,339.544,128,256,27.12
seresnext101_32x4d,374.73,510.176,192,224,48.96
regnetv_064,372.69,513.522,192,224,30.58
regnetz_040h,372.64,341.593,128,256,28.94
legacy_seresnext101_32x4d,372.06,513.705,192,224,48.96
deit3_base_patch16_224_in21ft1k,371.79,515.431,192,224,86.59
deit3_base_patch16_224,371.73,515.464,192,224,86.59
tf_efficientnet_b3,370.15,344.089,128,300,12.23
tf_efficientnet_b3_ap,370.14,344.111,128,300,12.23
tf_efficientnet_b3_ns,370.1,344.134,128,300,12.23
resnet152,370.08,516.516,192,224,60.19
vit_relpos_base_patch16_clsgap_224,369.76,518.105,192,224,86.43
vit_relpos_base_patch16_cls_224,369.34,518.67,192,224,86.43
resnetv2_50d_frn,369.16,345.594,128,224,25.59
gluon_resnet152_v1b,369.02,517.998,192,224,60.19
tv_resnet152,369.0,518.088,192,224,60.19
regnetz_b16_evos,365.3,348.518,128,224,9.74
sequencer2d_m,363.12,525.505,192,224,38.31
ese_vovnet99b_iabn,362.9,1055.043,384,224,63.2
resnetv2_152d,360.99,529.48,192,224,60.2
beit_base_patch16_224,358.29,534.776,192,224,86.53
xcit_tiny_12_p16_384_dist,357.91,534.55,192,384,6.72
vit_relpos_base_patch16_224,355.33,539.194,192,224,86.43
gluon_resnet152_v1c,354.77,538.797,192,224,60.21
regnetz_d32,354.52,359.397,128,256,27.58
swinv2_tiny_window8_256,354.35,540.569,192,256,28.35
resnetv2_50d_evos,353.36,270.506,96,224,25.59
dpn92,353.0,723.617,256,224,37.67
vgg19,352.0,1090.655,384,224,143.67
gluon_resnet152_v1d,351.06,544.563,192,224,60.21
densenet161,346.02,367.416,128,224,28.68
xception41p,344.85,370.318,128,299,26.91
gluon_resnet152_v1s,344.7,368.96,128,224,60.32
mobilevitv2_200,342.4,372.843,128,256,18.45
tnt_s_patch16_224,342.25,559.037,192,224,23.76
mobilevitv2_200_in22ft1k,342.08,373.147,128,256,18.45
eca_nfnet_l1,341.07,561.084,192,256,41.41
hrnet_w32,340.54,747.043,256,224,41.23
dla169,338.11,565.259,192,224,53.39
convnext_base_in22ft1k,337.76,377.102,128,224,88.59
convnext_base,336.98,378.091,128,224,88.59
repvgg_b2,335.01,1527.215,512,224,89.02
repvgg_b3g4,334.01,1148.557,384,224,83.83
vgg13,331.37,1544.923,512,224,133.05
pit_b_224,331.17,385.577,128,224,73.76
vgg19_bn,330.96,773.109,256,224,143.68
pit_b_distilled_224,329.46,387.568,128,224,74.79
regnetx_120,327.41,780.952,256,224,46.11
twins_pcpvt_large,322.96,392.17,128,224,60.99
hrnet_w30,321.86,790.607,256,224,37.71
legacy_seresnet152,319.56,397.245,128,224,66.82
inception_v4,316.87,603.734,192,299,42.68
seresnet152,313.75,608.677,192,224,66.82
vit_small_patch16_36x1_224,310.56,409.448,128,224,64.67
dla102x2,309.09,412.537,128,224,41.28
xcit_small_24_p16_224_dist,307.83,412.466,128,224,47.67
convmixer_1024_20_ks9_p14,307.81,830.813,256,224,24.38
vit_small_patch16_18x2_224,307.61,413.3,128,224,64.67
xcit_small_24_p16_224,307.46,412.867,128,224,47.67
regnety_120,307.05,623.971,192,224,51.82
poolformer_m36,303.34,420.132,128,224,56.17
efficientnet_el_pruned,301.49,423.464,128,300,10.59
efficientnet_el,301.45,423.5,128,300,10.59
swinv2_cr_small_ns_224,300.41,423.619,128,224,49.7
swinv2_cr_small_224,297.65,427.521,128,224,49.7
mixnet_xxl,297.33,428.503,128,224,23.96
cait_s24_224,296.96,428.341,128,224,46.92
nest_small,296.72,321.888,96,224,38.35
coat_tiny,296.44,429.708,128,224,5.5
tf_efficientnet_el,295.51,432.07,128,300,10.59
jx_nest_small,294.83,323.932,96,224,38.35
efficientnet_b4,293.1,325.442,96,320,19.34
xception41,293.07,435.505,128,299,26.97
xcit_tiny_12_p8_224_dist,291.52,437.287,128,224,6.71
tresnet_xl,291.4,875.028,256,224,78.44
resnext101_64x4d,291.33,437.816,128,224,83.46
gluon_resnext101_64x4d,291.25,437.881,128,224,83.46
swin_s3_small_224,289.76,439.818,128,224,49.74
wide_resnet101_2,289.62,661.356,192,224,126.89
xcit_tiny_12_p8_224,289.33,440.549,128,224,6.71
twins_svt_large,289.11,440.647,128,224,99.27
resnet152d,281.47,452.46,128,256,60.21
swin_base_patch4_window7_224,279.66,455.817,128,224,87.77
convnext_tiny_384_in22ft1k,278.8,343.389,96,384,28.59
resnet200,276.62,459.688,128,224,64.67
ssl_resnext101_32x8d,276.52,461.341,128,224,88.79
ig_resnext101_32x8d,276.39,461.582,128,224,88.79
resnext101_32x8d,276.22,461.854,128,224,88.79
swsl_resnext101_32x8d,276.22,461.764,128,224,88.79
repvgg_b3,271.93,1411.039,384,224,123.09
nfnet_f1,271.43,705.161,192,224,132.63
resnetv2_50d_evob,268.92,355.729,96,224,25.59
gmlp_b16_224,268.22,356.298,96,224,73.08
dpn98,267.62,476.602,128,224,61.57
regnetx_160,266.55,719.244,192,224,54.28
regnety_160,264.44,724.758,192,224,83.59
gluon_seresnext101_64x4d,264.12,482.344,128,224,88.23
ens_adv_inception_resnet_v2,261.47,730.952,192,299,55.84
inception_resnet_v2,261.44,730.919,192,299,55.84
xception65p,259.32,492.32,128,299,39.82
efficientnet_lite4,255.23,249.374,64,380,13.01
vit_base_patch16_rpn_224,254.51,753.593,192,224,86.54
resnest101e,253.9,501.575,128,256,48.28
crossvit_base_240,253.73,376.737,96,240,105.03
seresnext101_32x8d,251.44,506.792,128,224,93.57
vit_relpos_base_patch16_rpn_224,250.62,765.002,192,224,86.41
vit_base_patch16_plus_240,248.4,514.352,128,240,117.56
tf_efficientnet_lite4,247.32,257.437,64,380,13.01
efficientnet_b3_gn,245.44,258.998,64,288,11.73
dm_nfnet_f1,244.51,521.138,128,224,132.63
seresnext101d_32x8d,242.49,525.503,128,224,93.59
seresnet152d,242.25,392.75,96,256,66.84
xcit_tiny_24_p16_384_dist,241.62,526.318,128,384,12.12
vit_small_patch16_384,239.05,266.865,64,384,22.2
vit_relpos_base_patch16_plus_240,238.57,535.322,128,240,117.38
vit_large_r50_s32_224,237.94,401.033,96,224,328.99
resnetrs152,237.63,535.199,128,256,86.62
swinv2_tiny_window16_256,237.41,403.072,96,256,28.35
seresnextaa101d_32x8d,228.0,559.15,128,224,93.59
xcit_medium_24_p16_224_dist,227.91,558.239,128,224,84.4
deit3_small_patch16_384_in21ft1k,227.77,280.008,64,384,22.21
deit3_small_patch16_384,227.76,280.015,64,384,22.21
xcit_medium_24_p16_224,227.75,558.491,128,224,84.4
vit_small_r26_s32_384,227.25,280.302,64,384,36.47
convit_base,224.86,568.198,128,224,86.54
gluon_xception65,224.1,426.474,96,299,39.92
swin_s3_base_224,223.36,426.944,96,224,71.13
tnt_b_patch16_224,222.94,572.213,128,224,65.41
xception65,222.93,428.728,96,299,39.92
coat_mini,222.88,572.209,128,224,10.34
volo_d2_224,222.28,430.111,96,224,58.68
xcit_small_12_p16_384_dist,221.79,430.984,96,384,26.25
poolformer_m48,220.53,432.741,96,224,73.47
hrnet_w40,219.45,869.959,192,224,57.56
vit_base_r50_s16_224,215.41,443.988,96,224,98.66
swinv2_cr_base_ns_224,213.88,446.428,96,224,87.88
sequencer2d_l,213.86,444.098,96,224,54.3
swinv2_small_window8_256,212.59,449.01,96,256,49.73
swinv2_cr_base_224,211.39,451.682,96,224,87.88
mobilevitv2_150_384_in22ft1k,210.38,303.23,64,384,10.59
nest_base,210.2,302.774,64,224,67.72
tresnet_m_448,209.55,913.447,192,448,31.39
efficientnetv2_m,207.96,304.545,64,320,54.14
jx_nest_base,207.78,306.371,64,224,67.72
regnetz_c16_evos,207.35,306.824,64,256,13.49
hrnet_w44,206.45,925.026,192,224,67.06
resnet200d,204.47,623.017,128,256,64.69
efficientnet_b3_g8_gn,203.44,312.836,64,288,14.25
hrnet_w48,202.15,628.427,128,224,77.47
densenet264,202.1,470.789,96,224,72.69
dpn131,198.25,643.486,128,224,79.25
tf_efficientnet_b4,194.83,326.399,64,380,19.34
tf_efficientnet_b4_ap,194.65,326.738,64,380,19.34
tf_efficientnet_b4_ns,194.23,327.375,64,380,19.34
xcit_nano_12_p8_384_dist,187.76,338.965,64,384,3.05
efficientnetv2_rw_m,187.31,338.14,64,320,53.24
dpn107,187.14,682.151,128,224,86.92
convnext_large_in22ft1k,187.05,511.402,96,224,197.77
convnext_large,187.01,511.523,96,224,197.77
nf_regnet_b5,186.49,512.09,96,384,49.74
xcit_tiny_24_p8_224_dist,183.21,520.533,96,224,12.11
xcit_tiny_24_p8_224,183.21,520.609,96,224,12.11
halonet_h1,177.48,359.151,64,256,8.1
hrnet_w64,176.04,722.362,128,224,128.06
mobilevitv2_175_384_in22ft1k,175.76,363.135,64,384,14.25
senet154,174.83,545.792,96,224,115.09
regnety_320,174.41,732.528,128,224,145.05
gluon_senet154,174.03,548.162,96,224,115.09
regnetz_e8,173.89,365.999,64,256,57.7
legacy_senet154,170.27,560.493,96,224,115.09
xception71,168.81,376.911,64,299,42.34
xcit_small_12_p8_224,168.52,377.961,64,224,26.21
xcit_small_12_p8_224_dist,168.32,378.375,64,224,26.21
vit_large_patch32_384,168.05,569.595,96,384,306.63
convnext_small_384_in22ft1k,164.94,386.292,64,384,50.22
mixer_l16_224,164.43,582.335,96,224,208.2
ecaresnet200d,161.74,392.334,64,256,64.69
seresnet200d,161.56,391.892,64,256,71.86
resnetrs200,160.52,394.222,64,256,93.21
densenet264d_iabn,158.12,804.924,128,224,72.74
regnetx_320,155.94,819.702,128,224,107.81
swin_large_patch4_window7_224,153.79,414.255,64,224,196.53
volo_d3_224,152.73,416.584,64,224,86.33
mobilevitv2_200_384_in22ft1k,150.68,317.559,48,384,18.45
swinv2_base_window8_256,150.28,423.39,64,256,87.92
resnetv2_50x1_bitm,149.04,321.21,48,448,25.55
nfnet_f2,148.92,641.446,96,256,193.78
swinv2_small_window16_256,142.83,445.591,64,256,49.73
tf_efficientnetv2_m,142.41,333.833,48,384,54.14
eca_nfnet_l2,142.2,672.291,96,320,56.72
tf_efficientnetv2_m_in21ft1k,141.35,336.246,48,384,54.14
regnetz_d8_evos,132.2,360.995,48,256,23.46
swinv2_cr_tiny_384,131.5,485.388,64,384,28.33
ig_resnext101_32x16d,130.47,734.203,96,224,194.03
ssl_resnext101_32x16d,130.4,734.63,96,224,194.03
swsl_resnext101_32x16d,130.37,734.771,96,224,194.03
xcit_large_24_p16_224,126.98,500.577,64,224,189.1
xcit_large_24_p16_224_dist,126.97,500.662,64,224,189.1
seresnet269d,125.7,503.318,64,256,113.67
dm_nfnet_f2,125.2,507.681,64,256,193.78
swinv2_cr_large_224,124.7,510.736,64,224,196.68
xcit_tiny_12_p8_384_dist,122.38,390.412,48,384,6.71
resnetrs270,121.5,520.761,64,256,129.86
crossvit_15_dagger_408,117.57,270.29,32,408,28.5
vit_large_patch16_224,117.08,544.981,64,224,304.33
vit_base_patch16_18x2_224,116.55,546.378,64,224,256.73
convnext_base_384_in22ft1k,115.97,412.084,48,384,88.59
convnext_xlarge_in22ft1k,115.91,550.445,64,224,350.2
deit3_large_patch16_224_in21ft1k,113.19,563.501,64,224,304.37
deit3_large_patch16_224,113.17,563.634,64,224,304.37
xcit_small_24_p16_384_dist,112.88,421.839,48,384,47.67
beit_large_patch16_224,107.8,591.544,64,224,304.43
swinv2_base_window16_256,103.68,460.461,48,256,87.92
swinv2_base_window12to16_192to256_22kft1k,103.56,461.021,48,256,87.92
tresnet_l_448,103.2,1236.839,128,448,55.99
volo_d1_384,99.39,320.613,32,384,26.78
cait_xxs24_384,97.83,488.033,48,384,12.03
vit_base_patch16_384,96.96,329.192,32,384,86.86
deit_base_patch16_384,96.37,331.171,32,384,86.86
volo_d4_224,95.75,498.748,48,224,192.96
deit_base_distilled_patch16_384,94.83,336.556,32,384,87.63
efficientnet_b5,93.71,338.901,32,456,30.39
deit3_base_patch16_384,93.22,342.328,32,384,86.88
deit3_base_patch16_384_in21ft1k,92.68,344.327,32,384,86.88
tf_efficientnet_b5,92.16,344.785,32,456,30.39
tf_efficientnet_b5_ns,92.11,344.939,32,456,30.39
tf_efficientnet_b5_ap,91.98,345.405,32,456,30.39
resnetv2_152x2_bit_teacher,89.37,355.711,32,224,236.34
crossvit_18_dagger_408,88.76,358.492,32,408,44.61
xcit_small_24_p8_224,87.25,546.829,48,224,47.63
xcit_small_24_p8_224_dist,86.87,549.24,48,224,47.63
convmixer_768_32,85.53,1121.025,96,224,21.11
vit_large_patch14_224,85.27,561.239,48,224,304.2
eca_nfnet_l3,84.61,563.702,48,352,72.04
resnetv2_101x1_bitm,84.61,187.399,16,448,44.54
beit_base_patch16_384,83.67,381.291,32,384,86.74
resnest200e,83.33,570.802,48,320,70.2
tf_efficientnetv2_l_in21ft1k,83.27,379.678,32,384,118.52
efficientnetv2_l,83.27,379.867,32,384,118.52
tf_efficientnetv2_l,82.74,382.367,32,384,118.52
ecaresnet269d,82.15,579.477,48,320,102.09
tresnet_xl_448,78.31,1222.487,96,448,78.44
xcit_medium_24_p16_384_dist,77.9,407.346,32,384,84.4
vit_large_r50_s32_384,77.49,410.426,32,384,329.09
swinv2_cr_small_384,76.31,416.793,32,384,49.7
swin_base_patch4_window12_384,74.03,430.327,32,384,87.9
pnasnet5large,68.77,461.392,32,331,86.06
resnetrs350,68.24,460.967,32,288,163.96
nfnet_f3,67.87,703.087,48,320,254.92
nasnetalarge,67.38,469.785,32,331,88.75
resmlp_big_24_distilled_224,67.03,475.867,32,224,129.14
resmlp_big_24_224_in22ft1k,67.03,475.857,32,224,129.14
resmlp_big_24_224,67.02,475.97,32,224,129.14
cait_xs24_384,65.59,485.229,32,384,26.67
convnext_large_384_in22ft1k,63.62,501.159,32,384,197.77
vit_base_patch8_224,63.42,377.591,24,224,86.58
cait_xxs36_384,63.23,502.345,32,384,17.37
ig_resnext101_32x32d,62.72,508.666,32,224,468.53
xcit_tiny_24_p8_384_dist,62.14,511.676,32,384,12.11
volo_d5_224,61.58,516.467,32,224,295.46
vit_base_resnet50_384,61.06,391.43,24,384,98.95
vit_base_r50_s16_384,61.0,391.773,24,384,98.95
swinv2_large_window12to16_192to256_22kft1k,60.93,391.352,24,256,196.74
xcit_medium_24_p8_224,60.43,526.111,32,224,84.32
xcit_medium_24_p8_224_dist,60.03,529.652,32,224,84.32
xcit_small_12_p8_384_dist,57.75,413.763,24,384,26.21
dm_nfnet_f3,57.26,554.375,32,320,254.92
volo_d2_384,55.56,286.247,16,384,58.87
efficientnet_b6,54.98,288.003,16,528,43.04
swinv2_cr_base_384,54.71,436.265,24,384,87.88
tf_efficientnet_b6,54.38,291.338,16,528,43.04
tf_efficientnet_b6_ns,54.21,292.241,16,528,43.04
tf_efficientnet_b6_ap,54.17,292.479,16,528,43.04
efficientnetv2_xl,53.77,291.666,16,384,208.12
tf_efficientnetv2_xl_in21ft1k,53.07,295.611,16,384,208.12
convmixer_1536_20,50.1,957.271,48,224,51.63
swinv2_cr_huge_224,49.51,482.114,24,224,657.83
cait_s24_384,49.37,483.331,24,384,47.06
resnetrs420,48.12,489.4,24,320,191.89
xcit_large_24_p16_384_dist,45.6,522.909,24,384,189.1
swin_large_patch4_window12_384,41.65,382.145,16,384,196.74
convnext_xlarge_384_in22ft1k,40.18,595.51,24,384,350.2
vit_huge_patch14_224,39.94,398.436,16,224,632.05
deit3_huge_patch14_224_in21ft1k,38.36,414.578,16,224,632.13
deit3_huge_patch14_224,38.33,414.855,16,224,632.13
nfnet_f4,36.85,646.047,24,384,316.07
resnest269e,35.75,664.499,24,416,110.93
resnetv2_50x3_bitm,34.88,457.822,16,448,217.32
xcit_large_24_p8_224_dist,33.7,471.344,16,224,188.93
xcit_large_24_p8_224,33.68,471.512,16,224,188.93
resnetv2_152x2_bit_teacher_384,32.69,487.138,16,384,236.34
ig_resnext101_32x48d,32.39,492.417,16,224,828.41
swinv2_cr_large_384,32.36,491.839,16,384,196.68
cait_s36_384,31.92,497.404,16,384,68.37
efficientnet_b7,31.74,248.492,8,600,66.35
dm_nfnet_f4,31.4,758.349,24,384,316.07
tf_efficientnet_b7,31.4,251.12,8,600,66.35
tf_efficientnet_b7_ns,31.37,251.394,8,600,66.35
tf_efficientnet_b7_ap,31.35,251.548,8,600,66.35
xcit_small_24_p8_384_dist,29.2,544.65,16,384,47.63
vit_large_patch16_384,29.07,411.127,12,384,304.72
deit3_large_patch16_384,28.22,423.365,12,384,304.76
deit3_large_patch16_384_in21ft1k,28.19,423.825,12,384,304.76
swinv2_base_window12to24_192to384_22kft1k,28.14,423.938,12,384,87.92
beit_large_patch16_384,25.12,475.56,12,384,305.0
volo_d3_448,23.79,333.686,8,448,86.63
nfnet_f5,22.84,694.067,16,416,377.21
resnetv2_152x2_bitm,22.69,350.236,8,448,236.34
vit_giant_patch14_224,22.29,356.113,8,224,1012.61
dm_nfnet_f5,20.95,756.72,16,416,377.21
xcit_medium_24_p8_384_dist,19.97,397.272,8,384,84.32
efficientnet_b8,19.9,297.659,6,672,87.41
tf_efficientnet_b8_ap,19.66,301.198,6,672,87.41
tf_efficientnet_b8,19.65,301.246,6,672,87.41
nfnet_f6,18.5,641.193,12,448,438.36
resnetv2_101x3_bitm,18.0,442.742,8,448,387.93
volo_d4_448,16.87,353.154,6,448,193.41
swinv2_large_window12to24_192to384_22kft1k,16.59,359.187,6,384,196.74
dm_nfnet_f6,15.07,522.261,8,448,438.36
swinv2_cr_huge_384,12.92,461.964,6,384,657.94
nfnet_f7,12.67,622.861,8,480,499.5
cait_m36_384,11.76,506.439,6,384,271.22
xcit_large_24_p8_384_dist,11.53,516.755,6,384,188.93
volo_d5_448,11.08,357.783,4,448,295.91
tf_efficientnet_l2_ns_475,10.91,360.832,4,475,480.31
beit_large_patch16_512,9.42,422.333,4,512,305.67
volo_d5_512,7.72,385.462,3,512,296.09
resnetv2_152x4_bitm,4.91,404.529,2,480,936.53
cait_m48_448,4.71,419.69,2,448,356.46
efficientnet_l2,3.43,285.826,1,800,480.31
tf_efficientnet_l2_ns,3.42,287.247,1,800,480.31
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nhwc-pt112-cu113-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,70939.06,14.424,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,53363.87,19.179,1024,224,0.03,0.92,1.59
lcnet_035,39908.29,25.648,1024,224,0.03,1.04,1.64
mobilenetv3_small_075,38048.72,26.902,1024,224,0.05,1.3,2.04
tinynet_d,35634.7,28.724,1024,152,0.05,1.42,2.34
lcnet_050,35231.0,29.055,1024,224,0.05,1.26,1.88
mobilenetv3_small_100,34913.55,29.319,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,31288.96,32.716,1024,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,30676.85,33.368,1024,224,0.05,1.3,2.04
lcnet_075,30088.74,34.022,1024,224,0.1,1.99,2.36
tf_mobilenetv3_small_100,28547.5,35.858,1024,224,0.06,1.42,2.54
lcnet_100,23945.91,42.753,1024,224,0.16,2.52,2.95
mnasnet_small,22244.35,46.024,1024,224,0.07,2.16,2.03
levit_128s,22002.58,46.529,1024,224,0.31,1.88,7.78
mobilenetv2_035,20937.19,48.897,1024,224,0.07,2.86,1.68
mnasnet_050,18984.06,53.93,1024,224,0.11,3.07,2.22
ghostnet_050,18415.88,55.593,1024,224,0.05,1.77,2.59
tinynet_c,17846.73,57.365,1024,184,0.11,2.87,2.46
mobilenetv2_050,16928.19,60.48,1024,224,0.1,3.64,1.97
semnasnet_050,16394.61,62.449,1024,224,0.11,3.44,2.08
lcnet_150,15508.0,66.02,1024,224,0.34,3.79,4.5
gernet_s,15282.73,66.993,1024,224,0.75,2.65,8.17
levit_128,14929.05,68.581,1024,224,0.41,2.71,9.21
cs3darknet_focus_s,14654.05,69.868,1024,256,0.69,2.7,3.27
cs3darknet_s,14422.34,70.991,1024,256,0.72,2.97,3.28
mobilenetv3_large_075,14412.2,71.04,1024,224,0.16,4.0,3.99
mixer_s32_224,13576.58,75.414,1024,224,1.0,2.28,19.1
resnet10t,13509.21,75.789,1024,224,1.1,2.43,5.44
mobilenetv3_rw,13202.47,77.551,1024,224,0.23,4.41,5.48
levit_192,13174.02,77.717,1024,224,0.66,3.2,10.95
mobilenetv3_large_100,12955.77,79.027,1024,224,0.23,4.41,5.48
mobilenetv3_large_100_miil,12954.49,79.035,1024,224,0.23,4.41,5.48
vit_small_patch32_224,12913.76,79.284,1024,224,1.15,2.5,22.88
hardcorenas_a,12748.25,80.313,1024,224,0.23,4.38,5.26
mnasnet_075,12700.79,80.614,1024,224,0.23,4.77,3.17
tf_mobilenetv3_large_075,12296.64,83.262,1024,224,0.16,4.0,3.99
tinynet_b,12104.09,84.587,1024,188,0.21,4.44,3.73
tf_mobilenetv3_large_minimal_100,11915.92,85.923,1024,224,0.22,4.4,3.92
hardcorenas_b,11667.78,87.752,1024,224,0.26,5.09,5.18
hardcorenas_c,11602.35,88.247,1024,224,0.28,5.01,5.52
ese_vovnet19b_slim_dw,11450.49,89.417,1024,224,0.4,5.28,1.9
mnasnet_b1,11305.32,90.567,1024,224,0.33,5.46,4.38
mnasnet_100,11303.72,90.578,1024,224,0.33,5.46,4.38
tf_mobilenetv3_large_100,11165.87,91.695,1024,224,0.23,4.41,5.48
gluon_resnet18_v1b,11046.16,92.691,1024,224,1.82,2.48,11.69
ssl_resnet18,11027.01,92.852,1024,224,1.82,2.48,11.69
resnet18,11023.6,92.881,1024,224,1.82,2.48,11.69
swsl_resnet18,11003.86,93.048,1024,224,1.82,2.48,11.69
semnasnet_075,10953.15,93.479,1024,224,0.23,5.54,2.91
regnetx_004,10946.78,93.533,1024,224,0.4,3.14,5.16
hardcorenas_d,10898.13,93.95,1024,224,0.3,4.93,7.5
mobilenetv2_075,10779.36,94.985,1024,224,0.22,5.86,2.64
ghostnet_100,10498.38,97.527,1024,224,0.15,3.55,5.18
seresnet18,10333.85,99.081,1024,224,1.82,2.49,11.78
vit_tiny_r_s16_p8_224,10273.23,99.666,1024,224,0.44,2.06,6.34
spnasnet_100,10240.55,99.983,1024,224,0.35,6.03,4.42
legacy_seresnet18,10026.58,102.118,1024,224,1.82,2.49,11.78
mnasnet_a1,9730.7,105.223,1024,224,0.32,6.23,3.89
semnasnet_100,9728.29,105.249,1024,224,0.32,6.23,3.89
tf_efficientnetv2_b0,9714.68,105.397,1024,224,0.73,4.77,7.14
tinynet_a,9706.17,105.487,1024,192,0.35,5.41,6.19
hardcorenas_f,9654.05,106.058,1024,224,0.35,5.57,8.2
mobilenetv2_100,9591.46,106.751,1024,224,0.31,6.68,3.5
levit_256,9580.42,106.874,1024,224,1.13,4.23,18.89
regnetx_002,9551.99,107.191,1024,224,0.2,2.16,2.68
hardcorenas_e,9521.78,107.531,1024,224,0.35,5.65,8.07
efficientnet_lite0,9415.07,108.751,1024,224,0.4,6.74,4.65
regnety_002,9227.09,110.966,1024,224,0.2,2.17,3.16
fbnetc_100,9182.63,111.504,1024,224,0.4,6.51,5.57
resnet18d,9153.04,111.864,1024,224,2.06,3.29,11.71
regnety_006,9048.64,113.154,1024,224,0.61,4.33,6.06
ese_vovnet19b_slim,8822.42,116.057,1024,224,1.69,3.52,3.17
ghostnet_130,8402.81,121.852,1024,224,0.24,4.6,7.36
regnetx_006,8395.84,121.954,1024,224,0.61,3.98,6.2
levit_256d,8131.84,125.914,1024,224,1.4,4.93,26.21
tf_efficientnet_lite0,8115.07,126.174,1024,224,0.4,6.74,4.65
regnetz_005,8104.32,126.341,1024,224,0.52,5.86,7.12
efficientnet_b0,8015.36,127.743,1024,224,0.4,6.75,5.29
xcit_nano_12_p16_224_dist,7700.28,132.971,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,7692.58,133.102,1024,224,0.56,4.17,3.05
mnasnet_140,7484.35,136.808,1024,224,0.6,7.71,7.12
rexnetr_100,7414.06,138.105,1024,224,0.43,7.72,4.88
resnet14t,7298.17,140.296,1024,224,1.69,5.8,10.08
mobilenetv2_110d,7233.3,141.556,1024,224,0.45,8.71,4.52
regnetx_008,7112.68,143.957,1024,224,0.81,5.15,7.26
tf_efficientnet_b0_ns,7058.22,145.067,1024,224,0.4,6.75,5.29
tf_efficientnet_b0_ap,7055.15,145.131,1024,224,0.4,6.75,5.29
tf_efficientnet_b0,7052.24,145.19,1024,224,0.4,6.75,5.29
edgenext_xx_small,6998.78,146.298,1024,256,0.33,4.21,1.33
dla46_c,6933.78,147.671,1024,224,0.58,4.5,1.3
deit_tiny_patch16_224,6855.38,149.36,1024,224,1.26,5.97,5.72
vit_tiny_patch16_224,6844.8,149.592,1024,224,1.26,5.97,5.72
regnety_008,6827.19,149.977,1024,224,0.81,5.25,6.26
gernet_m,6753.27,151.619,1024,224,3.02,5.24,21.14
deit_tiny_distilled_patch16_224,6720.97,152.347,1024,224,1.27,6.01,5.91
efficientnet_b1_pruned,6608.04,154.952,1024,240,0.4,6.21,6.33
hrnet_w18_small,6603.38,155.061,1024,224,1.61,5.72,13.19
gluon_resnet34_v1b,6434.28,159.136,1024,224,3.67,3.74,21.8
semnasnet_140,6428.21,159.287,1024,224,0.6,8.87,6.11
tv_resnet34,6406.04,159.838,1024,224,3.67,3.74,21.8
resnet34,6404.45,159.878,1024,224,3.67,3.74,21.8
ese_vovnet19b_dw,6353.89,161.15,1024,224,1.34,8.25,6.54
rexnet_100,6291.85,162.738,1024,224,0.41,7.44,4.8
mobilenetv2_140,6258.1,163.617,1024,224,0.6,9.57,6.11
mobilevitv2_050,6228.2,164.403,1024,256,0.48,8.04,1.37
efficientnet_lite1,6215.56,164.736,1024,240,0.62,10.14,5.42
tf_efficientnetv2_b1,6143.78,166.661,1024,240,1.21,7.34,8.14
visformer_tiny,6090.13,168.13,1024,224,1.27,5.72,10.32
fbnetv3_b,6012.29,170.307,1024,256,0.55,9.1,8.6
seresnet34,5988.65,170.978,1024,224,3.67,3.74,21.96
resnet26,5923.37,172.863,1024,224,2.36,7.35,16.0
efficientnet_es,5871.09,174.403,1024,224,1.81,8.73,5.44
efficientnet_es_pruned,5866.42,174.542,1024,224,1.81,8.73,5.44
selecsls42,5796.58,176.643,1024,224,2.94,4.62,30.35
pit_ti_distilled_224,5792.34,176.773,1024,224,0.71,6.23,5.1
legacy_seresnet34,5780.67,177.13,1024,224,3.67,3.74,21.96
selecsls42b,5766.92,177.544,1024,224,2.98,4.62,32.46
pit_ti_224,5764.0,177.643,1024,224,0.7,6.19,4.85
resnet34d,5748.0,178.138,1024,224,3.91,4.54,21.82
levit_384,5659.16,180.934,1024,224,2.36,6.26,39.13
tf_efficientnet_es,5608.15,182.58,1024,224,1.81,8.73,5.44
resnetblur18,5572.02,183.764,1024,224,2.34,3.39,11.69
tf_efficientnet_lite1,5541.93,184.761,1024,240,0.62,10.14,5.42
rexnetr_130,5487.71,186.587,1024,224,0.68,9.81,7.61
cs3darknet_m,5481.96,186.783,1024,288,2.63,6.69,9.31
mixnet_s,5402.43,189.533,1024,224,0.25,6.25,4.13
regnety_004,5382.71,190.227,1024,224,0.41,3.89,4.34
skresnet18,5371.9,190.61,1024,224,1.82,3.24,11.96
darknet17,5347.86,143.598,768,256,3.26,7.18,14.3
mobilevit_xxs,5306.36,192.964,1024,256,0.42,8.34,1.27
cs3darknet_focus_m,5289.88,193.566,1024,288,2.51,6.19,9.3
mobilenetv2_120d,5178.64,197.724,1024,224,0.69,11.97,5.83
repvgg_b0,5161.18,198.394,1024,224,3.41,6.15,15.82
xcit_tiny_12_p16_224_dist,5107.76,200.467,1024,224,1.24,6.29,6.72
xcit_tiny_12_p16_224,5104.48,200.597,1024,224,1.24,6.29,6.72
resnet26d,5093.48,201.03,1024,224,2.6,8.15,16.01
tf_mixnet_s,4981.9,205.528,1024,224,0.25,6.25,4.13
vit_base_patch32_224_sam,4925.79,207.875,1024,224,4.41,5.01,88.22
vit_base_patch32_224,4923.14,207.986,1024,224,4.41,5.01,88.22
selecsls60,4922.02,208.033,1024,224,3.59,5.52,30.67
mixer_b32_224,4909.46,208.566,1024,224,3.24,6.29,60.29
selecsls60b,4902.59,208.858,1024,224,3.63,5.52,32.77
rexnetr_150,4887.15,209.518,1024,224,0.89,11.13,9.78
nf_resnet26,4834.78,211.787,1024,224,2.41,7.35,16.0
darknet21,4804.05,159.854,768,256,3.93,7.47,20.86
resmlp_12_distilled_224,4801.62,213.251,1024,224,3.01,5.5,15.35
resmlp_12_224,4801.47,213.257,1024,224,3.01,5.5,15.35
efficientnet_lite2,4791.14,213.716,1024,260,0.89,12.9,6.09
pit_xs_224,4790.75,213.733,1024,224,1.4,7.71,10.62
fbnetv3_d,4788.83,213.819,1024,256,0.68,11.1,10.31
pit_xs_distilled_224,4740.73,215.989,1024,224,1.41,7.76,11.0
dla34,4712.5,217.283,1024,224,3.07,5.02,15.74
sedarknet21,4615.23,166.394,768,256,3.93,7.47,20.95
resnext26ts,4512.74,226.902,1024,256,2.43,10.52,10.3
tf_efficientnetv2_b2,4506.54,227.212,1024,260,1.72,9.84,10.1
mixer_s16_224,4471.0,229.021,1024,224,3.79,5.97,18.53
legacy_seresnext26_32x4d,4467.81,229.184,1024,224,2.49,9.39,16.79
edgenext_x_small,4458.42,229.664,1024,256,0.68,7.5,2.34
gernet_l,4450.89,230.055,1024,256,4.57,8.0,31.08
tf_efficientnet_b1,4403.29,232.542,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ns,4402.75,232.57,1024,240,0.71,10.88,7.79
tf_efficientnet_b1_ap,4402.24,232.597,1024,240,0.71,10.88,7.79
eca_resnext26ts,4354.84,235.13,1024,256,2.43,10.52,10.3
seresnext26ts,4350.24,235.378,1024,256,2.43,10.52,10.39
tf_efficientnet_lite2,4300.72,238.087,1024,260,0.89,12.9,6.09
gcresnext26ts,4295.78,238.361,1024,256,2.43,10.53,10.48
rexnet_130,4282.77,239.085,1024,224,0.68,9.71,7.56
efficientnet_b1,4273.32,239.615,1024,256,0.77,12.22,7.79
gmlp_ti16_224,4143.15,247.143,1024,224,1.34,7.55,5.87
efficientnet_b0_g16_evos,4127.77,248.064,1024,224,1.01,7.42,8.11
crossvit_tiny_240,4122.07,248.408,1024,240,1.57,9.08,7.01
nf_ecaresnet26,4104.24,249.486,1024,224,2.41,7.36,16.0
efficientnet_b2_pruned,4103.05,249.558,1024,260,0.73,9.13,8.31
nf_seresnet26,4102.39,249.599,1024,224,2.41,7.36,17.4
mobilevitv2_075,4066.07,251.829,1024,256,1.05,12.06,2.87
vit_small_patch32_384,4025.63,254.359,1024,384,3.45,8.25,22.92
ecaresnext50t_32x4d,4000.29,255.97,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,3998.13,256.108,1024,224,2.7,10.09,15.41
seresnext26tn_32x4d,3995.56,256.274,1024,224,2.7,10.09,16.81
seresnext26t_32x4d,3993.92,256.378,1024,224,2.7,10.09,16.81
seresnext26d_32x4d,3982.98,257.083,1024,224,2.73,10.19,16.81
resnet26t,3922.56,261.042,1024,256,3.35,10.52,16.01
dla46x_c,3917.72,261.363,1024,224,0.54,5.66,1.07
rexnet_150,3870.49,264.554,1024,224,0.9,11.21,9.73
resnetv2_50,3868.33,264.701,1024,224,4.11,11.11,25.55
crossvit_9_240,3865.11,264.922,1024,240,1.85,9.52,8.55
convnext_nano_ols,3859.14,265.333,1024,224,2.5,8.37,15.6
nf_regnet_b0,3819.49,268.086,1024,256,0.64,5.58,8.76
ecaresnet50d_pruned,3812.92,268.549,1024,224,2.53,6.43,19.94
regnetx_016,3808.7,268.846,1024,224,1.62,7.93,9.19
crossvit_9_dagger_240,3792.18,270.018,1024,240,1.99,9.97,8.78
dla60x_c,3787.69,270.337,1024,224,0.59,6.01,1.32
convnext_nano_hnf,3786.83,270.399,1024,224,2.46,8.37,15.59
ecaresnetlight,3729.3,274.57,1024,224,4.11,8.42,30.16
poolformer_s12,3722.39,275.081,1024,224,1.82,5.53,11.92
gmixer_12_224,3686.02,277.795,1024,224,2.67,7.26,12.7
gluon_resnet50_v1b,3677.5,278.438,1024,224,4.11,11.11,25.56
resnet50,3676.35,278.526,1024,224,4.11,11.11,25.56
ssl_resnet50,3674.86,278.638,1024,224,4.11,11.11,25.56
tv_resnet50,3673.31,278.756,1024,224,4.11,11.11,25.56
swsl_resnet50,3672.81,278.794,1024,224,4.11,11.11,25.56
dpn68,3650.67,280.484,1024,224,2.35,10.47,12.61
dpn68b,3606.63,283.907,1024,224,2.35,10.47,12.61
botnet26t_256,3555.59,287.983,1024,256,3.32,11.98,12.49
regnety_016,3516.07,291.222,1024,224,1.63,8.04,11.2
repvgg_a2,3514.85,291.324,1024,224,5.7,6.26,28.21
resnetv2_50t,3513.08,291.47,1024,224,4.32,11.82,25.57
efficientnet_em,3504.92,292.149,1024,240,3.04,14.34,6.9
mixnet_m,3496.31,292.868,1024,224,0.36,8.19,5.01
resnetv2_50d,3496.21,292.876,1024,224,4.35,11.92,25.57
rexnetr_200,3492.0,219.921,768,224,1.59,15.11,16.52
halonet26t,3480.88,294.167,1024,256,3.19,11.69,12.48
gluon_resnet50_v1c,3476.97,294.498,1024,224,4.35,11.92,25.58
resnet32ts,3465.97,295.433,1024,256,4.63,11.58,17.96
bat_resnext26ts,3457.26,296.173,1024,256,2.53,12.51,10.73
resnet33ts,3415.09,299.834,1024,256,4.76,11.66,19.68
tf_efficientnet_b2_ns,3414.14,299.918,1024,260,1.02,13.83,9.11
tf_efficientnet_b2,3412.6,300.052,1024,260,1.02,13.83,9.11
tf_efficientnet_b2_ap,3412.15,300.092,1024,260,1.02,13.83,9.11
tf_efficientnet_em,3389.12,302.132,1024,240,3.04,14.34,6.9
resnet50t,3345.79,306.045,1024,224,4.32,11.82,25.57
gluon_resnet50_v1d,3337.44,306.809,1024,224,4.35,11.92,25.58
resnet50d,3336.65,306.882,1024,224,4.35,11.92,25.58
vit_tiny_r_s16_p8_384,3314.06,154.482,512,384,1.34,6.49,6.36
legacy_seresnet50,3311.17,309.245,1024,224,3.88,10.6,28.09
seresnet33ts,3304.6,309.859,1024,256,4.76,11.66,19.78
tf_mixnet_m,3303.11,309.997,1024,224,0.36,8.19,5.01
eca_resnet33ts,3297.96,310.484,1024,256,4.76,11.66,19.68
convit_tiny,3289.83,311.251,1024,224,1.26,7.94,5.71
gcresnet33ts,3263.32,313.778,1024,256,4.76,11.68,19.88
vit_small_resnet26d_224,3252.42,314.83,1024,224,5.07,11.12,63.61
vovnet39a,3229.96,317.018,1024,224,7.09,6.73,22.6
efficientnet_b2a,3221.33,317.868,1024,288,1.12,16.2,9.11
efficientnet_b2,3221.08,317.894,1024,288,1.12,16.2,9.11
efficientnet_b3_pruned,3195.08,320.48,1024,300,1.04,11.86,9.86
seresnet50,3183.22,321.675,1024,224,4.11,11.13,28.09
cs3darknet_l,3171.55,322.859,1024,288,6.16,10.83,21.16
cs3darknet_focus_l,3146.07,325.473,1024,288,5.9,10.16,21.15
res2net50_48w_2s,3141.04,325.995,1024,224,4.18,11.72,25.29
eca_vovnet39b,3123.78,327.796,1024,224,7.09,6.74,22.6
ese_vovnet39b,3117.85,328.419,1024,224,7.09,6.74,24.57
mobilevit_xs,3095.24,248.113,768,256,1.05,16.33,2.32
resnext50_32x4d,3092.67,331.094,1024,224,4.26,14.4,25.03
ssl_resnext50_32x4d,3089.61,331.422,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,3087.43,331.656,1024,224,4.26,14.4,25.03
swsl_resnext50_32x4d,3086.28,331.779,1024,224,4.26,14.4,25.03
tv_resnext50_32x4d,3085.42,331.872,1024,224,4.26,14.4,25.03
hrnet_w18_small_v2,3075.57,332.934,1024,224,2.62,9.65,15.6
eca_botnext26ts_256,3069.01,333.646,1024,256,2.46,11.6,10.59
dla60,3049.62,335.767,1024,224,4.26,10.16,22.04
vgg11,3048.59,167.933,512,224,7.61,7.44,132.86
skresnet34,3035.0,337.386,1024,224,3.67,5.13,22.28
mobilevitv2_100,3028.13,253.611,768,256,1.84,16.08,4.9
vit_small_patch16_224,3005.65,340.679,1024,224,4.61,11.95,22.05
deit_small_patch16_224,3005.16,340.735,1024,224,4.61,11.95,22.05
eca_halonext26ts,2980.4,343.567,1024,256,2.44,11.46,10.76
resnetaa50d,2972.16,344.518,1024,224,5.39,12.44,25.58
ecaresnet101d_pruned,2963.47,345.529,1024,224,3.48,7.69,24.88
coat_lite_tiny,2957.59,346.216,1024,224,1.6,11.65,5.72
cs3sedarknet_l,2955.87,346.418,1024,288,6.16,10.83,21.91
deit_small_distilled_patch16_224,2950.21,347.082,1024,224,4.63,12.02,22.44
gluon_resnet50_v1s,2947.99,347.343,1024,224,5.47,13.52,25.68
resnetrs50,2945.06,347.688,1024,224,4.48,12.14,35.69
seresnet50t,2939.07,348.397,1024,224,4.32,11.83,28.1
densenet121,2936.15,348.744,1024,224,2.87,6.9,7.98
pit_s_224,2935.87,348.777,1024,224,2.88,11.56,23.46
ecaresnet50d,2934.67,348.92,1024,224,4.35,11.93,25.58
tv_densenet121,2927.3,349.799,1024,224,2.87,6.9,7.98
selecsls84,2927.1,349.822,1024,224,5.9,7.57,50.95
pit_s_distilled_224,2909.88,351.892,1024,224,2.9,11.64,24.04
vit_relpos_base_patch32_plus_rpn_256,2858.63,358.203,1024,256,7.68,8.01,119.42
deit3_small_patch16_224_in21ft1k,2858.2,358.255,1024,224,4.61,11.95,22.06
deit3_small_patch16_224,2853.97,358.786,1024,224,4.61,11.95,22.06
resnext50d_32x4d,2849.78,359.313,1024,224,4.5,15.2,25.05
vit_relpos_small_patch16_rpn_224,2814.18,363.86,1024,224,4.59,13.05,21.97
densenet121d,2808.28,364.624,1024,224,3.11,7.7,8.0
cspresnet50,2805.19,365.024,1024,256,4.54,11.5,21.62
vit_relpos_small_patch16_224,2800.45,365.643,1024,224,4.59,13.05,21.98
gcresnext50ts,2798.56,365.89,1024,256,3.75,15.46,15.67
vit_srelpos_small_patch16_224,2795.09,366.343,1024,224,4.59,12.16,21.97
coat_lite_mini,2774.65,369.044,1024,224,2.0,12.25,11.01
haloregnetz_b,2774.49,369.064,1024,224,1.97,11.94,11.68
vit_base_patch32_plus_256,2772.64,369.31,1024,256,7.79,7.76,119.48
rexnet_200,2762.14,278.034,768,224,1.56,14.91,16.37
res2net50_26w_4s,2757.69,371.313,1024,224,4.28,12.61,25.7
seresnext50_32x4d,2741.98,373.44,1024,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,2737.7,374.024,1024,224,4.26,14.42,27.56
legacy_seresnext50_32x4d,2737.27,374.083,1024,224,4.26,14.42,27.56
xcit_tiny_24_p16_224_dist,2733.14,374.648,1024,224,2.34,11.82,12.12
xcit_tiny_24_p16_224,2731.46,374.879,1024,224,2.34,11.82,12.12
xcit_nano_12_p16_384_dist,2727.34,375.445,1024,384,1.64,12.15,3.05
dla60x,2724.01,375.903,1024,224,3.54,13.8,17.35
gcresnet50t,2718.77,376.628,1024,256,5.42,14.67,25.9
vgg11_bn,2701.91,189.486,512,224,7.62,7.44,132.87
visformer_small,2695.9,379.825,1024,224,4.88,11.43,40.22
lambda_resnet26rpt_256,2689.85,380.679,1024,256,3.16,11.87,10.99
mixnet_l,2682.98,286.237,768,224,0.58,10.84,7.33
resnetblur50,2682.32,381.746,1024,224,5.16,12.02,25.56
vovnet57a,2674.97,382.797,1024,224,8.95,7.52,36.64
efficientnet_lite3,2659.04,192.54,512,300,1.65,21.85,8.2
cspresnet50d,2649.78,386.434,1024,256,4.86,12.55,21.64
seresnetaa50d,2644.86,387.154,1024,224,5.4,12.46,28.11
efficientnetv2_rw_t,2633.28,388.856,1024,288,3.19,16.42,13.65
cspresnet50w,2624.25,390.195,1024,256,5.04,12.19,28.12
twins_svt_small,2599.22,393.951,1024,224,2.94,13.75,24.06
tf_efficientnetv2_b3,2587.08,395.8,1024,300,3.04,15.74,14.36
nf_regnet_b2,2583.08,396.413,1024,272,1.22,9.27,14.31
vit_base_resnet26d_224,2575.01,397.656,1024,224,6.97,13.16,101.4
ese_vovnet57b,2572.63,398.024,1024,224,8.95,7.52,38.61
fbnetv3_g,2570.51,398.353,1024,288,1.77,21.09,16.62
gc_efficientnetv2_rw_t,2557.73,400.342,1024,288,3.2,16.45,13.68
tf_mixnet_l,2550.98,301.047,768,224,0.58,10.84,7.33
nf_regnet_b1,2527.71,405.098,1024,288,1.02,9.2,10.22
res2net50_14w_8s,2514.56,407.217,1024,224,4.21,13.28,25.06
inception_v3,2512.32,407.579,1024,299,5.73,8.97,23.83
densenetblur121d,2509.93,407.967,1024,224,3.11,7.9,8.0
adv_inception_v3,2509.35,408.057,1024,299,5.73,8.97,23.83
tf_inception_v3,2505.31,408.714,1024,299,5.73,8.97,23.83
gluon_inception_v3,2501.93,409.271,1024,299,5.73,8.97,23.83
resnetblur50d,2498.32,409.863,1024,224,5.4,12.82,25.58
nf_ecaresnet50,2492.25,410.862,1024,224,4.21,11.13,25.56
nf_seresnet50,2488.35,411.506,1024,224,4.21,11.13,28.09
resmlp_24_224,2465.19,415.371,1024,224,5.96,10.91,30.02
resmlp_24_distilled_224,2463.93,415.584,1024,224,5.96,10.91,30.02
mobilevit_s,2450.02,313.456,768,256,2.03,19.94,5.58
regnetx_032,2449.76,417.988,1024,224,3.2,11.37,15.3
cspresnext50,2440.84,419.515,1024,256,4.05,15.86,20.57
dla60_res2net,2430.52,421.296,1024,224,4.15,12.34,20.85
resnest14d,2424.63,422.32,1024,224,2.76,7.33,10.61
densenet169,2421.68,422.835,1024,224,3.4,7.3,14.15
convnext_tiny_hnfd,2418.1,423.46,1024,224,4.47,13.44,28.59
convnext_tiny_hnf,2414.45,424.101,1024,224,4.47,13.44,28.59
tf_efficientnet_lite3,2396.12,213.668,512,300,1.65,21.85,8.2
sehalonet33ts,2392.73,427.951,1024,256,3.55,14.7,13.69
efficientnet_cc_b0_4e,2389.83,428.47,1024,224,0.41,9.42,13.31
efficientnet_cc_b0_8e,2386.88,429.0,1024,224,0.42,9.42,24.01
convnext_tiny_in22ft1k,2380.94,430.068,1024,224,4.47,13.44,28.59
convnext_tiny,2379.5,430.329,1024,224,4.47,13.44,28.59
regnetz_b16,2348.93,435.929,1024,288,2.39,16.43,9.72
resnetv2_101,2321.74,441.036,1024,224,7.83,16.23,44.54
convnext_nano,2304.31,444.37,1024,288,4.06,13.84,15.59
tf_efficientnet_cc_b0_4e,2293.39,446.488,1024,224,0.41,9.42,13.31
semobilevit_s,2279.96,336.836,768,256,2.03,19.95,5.74
gluon_resnet101_v1b,2249.95,455.108,1024,224,7.83,16.23,44.55
tv_resnet101,2246.24,455.861,1024,224,7.83,16.23,44.55
resnet101,2246.09,455.891,1024,224,7.83,16.23,44.55
mobilevitv2_125,2233.52,343.842,768,256,2.86,20.1,7.48
skresnet50,2232.97,458.569,1024,224,4.11,12.5,25.8
ecaresnet26t,2203.93,464.611,1024,320,5.24,16.44,16.01
resnetv2_101d,2180.36,469.635,1024,224,8.07,17.04,44.56
gluon_resnet101_v1c,2174.26,470.952,1024,224,8.08,17.04,44.57
twins_pcpvt_small,2160.75,473.897,1024,224,3.83,18.08,24.11
xcit_small_12_p16_224_dist,2141.07,478.253,1024,224,4.82,12.58,26.25
xcit_small_12_p16_224,2140.23,478.441,1024,224,4.82,12.58,26.25
cs3darknet_focus_x,2138.85,478.75,1024,256,8.03,10.69,35.02
edgenext_small,2120.03,482.998,1024,320,1.97,14.16,5.59
gluon_resnet101_v1d,2116.52,483.8,1024,224,8.08,17.04,44.57
tf_efficientnet_cc_b0_8e,2114.86,484.181,1024,224,0.42,9.42,24.01
vgg13,2106.04,486.207,1024,224,11.31,12.25,133.05
skresnet50d,2104.19,486.637,1024,224,4.36,13.31,25.82
xcit_nano_12_p8_224_dist,2091.47,489.594,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,2088.8,490.222,1024,224,2.16,15.71,3.05
sebotnet33ts_256,2061.15,248.392,512,256,3.89,17.46,13.7
efficientnet_b0_gn,2058.74,373.032,768,224,0.42,6.75,5.29
wide_resnet50_2,2057.11,497.774,1024,224,11.43,14.4,68.88
dla102,2034.05,503.416,1024,224,7.19,14.18,33.27
vit_base_resnet50d_224,2032.15,503.887,1024,224,8.73,16.92,110.97
resnet51q,2003.15,511.181,1024,288,8.07,20.94,35.7
legacy_seresnet101,1997.44,512.643,1024,224,7.61,15.74,49.33
regnetx_040,1987.99,515.08,1024,224,3.99,12.2,22.12
gmlp_s16_224,1983.97,516.124,1024,224,4.42,15.1,19.42
res2net50_26w_6s,1970.9,519.546,1024,224,6.33,15.28,37.05
resnetaa101d,1964.05,521.361,1024,224,9.12,17.56,44.57
gluon_resnet101_v1s,1954.76,523.835,1024,224,9.19,18.64,44.67
seresnet101,1951.77,524.639,1024,224,7.84,16.27,49.33
repvgg_b1,1949.45,525.265,1024,224,13.16,10.64,57.42
crossvit_small_240,1948.12,525.623,1024,240,5.63,18.17,26.86
cs3sedarknet_xdw,1942.13,527.244,1024,256,5.97,17.18,21.6
resnetaa50,1934.76,529.254,1024,288,8.52,19.24,25.56
swin_tiny_patch4_window7_224,1924.88,531.966,1024,224,4.51,17.06,28.29
resnext101_32x4d,1924.17,532.166,1024,224,8.01,21.23,44.18
ssl_resnext101_32x4d,1923.99,532.215,1024,224,8.01,21.23,44.18
poolformer_s24,1923.48,532.355,1024,224,3.41,10.68,21.39
gluon_resnext101_32x4d,1922.64,532.587,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1922.44,532.644,1024,224,8.01,21.23,44.18
vit_relpos_medium_patch16_cls_224,1918.8,533.655,1024,224,8.03,18.24,38.76
vit_relpos_medium_patch16_rpn_224,1917.82,533.927,1024,224,7.97,17.02,38.73
vit_relpos_medium_patch16_224,1911.97,535.56,1024,224,7.97,17.02,38.75
vit_srelpos_medium_patch16_224,1908.42,536.559,1024,224,7.96,16.21,38.74
resnest50d_1s4x24d,1891.51,541.355,1024,224,4.43,13.57,25.68
darknet53,1883.2,407.805,768,288,11.78,15.68,41.61
gmixer_24_224,1881.95,544.104,1024,224,5.28,14.45,24.72
darknetaa53,1875.8,409.414,768,288,10.08,15.68,36.02
densenet201,1868.67,547.971,1024,224,4.34,7.85,20.01
halonet50ts,1867.02,548.456,1024,256,5.3,19.2,22.73
mobilevitv2_150,1865.71,274.416,512,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,1864.94,274.529,512,256,4.09,24.11,10.59
tf_efficientnet_b3_ns,1862.24,274.927,512,300,1.87,23.83,12.23
tf_efficientnet_b3,1861.19,275.081,512,300,1.87,23.83,12.23
tf_efficientnet_b3_ap,1860.71,275.153,512,300,1.87,23.83,12.23
nf_resnet101,1854.11,552.273,1024,224,8.01,16.23,44.55
dla102x,1853.94,552.322,1024,224,5.89,19.42,26.31
ecaresnet101d,1853.67,552.405,1024,224,8.08,17.07,44.57
vgg13_bn,1850.18,276.718,512,224,11.33,12.25,133.05
cspdarknet53,1837.13,418.032,768,256,6.57,16.81,27.64
efficientnet_b3a,1829.01,279.921,512,320,2.01,26.52,12.23
efficientnet_b3,1828.85,279.946,512,320,2.01,26.52,12.23
mixnet_xl,1821.62,281.057,512,224,0.93,14.57,11.9
resnet61q,1810.56,565.559,1024,288,9.87,21.52,36.85
vit_small_r26_s32_224,1806.33,566.883,1024,224,3.56,9.85,36.43
xcit_tiny_12_p16_384_dist,1805.03,567.29,1024,384,3.64,18.26,6.72
edgenext_small_rw,1803.36,567.813,1024,320,2.46,14.85,7.83
crossvit_15_240,1792.63,571.217,1024,240,5.81,19.77,27.53
resnest26d,1781.59,574.753,1024,224,3.64,9.97,17.07
nf_resnet50,1773.35,577.425,1024,288,6.88,18.37,25.56
hrnet_w18,1766.17,579.773,1024,224,4.32,16.31,21.3
crossvit_15_dagger_240,1755.64,583.25,1024,240,6.13,20.43,28.21
swin_s3_tiny_224,1746.31,586.364,1024,224,4.64,19.13,28.33
resnetblur101d,1744.07,587.12,1024,224,9.12,17.94,44.57
res2net101_26w_4s,1715.56,596.875,1024,224,8.1,18.45,45.21
cait_xxs24_224,1707.34,599.75,1024,224,2.53,20.29,11.96
nf_regnet_b3,1702.33,601.516,1024,320,2.05,14.61,18.59
seresnext101_32x4d,1701.46,601.825,1024,224,8.02,21.26,48.96
gluon_seresnext101_32x4d,1701.17,601.927,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1697.89,603.09,1024,224,8.02,21.26,48.96
resnetv2_50d_frn,1691.2,605.475,1024,224,4.33,11.92,25.59
vgg16,1690.51,605.724,1024,224,15.47,13.56,138.36
repvgg_b1g4,1670.27,613.064,1024,224,8.15,10.64,39.97
res2net50_26w_8s,1662.2,616.038,1024,224,8.37,17.95,48.4
resmlp_36_224,1656.29,618.237,1024,224,8.91,16.33,44.69
resmlp_36_distilled_224,1655.44,618.553,1024,224,8.91,16.33,44.69
regnetz_c16,1654.15,309.514,512,320,3.92,25.88,13.46
sequencer2d_s,1646.91,621.756,1024,224,4.96,11.31,27.65
efficientnet_b0_g8_gn,1636.03,469.418,768,224,0.66,6.75,6.56
botnet50ts_256,1633.5,313.424,512,256,5.54,22.23,22.74
vit_large_patch32_224,1629.91,628.244,1024,224,15.39,13.3,306.54
ese_vovnet39b_evos,1627.7,629.096,1024,224,7.07,6.74,24.58
cs3darknet_x,1627.54,629.156,1024,288,10.6,14.36,35.05
resnetv2_50d_evob,1620.95,631.713,1024,224,4.33,11.92,25.59
efficientnet_cc_b1_8e,1619.01,632.471,1024,240,0.75,15.44,39.72
resnetv2_152,1614.21,634.351,1024,224,11.55,22.56,60.19
xception41p,1587.33,322.543,512,299,9.25,39.86,26.91
regnetx_064,1586.15,484.18,768,224,6.49,16.37,26.21
coat_lite_small,1583.04,646.84,1024,224,3.96,22.09,19.84
swinv2_cr_tiny_224,1581.48,647.481,1024,224,4.66,28.45,28.33
xception,1578.2,486.619,768,299,8.4,35.83,22.86
gluon_resnet152_v1b,1573.68,650.689,1024,224,11.56,22.56,60.19
tv_resnet152,1573.22,650.883,1024,224,11.56,22.56,60.19
resnetv2_50x1_bit_distilled,1572.94,650.997,1024,224,4.23,11.11,25.55
resnet152,1571.71,651.505,1024,224,11.56,22.56,60.19
tf_efficientnet_cc_b1_8e,1564.9,654.344,1024,240,0.75,15.44,39.72
halo2botnet50ts_256,1559.2,656.732,1024,256,5.02,21.78,22.64
mixer_l32_224,1558.59,656.992,1024,224,11.27,19.86,206.94
vit_tiny_patch16_384,1557.53,657.441,1024,384,4.7,25.39,5.79
mobilevitv2_175,1554.07,329.447,512,256,5.54,28.13,14.25
swinv2_cr_tiny_ns_224,1551.84,659.847,1024,224,4.66,28.45,28.33
mobilevitv2_175_in22ft1k,1551.58,329.973,512,256,5.54,28.13,14.25
vit_base_patch32_384,1550.87,660.263,1024,384,13.06,16.5,88.3
cs3sedarknet_x,1549.02,661.048,1024,288,10.6,14.37,35.4
resnetv2_152d,1545.41,662.596,1024,224,11.8,23.36,60.2
nf_ecaresnet101,1540.66,664.639,1024,224,8.01,16.27,44.55
nf_seresnet101,1538.7,665.483,1024,224,8.02,16.27,49.33
gluon_resnet152_v1c,1536.27,666.538,1024,224,11.8,23.36,60.21
efficientnet_el,1524.59,335.818,512,300,8.0,30.7,10.59
efficientnet_el_pruned,1523.29,336.105,512,300,8.0,30.7,10.59
gluon_resnet152_v1d,1508.62,678.753,1024,224,11.8,23.36,60.21
vgg16_bn,1504.44,340.315,512,224,15.5,13.56,138.37
twins_pcpvt_base,1494.56,685.139,1024,224,6.68,25.25,43.83
tf_efficientnet_el,1481.51,345.582,512,300,8.0,30.7,10.59
cs3edgenet_x,1479.52,692.101,1024,288,14.59,16.36,47.82
vit_base_r26_s32_224,1479.31,692.202,1024,224,6.81,12.36,101.38
skresnext50_32x4d,1465.64,698.657,1024,224,4.5,17.18,27.48
convnext_small,1452.43,705.012,1024,224,8.71,21.56,50.22
convnext_small_in22ft1k,1450.42,705.987,1024,224,8.71,21.56,50.22
hrnet_w32,1444.27,708.991,1024,224,8.97,22.02,41.23
ese_vovnet99b,1442.7,709.767,1024,224,16.51,11.27,63.2
mixer_b16_224,1424.87,718.648,1024,224,12.62,14.53,59.88
gluon_resnet152_v1s,1424.84,718.665,1024,224,12.92,24.96,60.32
ecaresnet50t,1422.0,720.1,1024,320,8.82,24.13,25.57
mixer_b16_224_miil,1421.45,720.381,1024,224,12.62,14.53,59.88
vgg19,1411.57,725.42,1024,224,19.63,14.86,143.67
regnety_032,1398.62,732.137,1024,288,5.29,18.61,19.44
convit_small,1398.4,732.254,1024,224,5.76,17.87,27.78
nest_tiny,1387.46,553.516,768,224,5.83,25.48,17.06
legacy_seresnet152,1382.46,740.694,1024,224,11.33,22.08,66.82
dla169,1382.4,740.729,1024,224,11.6,20.2,53.39
xcit_tiny_12_p8_224,1374.74,744.858,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224_dist,1374.04,745.236,1024,224,4.81,23.6,6.71
densenet161,1366.11,749.563,1024,224,7.79,11.06,28.68
jx_nest_tiny,1362.5,563.656,768,224,5.83,25.48,17.06
seresnet152,1361.36,752.174,1024,224,11.57,22.61,66.82
mobilevitv2_200_in22ft1k,1354.63,283.461,384,256,7.22,32.15,18.45
mobilevitv2_200,1354.25,283.54,384,256,7.22,32.15,18.45
xception41,1347.67,379.903,512,299,9.28,39.86,26.97
inception_v4,1323.37,773.767,1024,299,12.28,15.09,42.68
twins_svt_base,1316.07,778.059,1024,224,8.59,26.33,56.07
vit_small_resnet50d_s16_224,1305.38,784.435,1024,224,13.48,24.82,57.53
dpn92,1303.44,785.601,1024,224,6.54,18.21,37.67
tresnet_m,1297.91,788.947,1024,224,5.74,7.31,31.39
poolformer_s36,1296.72,789.674,1024,224,5.0,15.82,30.86
sequencer2d_m,1285.21,796.745,1024,224,6.55,14.26,38.31
crossvit_18_240,1273.5,804.072,1024,240,9.05,26.26,43.27
regnetx_080,1271.94,805.056,1024,224,8.02,14.06,39.57
dla102x2,1271.93,402.524,512,224,9.34,29.91,41.28
vgg19_bn,1265.83,404.467,512,224,19.66,14.86,143.68
efficientnet_lite4,1263.67,303.867,384,380,4.04,45.66,13.01
crossvit_18_dagger_240,1245.15,822.375,1024,240,9.5,27.03,44.27
res2next50,1241.53,824.775,1024,224,4.2,13.71,24.67
volo_d1_224,1235.2,829.0,1024,224,6.94,24.43,26.63
efficientnetv2_s,1221.73,838.141,1024,384,8.44,35.77,21.46
resnest50d,1214.35,843.233,1024,224,5.4,14.36,27.48
tf_efficientnetv2_s_in21ft1k,1191.03,859.741,1024,384,8.44,35.77,21.46
tf_efficientnetv2_s,1191.03,859.748,1024,384,8.44,35.77,21.46
dpn98,1188.11,861.858,1024,224,11.73,25.2,61.57
mixnet_xxl,1187.83,323.267,384,224,2.04,23.43,23.96
swin_small_patch4_window7_224,1183.48,865.227,1024,224,8.77,27.47,49.61
regnetz_d8,1180.44,867.458,1024,320,6.19,37.08,23.37
hrnet_w30,1180.28,867.576,1024,224,8.15,21.21,37.71
gluon_resnext101_64x4d,1176.11,870.653,1024,224,15.52,31.21,83.46
efficientnetv2_rw_s,1166.72,877.658,1024,384,8.72,38.03,23.94
tf_efficientnet_lite4,1164.88,329.636,384,380,4.04,45.66,13.01
swinv2_tiny_window8_256,1158.39,883.971,1024,256,5.96,24.57,28.35
wide_resnet101_2,1155.6,886.11,1024,224,22.8,21.23,126.89
repvgg_b2,1154.84,886.691,1024,224,20.45,12.9,89.02
vit_base_patch16_224_miil,1153.59,887.648,1024,224,17.58,23.9,86.54
resnet50_gn,1150.43,890.083,1024,224,4.14,11.11,25.56
resnet200,1149.46,890.84,1024,224,15.07,32.19,64.67
cait_xxs36_224,1148.13,891.873,1024,224,3.77,30.34,17.3
xception65p,1140.81,448.791,512,299,13.91,52.48,39.82
regnetz_040,1140.64,336.641,384,320,6.35,37.78,27.12
xcit_small_24_p16_224_dist,1137.7,900.049,1024,224,9.1,23.64,47.67
xcit_small_24_p16_224,1136.58,900.934,1024,224,9.1,23.64,47.67
regnetz_040h,1135.68,338.111,384,320,6.43,37.94,28.94
deit_base_patch16_224,1133.84,903.113,1024,224,17.58,23.9,86.57
vit_base_patch16_224,1133.45,903.419,1024,224,17.58,23.9,86.57
vit_base_patch16_224_sam,1132.11,904.493,1024,224,17.58,23.9,86.57
regnetz_d32,1128.13,907.679,1024,320,9.33,37.08,27.58
dla60_res2next,1127.83,907.922,1024,224,3.49,13.17,17.03
eca_nfnet_l0,1126.64,908.88,1024,288,7.12,17.29,24.14
resnetrs101,1123.2,911.667,1024,288,13.56,28.53,63.62
nfnet_l0,1123.1,911.747,1024,288,7.13,17.29,35.07
cs3se_edgenet_x,1120.46,913.898,1024,320,18.01,20.21,50.72
deit_base_distilled_patch16_224,1119.36,914.798,1024,224,17.68,24.05,87.34
vit_base_patch16_rpn_224,1111.53,921.235,1024,224,17.49,23.75,86.54
inception_resnet_v2,1108.79,923.511,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,1107.31,924.747,1024,299,13.18,25.06,55.84
deit3_base_patch16_224,1093.88,936.101,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1092.45,937.33,1024,224,17.58,23.9,86.59
gluon_seresnext101_64x4d,1088.94,940.353,1024,224,15.53,31.25,88.23
vit_relpos_base_patch16_clsgap_224,1081.95,946.422,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1081.77,946.586,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_rpn_224,1080.35,947.826,1024,224,17.51,24.97,86.41
vit_relpos_base_patch16_224,1079.52,948.56,1024,224,17.51,24.97,86.43
tnt_s_patch16_224,1078.48,949.47,1024,224,5.24,24.37,23.76
twins_pcpvt_large,1066.87,959.801,1024,224,9.84,35.82,60.99
ssl_resnext101_32x8d,1054.92,970.677,1024,224,16.48,31.21,88.79
resnext101_32x8d,1054.71,970.869,1024,224,16.48,31.21,88.79
ig_resnext101_32x8d,1054.19,971.349,1024,224,16.48,31.21,88.79
swsl_resnext101_32x8d,1053.69,971.812,1024,224,16.48,31.21,88.79
beit_base_patch16_224,1049.2,975.962,1024,224,17.58,23.9,86.53
resnest50d_4s2x40d,1042.05,982.666,1024,224,4.4,17.94,30.42
coat_tiny,1040.04,984.566,1024,224,4.35,27.2,5.5
resnet101d,1029.19,994.942,1024,320,16.48,34.77,44.57
convnext_base,1012.72,1011.124,1024,224,15.38,28.75,88.59
convnext_base_in22ft1k,1011.75,1012.088,1024,224,15.38,28.75,88.59
efficientnet_b4,993.87,386.356,384,384,4.51,50.04,19.34
pit_b_224,992.43,515.895,512,224,12.42,32.94,73.76
pit_b_distilled_224,988.79,517.791,512,224,12.5,33.07,74.79
gluon_xception65,981.26,521.764,512,299,13.96,52.48,39.92
xception65,975.37,524.918,512,299,13.96,52.48,39.92
vit_small_patch16_36x1_224,973.92,1051.404,1024,224,13.71,35.69,64.67
repvgg_b3,972.06,1053.416,1024,224,29.16,15.1,123.09
repvgg_b2g4,969.39,1056.316,1024,224,12.63,12.9,61.76
xcit_tiny_24_p16_384_dist,969.37,1056.345,1024,384,6.87,34.29,12.12
swinv2_cr_small_224,967.97,1057.869,1024,224,9.07,50.27,49.7
swinv2_cr_small_ns_224,957.86,1069.034,1024,224,9.08,50.27,49.7
vit_small_patch16_18x2_224,951.03,1076.715,1024,224,13.71,35.69,64.67
twins_svt_large,923.66,1108.616,1024,224,15.15,35.1,99.27
tf_efficientnet_b4,922.69,416.164,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ap,922.5,416.247,384,380,4.49,49.49,19.34
tf_efficientnet_b4_ns,922.41,416.289,384,380,4.49,49.49,19.34
hrnet_w40,910.46,1124.691,1024,224,12.75,25.29,57.56
regnetz_b16_evos,903.54,849.98,768,288,2.36,16.43,9.74
cait_s24_224,902.24,1134.941,1024,224,9.35,40.58,46.92
nfnet_f0,901.6,1135.748,1024,256,12.62,18.05,71.49
nf_regnet_b4,900.78,1136.78,1024,384,4.7,28.61,30.21
poolformer_m36,896.53,1142.174,1024,224,8.8,22.02,56.17
nest_small,884.77,868.006,768,224,10.35,40.04,38.35
hrnet_w48,875.56,1169.527,1024,224,17.34,28.56,77.47
jx_nest_small,874.29,878.41,768,224,10.35,40.04,38.35
dpn131,866.66,1181.531,1024,224,16.09,32.97,79.25
swin_s3_small_224,854.8,898.443,768,224,9.43,37.84,49.74
regnety_040,854.48,898.782,768,288,6.61,20.3,20.65
regnetv_040,854.18,899.093,768,288,6.6,20.3,20.64
regnety_080,846.16,605.071,512,288,13.22,29.69,39.18
resnetv2_50d_evos,844.23,1212.929,1024,288,7.15,19.7,25.59
coat_mini,836.76,1223.748,1024,224,6.82,33.68,10.34
swin_base_patch4_window7_224,836.19,1224.59,1024,224,15.47,36.63,87.77
repvgg_b3g4,835.5,1225.597,1024,224,17.89,15.1,83.83
sequencer2d_l,832.91,1229.407,1024,224,9.74,22.12,54.3
dm_nfnet_f0,831.16,1232.004,1024,256,12.62,18.05,71.49
mobilevitv2_150_384_in22ft1k,826.94,309.566,256,384,9.2,54.25,10.59
gmlp_b16_224,825.63,1240.256,1024,224,15.78,30.21,73.08
convnext_tiny_384_in22ft1k,814.55,628.553,512,384,13.14,39.48,28.59
xcit_medium_24_p16_224_dist,812.18,1260.787,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,812.16,1260.822,1024,224,16.13,31.71,84.4
regnetx_120,810.53,631.674,512,224,12.13,21.37,46.11
xcit_small_12_p16_384_dist,800.29,1279.521,1024,384,14.14,36.51,26.25
densenet264,798.84,1281.845,1024,224,12.95,12.8,72.69
hrnet_w44,790.45,1295.441,1024,224,14.94,26.92,67.06
crossvit_base_240,787.5,975.226,768,240,21.22,36.33,105.03
regnety_120,783.87,653.154,512,224,12.14,21.38,51.82
swinv2_tiny_window16_256,765.77,668.599,512,256,6.68,39.02,28.35
resnetv2_50d_gn,751.14,1022.427,768,288,7.24,19.7,25.57
xception71,749.93,682.721,512,299,18.09,69.92,42.34
vit_large_r50_s32_224,739.74,1384.252,1024,224,19.58,24.41,328.99
vit_base_patch16_plus_240,737.95,1387.618,1024,240,27.41,33.08,117.56
dpn107,736.42,1390.497,1024,224,18.38,33.46,86.92
resnet152d,736.09,1391.121,1024,320,24.08,47.67,60.21
ecaresnet200d,730.76,1401.271,1024,256,20.0,43.15,64.69
seresnet200d,728.63,1405.363,1024,256,20.01,43.15,71.86
vit_relpos_base_patch16_plus_240,727.74,1407.074,1024,240,27.3,34.33,117.38
xcit_tiny_24_p8_224,725.69,1411.06,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,725.66,1411.108,1024,224,9.21,45.39,12.11
hrnet_w64,719.99,1422.231,1024,224,28.97,35.09,128.06
regnety_040s_gn,719.08,1068.018,768,224,4.03,12.29,20.65
xcit_nano_12_p8_384_dist,713.49,1435.191,1024,384,6.34,46.08,3.05
swinv2_small_window8_256,712.51,1437.16,1024,256,11.58,40.14,49.73
convit_base,706.35,1449.693,1024,224,17.52,31.77,86.54
resnext101_64x4d,704.57,1090.007,768,288,25.66,51.59,83.46
swin_s3_base_224,695.14,1473.064,1024,224,13.69,48.26,71.13
vit_small_patch16_384,693.62,1107.217,768,384,15.52,50.78,22.2
tnt_b_patch16_224,691.83,1480.107,1024,224,14.09,39.01,65.41
swinv2_cr_base_224,689.84,1484.394,1024,224,15.86,59.66,87.88
regnety_064,684.37,748.122,512,288,10.56,27.11,30.58
swinv2_cr_base_ns_224,683.96,1497.137,1024,224,15.86,59.66,87.88
volo_d2_224,679.94,1506.002,1024,224,14.34,41.34,58.68
mobilevitv2_175_384_in22ft1k,679.77,376.586,256,384,12.47,63.29,14.25
regnetv_064,678.0,755.149,512,288,10.55,27.11,30.58
poolformer_m48,676.06,1514.652,1024,224,11.59,29.17,73.47
deit3_small_patch16_384,669.71,1146.752,768,384,15.52,50.78,22.21
deit3_small_patch16_384_in21ft1k,669.36,1147.345,768,384,15.52,50.78,22.21
legacy_senet154,660.41,1550.529,1024,224,20.77,38.69,115.09
senet154,659.33,1553.081,1024,224,20.77,38.69,115.09
gluon_senet154,659.08,1553.657,1024,224,20.77,38.69,115.09
regnetx_160,650.99,786.486,512,224,15.99,25.52,54.28
resnetrs152,646.22,1584.595,1024,320,24.34,48.14,86.62
seresnet152d,642.19,1594.525,1024,320,24.09,47.72,66.84
regnetz_e8,633.28,1212.715,768,320,15.46,63.94,57.7
tresnet_l,630.57,1623.908,1024,224,10.88,11.9,55.99
nest_base,629.43,813.42,512,224,17.96,53.39,67.72
ese_vovnet99b_iabn,628.09,1630.325,1024,224,16.49,11.27,63.2
jx_nest_base,622.33,822.697,512,224,17.96,53.39,67.72
vit_small_r26_s32_384,613.78,834.168,512,384,10.43,29.85,36.47
vit_base_r50_s16_224,610.7,1676.739,1024,224,21.66,35.29,98.66
xcit_small_12_p8_224,609.86,1679.054,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,609.57,1679.853,1024,224,18.69,47.21,26.21
efficientnetv2_m,603.09,1697.911,1024,416,18.6,67.5,54.14
mobilevitv2_200_384_in22ft1k,594.06,323.186,192,384,16.24,72.34,18.45
seresnext101_32x8d,590.8,1299.927,768,288,27.24,51.63,93.57
resnest101e,588.3,1305.448,768,256,13.38,28.66,48.28
regnetz_c16_evos,588.05,870.656,512,320,3.86,25.88,13.49
convmixer_768_32,578.91,1768.818,1024,224,19.55,25.95,21.11
seresnext101d_32x8d,575.36,1334.812,768,288,27.64,52.95,93.59
seresnet269d,570.63,1794.5,1024,256,26.59,53.6,113.67
convnext_large,561.47,1823.764,1024,224,34.4,43.13,197.77
convnext_large_in22ft1k,560.92,1825.557,1024,224,34.4,43.13,197.77
resnet200d,544.65,1880.08,1024,320,31.25,67.33,64.69
efficientnetv2_rw_m,534.87,1435.852,768,416,21.49,79.62,53.24
seresnextaa101d_32x8d,521.71,1472.057,768,288,28.51,56.44,93.59
vit_large_patch32_384,517.33,1979.373,1024,384,45.31,43.86,306.63
swinv2_base_window8_256,509.01,2011.746,1024,256,20.37,52.59,87.92
eca_nfnet_l1,497.06,2060.113,1024,320,14.92,34.42,41.41
convnext_small_384_in22ft1k,496.5,1031.206,512,384,25.58,63.37,50.22
mixer_l16_224,495.85,2065.122,1024,224,44.6,41.69,208.2
efficientnet_b5,493.19,519.054,256,456,10.46,98.86,30.39
halonet_h1,492.19,520.115,256,256,3.0,51.17,8.1
regnety_320,483.53,1058.87,512,224,32.34,30.26,145.05
swin_large_patch4_window7_224,480.21,1599.271,768,224,34.53,54.94,196.53
swinv2_small_window16_256,477.23,1072.842,512,256,12.82,66.29,49.73
volo_d3_224,474.32,2158.852,1024,224,20.78,60.09,86.33
resnetrs200,471.22,2173.072,1024,320,31.51,67.81,93.21
tf_efficientnet_b5_ns,469.35,545.419,256,456,10.46,98.86,30.39
tf_efficientnet_b5_ap,469.08,545.738,256,456,10.46,98.86,30.39
tf_efficientnet_b5,468.97,545.864,256,456,10.46,98.86,30.39
xcit_tiny_12_p8_384_dist,468.35,2186.374,1024,384,14.13,69.14,6.71
tresnet_xl,466.16,2196.648,1024,224,15.17,15.34,78.44
efficientnet_b3_gn,464.67,550.914,256,320,2.14,28.83,11.73
xcit_large_24_p16_224_dist,454.83,2251.393,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,454.8,2251.543,1024,224,35.86,47.27,189.1
tf_efficientnetv2_m_in21ft1k,444.81,1726.544,768,480,24.76,89.84,54.14
tf_efficientnetv2_m,444.79,1726.633,768,480,24.76,89.84,54.14
xcit_small_24_p16_384_dist,426.37,2401.669,1024,384,26.72,68.58,47.67
regnety_160,422.88,908.045,384,288,26.37,38.07,83.59
nf_regnet_b5,413.3,1238.797,512,456,11.7,61.95,49.74
swinv2_cr_tiny_384,408.94,625.992,256,384,15.34,161.01,28.33
swinv2_cr_large_224,406.2,1890.673,768,224,35.1,78.42,196.68
resnetv2_50x1_bitm,400.73,958.233,384,448,16.62,44.46,25.55
regnetz_d8_evos,392.85,1954.947,768,320,7.03,38.92,23.46
convmixer_1024_20_ks9_p14,390.62,2621.469,1024,224,5.55,5.51,24.38
efficientnet_b3_g8_gn,373.24,685.874,256,320,3.2,28.83,14.25
vit_large_patch16_224,370.72,2762.206,1024,224,61.6,63.52,304.33
convnext_xlarge_in22ft1k,368.8,1388.275,512,224,60.98,57.5,350.2
crossvit_15_dagger_408,368.65,694.421,256,408,21.45,95.05,28.5
vit_base_patch16_18x2_224,361.92,2829.305,1024,224,52.51,71.38,256.73
deit3_large_patch16_224_in21ft1k,358.3,2857.929,1024,224,61.6,63.52,304.37
deit3_large_patch16_224,357.82,2861.791,1024,224,61.6,63.52,304.37
swinv2_base_window16_256,346.22,1109.109,384,256,22.02,84.71,87.92
swinv2_base_window12to16_192to256_22kft1k,345.97,1109.898,384,256,22.02,84.71,87.92
nasnetalarge,345.75,1110.628,384,331,23.89,90.56,88.75
convnext_base_384_in22ft1k,345.74,1110.63,384,384,45.21,84.49,88.59
beit_large_patch16_224,343.01,2985.299,1024,224,61.6,63.52,304.43
ssl_resnext101_32x16d,338.92,1510.65,512,224,36.27,51.18,194.03
ig_resnext101_32x16d,338.75,1511.419,512,224,36.27,51.18,194.03
swsl_resnext101_32x16d,338.52,1512.441,512,224,36.27,51.18,194.03
tresnet_m_448,325.14,3149.347,1024,448,22.94,29.21,31.39
pnasnet5large,323.98,1185.25,384,331,25.04,92.89,86.06
regnetx_320,320.95,1196.437,384,224,31.81,36.3,107.81
xcit_small_24_p8_224,319.26,3207.434,1024,224,35.81,90.78,47.63
xcit_small_24_p8_224_dist,319.16,3208.44,1024,224,35.81,90.78,47.63
volo_d1_384,315.83,1621.098,512,384,22.75,108.55,26.78
nfnet_f1,306.71,3338.679,1024,320,35.97,46.77,132.63
ecaresnet269d,304.87,3358.754,1024,352,50.25,101.25,102.09
volo_d4_224,303.38,3375.331,1024,224,44.34,80.22,192.96
xcit_medium_24_p16_384_dist,297.67,2580.013,768,384,47.39,91.64,84.4
resnetrs270,296.93,3448.599,1024,352,51.13,105.48,129.86
resnetv2_152x2_bit_teacher,290.64,2642.472,768,224,46.95,45.11,236.34
vit_base_patch16_384,289.22,1327.696,384,384,55.54,101.56,86.86
deit_base_patch16_384,289.04,1328.502,384,384,55.54,101.56,86.86
deit_base_distilled_patch16_384,285.12,1346.806,384,384,55.65,101.82,87.63
dm_nfnet_f1,282.58,3623.721,1024,320,35.97,46.77,132.63
deit3_base_patch16_384,281.04,1366.341,384,384,55.54,101.56,86.88
deit3_base_patch16_384_in21ft1k,280.92,1366.918,384,384,55.54,101.56,86.88
efficientnet_b6,279.71,457.611,128,528,19.4,167.39,43.04
cait_xxs24_384,275.0,3723.573,1024,384,9.63,122.66,12.03
vit_large_patch14_224,271.58,3770.566,1024,224,81.08,88.79,304.2
crossvit_18_dagger_408,269.56,712.259,192,408,32.47,124.87,44.61
tf_efficientnet_b6_ap,267.5,478.495,128,528,19.4,167.39,43.04
tf_efficientnet_b6,267.48,478.534,128,528,19.4,167.39,43.04
tf_efficientnet_b6_ns,267.38,478.715,128,528,19.4,167.39,43.04
efficientnetv2_l,254.55,2011.402,512,480,56.4,157.99,118.52
resnetv2_101x1_bitm,252.46,1521.025,384,448,31.65,64.93,44.54
tf_efficientnetv2_l,251.41,2036.496,512,480,56.4,157.99,118.52
tf_efficientnetv2_l_in21ft1k,251.09,2039.122,512,480,56.4,157.99,118.52
swinv2_cr_small_384,250.5,1021.951,256,384,29.7,298.03,49.7
beit_base_patch16_384,248.36,1546.143,384,384,55.54,101.56,86.74
xcit_tiny_24_p8_384_dist,246.6,4152.437,1024,384,27.05,132.95,12.11
vit_large_r50_s32_384,246.12,2080.308,512,384,57.43,76.52,329.09
eca_nfnet_l2,237.09,3239.214,768,384,30.05,68.28,56.72
resmlp_big_24_224,228.25,4486.35,1024,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,227.96,4491.908,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,227.94,4492.471,1024,224,100.23,87.31,129.14
xcit_medium_24_p8_224_dist,222.27,3455.29,768,224,63.53,121.23,84.32
xcit_medium_24_p8_224,222.27,3455.239,768,224,63.53,121.23,84.32
swin_base_patch4_window12_384,221.01,868.732,192,384,47.19,134.78,87.9
swinv2_large_window12to16_192to256_22kft1k,212.32,1205.699,256,256,47.81,121.53,196.74
xcit_small_12_p8_384_dist,207.32,1852.22,384,384,54.92,138.29,26.21
resnest200e,201.89,2536.056,512,320,35.69,82.78,70.2
volo_d5_224,200.39,5110.009,1024,224,72.4,118.11,295.46
resnetrs350,194.78,3942.989,768,384,77.59,154.74,163.96
convnext_large_384_in22ft1k,191.43,1337.313,256,384,101.1,126.74,197.77
cait_xs24_384,190.0,4042.088,768,384,19.28,183.98,26.67
vit_base_patch8_224,188.23,1360.04,256,224,78.22,161.69,86.58
cait_xxs36_384,183.87,5569.221,1024,384,14.35,183.7,17.37
swinv2_cr_base_384,178.69,1432.608,256,384,50.57,333.68,87.88
vit_base_r50_s16_384,176.73,1448.509,256,384,67.43,135.03,98.95
vit_base_resnet50_384,176.72,1448.639,256,384,67.43,135.03,98.95
volo_d2_384,176.17,2179.696,384,384,46.17,184.51,58.87
swinv2_cr_huge_224,175.72,2185.293,384,224,115.97,121.08,657.83
nfnet_f2,172.68,5929.942,1024,352,63.22,79.06,193.78
xcit_large_24_p16_384_dist,168.33,3041.628,512,384,105.35,137.17,189.1
densenet264d_iabn,166.37,6155.083,1024,224,13.47,14.0,72.74
efficientnet_b7,161.46,594.574,96,600,38.33,289.94,66.35
efficientnetv2_xl,159.28,2410.894,384,512,93.85,247.32,208.12
dm_nfnet_f2,159.16,4825.445,768,352,63.22,79.06,193.78
tf_efficientnetv2_xl_in21ft1k,158.05,2429.572,384,512,93.85,247.32,208.12
tf_efficientnet_b7,155.86,615.938,96,600,38.33,289.94,66.35
tf_efficientnet_b7_ap,155.83,616.029,96,600,38.33,289.94,66.35
tf_efficientnet_b7_ns,155.78,616.248,96,600,38.33,289.94,66.35
tresnet_l_448,151.99,6737.079,1024,448,43.5,47.56,55.99
cait_s24_384,148.32,3451.871,512,384,32.17,245.31,47.06
vit_huge_patch14_224,146.38,6995.606,1024,224,167.4,139.41,632.05
ig_resnext101_32x32d,143.03,1789.868,256,224,87.29,91.12,468.53
resnetrs420,142.89,5374.778,768,416,108.45,213.79,191.89
deit3_huge_patch14_224,142.28,7197.12,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224_in21ft1k,142.06,7208.264,1024,224,167.4,139.41,632.13
eca_nfnet_l3,132.47,3864.977,512,448,52.55,118.4,72.04
swin_large_patch4_window12_384,130.79,978.633,128,384,104.08,202.16,196.74
convnext_xlarge_384_in22ft1k,126.14,2029.4,256,384,179.2,168.99,350.2
xcit_large_24_p8_224,125.6,4076.305,512,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,125.51,4079.48,512,224,141.23,181.56,188.93
tresnet_xl_448,111.87,9153.659,1024,448,60.65,61.31,78.44
xcit_small_24_p8_384_dist,108.62,3535.115,384,384,105.24,265.91,47.63
swinv2_cr_large_384,108.25,1182.395,128,384,108.95,404.96,196.68
efficientnet_b8,101.79,943.075,96,672,63.48,442.89,87.41
resnetv2_50x3_bitm,101.1,1266.075,128,448,145.7,133.37,217.32
cait_s36_384,99.17,5162.791,512,384,47.99,367.4,68.37
tf_efficientnet_b8,98.82,971.416,96,672,63.48,442.89,87.41
tf_efficientnet_b8_ap,98.81,971.518,96,672,63.48,442.89,87.41
resnetv2_152x2_bit_teacher_384,98.49,2599.208,256,384,136.16,132.56,236.34
vit_large_patch16_384,97.65,2621.481,256,384,191.21,270.24,304.72
vit_giant_patch14_224,95.7,8025.142,768,224,267.18,192.64,1012.61
deit3_large_patch16_384,94.92,2697.101,256,384,191.21,270.24,304.76
deit3_large_patch16_384_in21ft1k,94.92,2697.083,256,384,191.21,270.24,304.76
swinv2_base_window12to24_192to384_22kft1k,94.52,677.087,64,384,55.25,280.36,87.92
nfnet_f3,93.84,8184.276,768,416,115.58,141.78,254.92
resnest269e,93.31,4115.183,384,416,77.69,171.98,110.93
dm_nfnet_f3,86.56,5914.801,512,416,115.58,141.78,254.92
beit_large_patch16_384,84.77,3019.757,256,384,191.21,270.24,305.0
volo_d3_448,76.33,2515.269,192,448,96.33,446.83,86.63
xcit_medium_24_p8_384_dist,75.97,3369.835,256,384,186.67,354.73,84.32
ig_resnext101_32x48d,74.47,2578.247,192,224,153.57,131.06,828.41
resnetv2_152x2_bitm,72.95,2631.902,192,448,184.99,180.43,236.34
tf_efficientnet_l2_ns_475,63.13,1013.713,64,475,172.11,609.89,480.31
resnetv2_101x3_bitm,60.06,2131.166,128,448,280.33,194.78,387.93
swinv2_large_window12to24_192to384_22kft1k,59.82,802.459,48,384,116.15,407.83,196.74
vit_gigantic_patch14_224,57.64,8883.342,512,224,483.95,275.37,1844.44
volo_d4_448,56.09,3423.322,192,448,197.13,527.35,193.41
convmixer_1536_20,53.95,18979.912,1024,224,48.68,33.03,51.63
swinv2_cr_giant_224,50.56,2531.46,128,224,483.85,309.15,2598.76
nfnet_f4,49.93,10254.329,512,512,216.26,262.26,316.07
swinv2_cr_huge_384,47.13,1357.909,64,384,352.04,583.18,657.94
dm_nfnet_f4,45.97,8353.673,384,512,216.26,262.26,316.07
xcit_large_24_p8_384_dist,42.84,4481.402,192,384,415.0,531.82,188.93
volo_d5_448,38.62,3314.317,128,448,315.06,737.92,295.91
nfnet_f5,37.03,10370.994,384,544,290.97,349.71,377.21
dm_nfnet_f5,33.93,11318.026,384,544,290.97,349.71,377.21
beit_large_patch16_512,33.87,2834.401,96,512,362.24,656.39,305.67
cait_m36_384,32.22,7945.944,256,384,173.11,734.81,271.22
nfnet_f6,28.33,13554.319,384,576,378.69,452.2,438.36
volo_d5_512,26.99,4742.789,128,512,425.09,1105.37,296.09
dm_nfnet_f6,26.14,9792.719,256,576,378.69,452.2,438.36
resnetv2_152x4_bitm,24.34,2629.892,64,480,844.84,414.26,936.53
efficientnet_l2,23.12,1037.889,24,800,479.12,1707.39,480.31
tf_efficientnet_l2_ns,22.7,1057.422,24,800,479.12,1707.39,480.31
nfnet_f7,22.34,11460.175,256,608,480.39,570.85,499.5
swinv2_cr_giant_384,14.63,2187.208,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.64,9385.159,128,448,329.41,1708.23,356.46
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/benchmark-infer-amp-nchw-pt113-cu117-rtx3090.csv | model,infer_samples_per_sec,infer_step_time,infer_batch_size,infer_img_size,infer_gmacs,infer_macts,param_count
tinynet_e,49277.65,20.77,1024,106,0.03,0.69,2.04
mobilenetv3_small_050,45562.75,22.464,1024,224,0.03,0.92,1.59
lcnet_035,41026.68,24.949,1024,224,0.03,1.04,1.64
lcnet_050,37575.13,27.242,1024,224,0.05,1.26,1.88
mobilenetv3_small_075,33062.39,30.961,1024,224,0.05,1.3,2.04
mobilenetv3_small_100,30012.26,34.109,1024,224,0.06,1.42,2.54
tf_mobilenetv3_small_minimal_100,28698.14,35.672,1024,224,0.06,1.41,2.04
tf_mobilenetv3_small_075,27407.51,37.352,1024,224,0.05,1.3,2.04
tinynet_d,27236.47,37.585,1024,152,0.05,1.42,2.34
tf_mobilenetv3_small_100,25103.65,40.781,1024,224,0.06,1.42,2.54
lcnet_075,24140.95,42.406,1024,224,0.1,1.99,2.36
mnasnet_small,20706.43,49.443,1024,224,0.07,2.16,2.03
levit_128s,20595.72,49.709,1024,224,0.31,1.88,7.78
lcnet_100,19684.75,52.01,1024,224,0.16,2.52,2.95
mobilenetv2_035,18358.82,55.767,1024,224,0.07,2.86,1.68
regnetx_002,18244.04,56.117,1024,224,0.2,2.16,2.68
ghostnet_050,17564.96,58.287,1024,224,0.05,1.77,2.59
regnety_002,17006.07,60.202,1024,224,0.2,2.17,3.16
mnasnet_050,15925.32,64.29,1024,224,0.11,3.07,2.22
vit_tiny_r_s16_p8_224,15068.38,67.946,1024,224,0.44,2.06,6.34
mobilenetv2_050,14843.74,68.974,1024,224,0.1,3.64,1.97
tinynet_c,14634.69,69.959,1024,184,0.11,2.87,2.46
semnasnet_050,14248.78,71.855,1024,224,0.11,3.44,2.08
levit_128,14164.26,72.284,1024,224,0.41,2.71,9.21
vit_small_patch32_224,13811.36,74.131,1024,224,1.15,2.5,22.88
mixer_s32_224,13352.85,76.677,1024,224,1.0,2.28,19.1
cs3darknet_focus_s,12798.44,79.999,1024,256,0.69,2.7,3.27
lcnet_150,12783.12,80.094,1024,224,0.34,3.79,4.5
cs3darknet_s,12395.11,82.602,1024,256,0.72,2.97,3.28
regnetx_004,12366.39,82.791,1024,224,0.4,3.14,5.16
mobilenetv3_large_075,12001.32,85.313,1024,224,0.16,4.0,3.99
levit_192,11882.81,86.163,1024,224,0.66,3.2,10.95
resnet10t,11615.84,88.145,1024,224,1.1,2.43,5.44
ese_vovnet19b_slim_dw,11539.4,88.729,1024,224,0.4,5.28,1.9
gernet_s,11496.77,89.058,1024,224,0.75,2.65,8.17
mobilenetv3_rw,10873.77,94.16,1024,224,0.23,4.41,5.48
mobilenetv3_large_100,10705.06,95.645,1024,224,0.23,4.41,5.48
hardcorenas_a,10554.34,97.012,1024,224,0.23,4.38,5.26
tf_mobilenetv3_large_075,10511.12,97.41,1024,224,0.16,4.0,3.99
tf_mobilenetv3_large_minimal_100,10371.16,98.725,1024,224,0.22,4.4,3.92
mnasnet_075,10345.17,98.972,1024,224,0.23,4.77,3.17
hardcorenas_b,9695.74,105.601,1024,224,0.26,5.09,5.18
regnety_004,9655.22,106.046,1024,224,0.41,3.89,4.34
ghostnet_100,9483.99,107.96,1024,224,0.15,3.55,5.18
hardcorenas_c,9481.05,107.994,1024,224,0.28,5.01,5.52
tf_mobilenetv3_large_100,9456.79,108.271,1024,224,0.23,4.41,5.48
regnetx_006,9408.22,108.83,1024,224,0.61,3.98,6.2
mobilenetv2_075,9313.88,109.932,1024,224,0.22,5.86,2.64
tinynet_b,9291.99,110.191,1024,188,0.21,4.44,3.73
mnasnet_b1,9286.4,110.258,1024,224,0.33,5.46,4.38
mnasnet_100,9263.52,110.53,1024,224,0.33,5.46,4.38
gluon_resnet18_v1b,9078.31,112.785,1024,224,1.82,2.48,11.69
semnasnet_075,9069.42,112.895,1024,224,0.23,5.54,2.91
resnet18,9045.63,113.192,1024,224,1.82,2.48,11.69
ssl_resnet18,9045.4,113.196,1024,224,1.82,2.48,11.69
swsl_resnet18,9040.4,113.258,1024,224,1.82,2.48,11.69
levit_256,8921.47,114.768,1024,224,1.13,4.23,18.89
hardcorenas_d,8879.46,115.311,1024,224,0.3,4.93,7.5
regnety_006,8666.48,118.144,1024,224,0.61,4.33,6.06
seresnet18,8542.99,119.851,1024,224,1.82,2.49,11.78
mobilenetv2_100,8507.29,120.356,1024,224,0.31,6.68,3.5
spnasnet_100,8342.04,122.741,1024,224,0.35,6.03,4.42
legacy_seresnet18,8310.8,123.202,1024,224,1.82,2.49,11.78
semnasnet_100,8284.16,123.599,1024,224,0.32,6.23,3.89
mnasnet_a1,8283.57,123.607,1024,224,0.32,6.23,3.89
regnetx_008,7852.75,130.39,1024,224,0.81,5.15,7.26
hardcorenas_f,7809.07,131.117,1024,224,0.35,5.57,8.2
hardcorenas_e,7730.97,132.444,1024,224,0.35,5.65,8.07
efficientnet_lite0,7722.75,132.584,1024,224,0.4,6.74,4.65
levit_256d,7689.03,133.165,1024,224,1.4,4.93,26.21
xcit_nano_12_p16_224_dist,7674.8,133.413,1024,224,0.56,4.17,3.05
xcit_nano_12_p16_224,7670.11,133.492,1024,224,0.56,4.17,3.05
resnet18d,7636.48,134.082,1024,224,2.06,3.29,11.71
ghostnet_130,7625.58,134.274,1024,224,0.24,4.6,7.36
tf_efficientnetv2_b0,7614.25,134.473,1024,224,0.73,4.77,7.14
ese_vovnet19b_slim,7588.4,134.932,1024,224,1.69,3.52,3.17
deit_tiny_distilled_patch16_224,7449.3,137.451,1024,224,1.27,6.01,5.91
deit_tiny_patch16_224,7398.73,138.391,1024,224,1.26,5.97,5.72
vit_tiny_patch16_224,7390.78,138.538,1024,224,1.26,5.97,5.72
regnety_008,7366.88,138.989,1024,224,0.81,5.25,6.26
tinynet_a,7358.6,139.145,1024,192,0.35,5.41,6.19
dla46_c,7311.64,140.038,1024,224,0.58,4.5,1.3
fbnetc_100,7303.94,140.187,1024,224,0.4,6.51,5.57
mobilevitv2_050,7248.37,141.262,1024,256,0.48,8.04,1.37
tf_efficientnet_lite0,6816.26,150.218,1024,224,0.4,6.74,4.65
pit_ti_distilled_224,6788.49,150.832,1024,224,0.71,6.23,5.1
pit_ti_224,6762.99,151.401,1024,224,0.7,6.19,4.85
efficientnet_b0,6687.26,153.115,1024,224,0.4,6.75,5.29
visformer_tiny,6618.81,154.698,1024,224,1.27,5.72,10.32
rexnet_100,6608.65,154.937,1024,224,0.41,7.44,4.8
mnasnet_140,6580.58,155.597,1024,224,0.6,7.71,7.12
efficientnet_b1_pruned,6513.48,157.201,1024,240,0.4,6.21,6.33
rexnetr_100,6491.35,157.737,1024,224,0.43,7.72,4.88
mobilenetv2_110d,6395.98,160.089,1024,224,0.45,8.71,4.52
resnet14t,6341.58,161.462,1024,224,1.69,5.8,10.08
regnetz_005,6208.75,164.916,1024,224,0.52,5.86,7.12
dla46x_c,6145.64,166.61,1024,224,0.54,5.66,1.07
nf_regnet_b0,6055.0,169.104,1024,256,0.64,5.58,8.76
tf_efficientnet_b0,5992.76,170.862,1024,224,0.4,6.75,5.29
hrnet_w18_small,5908.15,173.308,1024,224,1.61,5.72,13.19
edgenext_xx_small,5886.07,173.957,1024,288,0.33,4.21,1.33
semnasnet_140,5856.63,174.833,1024,224,0.6,8.87,6.11
resnetblur18,5839.81,175.336,1024,224,2.34,3.39,11.69
ese_vovnet19b_dw,5825.11,175.779,1024,224,1.34,8.25,6.54
dla60x_c,5790.89,176.817,1024,224,0.59,6.01,1.32
mobilenetv2_140,5780.41,177.139,1024,224,0.6,9.57,6.11
skresnet18,5648.81,181.265,1024,224,1.82,3.24,11.96
mobilevit_xxs,5528.18,185.22,1024,256,0.42,8.34,1.27
efficientnet_b0_gn,5401.88,189.551,1024,224,0.42,6.75,5.29
convnext_atto,5364.13,190.886,1024,288,0.91,6.3,3.7
gluon_resnet34_v1b,5344.34,191.593,1024,224,3.67,3.74,21.8
resnet34,5335.05,191.926,1024,224,3.67,3.74,21.8
efficientnet_lite1,5334.12,191.959,1024,240,0.62,10.14,5.42
tv_resnet34,5332.7,192.011,1024,224,3.67,3.74,21.8
vit_base_patch32_224,5287.0,193.67,1024,224,4.41,5.01,88.22
vit_base_patch32_clip_224,5281.4,193.877,1024,224,4.41,5.01,88.22
levit_384,5276.74,194.047,1024,224,2.36,6.26,39.13
pit_xs_distilled_224,5241.4,195.357,1024,224,1.41,7.76,11.0
pit_xs_224,5237.09,195.517,1024,224,1.4,7.71,10.62
selecsls42,5225.99,195.932,1024,224,2.94,4.62,30.35
selecsls42b,5201.55,196.853,1024,224,2.98,4.62,32.46
gernet_m,5124.67,199.807,1024,224,3.02,5.24,21.14
pvt_v2_b0,5122.72,199.882,1024,224,0.57,7.99,3.67
tf_efficientnetv2_b1,5122.21,199.903,1024,240,1.21,7.34,8.14
mixnet_s,5079.84,201.57,1024,224,0.25,6.25,4.13
convnext_atto_ols,5062.64,202.255,1024,288,0.96,6.8,3.7
seresnet34,5028.88,203.611,1024,224,3.67,3.74,21.96
rexnetr_130,5003.96,204.626,1024,224,0.68,9.81,7.61
fbnetv3_b,5003.0,204.666,1024,256,0.55,9.1,8.6
mixer_b32_224,4982.51,205.508,1024,224,3.24,6.29,60.29
xcit_tiny_12_p16_224_dist,4879.26,209.853,1024,224,1.24,6.29,6.72
legacy_seresnet34,4875.12,210.034,1024,224,3.67,3.74,21.96
xcit_tiny_12_p16_224,4870.16,210.244,1024,224,1.24,6.29,6.72
resnet34d,4834.78,211.786,1024,224,3.91,4.54,21.82
tf_efficientnet_lite1,4822.03,212.348,1024,240,0.62,10.14,5.42
resnet26,4794.98,213.545,1024,224,2.36,7.35,16.0
mobilenetv2_120d,4786.27,213.934,1024,224,0.69,11.97,5.83
rexnet_130,4770.1,214.659,1024,224,0.68,9.71,7.56
efficientnet_b0_g16_evos,4743.69,215.854,1024,224,1.01,7.42,8.11
efficientnet_es,4736.89,216.163,1024,224,1.81,8.73,5.44
efficientnet_es_pruned,4735.25,216.239,1024,224,1.81,8.73,5.44
tf_mixnet_s,4735.17,216.242,1024,224,0.25,6.25,4.13
gmlp_ti16_224,4709.0,217.445,1024,224,1.34,7.55,5.87
convnext_femto,4672.08,219.162,1024,288,1.3,7.56,5.22
mobilevitv2_075,4638.17,220.764,1024,256,1.05,12.06,2.87
resmlp_12_224,4601.92,222.504,1024,224,3.01,5.5,15.35
resmlp_12_distilled_224,4597.97,222.695,1024,224,3.01,5.5,15.35
gmixer_12_224,4543.02,225.388,1024,224,2.67,7.26,12.7
fbnetv3_d,4532.2,225.927,1024,256,0.68,11.1,10.31
tf_efficientnet_es,4518.93,226.591,1024,224,1.81,8.73,5.44
selecsls60,4510.1,227.034,1024,224,3.59,5.52,30.67
mixer_s16_224,4509.29,227.075,1024,224,3.79,5.97,18.53
regnetx_016,4507.02,227.189,1024,224,1.62,7.93,9.19
selecsls60b,4490.35,228.033,1024,224,3.63,5.52,32.77
cs3darknet_focus_m,4487.64,228.171,1024,288,2.51,6.19,9.3
dla34,4481.03,228.505,1024,224,3.07,5.02,15.74
crossvit_tiny_240,4476.83,228.722,1024,240,1.57,9.08,7.01
convnext_femto_ols,4473.25,228.904,1024,288,1.35,8.06,5.23
vit_tiny_r_s16_p8_384,4463.13,229.423,1024,384,1.34,6.49,6.36
cs3darknet_m,4452.94,229.949,1024,288,2.63,6.69,9.31
repvgg_b0,4433.11,230.978,1024,224,3.41,6.15,15.82
resnet26d,4354.59,235.143,1024,224,2.6,8.15,16.01
rexnetr_150,4349.97,235.392,1024,224,0.89,11.13,9.78
resnetaa34d,4309.77,237.588,1024,224,4.43,5.07,21.82
efficientnet_b2_pruned,4309.58,237.598,1024,260,0.73,9.13,8.31
darknet17,4296.61,238.316,1024,256,3.26,7.18,14.3
vit_small_patch32_384,4250.58,240.897,1024,384,3.45,8.25,22.92
crossvit_9_240,4201.98,243.683,1024,240,1.85,9.52,8.55
nf_resnet26,4197.39,243.949,1024,224,2.41,7.35,16.0
efficientnet_b0_g8_gn,4190.39,244.357,1024,224,0.66,6.75,6.56
rexnet_150,4186.31,244.594,1024,224,0.9,11.21,9.73
ecaresnet50d_pruned,4182.62,244.81,1024,224,2.53,6.43,19.94
efficientformer_l1,4075.83,251.225,1024,224,1.3,5.53,12.29
poolformer_s12,4050.19,252.815,1024,224,1.82,5.53,11.92
regnety_016,4035.9,253.712,1024,224,1.63,8.04,11.2
efficientnet_lite2,4013.48,255.128,1024,260,0.89,12.9,6.09
crossvit_9_dagger_240,3992.98,256.437,1024,240,1.99,9.97,8.78
efficientnet_cc_b0_8e,3929.29,260.595,1024,224,0.42,9.42,24.01
efficientnet_cc_b0_4e,3918.01,261.346,1024,224,0.41,9.42,13.31
darknet21,3914.26,261.596,1024,256,3.93,7.47,20.86
efficientnet_b1,3876.9,264.116,1024,256,0.77,12.22,7.79
tf_efficientnet_b1,3834.3,267.052,1024,240,0.71,10.88,7.79
resnest14d,3793.21,269.944,1024,224,2.76,7.33,10.61
sedarknet21,3784.73,270.549,1024,256,3.93,7.47,20.95
resnext26ts,3775.5,271.211,1024,256,2.43,10.52,10.3
tf_efficientnetv2_b2,3727.06,274.735,1024,260,1.72,9.84,10.1
convnext_pico,3702.78,276.537,1024,288,2.27,10.08,9.05
edgenext_x_small,3692.42,277.311,1024,288,0.68,7.5,2.34
tf_efficientnet_cc_b0_8e,3691.33,277.395,1024,224,0.42,9.42,24.01
dpn48b,3689.99,277.494,1024,224,1.69,8.92,9.13
eca_resnext26ts,3675.59,278.583,1024,256,2.43,10.52,10.3
seresnext26ts,3670.33,278.98,1024,256,2.43,10.52,10.39
tf_efficientnet_cc_b0_4e,3665.41,279.357,1024,224,0.41,9.42,13.31
tf_efficientnet_lite2,3662.0,279.618,1024,260,0.89,12.9,6.09
nf_ecaresnet26,3619.99,282.862,1024,224,2.41,7.36,16.0
nf_seresnet26,3618.8,282.955,1024,224,2.41,7.36,17.4
gcresnext26ts,3594.7,284.852,1024,256,2.43,10.53,10.48
mobilevitv2_100,3589.19,213.964,768,256,1.84,16.08,4.9
gernet_l,3556.24,287.933,1024,256,4.57,8.0,31.08
legacy_seresnext26_32x4d,3545.88,288.774,1024,224,2.49,9.39,16.79
convnext_pico_ols,3532.27,289.886,1024,288,2.37,10.74,9.06
resnet26t,3503.33,292.28,1024,256,3.35,10.52,16.01
repvgg_a2,3454.82,296.386,1024,224,5.7,6.26,28.21
mixnet_m,3418.52,299.526,1024,224,0.36,8.19,5.01
efficientnet_b3_pruned,3356.7,305.049,1024,300,1.04,11.86,9.86
nf_regnet_b1,3352.23,305.456,1024,288,1.02,9.2,10.22
ecaresnext50t_32x4d,3339.2,306.649,1024,224,2.7,10.09,15.41
ecaresnext26t_32x4d,3337.18,306.833,1024,224,2.7,10.09,15.41
seresnext26tn_32x4d,3327.66,307.711,1024,224,2.7,10.09,16.81
seresnext26t_32x4d,3327.23,307.751,1024,224,2.7,10.09,16.81
seresnext26d_32x4d,3303.57,309.954,1024,224,2.73,10.19,16.81
tf_mixnet_m,3301.19,310.17,1024,224,0.36,8.19,5.01
convit_tiny,3286.62,311.554,1024,224,1.26,7.94,5.71
mobilevit_xs,3278.19,234.265,768,256,1.05,16.33,2.32
pit_s_224,3268.88,313.245,1024,224,2.88,11.56,23.46
pit_s_distilled_224,3266.72,313.452,1024,224,2.9,11.64,24.04
skresnet34,3242.45,315.8,1024,224,3.67,5.13,22.28
eca_botnext26ts_256,3224.24,317.583,1024,256,2.46,11.6,10.59
ecaresnet101d_pruned,3223.88,317.616,1024,224,3.48,7.69,24.88
deit_small_distilled_patch16_224,3220.79,317.922,1024,224,4.63,12.02,22.44
ecaresnetlight,3215.57,318.439,1024,224,4.11,8.42,30.16
deit_small_patch16_224,3209.05,319.085,1024,224,4.61,11.95,22.05
vit_small_patch16_224,3199.98,319.99,1024,224,4.61,11.95,22.05
eca_halonext26ts,3173.71,322.639,1024,256,2.44,11.46,10.76
convnextv2_atto,3162.98,323.733,1024,288,0.91,6.3,3.71
resnetv2_50,3158.28,324.214,1024,224,4.11,11.11,25.55
nf_regnet_b2,3133.63,326.765,1024,272,1.22,9.27,14.31
rexnetr_200,3133.12,245.111,768,224,1.59,15.11,16.52
botnet26t_256,3123.98,327.772,1024,256,3.32,11.98,12.49
coat_lite_tiny,3113.54,328.874,1024,224,1.6,11.65,5.72
vit_small_r26_s32_224,3112.34,329.001,1024,224,3.56,9.85,36.43
bat_resnext26ts,3103.95,329.89,1024,256,2.53,12.51,10.73
halonet26t,3103.39,329.95,1024,256,3.19,11.69,12.48
pvt_v2_b1,3095.14,330.828,1024,224,2.12,15.39,14.01
cspresnet50,3063.22,334.278,1024,256,4.54,11.5,21.62
resnet32ts,3055.79,335.09,1024,256,4.63,11.58,17.96
rexnet_200,3051.5,251.668,768,224,1.56,14.91,16.37
lambda_resnet26t,3046.2,336.144,1024,256,3.02,11.87,10.96
ssl_resnet50,3030.48,337.887,1024,224,4.11,11.11,25.56
gluon_resnet50_v1b,3027.43,338.23,1024,224,4.11,11.11,25.56
tv_resnet50,3027.39,338.232,1024,224,4.11,11.11,25.56
swsl_resnet50,3027.07,338.268,1024,224,4.11,11.11,25.56
resnet50,3025.4,338.455,1024,224,4.11,11.11,25.56
deit3_small_patch16_224_in21ft1k,3023.02,338.721,1024,224,4.61,11.95,22.06
deit3_small_patch16_224,3017.77,339.312,1024,224,4.61,11.95,22.06
tresnet_m,3006.54,340.578,1024,224,5.74,7.31,31.39
resnet33ts,3005.78,340.665,1024,256,4.76,11.66,19.68
vit_small_resnet26d_224,2994.08,341.995,1024,224,5.07,11.12,63.61
resnetv2_50t,2989.06,342.569,1024,224,4.32,11.82,25.57
regnetx_032,2988.15,342.675,1024,224,3.2,11.37,15.3
dpn68b,2981.13,343.481,1024,224,2.35,10.47,12.61
hrnet_w18_small_v2,2978.67,343.765,1024,224,2.62,9.65,15.6
dpn68,2975.29,344.155,1024,224,2.35,10.47,12.61
resnetv2_50d,2971.15,344.633,1024,224,4.35,11.92,25.57
efficientnet_em,2938.12,348.51,1024,240,3.04,14.34,6.9
vit_base_patch32_plus_256,2934.64,348.925,1024,256,7.79,7.76,119.48
coat_lite_mini,2921.75,350.462,1024,224,2.0,12.25,11.01
tf_efficientnet_b2,2919.63,350.718,1024,260,1.02,13.83,9.11
seresnet33ts,2919.51,350.732,1024,256,4.76,11.66,19.78
eca_resnet33ts,2917.21,351.008,1024,256,4.76,11.66,19.68
haloregnetz_b,2890.29,354.276,1024,224,1.97,11.94,11.68
coatnet_pico_rw_224,2884.58,354.98,1024,224,2.05,14.62,10.85
dla60,2883.99,355.049,1024,224,4.26,10.16,22.04
gluon_resnet50_v1c,2872.58,356.463,1024,224,4.35,11.92,25.58
resnet50t,2869.49,356.844,1024,224,4.32,11.82,25.57
gcresnet33ts,2863.36,357.609,1024,256,4.76,11.68,19.88
gluon_resnet50_v1d,2853.24,358.879,1024,224,4.35,11.92,25.58
cspresnet50d,2852.98,358.911,1024,256,4.86,12.55,21.64
resnet50d,2850.55,359.218,1024,224,4.35,11.92,25.58
vovnet39a,2845.31,359.878,1024,224,7.09,6.73,22.6
cspresnet50w,2835.31,361.148,1024,256,5.04,12.19,28.12
vgg11,2827.53,362.143,1024,224,7.61,7.44,132.86
tf_efficientnet_em,2826.28,362.303,1024,240,3.04,14.34,6.9
visformer_small,2818.88,363.251,1024,224,4.88,11.43,40.22
vit_relpos_small_patch16_224,2792.87,366.637,1024,224,4.59,13.05,21.98
vit_relpos_base_patch32_plus_rpn_256,2784.26,367.771,1024,256,7.68,8.01,119.42
vit_srelpos_small_patch16_224,2781.72,368.106,1024,224,4.59,12.16,21.97
resnest26d,2772.97,369.267,1024,224,3.64,9.97,17.07
cs3darknet_focus_l,2770.5,369.596,1024,288,5.9,10.16,21.15
efficientnet_b2a,2767.64,369.979,1024,288,1.12,16.2,9.11
efficientnet_b2,2766.98,370.065,1024,288,1.12,16.2,9.11
ese_vovnet39b,2760.12,370.986,1024,224,7.09,6.74,24.57
legacy_seresnet50,2753.49,371.881,1024,224,3.88,10.6,28.09
densenet121,2749.79,372.378,1024,224,2.87,6.9,7.98
tv_densenet121,2747.16,372.735,1024,224,2.87,6.9,7.98
eca_vovnet39b,2736.53,374.185,1024,224,7.09,6.74,22.6
coatnet_nano_cc_224,2716.19,376.986,1024,224,2.24,15.02,13.76
convnextv2_femto,2710.95,377.714,1024,288,1.3,7.56,5.23
resnetv2_50x1_bit_distilled,2704.93,378.554,1024,224,4.23,11.11,25.55
selecsls84,2697.2,379.64,1024,224,5.9,7.57,50.95
flexivit_small,2693.55,380.153,1024,240,5.35,14.18,22.06
twins_svt_small,2691.25,380.48,1024,224,2.94,13.75,24.06
mixnet_l,2678.25,382.327,1024,224,0.58,10.84,7.33
seresnet50,2674.61,382.848,1024,224,4.11,11.13,28.09
xcit_nano_12_p16_384_dist,2668.39,383.74,1024,384,1.64,12.15,3.05
cs3darknet_l,2649.93,386.412,1024,288,6.16,10.83,21.16
coatnet_nano_rw_224,2633.36,388.844,1024,224,2.41,15.41,15.14
coatnext_nano_rw_224,2627.24,389.75,1024,224,2.47,12.8,14.7
xcit_tiny_24_p16_224_dist,2617.14,391.253,1024,224,2.34,11.82,12.12
densenet121d,2616.98,391.278,1024,224,3.11,7.7,8.0
xcit_tiny_24_p16_224,2614.91,391.584,1024,224,2.34,11.82,12.12
resnet50_gn,2599.07,393.975,1024,224,4.14,11.11,25.56
vit_relpos_small_patch16_rpn_224,2596.73,394.33,1024,224,4.59,13.05,21.97
res2net50_48w_2s,2593.21,394.865,1024,224,4.18,11.72,25.29
mobilevit_s,2587.93,296.749,768,256,2.03,19.94,5.58
convnext_nano,2579.36,396.983,1024,288,4.06,13.84,15.59
tf_mixnet_l,2577.4,397.288,1024,224,0.58,10.84,7.33
resnetaa50d,2573.35,397.912,1024,224,5.39,12.44,25.58
vgg11_bn,2556.04,400.607,1024,224,7.62,7.44,132.87
seresnet50t,2550.33,401.504,1024,224,4.32,11.83,28.1
ecaresnet50d,2544.16,402.478,1024,224,4.35,11.93,25.58
gcvit_xxtiny,2518.13,406.639,1024,224,2.14,15.36,12.0
cs3sedarknet_l,2502.51,409.176,1024,288,6.16,10.83,21.91
resnetrs50,2497.73,409.96,1024,224,4.48,12.14,35.69
mobilevitv2_125,2489.87,308.438,768,256,2.86,20.1,7.48
resnetblur50,2484.87,412.08,1024,224,5.16,12.02,25.56
cspresnext50,2483.24,412.352,1024,256,4.05,15.86,20.57
gluon_resnet50_v1s,2459.02,416.413,1024,224,5.47,13.52,25.68
efficientnet_cc_b1_8e,2458.85,416.443,1024,240,0.75,15.44,39.72
vit_base_resnet26d_224,2458.01,416.584,1024,224,6.97,13.16,101.4
densenetblur121d,2444.58,418.873,1024,224,3.11,7.9,8.0
tv_resnext50_32x4d,2431.41,421.143,1024,224,4.26,14.4,25.03
ssl_resnext50_32x4d,2431.35,421.155,1024,224,4.26,14.4,25.03
swsl_resnext50_32x4d,2430.87,421.236,1024,224,4.26,14.4,25.03
resnext50_32x4d,2429.56,421.462,1024,224,4.26,14.4,25.03
gluon_resnext50_32x4d,2428.35,421.674,1024,224,4.26,14.4,25.03
dla60x,2414.82,424.035,1024,224,3.54,13.8,17.35
efficientnet_lite3,2407.43,212.664,512,300,1.65,21.85,8.2
regnetx_040,2406.98,425.416,1024,224,3.99,12.2,22.12
semobilevit_s,2404.63,319.371,768,256,2.03,19.95,5.74
gcresnext50ts,2402.57,426.196,1024,256,3.75,15.46,15.67
regnety_040s_gn,2385.11,429.317,1024,224,4.03,12.29,20.65
resnetblur50d,2367.52,432.507,1024,224,5.4,12.82,25.58
vovnet57a,2360.79,433.737,1024,224,8.95,7.52,36.64
tf_efficientnet_cc_b1_8e,2357.71,434.307,1024,240,0.75,15.44,39.72
resmlp_24_distilled_224,2351.85,435.39,1024,224,5.96,10.91,30.02
resmlp_24_224,2345.81,436.509,1024,224,5.96,10.91,30.02
res2net50_14w_8s,2341.48,437.317,1024,224,4.21,13.28,25.06
coatnet_rmlp_nano_rw_224,2340.53,437.494,1024,224,2.62,20.34,15.15
sehalonet33ts,2339.44,328.271,768,256,3.55,14.7,13.69
res2net50_26w_4s,2338.49,437.876,1024,224,4.28,12.61,25.7
convnext_nano_ols,2328.37,439.779,1024,288,4.38,15.5,15.65
lambda_resnet26rpt_256,2324.88,165.158,384,256,3.16,11.87,10.99
gmixer_24_224,2324.82,440.451,1024,224,5.28,14.45,24.72
gcresnet50t,2321.78,441.028,1024,256,5.42,14.67,25.9
resnext50d_32x4d,2317.05,441.929,1024,224,4.5,15.2,25.05
resnest50d_1s4x24d,2309.9,443.296,1024,224,4.43,13.57,25.68
seresnetaa50d,2309.78,443.319,1024,224,5.4,12.46,28.11
dla60_res2net,2301.91,444.834,1024,224,4.15,12.34,20.85
vit_base_r26_s32_224,2301.77,444.864,1024,224,6.81,12.36,101.38
twins_pcpvt_small,2290.09,447.132,1024,224,3.83,18.08,24.11
regnetz_b16,2286.62,447.81,1024,288,2.39,16.43,9.72
ese_vovnet57b,2267.23,451.64,1024,224,8.95,7.52,38.61
gluon_inception_v3,2265.31,452.024,1024,299,5.73,8.97,23.83
inception_v3,2260.97,452.888,1024,299,5.73,8.97,23.83
adv_inception_v3,2258.89,453.305,1024,299,5.73,8.97,23.83
tf_inception_v3,2255.73,453.943,1024,299,5.73,8.97,23.83
densenet169,2232.91,458.582,1024,224,3.4,7.3,14.15
tf_efficientnetv2_b3,2223.64,460.493,1024,300,3.04,15.74,14.36
nf_ecaresnet50,2211.52,463.019,1024,224,4.21,11.13,25.56
nf_seresnet50,2207.21,463.921,1024,224,4.21,11.13,28.09
skresnet50,2206.75,464.017,1024,224,4.11,12.5,25.8
edgenext_small,2206.31,464.109,1024,320,1.97,14.16,5.59
seresnext50_32x4d,2197.09,466.058,1024,224,4.26,14.42,27.56
gluon_seresnext50_32x4d,2196.94,466.091,1024,224,4.26,14.42,27.56
xcit_small_12_p16_224_dist,2195.81,466.33,1024,224,4.82,12.58,26.25
legacy_seresnext50_32x4d,2193.34,466.856,1024,224,4.26,14.42,27.56
xcit_small_12_p16_224,2190.16,467.534,1024,224,4.82,12.58,26.25
repvgg_b1g4,2188.83,467.817,1024,224,8.15,10.64,39.97
tf_efficientnet_lite3,2188.37,233.953,512,300,1.65,21.85,8.2
efficientnetv2_rw_t,2170.03,471.87,1024,288,3.19,16.42,13.65
gmlp_s16_224,2164.56,473.061,1024,224,4.42,15.1,19.42
dla60_res2next,2126.26,481.583,1024,224,3.49,13.17,17.03
gc_efficientnetv2_rw_t,2126.09,481.621,1024,288,3.2,16.45,13.68
skresnet50d,2112.57,484.703,1024,224,4.36,13.31,25.82
mobilevitv2_150,2105.0,243.219,512,256,4.09,24.11,10.59
mobilevitv2_150_in22ft1k,2104.51,243.274,512,256,4.09,24.11,10.59
convnextv2_pico,2092.16,489.434,1024,288,2.27,10.08,9.07
poolformer_s24,2090.38,489.851,1024,224,3.41,10.68,21.39
cs3sedarknet_xdw,2090.04,489.929,1024,256,5.97,17.18,21.6
res2next50,2085.23,491.055,1024,224,4.2,13.71,24.67
cspdarknet53,2084.51,491.231,1024,256,6.57,16.81,27.64
fbnetv3_g,2084.48,491.238,1024,288,1.77,21.09,16.62
crossvit_small_240,2074.04,493.709,1024,240,5.63,18.17,26.86
deit3_medium_patch16_224_in21ft1k,2064.27,496.046,1024,224,8.0,15.93,38.85
deit3_medium_patch16_224,2063.34,496.268,1024,224,8.0,15.93,38.85
xcit_nano_12_p8_224_dist,2049.01,499.742,1024,224,2.16,15.71,3.05
xcit_nano_12_p8_224,2044.48,500.848,1024,224,2.16,15.71,3.05
nf_regnet_b3,2035.39,503.085,1024,320,2.05,14.61,18.59
cs3darknet_focus_x,2017.73,507.488,1024,256,8.03,10.69,35.02
vit_relpos_medium_patch16_cls_224,2000.38,511.89,1024,224,8.03,18.24,38.76
lambda_resnet50ts,1991.21,514.246,1024,256,5.07,17.48,21.54
swin_tiny_patch4_window7_224,1978.72,517.495,1024,224,4.51,17.06,28.29
sebotnet33ts_256,1959.75,195.932,384,256,3.89,17.46,13.7
coatnet_0_rw_224,1957.32,523.148,1024,224,4.43,18.73,27.44
ecaresnet26t,1953.32,524.224,1024,320,5.24,16.44,16.01
regnetx_080,1942.5,527.144,1024,224,8.02,14.06,39.57
gcvit_xtiny,1941.57,527.393,1024,224,2.93,20.26,19.98
resnetv2_101,1925.46,531.806,1024,224,7.83,16.23,44.54
regnetx_064,1920.06,533.303,1024,224,6.49,16.37,26.21
mixnet_xl,1918.85,533.64,1024,224,0.93,14.57,11.9
edgenext_small_rw,1912.9,535.3,1024,320,2.46,14.85,7.83
vit_relpos_medium_patch16_224,1907.96,536.687,1024,224,7.97,17.02,38.75
vit_srelpos_medium_patch16_224,1900.57,538.773,1024,224,7.96,16.21,38.74
resnest50d,1896.74,539.858,1024,224,5.4,14.36,27.48
crossvit_15_240,1894.86,540.397,1024,240,5.81,19.77,27.53
vit_base_resnet50d_224,1892.78,540.989,1024,224,8.73,16.92,110.97
gluon_resnet101_v1b,1879.26,544.883,1024,224,7.83,16.23,44.55
tv_resnet101,1878.26,545.172,1024,224,7.83,16.23,44.55
resnet101,1875.25,546.047,1024,224,7.83,16.23,44.55
dla102,1873.79,546.472,1024,224,7.19,14.18,33.27
efficientformer_l3,1868.08,548.142,1024,224,3.93,12.01,31.41
maxvit_rmlp_pico_rw_256,1866.73,411.402,768,256,1.85,24.86,7.52
resnetv2_101d,1855.94,551.727,1024,224,8.07,17.04,44.56
pvt_v2_b2,1835.92,557.745,1024,224,4.05,27.53,25.36
maxvit_pico_rw_256,1829.44,419.787,768,256,1.83,22.3,7.46
vgg13,1820.36,562.512,1024,224,11.31,12.25,133.05
lamhalobotnet50ts_256,1818.57,563.067,1024,256,5.02,18.44,22.57
crossvit_15_dagger_240,1817.96,563.255,1024,240,6.13,20.43,28.21
gluon_resnet101_v1c,1816.14,563.82,1024,224,8.08,17.04,44.57
res2net50_26w_6s,1811.81,565.168,1024,224,6.33,15.28,37.05
gluon_resnet101_v1d,1808.21,566.295,1024,224,8.08,17.04,44.57
swin_s3_tiny_224,1803.67,567.72,1024,224,4.64,19.13,28.33
coatnet_rmlp_0_rw_224,1803.63,567.733,1024,224,4.72,24.89,27.45
vit_relpos_medium_patch16_rpn_224,1770.72,578.284,1024,224,7.97,17.02,38.73
halonet50ts,1765.73,579.917,1024,256,5.3,19.2,22.73
repvgg_b1,1760.92,581.5,1024,224,13.16,10.64,57.42
coatnet_bn_0_rw_224,1753.99,583.799,1024,224,4.67,22.04,27.44
wide_resnet50_2,1747.87,585.844,1024,224,11.43,14.4,68.88
efficientnet_b3,1741.21,294.036,512,320,2.01,26.52,12.23
efficientnet_b3a,1740.84,294.1,512,320,2.01,26.52,12.23
densenet201,1738.22,589.096,1024,224,4.34,7.85,20.01
coatnet_0_224,1727.45,296.376,512,224,4.58,24.01,25.04
darknetaa53,1721.33,594.876,1024,288,10.08,15.68,36.02
tf_efficientnet_b3,1720.61,297.558,512,300,1.87,23.83,12.23
cait_xxs24_224,1720.1,595.301,1024,224,2.53,20.29,11.96
vit_large_patch32_224,1718.53,595.845,1024,224,15.41,13.32,327.9
mobilevitv2_175,1697.71,301.572,512,256,5.54,28.13,14.25
mobilevitv2_175_in22ft1k,1697.51,301.606,512,256,5.54,28.13,14.25
xcit_tiny_12_p16_384_dist,1694.92,604.145,1024,384,3.64,18.26,6.72
pvt_v2_b2_li,1694.45,604.311,1024,224,3.91,27.6,22.55
coat_lite_small,1694.41,604.328,1024,224,3.96,22.09,19.84
resnetaa101d,1692.59,604.976,1024,224,9.12,17.56,44.57
legacy_seresnet101,1686.93,607.005,1024,224,7.61,15.74,49.33
tresnet_v2_l,1685.52,607.515,1024,224,8.81,16.34,46.17
hrnet_w18,1679.12,609.832,1024,224,4.32,16.31,21.3
vit_medium_patch16_gap_240,1667.0,614.264,1024,240,9.22,18.81,44.4
vit_tiny_patch16_384,1660.88,616.528,1024,384,4.7,25.39,5.79
regnetv_040,1659.81,616.926,1024,288,6.6,20.3,20.64
convnext_tiny_hnf,1659.73,616.951,1024,288,7.39,22.21,28.59
seresnet101,1655.13,618.666,1024,224,7.84,16.27,49.33
vit_base_patch32_384,1651.29,620.109,1024,384,13.06,16.5,88.3
vit_base_patch32_clip_384,1649.72,620.7,1024,384,13.06,16.5,88.3
regnety_040,1647.66,621.47,1024,288,6.61,20.3,20.65
regnety_032,1645.25,622.383,1024,288,5.29,18.61,19.44
gluon_resnet101_v1s,1642.29,623.505,1024,224,9.19,18.64,44.67
vgg13_bn,1634.19,626.596,1024,224,11.33,12.25,133.05
resnetaa50,1631.05,627.803,1024,288,8.52,19.24,25.56
mixer_b16_224_miil,1628.71,628.706,1024,224,12.62,14.53,59.88
mixer_b16_224,1627.79,629.061,1024,224,12.62,14.53,59.88
convnext_tiny,1626.95,629.384,1024,288,7.39,22.21,28.59
nf_resnet101,1620.77,631.785,1024,224,8.01,16.23,44.55
swinv2_cr_tiny_224,1618.15,632.807,1024,224,4.66,28.45,28.33
ecaresnet101d,1609.33,636.276,1024,224,8.08,17.07,44.57
twins_pcpvt_base,1605.41,637.831,1024,224,6.68,25.25,43.83
dla102x,1601.78,639.274,1024,224,5.89,19.42,26.31
ese_vovnet39b_evos,1601.47,639.4,1024,224,7.07,6.74,24.58
darknet53,1597.03,641.177,1024,288,11.78,15.68,41.61
resnetblur101d,1596.24,641.494,1024,224,9.12,17.94,44.57
resnet51q,1592.08,643.172,1024,288,8.07,20.94,35.7
swinv2_cr_tiny_ns_224,1591.39,643.448,1024,224,4.66,28.45,28.33
mixer_l32_224,1583.03,646.85,1024,224,11.27,19.86,206.94
resmlp_36_distilled_224,1577.86,648.967,1024,224,8.91,16.33,44.69
resmlp_36_224,1577.4,649.158,1024,224,8.91,16.33,44.69
resnetv2_50d_gn,1561.87,655.61,1024,288,7.24,19.7,25.57
botnet50ts_256,1556.81,246.643,384,256,5.54,22.23,22.74
nf_resnet50,1548.83,661.132,1024,288,6.88,18.37,25.56
resnetv2_50d_frn,1547.35,661.764,1024,224,4.33,11.92,25.59
halo2botnet50ts_256,1546.64,496.545,768,256,5.02,21.78,22.64
mvitv2_tiny,1534.63,667.247,1024,224,4.7,21.16,24.17
gluon_resnext101_32x4d,1505.04,680.366,1024,224,8.01,21.23,44.18
swsl_resnext101_32x4d,1504.46,680.63,1024,224,8.01,21.23,44.18
cs3darknet_x,1504.38,680.665,1024,288,10.6,14.36,35.05
ssl_resnext101_32x4d,1503.93,680.869,1024,224,8.01,21.23,44.18
resnext101_32x4d,1503.63,681.005,1024,224,8.01,21.23,44.18
resnest50d_4s2x40d,1497.58,683.755,1024,224,4.4,17.94,30.42
convnextv2_nano,1488.75,515.858,768,288,4.06,13.84,15.62
skresnext50_32x4d,1478.83,692.427,1024,224,4.5,17.18,27.48
mobilevitv2_200,1478.44,519.454,768,256,7.22,32.15,18.45
tresnet_l,1477.44,693.076,1024,224,10.88,11.9,55.99
mobilevitv2_200_in22ft1k,1477.37,519.83,768,256,7.22,32.15,18.45
vgg16,1475.59,693.946,1024,224,15.47,13.56,138.36
regnetz_c16,1475.58,693.953,1024,320,3.92,25.88,13.46
resnetv2_50d_evob,1468.61,697.244,1024,224,4.33,11.92,25.59
vit_medium_patch16_gap_256,1467.03,697.996,1024,256,10.59,22.15,38.86
res2net50_26w_8s,1466.52,698.239,1024,224,8.37,17.95,48.4
sequencer2d_s,1465.84,698.562,1024,224,4.96,11.31,27.65
eca_nfnet_l0,1461.61,700.586,1024,288,7.12,17.29,24.14
nfnet_l0,1460.27,701.228,1024,288,7.13,17.29,35.07
cs3sedarknet_x,1435.72,713.217,1024,288,10.6,14.37,35.4
resnet61q,1434.01,714.068,1024,288,9.87,21.52,36.85
res2net101_26w_4s,1424.71,718.728,1024,224,8.1,18.45,45.21
repvgg_b2g4,1415.15,723.581,1024,224,12.63,12.9,61.76
nest_tiny,1413.2,543.434,768,224,5.83,25.48,17.06
poolformer_s36,1408.65,726.922,1024,224,5.0,15.82,30.86
maxvit_rmlp_nano_rw_256,1404.06,546.971,768,256,4.47,31.92,15.5
convit_small,1397.72,732.608,1024,224,5.76,17.87,27.78
jx_nest_tiny,1387.89,553.347,768,224,5.83,25.48,17.06
maxvit_nano_rw_256,1378.18,557.246,768,256,4.46,30.28,15.45
nf_ecaresnet101,1373.28,745.649,1024,224,8.01,16.27,44.55
nf_seresnet101,1369.04,747.958,1024,224,8.02,16.27,49.33
gluon_seresnext101_32x4d,1358.35,753.84,1024,224,8.02,21.26,48.96
legacy_seresnext101_32x4d,1357.27,754.442,1024,224,8.02,21.26,48.96
efficientnet_b3_gn,1357.0,282.964,384,320,2.14,28.83,11.73
nfnet_f0,1356.65,754.786,1024,256,12.62,18.05,71.49
seresnext101_32x4d,1356.0,755.148,1024,224,8.02,21.26,48.96
resnetv2_152,1353.28,756.668,1024,224,11.55,22.56,60.19
xception,1353.17,567.542,768,299,8.4,35.83,22.86
twins_svt_base,1350.54,758.199,1024,224,8.59,26.33,56.07
crossvit_18_240,1343.82,761.996,1024,240,9.05,26.26,43.27
ese_vovnet99b_iabn,1343.72,762.049,1024,224,16.49,11.27,63.2
maxxvit_rmlp_nano_rw_256,1341.45,763.341,1024,256,4.37,26.05,16.78
regnetx_120,1339.05,764.708,1024,224,12.13,21.37,46.11
vgg16_bn,1336.79,765.998,1024,224,15.5,13.56,138.37
dpn92,1330.6,769.562,1024,224,6.54,18.21,37.67
tv_resnet152,1329.75,770.054,1024,224,11.56,22.56,60.19
gcvit_tiny,1328.61,770.718,1024,224,4.79,29.82,28.22
gluon_resnet152_v1b,1328.2,770.954,1024,224,11.56,22.56,60.19
resnet152,1327.13,771.578,1024,224,11.56,22.56,60.19
ese_vovnet99b,1316.93,777.554,1024,224,16.51,11.27,63.2
pvt_v2_b3,1316.31,777.917,1024,224,6.92,37.7,45.24
xcit_tiny_12_p8_224_dist,1300.55,787.348,1024,224,4.81,23.6,6.71
xcit_tiny_12_p8_224,1299.96,787.704,1024,224,4.81,23.6,6.71
crossvit_18_dagger_240,1298.96,788.312,1024,240,9.5,27.03,44.27
hrnet_w32,1297.82,789.002,1024,224,8.97,22.02,41.23
gluon_resnet152_v1c,1296.47,789.825,1024,224,11.8,23.36,60.21
resnetv2_152d,1296.37,789.881,1024,224,11.8,23.36,60.2
gluon_resnet152_v1d,1293.21,791.811,1024,224,11.8,23.36,60.21
vit_small_resnet50d_s16_224,1288.35,794.801,1024,224,13.48,24.82,57.53
cs3edgenet_x,1281.15,799.266,1024,288,14.59,16.36,47.82
edgenext_base,1272.74,804.548,1024,320,6.01,24.32,18.51
regnety_120,1268.38,807.318,1024,224,12.14,21.38,51.82
dla169,1258.34,813.753,1024,224,11.6,20.2,53.39
hrnet_w30,1252.2,817.74,1024,224,8.15,21.21,37.71
xception41p,1249.06,409.896,512,299,9.25,39.86,26.91
maxxvitv2_nano_rw_256,1248.81,819.967,1024,256,6.26,23.05,23.7
ecaresnet50t,1243.91,823.198,1024,320,8.82,24.13,25.57
vgg19,1237.03,827.774,1024,224,19.63,14.86,143.67
swin_small_patch4_window7_224,1228.67,833.406,1024,224,8.77,27.47,49.61
efficientnet_el_pruned,1220.93,838.69,1024,300,8.0,30.7,10.59
densenet161,1220.41,839.05,1024,224,7.79,11.06,28.68
efficientnet_el,1218.76,840.187,1024,300,8.0,30.7,10.59
deit_base_distilled_patch16_224,1211.4,845.292,1024,224,17.68,24.05,87.34
vit_base_patch16_224,1209.0,846.969,1024,224,17.58,23.9,86.57
vit_base_patch16_224_miil,1208.72,847.163,1024,224,17.59,23.91,94.4
deit_base_patch16_224,1208.56,847.275,1024,224,17.58,23.9,86.57
vit_base_patch16_clip_224,1205.77,849.236,1024,224,17.58,23.9,86.57
gluon_resnet152_v1s,1205.41,849.488,1024,224,12.92,24.96,60.32
coatnet_rmlp_1_rw_224,1201.89,851.979,1024,224,7.85,35.47,41.69
maxvit_tiny_rw_224,1200.3,853.107,1024,224,5.11,33.11,29.06
mixnet_xxl,1193.04,643.721,768,224,2.04,23.43,23.96
tf_efficientnet_el,1192.11,858.967,1024,300,8.0,30.7,10.59
swinv2_tiny_window8_256,1191.01,859.761,1024,256,5.96,24.57,28.35
volo_d1_224,1190.57,860.079,1024,224,6.94,24.43,26.63
repvgg_b2,1183.91,864.916,1024,224,20.45,12.9,89.02
legacy_seresnet152,1181.09,866.978,1024,224,11.33,22.08,66.82
xcit_small_24_p16_224_dist,1175.31,871.245,1024,224,9.1,23.64,47.67
xcit_small_24_p16_224,1174.76,871.656,1024,224,9.1,23.64,47.67
inception_v4,1168.76,876.127,1024,299,12.28,15.09,42.68
seresnet152,1166.02,878.19,1024,224,11.57,22.61,66.82
twins_pcpvt_large,1163.18,880.331,1024,224,9.84,35.82,60.99
deit3_base_patch16_224,1159.4,883.201,1024,224,17.58,23.9,86.59
deit3_base_patch16_224_in21ft1k,1159.14,883.404,1024,224,17.58,23.9,86.59
cait_xxs36_224,1156.4,885.493,1024,224,3.77,30.34,17.3
vit_base_patch32_clip_448,1154.9,886.645,1024,448,17.93,23.9,88.34
regnetx_160,1153.07,888.048,1024,224,15.99,25.52,54.28
dm_nfnet_f0,1152.75,888.293,1024,256,12.62,18.05,71.49
sequencer2d_m,1147.71,892.201,1024,224,6.55,14.26,38.31
repvgg_b3g4,1145.87,893.631,1024,224,17.89,15.1,83.83
mvitv2_small_cls,1144.7,894.542,1024,224,7.04,28.17,34.87
mvitv2_small,1143.83,895.224,1024,224,7.0,28.08,34.87
efficientnet_lite4,1139.64,336.935,384,380,4.04,45.66,13.01
tnt_s_patch16_224,1135.12,902.091,1024,224,5.24,24.37,23.76
convmixer_1024_20_ks9_p14,1130.85,905.497,1024,224,5.55,5.51,24.38
vgg19_bn,1127.16,908.464,1024,224,19.66,14.86,143.68
vit_relpos_base_patch16_clsgap_224,1124.58,910.547,1024,224,17.6,25.12,86.43
vit_relpos_base_patch16_cls_224,1122.76,912.026,1024,224,17.6,25.12,86.43
coatnet_rmlp_1_rw2_224,1119.61,914.591,1024,224,8.11,40.13,41.72
beit_base_patch16_224,1109.32,923.073,1024,224,17.58,23.9,86.53
xception41,1107.6,462.251,512,299,9.28,39.86,26.97
tresnet_xl,1106.51,925.423,1024,224,15.17,15.34,78.44
beitv2_base_patch16_224,1106.05,925.798,1024,224,17.58,23.9,86.53
coat_tiny,1099.16,931.604,1024,224,4.35,27.2,5.5
vit_base_patch16_gap_224,1085.51,943.323,1024,224,17.49,25.59,86.57
maxvit_tiny_tf_224,1081.57,710.062,768,224,5.6,35.78,30.92
vit_relpos_base_patch16_224,1078.21,949.713,1024,224,17.51,24.97,86.43
nf_regnet_b4,1075.82,951.823,1024,384,4.7,28.61,30.21
coatnet_1_rw_224,1074.48,953.005,1024,224,8.04,34.6,41.72
dla102x2,1070.83,956.252,1024,224,9.34,29.91,41.28
pit_b_224,1066.8,479.928,512,224,12.42,32.94,73.76
pit_b_distilled_224,1063.31,481.504,512,224,12.5,33.07,74.79
tf_efficientnet_lite4,1058.68,362.703,384,380,4.04,45.66,13.01
efficientnetv2_s,1057.28,968.508,1024,384,8.44,35.77,21.46
vit_large_r50_s32_224,1034.79,989.556,1024,224,19.58,24.41,328.99
vit_small_patch16_36x1_224,1032.1,992.142,1024,224,13.71,35.69,64.67
efficientnet_b3_g8_gn,1031.26,496.465,512,320,3.2,28.83,14.25
tf_efficientnetv2_s,1029.13,995.002,1024,384,8.44,35.77,21.46
flexivit_base,1028.55,995.558,1024,240,20.29,28.36,86.59
vit_base_patch16_rpn_224,1016.66,1007.208,1024,224,17.49,23.75,86.54
vit_small_r26_s32_384,1011.11,1012.73,1024,384,10.43,29.85,36.47
vit_small_patch16_18x2_224,1005.34,1018.547,1024,224,13.71,35.69,64.67
swinv2_cr_small_224,1000.71,1023.259,1024,224,9.07,50.27,49.7
efficientnetv2_rw_s,995.91,1028.19,1024,384,8.72,38.03,23.94
wide_resnet101_2,995.32,1028.801,1024,224,22.8,21.23,126.89
swinv2_cr_small_ns_224,989.25,1035.114,1024,224,9.08,50.27,49.7
vit_relpos_base_patch16_rpn_224,986.84,1037.641,1024,224,17.51,24.97,86.41
coatnet_1_224,984.69,519.944,512,224,8.7,39.0,42.23
resnet200,983.36,1041.314,1024,224,15.07,32.19,64.67
dpn98,982.09,1042.657,1024,224,11.73,25.2,61.57
convnext_small,981.97,1042.782,1024,288,14.39,35.65,50.22
cs3se_edgenet_x,975.89,1049.279,1024,320,18.01,20.21,50.72
regnety_080,969.67,1056.01,1024,288,13.22,29.69,39.18
poolformer_m36,966.97,1058.965,1024,224,8.8,22.02,56.17
resnest101e,963.69,1062.57,1024,256,13.38,28.66,48.28
regnetz_b16_evos,955.65,803.632,768,288,2.36,16.43,9.74
twins_svt_large,954.95,1072.291,1024,224,15.15,35.1,99.27
pvt_v2_b4,952.02,1075.594,1024,224,10.14,53.74,62.56
gluon_resnext101_64x4d,944.48,1084.183,1024,224,15.52,31.21,83.46
regnetv_064,944.32,1084.367,1024,288,10.55,27.11,30.58
regnety_064,944.18,1084.526,1024,288,10.56,27.11,30.58
maxvit_rmlp_tiny_rw_256,941.64,815.588,768,256,6.77,46.92,29.15
regnetz_d8,936.16,1093.814,1024,320,6.19,37.08,23.37
resnetrs101,936.12,1093.858,1024,288,13.56,28.53,63.62
regnetz_d32,933.58,1096.833,1024,320,9.33,37.08,27.58
ig_resnext101_32x8d,930.9,1099.997,1024,224,16.48,31.21,88.79
swsl_resnext101_32x8d,930.28,1100.725,1024,224,16.48,31.21,88.79
resnext101_32x8d,929.98,1101.084,1024,224,16.48,31.21,88.79
ssl_resnext101_32x8d,929.0,1102.24,1024,224,16.48,31.21,88.79
convnextv2_tiny,925.13,553.423,512,288,7.39,22.21,28.64
convnextv2_small,924.53,1107.57,1024,224,8.71,21.56,50.32
maxvit_tiny_rw_256,921.72,833.209,768,256,6.74,44.35,29.07
inception_resnet_v2,917.69,1115.834,1024,299,13.18,25.06,55.84
ens_adv_inception_resnet_v2,917.66,1115.871,1024,299,13.18,25.06,55.84
maxxvit_rmlp_tiny_rw_256,914.74,1119.428,1024,256,6.66,39.76,29.64
xcit_tiny_24_p16_384_dist,912.61,1122.045,1024,384,6.87,34.29,12.12
cait_s24_224,908.65,1126.929,1024,224,9.35,40.58,46.92
pvt_v2_b5,904.89,1131.615,1024,224,11.76,50.92,81.96
nest_small,902.63,850.834,768,224,10.35,40.04,38.35
repvgg_b3,901.73,1135.583,1024,224,29.16,15.1,123.09
maxvit_tiny_pm_256,896.67,1141.994,1024,256,6.61,47.9,30.09
xception65p,896.53,571.079,512,299,13.91,52.48,39.82
swin_s3_small_224,896.35,856.792,768,224,9.43,37.84,49.74
jx_nest_small,892.32,860.663,768,224,10.35,40.04,38.35
efficientnet_b4,890.89,431.018,384,384,4.51,50.04,19.34
gmlp_b16_224,885.75,1156.072,1024,224,15.78,30.21,73.08
gluon_seresnext101_64x4d,885.23,1156.747,1024,224,15.53,31.25,88.23
hrnet_w40,881.9,1161.12,1024,224,12.75,25.29,57.56
efficientformer_l7,877.43,1167.027,1024,224,10.17,24.45,82.23
coat_mini,874.29,1171.227,1024,224,6.82,33.68,10.34
resnet101d,871.81,1174.559,1024,320,16.48,34.77,44.57
swin_base_patch4_window7_224,870.1,1176.867,1024,224,15.47,36.63,87.77
regnetz_040,868.17,884.605,768,320,6.35,37.78,27.12
regnetz_040h,862.76,890.151,768,320,6.43,37.94,28.94
mobilevitv2_150_384_in22ft1k,848.7,301.627,256,384,9.2,54.25,10.59
resnetv2_50d_evos,844.34,909.573,768,288,7.15,19.7,25.59
tf_efficientnet_b4,838.16,458.136,384,380,4.49,49.49,19.34
crossvit_base_240,835.31,919.411,768,240,21.22,36.33,105.03
vit_base_r50_s16_224,821.15,1247.01,1024,224,21.67,35.31,114.69
xcit_medium_24_p16_224_dist,819.59,1249.397,1024,224,16.13,31.71,84.4
xcit_medium_24_p16_224,818.73,1250.697,1024,224,16.13,31.71,84.4
gcvit_small,807.46,1268.151,1024,224,8.57,41.61,51.09
gluon_xception65,806.21,635.055,512,299,13.96,52.48,39.92
xception65,800.01,639.983,512,299,13.96,52.48,39.92
mvitv2_base,799.31,1281.092,1024,224,10.16,40.5,51.47
hrnet_w44,789.29,1297.348,1024,224,14.94,26.92,67.06
vit_base_patch16_plus_240,780.68,1311.665,1024,240,27.41,33.08,117.56
hrnet_w48,780.39,1312.147,1024,224,17.34,28.56,77.47
swinv2_tiny_window16_256,778.19,657.926,512,256,6.68,39.02,28.35
tresnet_m_448,775.99,1319.596,1024,448,22.94,29.21,31.39
xcit_small_12_p16_384_dist,760.88,1345.804,1024,384,14.14,36.51,26.25
vit_small_patch16_384,750.95,1022.685,768,384,15.52,50.78,22.2
maxvit_rmlp_small_rw_224,745.49,1373.585,1024,224,10.75,49.3,64.9
sequencer2d_l,742.48,1379.149,1024,224,9.74,22.12,54.3
swinv2_small_window8_256,738.39,1386.788,1024,256,11.58,40.14,49.73
swin_s3_base_224,730.45,1401.854,1024,224,13.69,48.26,71.13
poolformer_m48,729.44,1403.808,1024,224,11.59,29.17,73.47
densenet264d_iabn,727.43,1407.671,1024,224,13.47,14.0,72.74
vit_relpos_base_patch16_plus_240,723.43,1415.468,1024,240,27.3,34.33,117.38
dpn131,722.72,1416.854,1024,224,16.09,32.97,79.25
tnt_b_patch16_224,722.12,1418.026,1024,224,14.09,39.01,65.41
deit3_small_patch16_384,717.36,1070.572,768,384,15.52,50.78,22.21
deit3_small_patch16_384_in21ft1k,716.76,1071.477,768,384,15.52,50.78,22.21
swinv2_cr_base_224,715.64,1430.874,1024,224,15.86,59.66,87.88
eca_nfnet_l1,713.15,1435.867,1024,320,14.92,34.42,41.41
coatnet_2_rw_224,709.88,721.237,512,224,15.09,49.22,73.87
swinv2_cr_base_ns_224,709.69,1442.871,1024,224,15.86,59.66,87.88
coatnet_rmlp_2_rw_224,708.85,722.285,512,224,15.18,54.78,73.88
convit_base,706.65,1449.076,1024,224,17.52,31.77,86.54
mobilevitv2_175_384_in22ft1k,703.41,363.928,256,384,12.47,63.29,14.25
maxvit_small_tf_224,701.58,729.767,512,224,11.66,53.17,68.93
densenet264,701.03,1460.686,1024,224,12.95,12.8,72.69
ecaresnet200d,694.19,1475.094,1024,256,20.0,43.15,64.69
resnetv2_50x1_bitm,691.29,740.624,512,448,16.62,44.46,25.55
seresnet200d,691.25,1481.355,1024,256,20.01,43.15,71.86
xcit_tiny_24_p8_224,684.73,1495.467,1024,224,9.21,45.39,12.11
xcit_tiny_24_p8_224_dist,684.22,1496.573,1024,224,9.21,45.39,12.11
convnext_base,682.42,1500.518,1024,288,25.43,47.53,88.59
volo_d2_224,663.51,1543.3,1024,224,14.34,41.34,58.68
coatnet_2_224,660.84,581.062,384,224,16.5,52.67,74.68
legacy_senet154,654.15,1565.387,1024,224,20.77,38.69,115.09
gluon_senet154,654.04,1565.641,1024,224,20.77,38.69,115.09
senet154,653.94,1565.866,1024,224,20.77,38.69,115.09
xcit_nano_12_p8_384_dist,646.53,1583.823,1024,384,6.34,46.08,3.05
dpn107,646.38,1584.202,1024,224,18.38,33.46,86.92
nest_base,640.55,799.298,512,224,17.96,53.39,67.72
jx_nest_base,633.53,808.151,512,224,17.96,53.39,67.72
mobilevitv2_200_384_in22ft1k,626.31,408.731,256,384,16.24,72.34,18.45
xception71,619.72,826.163,512,299,18.09,69.92,42.34
hrnet_w64,618.15,1656.539,1024,224,28.97,35.09,128.06
resnet152d,618.09,1656.699,1024,320,24.08,47.67,60.21
regnetz_c16_evos,604.19,847.399,512,320,3.86,25.88,13.49
gcvit_base,594.61,1722.135,1024,224,14.87,55.48,90.32
regnety_160,594.3,1292.258,768,288,26.37,38.07,83.59
maxxvit_rmlp_small_rw_256,588.15,1741.023,1024,256,14.67,58.38,66.01
xcit_small_12_p8_224,582.04,1759.324,1024,224,18.69,47.21,26.21
xcit_small_12_p8_224_dist,581.74,1760.224,1024,224,18.69,47.21,26.21
maxvit_rmlp_small_rw_256,575.72,1333.976,768,256,14.15,66.09,64.9
regnetx_320,551.07,1393.631,768,224,31.81,36.3,107.81
seresnet152d,547.51,1870.27,1024,320,24.09,47.72,66.84
resnetrs152,544.33,1881.196,1024,320,24.34,48.14,86.62
vit_large_patch32_384,543.23,1884.997,1024,384,45.31,43.86,306.63
halonet_h1,540.47,473.65,256,256,3.0,51.17,8.1
seresnet269d,540.42,1894.818,1024,256,26.59,53.6,113.67
swinv2_base_window8_256,529.22,1451.182,768,256,20.37,52.59,87.92
maxxvitv2_rmlp_base_rw_224,523.43,1956.308,1024,224,24.2,62.77,116.09
resnext101_64x4d,521.77,1962.525,1024,288,25.66,51.59,83.46
regnetz_e8,521.5,1472.647,768,320,15.46,63.94,57.7
mixer_l16_224,518.26,1975.807,1024,224,44.6,41.69,208.2
vit_medium_patch16_gap_384,508.63,1006.611,512,384,26.08,67.54,39.03
swin_large_patch4_window7_224,501.11,1532.586,768,224,34.53,54.94,196.53
regnety_320,490.98,2085.591,1024,224,32.34,30.26,145.05
swinv2_small_window16_256,487.64,1049.932,512,256,12.82,66.29,49.73
seresnext101_32x8d,483.23,2119.074,1024,288,27.24,51.63,93.57
vit_small_patch8_224,478.05,1071.009,512,224,22.44,80.84,21.67
ig_resnext101_32x16d,477.64,2143.862,1024,224,36.27,51.18,194.03
swsl_resnext101_32x16d,476.69,2148.145,1024,224,36.27,51.18,194.03
ssl_resnext101_32x16d,476.06,2150.954,1024,224,36.27,51.18,194.03
seresnext101d_32x8d,475.05,2155.547,1024,288,27.64,52.95,93.59
nf_regnet_b5,470.14,1089.029,512,456,11.7,61.95,49.74
xcit_large_24_p16_224_dist,468.86,2184.017,1024,224,35.86,47.27,189.1
xcit_large_24_p16_224,468.75,2184.529,1024,224,35.86,47.27,189.1
volo_d3_224,463.72,2208.199,1024,224,20.78,60.09,86.33
nfnet_f1,463.52,2209.163,1024,320,35.97,46.77,132.63
efficientnet_b5,460.91,555.412,256,448,9.59,93.56,30.39
resnet200d,453.15,2259.739,1024,320,31.25,67.33,64.69
efficientnetv2_m,451.89,2266.018,1024,416,18.6,67.5,54.14
seresnextaa101d_32x8d,447.26,2289.498,1024,288,28.51,56.44,93.59
efficientnetv2_rw_m,437.1,1757.005,768,416,21.49,79.62,53.24
swinv2_cr_large_224,422.08,1819.551,768,224,35.1,78.42,196.68
coatnet_rmlp_3_rw_224,421.87,910.226,384,224,33.56,79.47,165.15
xcit_tiny_12_p8_384_dist,421.04,2432.044,1024,384,14.13,69.14,6.71
swinv2_cr_tiny_384,419.77,609.847,256,384,15.34,161.01,28.33
maxvit_rmlp_base_rw_224,419.03,1832.808,768,224,23.15,92.64,116.14
resnetv2_152x2_bit_teacher,418.89,2444.553,1024,224,46.95,45.11,236.34
resnetv2_101x1_bitm,418.36,1223.813,512,448,31.65,64.93,44.54
dm_nfnet_f1,409.02,1877.643,768,320,35.97,46.77,132.63
xcit_small_24_p16_384_dist,407.47,2513.062,1024,384,26.72,68.58,47.67
coatnet_3_rw_224,404.39,633.033,256,224,33.44,73.83,181.81
tf_efficientnet_b5,403.59,634.298,256,456,10.46,98.86,30.39
convnextv2_base,402.92,1270.715,512,288,25.43,47.53,88.72
resnetrs200,396.11,2585.123,1024,320,31.51,67.81,93.21
tresnet_l_448,395.6,2588.481,1024,448,43.5,47.56,55.99
eva_large_patch14_196,391.22,2617.408,1024,196,61.57,63.52,304.14
vit_large_patch16_224,389.92,2626.132,1024,224,61.6,63.52,304.33
regnetz_d8_evos,389.86,1969.937,768,320,7.03,38.92,23.46
maxvit_base_tf_224,387.71,1320.545,512,224,24.04,95.01,119.47
coatnet_3_224,387.35,660.882,256,224,36.56,79.01,166.97
crossvit_15_dagger_408,386.57,662.227,256,408,21.45,95.05,28.5
vit_base_patch16_18x2_224,384.3,2664.545,1024,224,52.51,71.38,256.73
deit3_large_patch16_224,376.93,2716.643,1024,224,61.6,63.52,304.37
deit3_large_patch16_224_in21ft1k,376.54,2719.504,1024,224,61.6,63.52,304.37
tf_efficientnetv2_m,374.38,2051.373,768,480,24.76,89.84,54.14
convnext_large,371.39,1378.579,512,288,56.87,71.29,197.77
beitv2_large_patch16_224,360.12,2843.465,1024,224,61.6,63.52,304.43
beit_large_patch16_224,359.86,2845.558,1024,224,61.6,63.52,304.43
swinv2_base_window12to16_192to256_22kft1k,359.31,1068.705,384,256,22.02,84.71,87.92
swinv2_base_window16_256,359.09,1069.342,384,256,22.02,84.71,87.92
eca_nfnet_l2,347.1,2212.621,768,384,30.05,68.28,56.72
flexivit_large,333.31,3072.173,1024,240,70.99,75.39,304.36
vit_large_r50_s32_384,332.86,3076.333,1024,384,57.43,76.52,329.09
maxxvitv2_rmlp_large_rw_224,330.79,3095.576,1024,224,44.14,87.15,215.42
resnest200e,317.25,3227.754,1024,320,35.69,82.78,70.2
maxvit_tiny_tf_384,317.22,807.002,256,384,17.53,123.42,30.98
convmixer_768_32,309.28,3310.892,1024,224,19.55,25.95,21.11
deit_base_patch16_384,306.13,1254.335,384,384,55.54,101.56,86.86
vit_base_patch16_384,306.13,1254.349,384,384,55.54,101.56,86.86
vit_base_patch16_clip_384,305.56,1256.673,384,384,55.54,101.56,86.86
xcit_small_24_p8_224_dist,305.18,3355.41,1024,224,35.81,90.78,47.63
deit_base_distilled_patch16_384,304.96,1259.16,384,384,55.65,101.82,87.63
xcit_small_24_p8_224,304.86,3358.887,1024,224,35.81,90.78,47.63
nasnetalarge,300.31,1278.679,384,331,23.89,90.56,88.75
volo_d1_384,299.05,1712.072,512,384,22.75,108.55,26.78
volo_d4_224,295.86,3461.069,1024,224,44.34,80.22,192.96
deit3_base_patch16_384,294.03,1305.985,384,384,55.54,101.56,86.88
deit3_base_patch16_384_in21ft1k,293.78,1307.085,384,384,55.54,101.56,86.88
tresnet_xl_448,292.43,2626.294,768,448,60.65,61.31,78.44
pnasnet5large,285.95,1342.894,384,331,25.04,92.89,86.06
vit_large_patch14_224,285.66,3584.705,1024,224,81.08,88.79,304.2
vit_large_patch14_clip_224,285.43,3587.599,1024,224,81.08,88.79,304.2
crossvit_18_dagger_408,283.82,901.967,256,408,32.47,124.87,44.61
xcit_medium_24_p16_384_dist,282.22,3628.317,1024,384,47.39,91.64,84.4
cait_xxs24_384,275.38,3718.492,1024,384,9.63,122.66,12.03
regnety_640,271.79,2825.663,768,224,64.16,42.5,281.38
maxvit_large_tf_224,268.97,1427.67,384,224,43.68,127.35,211.79
nfnet_f2,263.0,3893.59,1024,352,63.22,79.06,193.78
beit_base_patch16_384,260.66,1473.146,384,384,55.54,101.56,86.74
swinv2_cr_small_384,258.79,989.214,256,384,29.7,298.03,49.7
ecaresnet269d,257.79,3972.16,1024,352,50.25,101.25,102.09
resnetrs270,249.11,4110.633,1024,352,51.13,105.48,129.86
mvitv2_large,248.64,2059.181,512,224,43.87,112.02,217.99
efficientnet_b6,246.42,519.432,128,528,19.4,167.39,43.04
convnext_xlarge,241.35,2121.412,512,288,100.8,95.05,350.2
convnextv2_large,238.64,1072.708,256,288,56.87,71.29,197.96
tf_efficientnet_b6,236.4,541.434,128,528,19.4,167.39,43.04
swin_base_patch4_window12_384,235.04,816.885,192,384,47.19,134.78,87.9
dm_nfnet_f2,234.34,3277.279,768,352,63.22,79.06,193.78
coatnet_4_224,228.52,1120.23,256,224,62.48,129.26,275.43
vit_base_r50_s16_384,227.31,1689.303,384,384,67.43,135.03,98.95
efficientnetv2_l,221.97,2306.653,512,480,56.4,157.99,118.52
xcit_tiny_24_p8_384_dist,221.23,4628.611,1024,384,27.05,132.95,12.11
ig_resnext101_32x32d,220.61,2320.857,512,224,87.29,91.12,468.53
swinv2_large_window12to16_192to256_22kft1k,219.46,1166.485,256,256,47.81,121.53,196.74
tf_efficientnetv2_l,219.35,2334.183,512,480,56.4,157.99,118.52
resmlp_big_24_224,214.31,4778.166,1024,224,100.23,87.31,129.14
resmlp_big_24_224_in22ft1k,214.13,4782.043,1024,224,100.23,87.31,129.14
resmlp_big_24_distilled_224,214.04,4784.169,1024,224,100.23,87.31,129.14
xcit_medium_24_p8_224_dist,210.1,4873.763,1024,224,63.53,121.23,84.32
xcit_medium_24_p8_224,210.01,4875.864,1024,224,63.53,121.23,84.32
maxvit_small_tf_384,208.79,919.556,192,384,35.87,183.65,69.02
vit_base_patch8_224,199.59,1282.637,256,224,78.22,161.69,86.58
eca_nfnet_l3,199.58,2565.434,512,448,52.55,118.4,72.04
volo_d5_224,196.25,5217.924,1024,224,72.4,118.11,295.46
xcit_small_12_p8_384_dist,194.27,2635.521,512,384,54.92,138.29,26.21
cait_xs24_384,192.73,3984.863,768,384,19.28,183.98,26.67
swinv2_cr_base_384,184.92,1384.392,256,384,50.57,333.68,87.88
cait_xxs36_384,184.35,5554.56,1024,384,14.35,183.7,17.37
swinv2_cr_huge_224,183.61,2091.395,384,224,115.97,121.08,657.83
convnext_xxlarge,183.01,2098.268,384,224,151.66,95.29,846.47
coatnet_rmlp_2_rw_384,178.88,715.532,128,384,47.69,209.43,73.88
convmixer_1536_20,173.51,5901.752,1024,224,48.68,33.03,51.63
volo_d2_384,168.46,1519.603,256,384,46.17,184.51,58.87
resnetrs350,168.28,6085.136,1024,384,77.59,154.74,163.96
xcit_large_24_p16_384_dist,160.71,4778.847,768,384,105.35,137.17,189.1
resnetv2_152x2_bit_teacher_384,159.55,1604.488,256,384,136.16,132.56,236.34
maxvit_xlarge_tf_224,155.79,1643.178,256,224,97.49,191.02,474.95
maxvit_tiny_tf_512,155.64,822.373,128,512,33.49,257.59,31.05
regnety_1280,155.18,2474.502,384,224,127.66,71.58,644.81
vit_huge_patch14_224,154.03,6647.897,1024,224,167.43,139.43,658.75
vit_huge_patch14_clip_224,153.92,6652.944,1024,224,167.4,139.41,632.05
maxxvitv2_rmlp_base_rw_384,153.34,1669.502,256,384,72.98,213.74,116.09
efficientnetv2_xl,152.49,3357.61,512,512,93.85,247.32,208.12
tf_efficientnetv2_xl,151.4,2536.254,384,512,93.85,247.32,208.12
deit3_huge_patch14_224_in21ft1k,149.08,6868.834,1024,224,167.4,139.41,632.13
deit3_huge_patch14_224,149.01,6871.974,1024,224,167.4,139.41,632.13
cait_s24_384,148.46,3448.684,512,384,32.17,245.31,47.06
resnest269e,147.61,3468.584,512,416,77.69,171.98,110.93
nfnet_f3,147.43,3472.717,512,416,115.58,141.78,254.92
efficientnet_b7,142.41,674.084,96,600,38.33,289.94,66.35
resnetv2_50x3_bitm,138.27,1388.564,192,448,145.7,133.37,217.32
tf_efficientnet_b7,137.89,696.181,96,600,38.33,289.94,66.35
swin_large_patch4_window12_384,137.6,930.229,128,384,104.08,202.16,196.74
ig_resnext101_32x48d,132.29,2902.628,384,224,153.57,131.06,828.41
dm_nfnet_f3,127.59,4012.898,512,416,115.58,141.78,254.92
coatnet_5_224,125.18,1022.512,128,224,145.49,194.24,687.47
maxvit_rmlp_base_rw_384,121.26,2111.079,256,384,70.97,318.95,116.14
xcit_large_24_p8_224,119.97,6401.598,768,224,141.23,181.56,188.93
xcit_large_24_p8_224_dist,119.94,6403.17,768,224,141.23,181.56,188.93
resnetrs420,119.93,6403.598,768,416,108.45,213.79,191.89
resnetv2_152x2_bitm,117.33,2181.801,256,448,184.99,180.43,236.34
maxvit_base_tf_384,113.69,1688.826,192,384,73.8,332.9,119.65
swinv2_cr_large_384,113.07,1132.03,128,384,108.95,404.96,196.68
eva_large_patch14_336,102.65,2493.904,256,336,191.1,270.24,304.53
vit_large_patch14_clip_336,102.47,2498.286,256,336,191.11,270.24,304.53
vit_large_patch16_384,102.37,2500.639,256,384,191.21,270.24,304.72
xcit_small_24_p8_384_dist,102.36,5001.728,512,384,105.24,265.91,47.63
eva_giant_patch14_224,101.75,10063.521,1024,224,267.18,192.64,1012.56
vit_giant_patch14_224,100.42,7648.057,768,224,267.18,192.64,1012.61
vit_giant_patch14_clip_224,100.32,7655.265,768,224,267.18,192.64,1012.65
cait_s36_384,99.37,5152.338,512,384,47.99,367.4,68.37
deit3_large_patch16_384,99.34,2577.037,256,384,191.21,270.24,304.76
deit3_large_patch16_384_in21ft1k,99.27,2578.907,256,384,191.21,270.24,304.76
regnety_2560,97.99,2612.623,256,224,257.07,87.48,826.14
maxvit_small_tf_512,97.85,981.11,96,512,67.26,383.77,69.13
swinv2_base_window12to24_192to384_22kft1k,95.95,666.98,64,384,55.25,280.36,87.92
efficientnet_b8,95.3,1007.298,96,672,63.48,442.89,87.41
tf_efficientnet_b8,92.65,1036.1,96,672,63.48,442.89,87.41
beit_large_patch16_384,88.55,2890.891,256,384,191.21,270.24,305.0
resnetv2_101x3_bitm,83.1,2310.491,192,448,280.33,194.78,387.93
maxvit_large_tf_384,80.34,1593.284,128,384,132.55,445.84,212.03
nfnet_f4,79.54,4827.723,384,512,216.26,262.26,316.07
volo_d3_448,73.5,2612.274,192,448,96.33,446.83,86.63
dm_nfnet_f4,71.41,3584.699,256,512,216.26,262.26,316.07
xcit_medium_24_p8_384_dist,70.91,5415.294,384,384,186.67,354.73,84.32
swinv2_large_window12to24_192to384_22kft1k,60.84,788.97,48,384,116.15,407.83,196.74
vit_gigantic_patch14_clip_224,60.15,8511.823,512,224,483.96,275.37,1844.91
vit_gigantic_patch14_224,60.11,8517.291,512,224,483.95,275.37,1844.44
nfnet_f5,58.02,4412.387,256,544,290.97,349.71,377.21
vit_huge_patch14_clip_336,57.29,4468.831,256,336,390.97,407.54,632.46
convnextv2_huge,56.06,1712.576,96,384,337.96,232.35,660.29
volo_d4_448,54.47,2349.801,128,448,197.13,527.35,193.41
tf_efficientnet_l2,54.12,1182.593,64,475,172.11,609.89,480.31
maxvit_base_tf_512,52.65,1823.292,96,512,138.02,703.99,119.88
swinv2_cr_giant_224,52.12,2455.882,128,224,483.85,309.15,2598.76
dm_nfnet_f5,50.7,5049.339,256,544,290.97,349.71,377.21
swinv2_cr_huge_384,48.86,1309.971,64,384,352.04,583.18,657.94
maxvit_xlarge_tf_384,46.24,2076.289,96,384,292.78,668.76,475.32
nfnet_f6,44.3,5778.548,256,576,378.69,452.2,438.36
xcit_large_24_p8_384_dist,40.2,6368.127,256,384,415.0,531.82,188.93
eva_giant_patch14_336,39.77,6436.237,256,336,620.64,550.67,1013.01
dm_nfnet_f6,39.62,6461.626,256,576,378.69,452.2,438.36
maxvit_large_tf_512,38.67,1654.908,64,512,244.75,942.15,212.33
volo_d5_448,37.56,3408.043,128,448,315.06,737.92,295.91
beit_large_patch16_512,35.36,2715.28,96,512,362.24,656.39,305.67
nfnet_f7,34.74,7370.0,256,608,480.39,570.85,499.5
cait_m36_384,32.36,7912.123,256,384,173.11,734.81,271.22
resnetv2_152x4_bitm,30.0,4266.89,128,480,844.84,414.26,936.53
volo_d5_512,26.35,4857.602,128,512,425.09,1105.37,296.09
maxvit_xlarge_tf_512,23.12,2076.455,48,512,534.14,1413.22,475.77
efficientnet_l2,21.26,1505.032,32,800,479.12,1707.39,480.31
swinv2_cr_giant_384,15.03,2129.6,32,384,1450.71,1394.86,2598.76
cait_m48_448,13.69,9353.048,128,448,329.41,1708.23,356.46
eva_giant_patch14_560,10.36,4631.037,48,560,1906.76,2577.17,1014.45
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-imagenet.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,90.052,9.948,99.048,0.952,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,89.970,10.030,99.012,0.988,305.08,448,1.000,bicubic
eva_giant_patch14_560.m30m_ft_in22k_in1k,89.786,10.214,98.992,1.008,"1,014.45",560,1.000,bicubic
eva02_large_patch14_448.mim_in22k_ft_in1k,89.622,10.378,98.950,1.050,305.08,448,1.000,bicubic
eva02_large_patch14_448.mim_m38m_ft_in1k,89.574,10.426,98.924,1.076,305.08,448,1.000,bicubic
eva_giant_patch14_336.m30m_ft_in22k_in1k,89.566,10.434,98.952,1.048,"1,013.01",336,1.000,bicubic
eva_giant_patch14_336.clip_ft_in1k,89.466,10.534,98.826,1.174,"1,013.01",336,1.000,bicubic
eva_large_patch14_336.in22k_ft_in22k_in1k,89.206,10.794,98.854,1.146,304.53,336,1.000,bicubic
eva_giant_patch14_224.clip_ft_in1k,88.880,11.120,98.680,1.320,"1,012.56",224,0.900,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_512,88.858,11.142,98.748,1.252,660.29,512,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,88.690,11.310,98.724,1.276,87.12,448,1.000,bicubic
convnextv2_huge.fcmae_ft_in22k_in1k_384,88.670,11.330,98.738,1.262,660.29,384,1.000,bicubic
eva_large_patch14_336.in22k_ft_in1k,88.670,11.330,98.722,1.278,304.53,336,1.000,bicubic
convnext_xxlarge.clip_laion2b_soup_ft_in1k,88.604,11.396,98.708,1.292,846.47,256,1.000,bicubic
beit_large_patch16_512.in22k_ft_in22k_in1k,88.596,11.404,98.656,1.344,305.67,512,1.000,bicubic
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,88.592,11.408,98.662,1.338,632.46,336,1.000,bicubic
eva_large_patch14_196.in22k_ft_in22k_in1k,88.574,11.426,98.658,1.342,304.14,196,1.000,bicubic
maxvit_xlarge_tf_512.in21k_ft_in1k,88.538,11.462,98.644,1.356,475.77,512,1.000,bicubic
beit_large_patch16_384.in22k_ft_in22k_in1k,88.402,11.598,98.608,1.392,305.00,384,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in22k_in1k,88.394,11.606,98.598,1.402,304.43,224,0.950,bicubic
tf_efficientnet_l2.ns_jft_in1k,88.352,11.648,98.648,1.352,480.31,800,0.960,bicubic
maxvit_xlarge_tf_384.in21k_ft_in1k,88.314,11.686,98.544,1.456,475.32,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,88.306,11.694,98.582,1.418,200.13,384,1.000,bicubic
vit_large_patch14_clip_336.openai_ft_in12k_in1k,88.268,11.732,98.526,1.474,304.53,336,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,88.256,11.744,98.552,1.448,632.05,224,1.000,bicubic
eva02_base_patch14_448.mim_in22k_ft_in1k,88.252,11.748,98.564,1.436,87.12,448,1.000,bicubic
tf_efficientnet_l2.ns_jft_in1k_475,88.234,11.766,98.546,1.454,480.31,475,0.936,bicubic
regnety_1280.swag_ft_in1k,88.230,11.770,98.686,1.314,644.81,384,1.000,bicubic
maxvit_large_tf_512.in21k_ft_in1k,88.224,11.776,98.598,1.402,212.33,512,1.000,bicubic
maxvit_base_tf_512.in21k_ft_in1k,88.220,11.780,98.530,1.470,119.88,512,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k_384,88.198,11.802,98.528,1.472,197.96,384,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,88.180,11.820,98.572,1.428,304.53,336,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in12k_in1k,88.174,11.826,98.546,1.454,304.20,224,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k_384,88.058,11.942,98.582,1.418,98.75,384,1.000,bicubic
maxvit_large_tf_384.in21k_ft_in1k,87.986,12.014,98.568,1.432,212.03,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,87.958,12.042,98.476,1.524,200.13,320,1.000,bicubic
eva_large_patch14_196.in22k_ft_in1k,87.932,12.068,98.498,1.502,304.14,196,1.000,bicubic
maxvit_base_tf_384.in21k_ft_in1k,87.922,12.078,98.544,1.456,119.65,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,87.894,12.106,98.408,1.592,304.20,224,1.000,bicubic
vit_large_patch14_clip_336.laion2b_ft_in1k,87.856,12.144,98.368,1.632,304.53,336,1.000,bicubic
vit_large_patch14_clip_224.openai_ft_in1k,87.854,12.146,98.426,1.574,304.20,224,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,87.848,12.152,98.446,1.554,200.13,384,1.000,bicubic
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,87.828,12.172,98.372,1.628,116.14,384,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k_384,87.752,12.248,98.556,1.444,350.20,384,1.000,bicubic
deit3_large_patch16_384.fb_in22k_ft_in1k,87.720,12.280,98.512,1.488,304.76,384,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k_384,87.644,12.356,98.416,1.584,88.72,384,1.000,bicubic
convformer_b36.sail_in22k_ft_in1k_384,87.602,12.398,98.434,1.566,99.88,384,1.000,bicubic
vit_huge_patch14_clip_224.laion2b_ft_in1k,87.588,12.412,98.218,1.782,632.05,224,1.000,bicubic
convnextv2_large.fcmae_ft_in22k_in1k,87.484,12.516,98.356,1.644,197.96,288,1.000,bicubic
beit_large_patch16_224.in22k_ft_in22k_in1k,87.478,12.522,98.304,1.696,304.43,224,0.900,bicubic
convnext_large.fb_in22k_ft_in1k_384,87.472,12.528,98.386,1.614,197.77,384,1.000,bicubic
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,87.464,12.536,98.374,1.626,116.09,384,1.000,bicubic
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,87.464,12.536,98.250,1.750,196.74,384,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k_384,87.446,12.554,98.308,1.692,56.20,384,1.000,bicubic
caformer_b36.sail_in22k_ft_in1k,87.420,12.580,98.328,1.672,98.75,224,1.000,bicubic
beitv2_large_patch16_224.in1k_ft_in1k,87.412,12.588,98.234,1.766,304.43,224,0.950,bicubic
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,87.382,12.618,98.312,1.688,73.88,384,1.000,bicubic
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,87.336,12.664,98.218,1.782,200.13,256,1.000,bicubic
convnext_xlarge.fb_in22k_ft_in1k,87.330,12.670,98.328,1.672,350.20,288,1.000,bicubic
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,87.288,12.712,98.334,1.666,149.39,384,1.000,bicubic
vit_large_patch14_clip_224.laion2b_ft_in1k,87.286,12.714,98.244,1.756,304.20,224,1.000,bicubic
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,87.206,12.794,98.034,1.966,86.86,384,1.000,bicubic
deit3_huge_patch14_224.fb_in22k_ft_in1k,87.186,12.814,98.260,1.740,632.13,224,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,87.134,12.866,98.222,1.778,88.59,384,1.000,bicubic
swin_large_patch4_window12_384.ms_in22k_ft_in1k,87.132,12.868,98.234,1.766,196.74,384,1.000,bicubic
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,87.096,12.904,98.234,1.766,87.92,384,1.000,bicubic
vit_large_patch16_384.augreg_in21k_ft_in1k,87.084,12.916,98.302,1.698,304.72,384,1.000,bicubic
volo_d5_512.sail_in1k,87.058,12.942,97.970,2.030,296.09,512,1.150,bicubic
convnext_large.fb_in22k_ft_in1k,87.026,12.974,98.204,1.796,197.77,288,1.000,bicubic
vit_base_patch16_clip_384.openai_ft_in12k_in1k,87.026,12.974,98.182,1.818,86.86,384,0.950,bicubic
convformer_b36.sail_in22k_ft_in1k,86.998,13.002,98.172,1.828,99.88,224,1.000,bicubic
convnextv2_base.fcmae_ft_in22k_in1k,86.998,13.002,98.168,1.832,88.72,288,1.000,bicubic
deit3_large_patch16_224.fb_in22k_ft_in1k,86.982,13.018,98.236,1.764,304.37,224,1.000,bicubic
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,86.952,13.048,98.106,1.894,196.74,256,0.900,bicubic
volo_d5_448.sail_in1k,86.952,13.048,97.938,2.062,295.91,448,1.150,bicubic
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,86.894,13.106,98.014,1.986,116.14,224,0.950,bicubic
convformer_m36.sail_in22k_ft_in1k_384,86.892,13.108,98.116,1.884,57.05,384,1.000,bicubic
caformer_s36.sail_in22k_ft_in1k_384,86.858,13.142,98.212,1.788,39.30,384,1.000,bicubic
tf_efficientnet_b7.ns_jft_in1k,86.840,13.160,98.092,1.908,66.35,600,0.949,bicubic
regnety_320.swag_ft_in1k,86.834,13.166,98.362,1.638,145.05,384,1.000,bicubic
tf_efficientnetv2_l.in21k_ft_in1k,86.802,13.198,98.136,1.864,118.52,480,1.000,bicubic
beit_base_patch16_384.in22k_ft_in22k_in1k,86.800,13.200,98.136,1.864,86.74,384,1.000,bicubic
convnext_base.fb_in22k_ft_in1k_384,86.796,13.204,98.264,1.736,88.59,384,1.000,bicubic
volo_d4_448.sail_in1k,86.792,13.208,97.884,2.116,193.41,448,1.150,bicubic
tf_efficientnetv2_xl.in21k_ft_in1k,86.748,13.252,98.014,1.986,208.12,512,1.000,bicubic
deit3_base_patch16_384.fb_in22k_ft_in1k,86.740,13.260,98.116,1.884,86.88,384,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,86.724,13.276,98.176,1.824,93.59,320,1.000,bicubic
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,86.642,13.358,98.020,1.980,116.09,224,0.950,bicubic
vit_base_patch16_clip_384.laion2b_ft_in1k,86.618,13.382,98.008,1.992,86.86,384,1.000,bicubic
maxvit_base_tf_512.in1k,86.602,13.398,97.918,2.082,119.88,512,1.000,bicubic
caformer_m36.sail_in22k_ft_in1k,86.594,13.406,98.024,1.976,56.20,224,1.000,bicubic
convnextv2_huge.fcmae_ft_in1k,86.580,13.420,97.972,2.028,660.29,288,1.000,bicubic
coatnet_2_rw_224.sw_in12k_ft_in1k,86.564,13.436,97.896,2.104,73.87,224,0.950,bicubic
maxvit_large_tf_512.in1k,86.526,13.474,97.880,2.120,212.33,512,1.000,bicubic
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,86.504,13.496,97.894,2.106,73.88,224,0.950,bicubic
convnext_base.clip_laiona_augreg_ft_in1k_384,86.502,13.498,97.968,2.032,88.59,384,1.000,bicubic
volo_d3_448.sail_in1k,86.502,13.498,97.710,2.290,86.63,448,1.000,bicubic
cait_m48_448.fb_dist_in1k,86.492,13.508,97.752,2.248,356.46,448,1.000,bicubic
seresnextaa101d_32x8d.sw_in12k_ft_in1k,86.484,13.516,98.030,1.970,93.59,288,1.000,bicubic
beitv2_base_patch16_224.in1k_ft_in22k_in1k,86.474,13.526,98.052,1.948,86.53,224,0.900,bicubic
tiny_vit_21m_512.dist_in22k_ft_in1k,86.458,13.542,97.890,2.110,21.27,512,1.000,bicubic
tf_efficientnet_b6.ns_jft_in1k,86.458,13.542,97.884,2.116,43.04,528,0.942,bicubic
swin_base_patch4_window12_384.ms_in22k_ft_in1k,86.438,13.562,98.066,1.934,87.90,384,1.000,bicubic
caformer_b36.sail_in1k_384,86.408,13.592,97.814,2.186,98.75,384,1.000,bicubic
convformer_s36.sail_in22k_ft_in1k_384,86.378,13.622,97.984,2.016,40.01,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,86.370,13.630,97.984,2.016,88.59,256,1.000,bicubic
dm_nfnet_f6.dm_in1k,86.362,13.638,97.896,2.104,438.36,576,0.956,bicubic
swin_large_patch4_window7_224.ms_in22k_ft_in1k,86.312,13.688,97.902,2.098,196.53,224,0.900,bicubic
maxvit_base_tf_384.in1k,86.302,13.698,97.798,2.202,119.65,384,1.000,bicubic
convnext_base.fb_in22k_ft_in1k,86.274,13.726,98.092,1.908,88.59,288,1.000,bicubic
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,86.268,13.732,97.882,2.118,87.92,256,0.900,bicubic
maxvit_large_tf_384.in1k,86.230,13.770,97.688,2.312,212.03,384,1.000,bicubic
vit_base_patch8_224.augreg2_in21k_ft_in1k,86.218,13.782,97.832,2.168,86.58,224,0.900,bicubic
vit_base_patch16_clip_384.openai_ft_in1k,86.206,13.794,97.876,2.124,86.86,384,1.000,bicubic
convnext_small.in12k_ft_in1k_384,86.182,13.818,97.922,2.078,50.22,384,1.000,bicubic
vit_large_r50_s32_384.augreg_in21k_ft_in1k,86.182,13.818,97.922,2.078,329.09,384,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,86.170,13.830,97.756,2.244,86.57,224,0.950,bicubic
caformer_m36.sail_in1k_384,86.166,13.834,97.820,2.180,56.20,384,1.000,bicubic
convnext_base.clip_laion2b_augreg_ft_in1k,86.158,13.842,97.680,2.320,88.59,256,1.000,bicubic
convformer_m36.sail_in22k_ft_in1k,86.148,13.852,97.850,2.150,57.05,224,1.000,bicubic
convnextv2_large.fcmae_ft_in1k,86.118,13.882,97.822,2.178,197.96,288,1.000,bicubic
tiny_vit_21m_384.dist_in22k_ft_in1k,86.108,13.892,97.710,2.290,21.23,384,1.000,bicubic
dm_nfnet_f5.dm_in1k,86.100,13.900,97.688,2.312,377.21,544,0.954,bicubic
tf_efficientnet_b5.ns_jft_in1k,86.088,13.912,97.756,2.244,30.39,456,0.934,bicubic
maxvit_small_tf_512.in1k,86.084,13.916,97.764,2.236,69.13,512,1.000,bicubic
volo_d5_224.sail_in1k,86.070,13.930,97.576,2.424,295.46,224,0.960,bicubic
cait_m36_384.fb_dist_in1k,86.058,13.942,97.730,2.270,271.22,384,1.000,bicubic
volo_d2_384.sail_in1k,86.042,13.958,97.574,2.426,58.87,384,1.000,bicubic
regnety_160.swag_ft_in1k,86.020,13.980,98.052,1.948,83.59,384,1.000,bicubic
xcit_large_24_p8_384.fb_dist_in1k,85.996,14.004,97.690,2.310,188.93,384,1.000,bicubic
vit_base_patch16_384.augreg_in21k_ft_in1k,85.994,14.006,98.002,1.998,86.86,384,1.000,bicubic
tf_efficientnetv2_m.in21k_ft_in1k,85.992,14.008,97.944,2.056,54.14,480,1.000,bicubic
regnety_160.lion_in12k_ft_in1k,85.988,14.012,97.834,2.166,83.59,288,1.000,bicubic
regnety_160.sw_in12k_ft_in1k,85.986,14.014,97.834,2.166,83.59,288,1.000,bicubic
regnety_1280.swag_lc_in1k,85.982,14.018,97.850,2.150,644.81,224,0.965,bicubic
vit_base_patch16_clip_224.openai_ft_in12k_in1k,85.942,14.058,97.728,2.272,86.57,224,0.950,bicubic
efficientnet_b5.sw_in12k_ft_in1k,85.896,14.104,97.736,2.264,30.39,448,1.000,bicubic
volo_d4_224.sail_in1k,85.872,14.128,97.472,2.528,192.96,224,0.960,bicubic
vit_large_patch16_224.augreg_in21k_ft_in1k,85.836,14.164,97.818,2.182,304.33,224,0.900,bicubic
dm_nfnet_f4.dm_in1k,85.836,14.164,97.664,2.336,316.07,512,0.951,bicubic
xcit_medium_24_p8_384.fb_dist_in1k,85.816,14.184,97.592,2.408,84.32,384,1.000,bicubic
deit3_large_patch16_384.fb_in1k,85.812,14.188,97.598,2.402,304.76,384,1.000,bicubic
vit_base_patch8_224.augreg_in21k_ft_in1k,85.798,14.202,97.790,2.210,86.58,224,0.900,bicubic
caformer_s36.sail_in22k_ft_in1k,85.790,14.210,97.826,2.174,39.30,224,1.000,bicubic
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,85.780,14.220,97.638,2.362,88.34,448,1.000,bicubic
convnext_small.fb_in22k_ft_in1k_384,85.778,14.222,97.890,2.110,50.22,384,1.000,bicubic
xcit_large_24_p16_384.fb_dist_in1k,85.754,14.246,97.538,2.462,189.10,384,1.000,bicubic
caformer_s36.sail_in1k_384,85.742,14.258,97.672,2.328,39.30,384,1.000,bicubic
convformer_b36.sail_in1k_384,85.740,14.260,97.524,2.476,99.88,384,1.000,bicubic
eva02_small_patch14_336.mim_in22k_ft_in1k,85.718,14.282,97.634,2.366,22.13,336,1.000,bicubic
deit3_base_patch16_224.fb_in22k_ft_in1k,85.700,14.300,97.746,2.254,86.59,224,1.000,bicubic
dm_nfnet_f3.dm_in1k,85.686,14.314,97.570,2.430,254.92,416,0.940,bicubic
maxvit_tiny_tf_512.in1k,85.664,14.336,97.584,2.416,31.05,512,1.000,bicubic
tf_efficientnetv2_l.in1k,85.664,14.336,97.474,2.526,118.52,480,1.000,bicubic
flexivit_large.1200ep_in1k,85.644,14.356,97.540,2.460,304.36,240,0.950,bicubic
beitv2_base_patch16_224.in1k_ft_in1k,85.594,14.406,97.506,2.494,86.53,224,0.900,bicubic
convformer_m36.sail_in1k_384,85.580,14.420,97.542,2.458,57.05,384,1.000,bicubic
xcit_small_24_p8_384.fb_dist_in1k,85.554,14.446,97.570,2.430,47.63,384,1.000,bicubic
flexivit_large.600ep_in1k,85.540,14.460,97.488,2.512,304.36,240,0.950,bicubic
maxvit_small_tf_384.in1k,85.540,14.460,97.462,2.538,69.02,384,1.000,bicubic
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,85.530,14.470,97.636,2.364,39.03,384,0.950,bicubic
caformer_b36.sail_in1k,85.504,14.496,97.310,2.690,98.75,224,1.000,bicubic
convnextv2_base.fcmae_ft_in1k,85.474,14.526,97.384,2.616,88.72,288,1.000,bicubic
vit_base_patch16_clip_224.laion2b_ft_in1k,85.470,14.530,97.576,2.424,86.57,224,1.000,bicubic
cait_s36_384.fb_dist_in1k,85.454,14.546,97.478,2.522,68.37,384,1.000,bicubic
xcit_medium_24_p16_384.fb_dist_in1k,85.424,14.576,97.406,2.594,84.40,384,1.000,bicubic
deit_base_distilled_patch16_384.fb_in1k,85.424,14.576,97.330,2.670,87.63,384,1.000,bicubic
caformer_s18.sail_in22k_ft_in1k_384,85.414,14.586,97.702,2.298,26.34,384,1.000,bicubic
convformer_s36.sail_in22k_ft_in1k,85.414,14.586,97.568,2.432,40.01,224,1.000,bicubic
volo_d3_224.sail_in1k,85.414,14.586,97.276,2.724,86.33,224,0.960,bicubic
xcit_large_24_p8_224.fb_dist_in1k,85.402,14.598,97.402,2.598,188.93,224,1.000,bicubic
regnety_120.sw_in12k_ft_in1k,85.400,14.600,97.582,2.418,51.82,288,1.000,bicubic
convformer_s36.sail_in1k_384,85.378,14.622,97.476,2.524,40.01,384,1.000,bicubic
tf_efficientnet_b8.ra_in1k,85.368,14.632,97.394,2.606,87.41,672,0.954,bicubic
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,85.366,14.634,97.660,2.340,88.30,384,1.000,bicubic
tf_efficientnet_b8.ap_in1k,85.364,14.636,97.292,2.708,87.41,672,0.954,bicubic
convnext_small.in12k_ft_in1k,85.330,14.670,97.546,2.454,50.22,288,1.000,bicubic
vit_base_patch16_clip_224.openai_ft_in1k,85.292,14.708,97.436,2.564,86.57,224,0.900,bicubic
flexivit_large.300ep_in1k,85.288,14.712,97.400,2.600,304.36,240,0.950,bicubic
swin_base_patch4_window7_224.ms_in22k_ft_in1k,85.272,14.728,97.564,2.436,87.77,224,0.900,bicubic
convnext_small.fb_in22k_ft_in1k,85.262,14.738,97.682,2.318,50.22,288,1.000,bicubic
volo_d1_384.sail_in1k,85.244,14.756,97.214,2.786,26.78,384,1.000,bicubic
mvitv2_large.fb_in1k,85.244,14.756,97.194,2.806,217.99,224,0.900,bicubic
caformer_m36.sail_in1k,85.232,14.768,97.200,2.800,56.20,224,1.000,bicubic
deit3_huge_patch14_224.fb_in1k,85.224,14.776,97.360,2.640,632.13,224,0.900,bicubic
vit_base_patch32_clip_384.openai_ft_in12k_in1k,85.214,14.786,97.404,2.596,88.30,384,0.950,bicubic
beit_base_patch16_224.in22k_ft_in22k_in1k,85.212,14.788,97.658,2.342,86.53,224,0.900,bicubic
tf_efficientnetv2_m.in1k,85.204,14.796,97.364,2.636,54.14,480,1.000,bicubic
inception_next_base.sail_in1k_384,85.202,14.798,97.414,2.586,86.67,384,1.000,bicubic
volo_d2_224.sail_in1k,85.202,14.798,97.190,2.810,58.68,224,0.960,bicubic
dm_nfnet_f2.dm_in1k,85.192,14.808,97.346,2.654,193.78,352,0.920,bicubic
tf_efficientnet_b4.ns_jft_in1k,85.160,14.840,97.468,2.532,19.34,380,0.922,bicubic
regnety_2560.seer_ft_in1k,85.150,14.850,97.438,2.562,"1,282.60",384,1.000,bicubic
tf_efficientnet_b7.ap_in1k,85.124,14.876,97.252,2.748,66.35,600,0.949,bicubic
convnext_tiny.in12k_ft_in1k_384,85.122,14.878,97.606,2.394,28.59,384,1.000,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k_384,85.106,14.894,97.628,2.372,28.64,384,1.000,bicubic
maxvit_tiny_tf_384.in1k,85.100,14.900,97.378,2.622,30.98,384,1.000,bicubic
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,85.098,14.902,97.438,2.562,468.53,224,0.875,bilinear
vit_base_patch16_224.augreg2_in21k_ft_in1k,85.094,14.906,97.530,2.470,86.57,224,0.900,bicubic
xcit_small_24_p16_384.fb_dist_in1k,85.090,14.910,97.312,2.688,47.67,384,1.000,bicubic
tiny_vit_21m_224.dist_in22k_ft_in1k,85.086,14.914,97.366,2.634,21.20,224,0.950,bicubic
xcit_small_12_p8_384.fb_dist_in1k,85.078,14.922,97.282,2.718,26.21,384,1.000,bicubic
xcit_medium_24_p8_224.fb_dist_in1k,85.074,14.926,97.274,2.726,84.32,224,1.000,bicubic
deit3_base_patch16_384.fb_in1k,85.074,14.926,97.250,2.750,86.88,384,1.000,bicubic
cait_s24_384.fb_dist_in1k,85.048,14.952,97.346,2.654,47.06,384,1.000,bicubic
regnetz_e8.ra3_in1k,85.034,14.966,97.272,2.728,57.70,320,1.000,bicubic
caformer_s18.sail_in1k_384,85.026,14.974,97.358,2.642,26.34,384,1.000,bicubic
resnetrs420.tf_in1k,85.004,14.996,97.124,2.876,191.89,416,1.000,bicubic
convformer_s18.sail_in22k_ft_in1k_384,84.998,15.002,97.570,2.430,26.77,384,1.000,bicubic
vit_base_r50_s16_384.orig_in21k_ft_in1k,84.976,15.024,97.290,2.710,98.95,384,1.000,bicubic
ecaresnet269d.ra2_in1k,84.968,15.032,97.222,2.778,102.09,352,1.000,bicubic
maxvit_large_tf_224.in1k,84.942,15.058,96.970,3.030,211.79,224,0.950,bicubic
tf_efficientnet_b7.ra_in1k,84.932,15.068,97.208,2.792,66.35,600,0.949,bicubic
resnetv2_152x4_bit.goog_in21k_ft_in1k,84.916,15.084,97.438,2.562,936.53,480,1.000,bilinear
xcit_large_24_p16_224.fb_dist_in1k,84.916,15.084,97.128,2.872,189.10,224,1.000,bicubic
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,84.910,15.090,96.958,3.042,41.72,224,0.950,bicubic
coat_lite_medium_384.in1k,84.878,15.122,97.372,2.628,44.57,384,1.000,bicubic
xcit_small_24_p8_224.fb_dist_in1k,84.868,15.132,97.190,2.810,47.63,224,1.000,bicubic
maxvit_base_tf_224.in1k,84.860,15.140,96.988,3.012,119.47,224,0.950,bicubic
convnext_large.fb_in1k,84.846,15.154,97.214,2.786,197.77,288,1.000,bicubic
deit3_small_patch16_384.fb_in22k_ft_in1k,84.824,15.176,97.486,2.514,22.21,384,1.000,bicubic
convformer_b36.sail_in1k,84.818,15.182,96.946,3.054,99.88,224,1.000,bicubic
efficientnetv2_rw_m.agc_in1k,84.810,15.190,97.152,2.848,53.24,416,1.000,bicubic
tf_efficientnet_b6.ap_in1k,84.788,15.212,97.138,2.862,43.04,528,0.942,bicubic
deit3_large_patch16_224.fb_in1k,84.774,15.226,97.036,2.964,304.37,224,0.900,bicubic
resnetrs350.tf_in1k,84.714,15.286,96.992,3.008,163.96,384,1.000,bicubic
xcit_small_12_p16_384.fb_dist_in1k,84.712,15.288,97.118,2.882,26.25,384,1.000,bicubic
dm_nfnet_f1.dm_in1k,84.702,15.298,97.182,2.818,132.63,320,0.910,bicubic
eca_nfnet_l2.ra3_in1k,84.700,15.300,97.266,2.734,56.72,384,1.000,bicubic
flexivit_base.1200ep_in1k,84.676,15.324,96.994,3.006,86.59,240,0.950,bicubic
davit_base.msft_in1k,84.642,15.358,97.020,2.980,87.95,224,0.950,bicubic
maxxvit_rmlp_small_rw_256.sw_in1k,84.624,15.376,97.068,2.932,66.01,256,0.950,bicubic
coatnet_rmlp_2_rw_224.sw_in1k,84.608,15.392,96.740,3.260,73.88,224,0.950,bicubic
swinv2_base_window16_256.ms_in1k,84.600,15.400,97.090,2.910,87.92,256,0.900,bicubic
fastvit_ma36.apple_dist_in1k,84.598,15.402,97.002,2.998,44.07,256,0.950,bicubic
seresnextaa101d_32x8d.ah_in1k,84.566,15.434,97.076,2.924,93.59,288,1.000,bicubic
deit3_medium_patch16_224.fb_in22k_ft_in1k,84.550,15.450,97.188,2.812,38.85,224,1.000,bicubic
regnety_320.swag_lc_in1k,84.548,15.452,97.442,2.558,145.05,224,0.965,bicubic
rexnetr_300.sw_in12k_ft_in1k,84.546,15.454,97.256,2.744,34.81,288,1.000,bicubic
vit_base_patch16_224.augreg_in21k_ft_in1k,84.532,15.468,97.294,2.706,86.57,224,0.900,bicubic
flexivit_base.600ep_in1k,84.524,15.476,96.936,3.064,86.59,240,0.950,bicubic
resnetv2_152x2_bit.goog_in21k_ft_in1k,84.510,15.490,97.434,2.566,236.34,448,1.000,bilinear
resnest269e.in1k,84.508,15.492,96.990,3.010,110.93,416,0.928,bicubic
caformer_s36.sail_in1k,84.506,15.494,96.996,3.004,39.30,224,1.000,bicubic
convformer_m36.sail_in1k,84.494,15.506,96.866,3.134,57.05,224,1.000,bicubic
regnetz_040_h.ra3_in1k,84.492,15.508,97.010,2.990,28.94,320,1.000,bicubic
maxvit_rmlp_small_rw_224.sw_in1k,84.492,15.508,96.758,3.242,64.90,224,0.900,bicubic
hrnet_w48_ssld.paddle_in1k,84.480,15.520,97.234,2.766,77.47,288,1.000,bilinear
swin_base_patch4_window12_384.ms_in1k,84.476,15.524,96.892,3.108,87.90,384,1.000,bicubic
convnext_tiny.in12k_ft_in1k,84.450,15.550,97.340,2.660,28.59,288,1.000,bicubic
mvitv2_base.fb_in1k,84.450,15.550,96.858,3.142,51.47,224,0.900,bicubic
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,84.446,15.554,97.210,2.790,38.86,256,0.950,bicubic
resnetrs200.tf_in1k,84.444,15.556,97.082,2.918,93.21,320,1.000,bicubic
gcvit_base.in1k,84.444,15.556,96.842,3.158,90.32,224,0.875,bicubic
resnetv2_101x3_bit.goog_in21k_ft_in1k,84.438,15.562,97.382,2.618,387.93,448,1.000,bilinear
regnety_1280.seer_ft_in1k,84.432,15.568,97.092,2.908,644.81,384,1.000,bicubic
convnext_base.fb_in1k,84.428,15.572,96.968,3.032,88.59,288,1.000,bicubic
resnetrs270.tf_in1k,84.428,15.572,96.968,3.032,129.86,352,1.000,bicubic
maxvit_small_tf_224.in1k,84.426,15.574,96.824,3.176,68.93,224,0.950,bicubic
vit_large_r50_s32_224.augreg_in21k_ft_in1k,84.418,15.582,97.172,2.828,328.99,224,0.900,bicubic
convnextv2_tiny.fcmae_ft_in22k_in1k,84.416,15.584,97.260,2.740,28.64,288,1.000,bicubic
tf_efficientnet_b7.aa_in1k,84.416,15.584,96.908,3.092,66.35,600,0.949,bicubic
flexivit_base.300ep_in1k,84.406,15.594,96.884,3.116,86.59,240,0.950,bicubic
convformer_s18.sail_in1k_384,84.402,15.598,97.112,2.888,26.77,384,1.000,bicubic
resmlp_big_24_224.fb_in22k_ft_in1k,84.398,15.602,97.112,2.888,129.14,224,0.875,bicubic
xcit_large_24_p8_224.fb_in1k,84.394,15.606,96.664,3.336,188.93,224,1.000,bicubic
seresnet152d.ra2_in1k,84.360,15.640,97.040,2.960,66.84,320,1.000,bicubic
seresnext101d_32x8d.ah_in1k,84.358,15.642,96.920,3.080,93.59,288,1.000,bicubic
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,84.302,15.698,97.176,2.824,88.79,224,0.875,bilinear
tf_efficientnetv2_s.in21k_ft_in1k,84.286,15.714,97.252,2.748,21.46,384,1.000,bicubic
xcit_medium_24_p16_224.fb_dist_in1k,84.286,15.714,96.932,3.068,84.40,224,1.000,bicubic
vit_base_patch16_224_miil.in21k_ft_in1k,84.266,15.734,96.804,3.196,86.54,224,0.875,bilinear
tf_efficientnet_b5.ap_in1k,84.258,15.742,96.974,3.026,30.39,456,0.934,bicubic
davit_small.msft_in1k,84.252,15.748,96.940,3.060,49.75,224,0.950,bicubic
swinv2_base_window8_256.ms_in1k,84.250,15.750,96.924,3.076,87.92,256,0.900,bicubic
regnetz_040.ra3_in1k,84.240,15.760,96.932,3.068,27.12,320,1.000,bicubic
xcit_small_12_p8_224.fb_dist_in1k,84.236,15.764,96.870,3.130,26.21,224,1.000,bicubic
swinv2_small_window16_256.ms_in1k,84.224,15.776,96.868,3.132,49.73,256,0.900,bicubic
maxvit_rmlp_tiny_rw_256.sw_in1k,84.224,15.776,96.778,3.222,29.15,256,0.950,bicubic
crossvit_18_dagger_408.in1k,84.202,15.798,96.818,3.182,44.61,408,1.000,bicubic
vit_base_patch16_384.orig_in21k_ft_in1k,84.200,15.800,97.218,2.782,86.86,384,1.000,bicubic
seresnext101_32x8d.ah_in1k,84.186,15.814,96.874,3.126,93.57,288,1.000,bicubic
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,84.166,15.834,97.198,2.802,194.03,224,0.875,bilinear
volo_d1_224.sail_in1k,84.162,15.838,96.776,3.224,26.63,224,0.960,bicubic
efficientvit_b3.r288_in1k,84.154,15.846,96.736,3.264,48.65,288,1.000,bicubic
regnetz_d8_evos.ch_in1k,84.126,15.874,97.012,2.988,23.46,320,1.000,bicubic
resnetaa101d.sw_in12k_ft_in1k,84.124,15.876,97.106,2.894,44.57,288,1.000,bicubic
tf_efficientnet_b6.aa_in1k,84.112,15.888,96.884,3.116,43.04,528,0.942,bicubic
inception_next_base.sail_in1k,84.092,15.908,96.796,3.204,86.67,224,0.950,bicubic
convnext_tiny.fb_in22k_ft_in1k_384,84.088,15.912,97.144,2.856,28.59,384,1.000,bicubic
caformer_s18.sail_in22k_ft_in1k,84.074,15.926,97.198,2.802,26.34,224,1.000,bicubic
cait_xs24_384.fb_dist_in1k,84.062,15.938,96.884,3.116,26.67,384,1.000,bicubic
convformer_s36.sail_in1k,84.060,15.940,96.746,3.254,40.01,224,1.000,bicubic
edgenext_base.in21k_ft_in1k,84.054,15.946,97.196,2.804,18.51,320,1.000,bicubic
regnetz_d8.ra3_in1k,84.052,15.948,96.996,3.004,23.37,320,1.000,bicubic
tf_efficientnet_b3.ns_jft_in1k,84.052,15.948,96.918,3.082,12.23,300,0.904,bicubic
vit_small_r26_s32_384.augreg_in21k_ft_in1k,84.048,15.952,97.328,2.672,36.47,384,1.000,bicubic
fastvit_sa36.apple_dist_in1k,84.026,15.974,96.854,3.146,31.53,256,0.900,bicubic
regnetz_d32.ra3_in1k,84.022,15.978,96.868,3.132,27.58,320,0.950,bicubic
resnetv2_50x3_bit.goog_in21k_ft_in1k,84.020,15.980,97.126,2.874,217.32,448,1.000,bilinear
eca_nfnet_l1.ra2_in1k,84.012,15.988,97.026,2.974,41.41,320,1.000,bicubic
resnet200d.ra2_in1k,83.964,16.036,96.826,3.174,64.69,320,1.000,bicubic
edgenext_base.usi_in1k,83.958,16.042,96.770,3.230,18.51,320,1.000,bicubic
regnety_080.ra3_in1k,83.926,16.074,96.890,3.110,39.18,288,1.000,bicubic
swin_s3_base_224.ms_in1k,83.920,16.080,96.672,3.328,71.13,224,0.900,bicubic
regnety_640.seer_ft_in1k,83.908,16.092,96.922,3.078,281.38,384,1.000,bicubic
tf_efficientnetv2_s.in1k,83.898,16.102,96.696,3.304,21.46,384,1.000,bicubic
tresnet_v2_l.miil_in21k_ft_in1k,83.894,16.106,96.490,3.510,46.17,224,0.875,bilinear
gcvit_small.in1k,83.892,16.108,96.658,3.342,51.09,224,0.875,bicubic
fastvit_ma36.apple_in1k,83.882,16.118,96.742,3.258,44.07,256,0.950,bicubic
xcit_small_24_p16_224.fb_dist_in1k,83.874,16.126,96.736,3.264,47.67,224,1.000,bicubic
swinv2_small_window8_256.ms_in1k,83.854,16.146,96.644,3.356,49.73,256,0.900,bicubic
resnest200e.in1k,83.844,16.156,96.884,3.116,70.20,320,0.909,bicubic
crossvit_15_dagger_408.in1k,83.840,16.160,96.778,3.222,28.50,408,1.000,bicubic
focalnet_base_lrf.ms_in1k,83.838,16.162,96.608,3.392,88.75,224,0.900,bicubic
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,83.836,16.164,97.126,2.874,236.34,384,1.000,bicubic
xcit_small_24_p8_224.fb_in1k,83.834,16.166,96.632,3.368,47.63,224,1.000,bicubic
focalnet_base_srf.ms_in1k,83.820,16.180,96.680,3.320,88.15,224,0.900,bicubic
tf_efficientnet_b5.ra_in1k,83.814,16.186,96.752,3.248,30.39,456,0.934,bicubic
efficientnetv2_rw_s.ra2_in1k,83.806,16.194,96.732,3.268,23.94,384,1.000,bicubic
vit_small_patch16_384.augreg_in21k_ft_in1k,83.804,16.196,97.100,2.900,22.20,384,1.000,bicubic
efficientvit_b3.r256_in1k,83.802,16.198,96.516,3.484,48.65,256,1.000,bicubic
deit3_base_patch16_224.fb_in1k,83.786,16.214,96.586,3.414,86.59,224,0.900,bicubic
regnety_160.swag_lc_in1k,83.782,16.218,97.280,2.720,83.59,224,0.965,bicubic
mvitv2_small.fb_in1k,83.770,16.230,96.576,3.424,34.87,224,0.900,bicubic
pit_b_distilled_224.in1k,83.766,16.234,96.468,3.532,74.79,224,0.900,bicubic
swin_s3_small_224.ms_in1k,83.756,16.244,96.452,3.548,49.74,224,0.900,bicubic
xcit_tiny_24_p8_384.fb_dist_in1k,83.746,16.254,96.710,3.290,12.11,384,1.000,bicubic
xcit_medium_24_p8_224.fb_in1k,83.746,16.254,96.400,3.600,84.32,224,1.000,bicubic
repvit_m2_3.dist_450e_in1k,83.742,16.258,96.644,3.356,23.69,224,0.950,bicubic
pvt_v2_b5.in1k,83.740,16.260,96.636,3.364,81.96,224,0.900,bicubic
convformer_s18.sail_in22k_ft_in1k,83.738,16.262,97.048,2.952,26.77,224,1.000,bicubic
regnety_064.ra3_in1k,83.720,16.280,96.722,3.278,30.58,288,1.000,bicubic
regnetv_064.ra3_in1k,83.716,16.284,96.742,3.258,30.58,288,1.000,bicubic
pvt_v2_b4.in1k,83.712,16.288,96.670,3.330,62.56,224,0.900,bicubic
resnetrs152.tf_in1k,83.702,16.298,96.612,3.388,86.62,320,1.000,bicubic
convnext_small.fb_in1k,83.700,16.300,96.808,3.192,50.22,288,1.000,bicubic
regnety_160.deit_in1k,83.690,16.310,96.780,3.220,83.59,288,1.000,bicubic
tf_efficientnet_b5.aa_in1k,83.688,16.312,96.712,3.288,30.39,456,0.934,bicubic
resnet152d.ra2_in1k,83.684,16.316,96.738,3.262,60.21,320,1.000,bicubic
twins_svt_large.in1k,83.678,16.322,96.588,3.412,99.27,224,0.900,bicubic
caformer_s18.sail_in1k,83.654,16.346,96.518,3.482,26.34,224,1.000,bicubic
efficientformerv2_l.snap_dist_in1k,83.632,16.368,96.558,3.442,26.32,224,0.950,bicubic
swin_base_patch4_window7_224.ms_in1k,83.606,16.394,96.452,3.548,87.77,224,0.900,bicubic
coat_lite_medium.in1k,83.600,16.400,96.728,3.272,44.57,224,0.900,bicubic
coatnet_1_rw_224.sw_in1k,83.596,16.404,96.382,3.618,41.72,224,0.950,bicubic
resmlp_big_24_224.fb_distilled_in1k,83.592,16.408,96.650,3.350,129.14,224,0.875,bicubic
inception_next_small.sail_in1k,83.578,16.422,96.598,3.402,49.37,224,0.875,bicubic
repvgg_d2se.rvgg_in1k,83.560,16.440,96.658,3.342,133.33,320,1.000,bilinear
cs3se_edgenet_x.c2ns_in1k,83.546,16.454,96.670,3.330,50.72,320,1.000,bicubic
nest_base_jx.goog_in1k,83.534,16.466,96.374,3.626,67.72,224,0.875,bicubic
repvit_m2_3.dist_300e_in1k,83.504,16.496,96.514,3.486,23.69,224,0.950,bicubic
maxvit_tiny_rw_224.sw_in1k,83.504,16.496,96.504,3.496,29.06,224,0.950,bicubic
fastvit_sa36.apple_in1k,83.500,16.500,96.630,3.370,31.53,256,0.900,bicubic
swinv2_cr_small_ns_224.sw_in1k,83.498,16.502,96.484,3.516,49.70,224,0.900,bicubic
focalnet_small_lrf.ms_in1k,83.494,16.506,96.580,3.420,50.34,224,0.900,bicubic
dm_nfnet_f0.dm_in1k,83.486,16.514,96.568,3.432,71.49,256,0.900,bicubic
convnextv2_tiny.fcmae_ft_in1k,83.464,16.536,96.718,3.282,28.64,288,1.000,bicubic
efficientvit_b3.r224_in1k,83.460,16.540,96.330,3.670,48.65,224,0.950,bicubic
resnet152.a1h_in1k,83.450,16.550,96.538,3.462,60.19,288,1.000,bicubic
cait_s24_224.fb_dist_in1k,83.442,16.558,96.574,3.426,46.92,224,1.000,bicubic
deit3_small_patch16_384.fb_in1k,83.428,16.572,96.674,3.326,22.21,384,1.000,bicubic
focalnet_small_srf.ms_in1k,83.416,16.584,96.438,3.562,49.89,224,0.900,bicubic
efficientnet_b4.ra2_in1k,83.414,16.586,96.598,3.402,19.34,384,1.000,bicubic
maxvit_tiny_tf_224.in1k,83.402,16.598,96.590,3.410,30.92,224,0.950,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k_384,83.400,16.600,96.582,3.418,18.45,384,1.000,bicubic
sequencer2d_l.in1k,83.394,16.606,96.496,3.504,54.30,224,0.875,bicubic
deit_base_distilled_patch16_224.fb_in1k,83.390,16.610,96.488,3.512,87.34,224,0.900,bicubic
gcvit_tiny.in1k,83.384,16.616,96.398,3.602,28.22,224,0.875,bicubic
efficientformer_l7.snap_dist_in1k,83.382,16.618,96.536,3.464,82.23,224,0.950,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k_384,83.374,16.626,96.744,3.256,15.62,384,1.000,bicubic
coatnet_rmlp_1_rw_224.sw_in1k,83.362,16.638,96.450,3.550,41.69,224,0.950,bicubic
vit_base_patch32_384.augreg_in21k_ft_in1k,83.352,16.648,96.840,3.160,88.30,384,1.000,bicubic
fastvit_sa24.apple_dist_in1k,83.342,16.658,96.552,3.448,21.55,256,0.900,bicubic
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,83.336,16.664,96.846,3.154,194.03,224,0.875,bilinear
xcit_small_12_p8_224.fb_in1k,83.334,16.666,96.482,3.518,26.21,224,1.000,bicubic
regnety_320.seer_ft_in1k,83.328,16.672,96.708,3.292,145.05,384,1.000,bicubic
xcit_small_12_p16_224.fb_dist_in1k,83.328,16.672,96.416,3.584,26.25,224,1.000,bicubic
swin_small_patch4_window7_224.ms_in22k_ft_in1k,83.298,16.702,96.964,3.036,49.61,224,0.900,bicubic
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,83.296,16.704,96.528,3.472,88.22,224,0.900,bicubic
tiny_vit_21m_224.in1k,83.254,16.746,96.592,3.408,21.20,224,0.950,bicubic
tf_efficientnet_b4.ap_in1k,83.250,16.750,96.396,3.604,19.34,380,0.922,bicubic
tiny_vit_11m_224.dist_in22k_ft_in1k,83.228,16.772,96.630,3.370,11.00,224,0.950,bicubic
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,83.226,16.774,96.760,3.240,44.18,224,0.875,bilinear
swin_small_patch4_window7_224.ms_in1k,83.208,16.792,96.316,3.684,49.61,224,0.900,bicubic
regnetv_040.ra3_in1k,83.190,16.810,96.658,3.342,20.64,288,1.000,bicubic
xception65.ra3_in1k,83.180,16.820,96.592,3.408,39.92,299,0.940,bicubic
tf_efficientnet_b5.in1k,83.176,16.824,96.536,3.464,30.39,456,0.934,bicubic
regnety_320.tv2_in1k,83.162,16.838,96.414,3.586,145.05,224,0.965,bicubic
resnext101_64x4d.c1_in1k,83.156,16.844,96.374,3.626,83.46,288,1.000,bicubic
rexnetr_200.sw_in12k_ft_in1k,83.138,16.862,96.636,3.364,16.52,288,1.000,bicubic
swinv2_cr_small_224.sw_in1k,83.136,16.864,96.108,3.892,49.70,224,0.900,bicubic
twins_pcpvt_large.in1k,83.130,16.870,96.604,3.396,60.99,224,0.900,bicubic
xception65p.ra3_in1k,83.126,16.874,96.482,3.518,39.82,299,0.940,bicubic
nest_small_jx.goog_in1k,83.124,16.876,96.320,3.680,38.35,224,0.875,bicubic
twins_svt_base.in1k,83.120,16.880,96.414,3.586,56.07,224,0.900,bicubic
pvt_v2_b3.in1k,83.118,16.882,96.556,3.444,45.24,224,0.900,bicubic
maxxvitv2_nano_rw_256.sw_in1k,83.110,16.890,96.324,3.676,23.70,256,0.950,bicubic
deit_base_patch16_384.fb_in1k,83.104,16.896,96.368,3.632,86.86,384,1.000,bicubic
efficientvit_b2.r288_in1k,83.100,16.900,96.304,3.696,24.33,288,1.000,bicubic
deit3_medium_patch16_224.fb_in1k,83.086,16.914,96.294,3.706,38.85,224,0.900,bicubic
deit3_small_patch16_224.fb_in22k_ft_in1k,83.076,16.924,96.776,3.224,22.06,224,1.000,bicubic
tresnet_m.miil_in21k_ft_in1k,83.070,16.930,96.110,3.890,31.39,224,0.875,bilinear
tresnet_xl.miil_in1k_448,83.058,16.942,96.172,3.828,78.44,448,0.875,bilinear
regnety_040.ra3_in1k,83.044,16.956,96.502,3.498,20.65,288,1.000,bicubic
maxxvit_rmlp_nano_rw_256.sw_in1k,83.042,16.958,96.350,3.650,16.78,256,0.950,bicubic
resnet101d.ra2_in1k,83.020,16.980,96.452,3.548,44.57,320,1.000,bicubic
tf_efficientnet_b4.aa_in1k,83.018,16.982,96.300,3.700,19.34,380,0.922,bicubic
resnetv2_101.a1h_in1k,83.000,17.000,96.454,3.546,44.54,288,1.000,bicubic
resnext101_64x4d.tv_in1k,82.992,17.008,96.244,3.756,83.46,224,0.875,bilinear
convformer_s18.sail_in1k,82.986,17.014,96.250,3.750,26.77,224,1.000,bicubic
ecaresnet101d.miil_in1k,82.984,17.016,96.542,3.458,44.57,288,0.950,bicubic
maxvit_rmlp_nano_rw_256.sw_in1k,82.954,17.046,96.266,3.734,15.50,256,0.950,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k_384,82.938,17.062,96.426,3.574,14.25,384,1.000,bicubic
maxvit_nano_rw_256.sw_in1k,82.928,17.072,96.220,3.780,15.45,256,0.950,bicubic
xcit_large_24_p16_224.fb_in1k,82.902,17.098,95.884,4.116,189.10,224,1.000,bicubic
resnest101e.in1k,82.884,17.116,96.322,3.678,48.28,256,0.875,bilinear
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,82.876,17.124,96.582,3.418,236.34,224,0.875,bicubic
convnext_nano.in12k_ft_in1k,82.862,17.138,96.556,3.444,15.59,288,1.000,bicubic
resnext101_32x8d.tv2_in1k,82.832,17.168,96.232,3.768,88.79,224,0.965,bilinear
resnetv2_50x1_bit.goog_distilled_in1k,82.824,17.176,96.518,3.482,25.55,224,0.875,bicubic
sequencer2d_m.in1k,82.812,17.188,96.274,3.726,38.31,224,0.875,bicubic
regnetx_320.tv2_in1k,82.810,17.190,96.208,3.792,107.81,224,0.965,bicubic
swinv2_tiny_window16_256.ms_in1k,82.804,17.196,96.236,3.764,28.35,256,0.900,bicubic
pnasnet5large.tf_in1k,82.782,17.218,96.040,3.960,86.06,331,0.911,bicubic
resnet101.a1h_in1k,82.778,17.222,96.310,3.690,44.55,288,1.000,bicubic
rexnet_300.nav_in1k,82.774,17.226,96.238,3.762,34.71,224,0.875,bicubic
vit_relpos_base_patch16_clsgap_224.sw_in1k,82.760,17.240,96.172,3.828,86.43,224,0.900,bicubic
nfnet_l0.ra2_in1k,82.750,17.250,96.516,3.484,35.07,288,1.000,bicubic
resnet152.a1_in1k,82.732,17.268,95.720,4.280,60.19,288,1.000,bicubic
regnety_032.ra_in1k,82.726,17.274,96.416,3.584,19.44,288,1.000,bicubic
twins_pcpvt_base.in1k,82.714,17.286,96.346,3.654,43.83,224,0.900,bicubic
cs3edgenet_x.c2_in1k,82.708,17.292,96.370,3.630,47.82,288,1.000,bicubic
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,82.698,17.302,96.632,3.368,88.79,224,0.875,bilinear
convnext_tiny.fb_in1k,82.698,17.302,96.144,3.856,28.59,288,1.000,bicubic
davit_tiny.msft_in1k,82.696,17.304,96.274,3.726,28.36,224,0.950,bicubic
efficientvit_b2.r256_in1k,82.690,17.310,96.094,3.906,24.33,256,1.000,bicubic
fastvit_sa24.apple_in1k,82.678,17.322,96.272,3.728,21.55,256,0.900,bicubic
tf_efficientnetv2_b3.in21k_ft_in1k,82.670,17.330,96.626,3.374,14.36,300,0.900,bicubic
convnextv2_nano.fcmae_ft_in22k_in1k,82.664,17.336,96.520,3.480,15.62,288,1.000,bicubic
cs3sedarknet_x.c2ns_in1k,82.658,17.342,96.350,3.650,35.40,288,1.000,bicubic
regnety_160.tv2_in1k,82.646,17.354,96.214,3.786,83.59,224,0.965,bicubic
xcit_medium_24_p16_224.fb_in1k,82.640,17.360,95.982,4.018,84.40,224,1.000,bicubic
regnetz_c16_evos.ch_in1k,82.636,17.364,96.474,3.526,13.49,320,0.950,bicubic
regnetz_c16.ra3_in1k,82.632,17.368,96.318,3.682,13.46,320,1.000,bicubic
nasnetalarge.tf_in1k,82.626,17.374,96.042,3.958,88.75,331,0.911,bicubic
poolformerv2_m48.sail_in1k,82.618,17.382,96.072,3.928,73.35,224,1.000,bicubic
tf_efficientnet_b4.in1k,82.608,17.392,96.128,3.872,19.34,380,0.922,bicubic
resnet152.a2_in1k,82.608,17.392,95.752,4.248,60.19,288,1.000,bicubic
resnetaa50d.sw_in12k_ft_in1k,82.600,17.400,96.498,3.502,25.58,288,1.000,bicubic
levit_384.fb_dist_in1k,82.596,17.404,96.018,3.982,39.13,224,0.900,bicubic
regnety_080_tv.tv2_in1k,82.594,17.406,96.248,3.752,39.38,224,0.965,bicubic
levit_conv_384.fb_dist_in1k,82.590,17.410,96.016,3.984,39.13,224,0.900,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k_384,82.586,17.414,96.314,3.686,10.59,384,1.000,bicubic
convnext_tiny_hnf.a2h_in1k,82.584,17.416,96.008,3.992,28.59,288,1.000,bicubic
vit_base_patch32_clip_224.laion2b_ft_in1k,82.582,17.418,96.200,3.800,88.22,224,0.900,bicubic
eca_nfnet_l0.ra2_in1k,82.578,17.422,96.492,3.508,24.14,288,1.000,bicubic
xcit_small_24_p16_224.fb_in1k,82.576,17.424,96.012,3.988,47.67,224,1.000,bicubic
vit_relpos_medium_patch16_cls_224.sw_in1k,82.572,17.428,96.068,3.932,38.76,224,0.900,bicubic
xcit_tiny_24_p16_384.fb_dist_in1k,82.570,17.430,96.276,3.724,12.12,384,1.000,bicubic
xcit_tiny_24_p8_224.fb_dist_in1k,82.566,17.434,96.172,3.828,12.11,224,1.000,bicubic
regnetx_160.tv2_in1k,82.566,17.434,96.058,3.942,54.28,224,0.965,bicubic
efficientformer_l3.snap_dist_in1k,82.548,17.452,96.250,3.750,31.41,224,0.950,bicubic
flexivit_small.1200ep_in1k,82.526,17.474,96.126,3.874,22.06,240,0.950,bicubic
resnet61q.ra2_in1k,82.524,17.476,96.130,3.870,36.85,288,1.000,bicubic
crossvit_18_dagger_240.in1k,82.518,17.482,96.068,3.932,44.27,240,0.875,bicubic
repvit_m1_5.dist_450e_in1k,82.512,17.488,96.112,3.888,14.64,224,0.950,bicubic
wide_resnet101_2.tv2_in1k,82.502,17.498,96.016,3.984,126.89,224,0.965,bilinear
vit_relpos_base_patch16_224.sw_in1k,82.496,17.504,96.138,3.862,86.43,224,0.900,bicubic
convnextv2_nano.fcmae_ft_in1k,82.486,17.514,96.226,3.774,15.62,288,1.000,bicubic
poolformer_m48.sail_in1k,82.482,17.518,95.966,4.034,73.47,224,0.950,bicubic
inception_next_tiny.sail_in1k,82.478,17.522,96.022,3.978,28.06,224,0.875,bicubic
vit_relpos_medium_patch16_224.sw_in1k,82.462,17.538,96.082,3.918,38.75,224,0.900,bicubic
gc_efficientnetv2_rw_t.agc_in1k,82.456,17.544,96.296,3.704,13.68,288,1.000,bicubic
pit_b_224.in1k,82.438,17.562,95.714,4.286,73.76,224,0.900,bicubic
mvitv2_tiny.fb_in1k,82.410,17.590,96.152,3.848,24.17,224,0.900,bicubic
coatnet_bn_0_rw_224.sw_in1k,82.400,17.600,96.186,3.814,27.44,224,0.950,bicubic
crossvit_18_240.in1k,82.400,17.600,96.060,3.940,43.27,240,0.875,bicubic
coatnet_0_rw_224.sw_in1k,82.390,17.610,95.836,4.164,27.44,224,0.950,bicubic
xcit_tiny_12_p8_384.fb_dist_in1k,82.388,17.612,96.220,3.780,6.71,384,1.000,bicubic
tf_efficientnet_b2.ns_jft_in1k,82.378,17.622,96.254,3.746,9.11,260,0.890,bicubic
repvit_m1_5.dist_300e_in1k,82.376,17.624,96.030,3.970,14.64,224,0.950,bicubic
coat_small.in1k,82.362,17.638,96.208,3.792,21.69,224,0.900,bicubic
flexivit_small.600ep_in1k,82.362,17.638,96.084,3.916,22.06,240,0.950,bicubic
resnet51q.ra2_in1k,82.360,17.640,96.186,3.814,35.70,288,1.000,bilinear
ecaresnet50t.ra2_in1k,82.352,17.648,96.140,3.860,25.57,320,0.950,bicubic
efficientnetv2_rw_t.ra2_in1k,82.350,17.650,96.192,3.808,13.65,288,1.000,bicubic
resnetv2_101x1_bit.goog_in21k_ft_in1k,82.342,17.658,96.520,3.480,44.54,448,1.000,bilinear
sequencer2d_s.in1k,82.340,17.660,96.028,3.972,27.65,224,0.875,bicubic
mobilevitv2_200.cvnets_in22k_ft_in1k,82.332,17.668,95.942,4.058,18.45,256,0.888,bicubic
crossvit_15_dagger_240.in1k,82.330,17.670,95.956,4.044,28.21,240,0.875,bicubic
resnet101.a1_in1k,82.322,17.678,95.632,4.368,44.55,288,1.000,bicubic
coat_lite_small.in1k,82.312,17.688,95.850,4.150,19.84,224,0.900,bicubic
vit_relpos_medium_patch16_rpn_224.sw_in1k,82.310,17.690,95.972,4.028,38.73,224,0.900,bicubic
mixer_b16_224.miil_in21k_ft_in1k,82.306,17.694,95.720,4.280,59.88,224,0.875,bilinear
convit_base.fb_in1k,82.290,17.710,95.936,4.064,86.54,224,0.875,bicubic
resnet152.tv2_in1k,82.286,17.714,96.004,3.996,60.19,224,0.965,bilinear
resnetrs101.tf_in1k,82.284,17.716,96.014,3.986,63.62,288,0.940,bicubic
wide_resnet50_2.racm_in1k,82.280,17.720,96.064,3.936,68.88,288,0.950,bicubic
tresnet_l.miil_in1k_448,82.276,17.724,95.978,4.022,55.99,448,0.875,bilinear
efficientnet_b3.ra2_in1k,82.246,17.754,96.118,3.882,12.23,320,1.000,bicubic
vit_srelpos_medium_patch16_224.sw_in1k,82.240,17.760,95.942,4.058,38.74,224,0.900,bicubic
resnet101.a2_in1k,82.236,17.764,95.730,4.270,44.55,288,1.000,bicubic
cs3darknet_x.c2ns_in1k,82.222,17.778,96.230,3.770,35.05,288,1.000,bicubic
poolformerv2_m36.sail_in1k,82.216,17.784,95.924,4.076,56.08,224,1.000,bicubic
crossvit_base_240.in1k,82.214,17.786,95.834,4.166,105.03,240,0.875,bicubic
cait_xxs36_384.fb_dist_in1k,82.204,17.796,96.144,3.856,17.37,384,1.000,bicubic
vit_base_patch16_rpn_224.sw_in1k,82.202,17.798,95.996,4.004,86.54,224,0.900,bicubic
seresnext50_32x4d.racm_in1k,82.196,17.804,96.148,3.852,27.56,288,0.950,bicubic
pvt_v2_b2_li.in1k,82.194,17.806,96.092,3.908,22.55,224,0.900,bicubic
flexivit_small.300ep_in1k,82.178,17.822,96.038,3.962,22.06,240,0.950,bicubic
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,82.172,17.828,96.224,3.776,25.03,224,0.875,bilinear
efficientformerv2_s2.snap_dist_in1k,82.166,17.834,95.910,4.090,12.71,224,0.950,bicubic
focalnet_tiny_lrf.ms_in1k,82.154,17.846,95.948,4.052,28.65,224,0.900,bicubic
efficientvit_b2.r224_in1k,82.148,17.852,95.706,4.294,24.33,224,0.950,bicubic
swin_s3_tiny_224.ms_in1k,82.144,17.856,95.954,4.046,28.33,224,0.900,bicubic
focalnet_tiny_srf.ms_in1k,82.138,17.862,95.968,4.032,28.43,224,0.900,bicubic
ecaresnet50t.a1_in1k,82.128,17.872,95.642,4.358,25.57,288,1.000,bicubic
visformer_small.in1k,82.106,17.894,95.878,4.122,40.22,224,0.900,bicubic
poolformer_m36.sail_in1k,82.102,17.898,95.698,4.302,56.17,224,0.950,bicubic
pvt_v2_b2.in1k,82.084,17.916,95.956,4.044,25.36,224,0.900,bicubic
tresnet_xl.miil_in1k,82.074,17.926,95.928,4.072,78.44,224,0.875,bilinear
halo2botnet50ts_256.a1h_in1k,82.060,17.940,95.634,4.366,22.64,256,0.950,bicubic
coatnet_rmlp_nano_rw_224.sw_in1k,82.050,17.950,95.878,4.122,15.15,224,0.900,bicubic
hrnet_w18_ssld.paddle_in1k,82.048,17.952,96.250,3.750,21.30,288,1.000,bilinear
fbnetv3_g.ra2_in1k,82.040,17.960,96.060,3.940,16.62,288,0.950,bilinear
resnext50_32x4d.a1h_in1k,82.014,17.986,95.934,4.066,25.03,288,1.000,bicubic
resnetv2_50d_evos.ah_in1k,82.002,17.998,95.900,4.100,25.59,288,1.000,bicubic
ecaresnet101d_pruned.miil_in1k,81.998,18.002,96.160,3.840,24.88,288,0.950,bicubic
deit_base_patch16_224.fb_in1k,81.992,18.008,95.736,4.264,86.57,224,0.900,bicubic
xception41p.ra3_in1k,81.972,18.028,95.802,4.198,26.91,299,0.940,bicubic
tf_efficientnetv2_b3.in1k,81.972,18.028,95.784,4.216,14.36,300,0.904,bicubic
xcit_small_12_p16_224.fb_in1k,81.970,18.030,95.812,4.188,26.25,224,1.000,bicubic
resnetv2_50d_gn.ah_in1k,81.958,18.042,95.928,4.072,25.57,288,1.000,bicubic
gcvit_xtiny.in1k,81.954,18.046,95.966,4.034,19.98,224,0.875,bicubic
coatnext_nano_rw_224.sw_in1k,81.942,18.058,95.916,4.084,14.70,224,0.900,bicubic
mobilevitv2_175.cvnets_in22k_ft_in1k,81.938,18.062,95.790,4.210,14.25,256,0.888,bicubic
vit_base_patch32_clip_224.openai_ft_in1k,81.930,18.070,95.966,4.034,88.22,224,0.900,bicubic
xcit_tiny_24_p8_224.fb_in1k,81.892,18.108,95.970,4.030,12.11,224,1.000,bicubic
resnet101.tv2_in1k,81.888,18.112,95.768,4.232,44.55,224,0.965,bilinear
vit_small_r26_s32_224.augreg_in21k_ft_in1k,81.864,18.136,96.022,3.978,36.43,224,0.900,bicubic
fastvit_sa12.apple_dist_in1k,81.854,18.146,95.710,4.290,11.58,256,0.900,bicubic
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,81.838,18.162,96.092,3.908,194.03,224,0.875,bilinear
swinv2_tiny_window8_256.ms_in1k,81.820,18.180,95.994,4.006,28.35,256,0.900,bicubic
tf_efficientnet_b3.ap_in1k,81.820,18.180,95.626,4.374,12.23,300,0.904,bicubic
pit_s_distilled_224.in1k,81.814,18.186,95.730,4.270,24.04,224,0.900,bicubic
swinv2_cr_tiny_ns_224.sw_in1k,81.802,18.198,95.818,4.182,28.33,224,0.900,bicubic
vit_base_patch16_224.orig_in21k_ft_in1k,81.790,18.210,96.126,3.874,86.57,224,0.900,bicubic
cs3sedarknet_l.c2ns_in1k,81.784,18.216,95.964,4.036,21.91,288,0.950,bicubic
regnety_032.tv2_in1k,81.756,18.244,95.844,4.156,19.44,224,0.965,bicubic
tresnet_m.miil_in1k_448,81.710,18.290,95.574,4.426,31.39,448,0.875,bilinear
coatnet_nano_rw_224.sw_in1k,81.696,18.304,95.646,4.354,15.14,224,0.900,bicubic
twins_svt_small.in1k,81.676,18.324,95.658,4.342,24.06,224,0.900,bicubic
halonet50ts.a1h_in1k,81.662,18.338,95.610,4.390,22.73,256,0.940,bicubic
ecaresnet50t.a2_in1k,81.658,18.342,95.550,4.450,25.57,288,1.000,bicubic
ecaresnet50d.miil_in1k,81.650,18.350,95.882,4.118,25.58,288,0.950,bicubic
tf_efficientnet_b3.aa_in1k,81.640,18.360,95.722,4.278,12.23,300,0.904,bicubic
rexnet_200.nav_in1k,81.636,18.364,95.666,4.334,16.37,224,0.875,bicubic
resnetaa50.a1h_in1k,81.614,18.386,95.802,4.198,25.56,288,1.000,bicubic
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,81.606,18.394,96.040,3.960,88.79,224,0.875,bilinear
wide_resnet50_2.tv2_in1k,81.606,18.394,95.760,4.240,68.88,224,0.965,bilinear
convnext_nano_ols.d1h_in1k,81.600,18.400,95.636,4.364,15.65,288,1.000,bicubic
poolformerv2_s36.sail_in1k,81.566,18.434,95.690,4.310,30.79,224,1.000,bicubic
edgenext_small.usi_in1k,81.564,18.436,95.712,4.288,5.59,320,1.000,bicubic
lamhalobotnet50ts_256.a1h_in1k,81.552,18.448,95.492,4.508,22.57,256,0.950,bicubic
regnetx_080.tv2_in1k,81.540,18.460,95.542,4.458,39.57,224,0.965,bicubic
tnt_s_patch16_224,81.536,18.464,95.736,4.264,23.76,224,0.900,bicubic
crossvit_15_240.in1k,81.536,18.464,95.690,4.310,27.53,240,0.875,bicubic
tf_efficientnet_lite4.in1k,81.530,18.470,95.664,4.336,13.01,380,0.920,bilinear
levit_256.fb_dist_in1k,81.524,18.476,95.494,4.506,18.89,224,0.900,bicubic
levit_conv_256.fb_dist_in1k,81.522,18.478,95.490,4.510,18.89,224,0.900,bicubic
vit_large_patch32_384.orig_in21k_ft_in1k,81.510,18.490,96.090,3.910,306.63,384,1.000,bicubic
repvit_m3.dist_in1k,81.502,18.498,95.568,4.432,10.68,224,0.950,bicubic
tiny_vit_11m_224.in1k,81.492,18.508,95.862,4.138,11.00,224,0.950,bicubic
mobilevitv2_150.cvnets_in22k_ft_in1k,81.488,18.512,95.668,4.332,10.59,256,0.888,bicubic
convnext_nano.d1h_in1k,81.482,18.518,95.658,4.342,15.59,288,1.000,bicubic
tresnet_l.miil_in1k,81.480,18.520,95.624,4.376,55.99,224,0.875,bilinear
resnext50_32x4d.a1_in1k,81.466,18.534,95.174,4.826,25.03,288,1.000,bicubic
vit_relpos_small_patch16_224.sw_in1k,81.462,18.538,95.820,4.180,21.98,224,0.900,bicubic
gcresnet50t.ra2_in1k,81.456,18.544,95.718,4.282,25.90,288,1.000,bicubic
resnet50d.a1_in1k,81.450,18.550,95.218,4.782,25.58,288,1.000,bicubic
poolformer_s36.sail_in1k,81.430,18.570,95.444,4.556,30.86,224,0.900,bicubic
nest_tiny_jx.goog_in1k,81.426,18.574,95.618,4.382,17.06,224,0.875,bicubic
convit_small.fb_in1k,81.420,18.580,95.744,4.256,27.78,224,0.875,bicubic
ecaresnetlight.miil_in1k,81.408,18.592,95.816,4.184,30.16,288,0.950,bicubic
resnetv2_50.a1h_in1k,81.398,18.602,95.726,4.274,25.55,288,1.000,bicubic
tf_efficientnet_b1.ns_jft_in1k,81.388,18.612,95.738,4.262,7.79,240,0.882,bicubic
vit_small_patch16_224.augreg_in21k_ft_in1k,81.386,18.614,96.136,3.864,22.05,224,0.900,bicubic
swin_tiny_patch4_window7_224.ms_in1k,81.376,18.624,95.544,4.456,28.29,224,0.900,bicubic
deit3_small_patch16_224.fb_in1k,81.370,18.630,95.456,4.544,22.06,224,0.900,bicubic
convmixer_1536_20.in1k,81.362,18.638,95.614,4.386,51.63,224,0.960,bicubic
resnet50d.ra2_in1k,81.356,18.644,95.738,4.262,25.58,288,0.950,bicubic
gernet_l.idstcv_in1k,81.354,18.646,95.530,4.470,31.08,256,0.875,bilinear
repvit_m1_1.dist_450e_in1k,81.312,18.688,95.560,4.440,8.80,224,0.950,bicubic
efficientnet_el.ra_in1k,81.312,18.688,95.536,4.464,10.59,300,0.904,bicubic
legacy_senet154.in1k,81.312,18.688,95.490,4.510,115.09,224,0.875,bilinear
resnext50_32x4d.a2_in1k,81.304,18.696,95.096,4.904,25.03,288,1.000,bicubic
seresnet50.ra2_in1k,81.284,18.716,95.652,4.348,28.09,288,0.950,bicubic
coat_mini.in1k,81.270,18.730,95.382,4.618,10.34,224,0.900,bicubic
gcresnext50ts.ch_in1k,81.230,18.770,95.542,4.458,15.67,288,1.000,bicubic
senet154.gluon_in1k,81.226,18.774,95.358,4.642,115.09,224,0.875,bicubic
res2net101d.in1k,81.218,18.782,95.350,4.650,45.23,224,0.875,bilinear
resnet50_gn.a1h_in1k,81.216,18.784,95.624,4.376,25.56,288,0.950,bicubic
deit_small_distilled_patch16_224.fb_in1k,81.216,18.784,95.384,4.616,22.44,224,0.900,bicubic
resnet50.a1_in1k,81.214,18.786,95.102,4.898,25.56,288,1.000,bicubic
xcit_tiny_12_p8_224.fb_dist_in1k,81.212,18.788,95.602,4.398,6.71,224,1.000,bicubic
resnext50_32x4d.tv2_in1k,81.182,18.818,95.340,4.660,25.03,224,0.965,bilinear
resnet50.fb_swsl_ig1b_ft_in1k,81.172,18.828,95.986,4.014,25.56,224,0.875,bilinear
sebotnet33ts_256.a1h_in1k,81.168,18.832,95.168,4.832,13.70,256,0.940,bicubic
resnet50d.a2_in1k,81.164,18.836,95.080,4.920,25.58,288,1.000,bicubic
lambda_resnet50ts.a1h_in1k,81.158,18.842,95.098,4.902,21.54,256,0.950,bicubic
resmlp_36_224.fb_distilled_in1k,81.148,18.852,95.478,4.522,44.69,224,0.875,bicubic
mobilevitv2_200.cvnets_in1k,81.134,18.866,95.362,4.638,18.45,256,0.888,bicubic
resnest50d_4s2x40d.in1k,81.120,18.880,95.560,4.440,30.42,224,0.875,bicubic
vit_small_patch16_384.augreg_in1k,81.116,18.884,95.574,4.426,22.20,384,1.000,bicubic
seresnet50.a2_in1k,81.106,18.894,95.222,4.778,28.09,288,1.000,bicubic
vit_base_patch16_384.augreg_in1k,81.102,18.898,95.328,4.672,86.86,384,1.000,bicubic
seresnet50.a1_in1k,81.102,18.898,95.120,4.880,28.09,288,1.000,bicubic
twins_pcpvt_small.in1k,81.092,18.908,95.648,4.352,24.11,224,0.900,bicubic
vit_srelpos_small_patch16_224.sw_in1k,81.092,18.908,95.570,4.430,21.97,224,0.900,bicubic
convnextv2_pico.fcmae_ft_in1k,81.086,18.914,95.480,4.520,9.07,288,0.950,bicubic
pit_s_224.in1k,81.086,18.914,95.330,4.670,23.46,224,0.900,bicubic
fastvit_s12.apple_dist_in1k,81.070,18.930,95.284,4.716,9.47,256,0.900,bicubic
haloregnetz_b.ra3_in1k,81.046,18.954,95.200,4.800,11.68,224,0.940,bicubic
resmlp_big_24_224.fb_in1k,81.036,18.964,95.018,4.982,129.14,224,0.875,bicubic
crossvit_small_240.in1k,81.018,18.982,95.456,4.544,26.86,240,0.875,bicubic
resnet152s.gluon_in1k,81.008,18.992,95.416,4.584,60.32,224,0.875,bicubic
resnest50d_1s4x24d.in1k,80.988,19.012,95.326,4.674,25.68,224,0.875,bicubic
cait_xxs24_384.fb_dist_in1k,80.972,19.028,95.640,4.360,12.03,384,1.000,bicubic
resnet50.d_in1k,80.972,19.028,95.430,4.570,25.56,288,1.000,bicubic
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,80.968,19.032,96.014,3.986,28.29,224,0.900,bicubic
resnest50d.in1k,80.960,19.040,95.382,4.618,27.48,224,0.875,bilinear
sehalonet33ts.ra2_in1k,80.958,19.042,95.272,4.728,13.69,256,0.940,bicubic
xcit_tiny_12_p16_384.fb_dist_in1k,80.938,19.062,95.414,4.586,6.72,384,1.000,bicubic
regnetx_032.tv2_in1k,80.926,19.074,95.278,4.722,15.30,224,0.965,bicubic
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,80.924,19.076,95.734,4.266,44.18,224,0.875,bilinear
resnet50.c1_in1k,80.912,19.088,95.552,4.448,25.56,288,1.000,bicubic
cs3darknet_l.c2ns_in1k,80.896,19.104,95.662,4.338,21.16,288,0.950,bicubic
seresnext101_64x4d.gluon_in1k,80.894,19.106,95.296,4.704,88.23,224,0.875,bicubic
seresnext101_32x4d.gluon_in1k,80.892,19.108,95.296,4.704,48.96,224,0.875,bicubic
cs3darknet_focus_l.c2ns_in1k,80.876,19.124,95.682,4.318,21.15,288,0.950,bicubic
tiny_vit_5m_224.dist_in22k_ft_in1k,80.876,19.124,95.664,4.336,5.39,224,0.950,bicubic
tf_efficientnet_b3.in1k,80.874,19.126,95.300,4.700,12.23,300,0.904,bicubic
resnet50.c2_in1k,80.870,19.130,95.534,4.466,25.56,288,1.000,bicubic
mobilevitv2_175.cvnets_in1k,80.860,19.140,95.256,4.744,14.25,256,0.888,bicubic
efficientnet_b3_pruned.in1k,80.852,19.148,95.244,4.756,9.86,300,0.904,bicubic
resnet50.tv2_in1k,80.848,19.152,95.434,4.566,25.56,224,0.965,bilinear
fastvit_sa12.apple_in1k,80.844,19.156,95.340,4.660,11.58,256,0.900,bicubic
repvit_m1_1.dist_300e_in1k,80.826,19.174,95.170,4.830,8.80,224,0.950,bicubic
regnety_320.pycls_in1k,80.810,19.190,95.238,4.762,145.05,224,0.875,bicubic
tresnet_m.miil_in1k,80.798,19.202,94.856,5.144,31.39,224,0.875,bilinear
ecaresnet50d_pruned.miil_in1k,80.790,19.210,95.570,4.430,19.94,288,0.950,bicubic
seresnet33ts.ra2_in1k,80.784,19.216,95.362,4.638,19.78,288,1.000,bicubic
resnet50.a2_in1k,80.772,19.228,94.988,5.012,25.56,288,1.000,bicubic
resmlp_24_224.fb_distilled_in1k,80.756,19.244,95.224,4.776,30.02,224,0.875,bicubic
poolformerv2_s24.sail_in1k,80.748,19.252,95.310,4.690,21.34,224,1.000,bicubic
gernet_m.idstcv_in1k,80.736,19.264,95.190,4.810,21.14,224,0.875,bilinear
regnetz_b16.ra3_in1k,80.728,19.272,95.518,4.482,9.72,288,1.000,bicubic
vit_base_patch32_224.augreg_in21k_ft_in1k,80.716,19.284,95.566,4.434,88.22,224,0.900,bicubic
resnet50.b1k_in1k,80.706,19.294,95.432,4.568,25.56,288,1.000,bicubic
resnext50_32x4d.ra_in1k,80.698,19.302,95.392,4.608,25.03,288,0.950,bicubic
resnet50.a1h_in1k,80.678,19.322,95.306,4.694,25.56,224,1.000,bicubic
eca_resnet33ts.ra2_in1k,80.672,19.328,95.364,4.636,19.68,288,1.000,bicubic
regnety_016.tv2_in1k,80.666,19.334,95.330,4.670,11.20,224,0.965,bicubic
resnext50d_32x4d.bt_in1k,80.664,19.336,95.420,4.580,25.05,288,0.950,bicubic
nf_resnet50.ra2_in1k,80.640,19.360,95.334,4.666,25.56,288,0.940,bicubic
eva02_tiny_patch14_336.mim_in22k_ft_in1k,80.630,19.370,95.526,4.474,5.76,336,1.000,bicubic
efficientnet_b2.ra_in1k,80.610,19.390,95.314,4.686,9.11,288,1.000,bicubic
gcresnet33ts.ra2_in1k,80.600,19.400,95.322,4.678,19.88,288,1.000,bicubic
resnext101_64x4d.gluon_in1k,80.600,19.400,94.992,5.008,83.46,224,0.875,bicubic
cspresnext50.ra_in1k,80.554,19.446,95.326,4.674,20.57,256,0.887,bilinear
resnet152.a3_in1k,80.546,19.454,95.000,5.000,60.19,224,0.950,bicubic
darknet53.c2ns_in1k,80.532,19.468,95.432,4.568,41.61,288,1.000,bicubic
maxvit_rmlp_pico_rw_256.sw_in1k,80.514,19.486,95.214,4.786,7.52,256,0.950,bicubic
darknetaa53.c2ns_in1k,80.506,19.494,95.322,4.678,36.02,288,1.000,bilinear
repvgg_b3.rvgg_in1k,80.506,19.494,95.254,4.746,123.09,224,0.875,bilinear
efficientformer_l1.snap_dist_in1k,80.498,19.502,94.988,5.012,12.29,224,0.950,bicubic
vit_small_patch32_384.augreg_in21k_ft_in1k,80.486,19.514,95.600,4.400,22.92,384,1.000,bicubic
mixnet_xl.ra_in1k,80.482,19.518,94.936,5.064,11.90,224,0.875,bicubic
resnet152d.gluon_in1k,80.476,19.524,95.202,4.798,60.21,224,0.875,bicubic
convnext_pico_ols.d1_in1k,80.462,19.538,95.252,4.748,9.06,288,1.000,bicubic
repvit_m2.dist_in1k,80.460,19.540,95.168,4.832,8.80,224,0.950,bicubic
inception_resnet_v2.tf_in1k,80.458,19.542,95.308,4.692,55.84,299,0.897,bicubic
edgenext_small_rw.sw_in1k,80.458,19.542,95.190,4.810,7.83,320,1.000,bicubic
resnet50.b2k_in1k,80.454,19.546,95.318,4.682,25.56,288,1.000,bicubic
xcit_tiny_24_p16_224.fb_dist_in1k,80.454,19.546,95.218,4.782,12.12,224,1.000,bicubic
repvit_m1_0.dist_450e_in1k,80.434,19.566,94.918,5.082,7.30,224,0.950,bicubic
resnet101d.gluon_in1k,80.426,19.574,95.024,4.976,44.57,224,0.875,bicubic
convnext_pico.d1_in1k,80.416,19.584,95.048,4.952,9.05,288,0.950,bicubic
regnety_120.pycls_in1k,80.380,19.620,95.126,4.874,51.82,224,0.875,bicubic
mobilevitv2_150.cvnets_in1k,80.370,19.630,95.074,4.926,10.59,256,0.888,bicubic
fastvit_t12.apple_dist_in1k,80.352,19.648,95.042,4.958,7.55,256,0.900,bicubic
ese_vovnet39b.ra_in1k,80.350,19.650,95.366,4.634,24.57,288,0.950,bicubic
resnetv2_50x1_bit.goog_in21k_ft_in1k,80.342,19.658,95.682,4.318,25.55,448,1.000,bilinear
resnext101_32x4d.gluon_in1k,80.340,19.660,94.930,5.070,44.18,224,0.875,bicubic
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,80.334,19.666,95.400,4.600,25.03,224,0.875,bilinear
rexnet_150.nav_in1k,80.324,19.676,95.176,4.824,9.73,224,0.875,bicubic
efficientvit_b1.r288_in1k,80.324,19.676,94.990,5.010,9.10,288,1.000,bicubic
tf_efficientnet_b2.ap_in1k,80.310,19.690,95.026,4.974,9.11,260,0.890,bicubic
resnet101s.gluon_in1k,80.304,19.696,95.152,4.848,44.67,224,0.875,bicubic
efficientnet_el_pruned.in1k,80.298,19.702,95.222,4.778,10.59,300,0.904,bicubic
regnety_160.pycls_in1k,80.298,19.702,94.964,5.036,83.59,224,0.875,bicubic
poolformer_s24.sail_in1k,80.294,19.706,95.074,4.926,21.39,224,0.900,bicubic
res2net50d.in1k,80.254,19.746,95.036,4.964,25.72,224,0.875,bilinear
tf_efficientnet_el.in1k,80.248,19.752,95.120,4.880,10.59,300,0.904,bicubic
regnetx_320.pycls_in1k,80.246,19.754,95.022,4.978,107.81,224,0.875,bicubic
vit_base_patch16_224.sam_in1k,80.238,19.762,94.756,5.244,86.57,224,0.900,bicubic
resnetblur50.bt_in1k,80.234,19.766,95.234,4.766,25.56,288,0.950,bicubic
legacy_seresnext101_32x4d.in1k,80.232,19.768,95.020,4.980,48.96,224,0.875,bilinear
repvgg_b3g4.rvgg_in1k,80.216,19.784,95.092,4.908,83.83,224,0.875,bilinear
tf_efficientnetv2_b2.in1k,80.196,19.804,95.042,4.958,10.10,260,0.890,bicubic
dpn107.mx_in1k,80.170,19.830,94.942,5.058,86.92,224,0.875,bicubic
convmixer_768_32.in1k,80.168,19.832,95.074,4.926,21.11,224,0.960,bicubic
skresnext50_32x4d.ra_in1k,80.164,19.836,94.640,5.360,27.48,224,0.875,bicubic
inception_v4.tf_in1k,80.156,19.844,94.970,5.030,42.68,299,0.875,bicubic
repvit_m1_0.dist_300e_in1k,80.126,19.874,94.744,5.256,7.30,224,0.950,bicubic
tf_efficientnet_b2.aa_in1k,80.084,19.916,94.906,5.094,9.11,260,0.890,bicubic
cspdarknet53.ra_in1k,80.068,19.932,95.078,4.922,27.64,256,0.887,bilinear
dpn92.mx_in1k,80.038,19.962,94.860,5.140,37.67,224,0.875,bicubic
inception_resnet_v2.tf_ens_adv_in1k,79.978,20.022,94.948,5.052,55.84,299,0.897,bicubic
resnet50.ram_in1k,79.976,20.024,95.052,4.948,25.56,288,0.950,bicubic
fastvit_s12.apple_in1k,79.942,20.058,94.794,5.206,9.47,256,0.900,bicubic
seresnext50_32x4d.gluon_in1k,79.924,20.076,94.824,5.176,27.56,224,0.875,bicubic
efficientnet_b2_pruned.in1k,79.920,20.080,94.852,5.148,8.31,260,0.890,bicubic
resnet152c.gluon_in1k,79.912,20.088,94.846,5.154,60.21,224,0.875,bicubic
resnetrs50.tf_in1k,79.894,20.106,94.974,5.026,35.69,224,0.910,bicubic
xception71.tf_in1k,79.874,20.126,94.928,5.072,42.34,299,0.903,bicubic
regnety_080.pycls_in1k,79.868,20.132,94.832,5.168,39.18,224,0.875,bicubic
regnetx_160.pycls_in1k,79.866,20.134,94.828,5.172,54.28,224,0.875,bicubic
ecaresnet26t.ra2_in1k,79.850,20.150,95.090,4.910,16.01,320,0.950,bicubic
deit_small_patch16_224.fb_in1k,79.848,20.152,95.044,4.956,22.05,224,0.900,bicubic
levit_conv_192.fb_dist_in1k,79.838,20.162,94.784,5.216,10.95,224,0.900,bicubic
levit_192.fb_dist_in1k,79.838,20.162,94.778,5.222,10.95,224,0.900,bicubic
resnet50.ra_in1k,79.836,20.164,94.966,5.034,25.56,288,0.950,bicubic
dpn131.mx_in1k,79.814,20.186,94.700,5.300,79.25,224,0.875,bicubic
resnet101.a3_in1k,79.814,20.186,94.614,5.386,44.55,224,0.950,bicubic
tf_efficientnet_lite3.in1k,79.806,20.194,94.914,5.086,8.20,300,0.904,bilinear
resmlp_36_224.fb_in1k,79.772,20.228,94.884,5.116,44.69,224,0.875,bicubic
cait_xxs36_224.fb_dist_in1k,79.746,20.254,94.874,5.126,17.30,224,1.000,bicubic
efficientvit_b1.r256_in1k,79.734,20.266,94.780,5.220,9.10,256,1.000,bicubic
gcvit_xxtiny.in1k,79.726,20.274,95.080,4.920,12.00,224,0.875,bicubic
resnet33ts.ra2_in1k,79.726,20.274,94.828,5.172,19.68,288,1.000,bicubic
regnety_064.pycls_in1k,79.716,20.284,94.766,5.234,30.58,224,0.875,bicubic
resnet152.gluon_in1k,79.696,20.304,94.730,5.270,60.19,224,0.875,bicubic
efficientformerv2_s1.snap_dist_in1k,79.692,20.308,94.716,5.284,6.19,224,0.950,bicubic
xcit_tiny_12_p8_224.fb_in1k,79.688,20.312,95.054,4.946,6.71,224,1.000,bicubic
fbnetv3_d.ra2_in1k,79.682,20.318,94.944,5.056,10.31,256,0.950,bilinear
mobilevitv2_125.cvnets_in1k,79.680,20.320,94.858,5.142,7.48,256,0.888,bicubic
dpn98.mx_in1k,79.670,20.330,94.654,5.346,61.57,224,0.875,bicubic
gmlp_s16_224.ra3_in1k,79.644,20.356,94.622,5.378,19.42,224,0.875,bicubic
resnet50.bt_in1k,79.640,20.360,94.892,5.108,25.56,288,0.950,bicubic
tf_efficientnet_b2.in1k,79.608,20.392,94.714,5.286,9.11,260,0.890,bicubic
regnetx_120.pycls_in1k,79.588,20.412,94.742,5.258,46.11,224,0.875,bicubic
cspresnet50.ra_in1k,79.582,20.418,94.710,5.290,21.62,256,0.887,bilinear
xception65.tf_in1k,79.556,20.444,94.658,5.342,39.92,299,0.903,bicubic
ecaresnet50t.a3_in1k,79.552,20.448,94.694,5.306,25.57,224,0.950,bicubic
resnet101c.gluon_in1k,79.538,20.462,94.584,5.416,44.57,224,0.875,bicubic
rexnet_130.nav_in1k,79.506,20.494,94.678,5.322,7.56,224,0.875,bicubic
eca_halonext26ts.c1_in1k,79.486,20.514,94.600,5.400,10.76,256,0.940,bicubic
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,79.484,20.516,94.138,5.862,119.42,256,0.900,bicubic
hrnet_w64.ms_in1k,79.476,20.524,94.652,5.348,128.06,224,0.875,bilinear
tf_efficientnetv2_b1.in1k,79.460,20.540,94.722,5.278,8.14,240,0.882,bicubic
xcit_tiny_24_p16_224.fb_in1k,79.448,20.552,94.878,5.122,12.12,224,1.000,bicubic
dla102x2.in1k,79.446,20.554,94.632,5.368,41.28,224,0.875,bilinear
regnetx_016.tv2_in1k,79.436,20.564,94.768,5.232,9.19,224,0.965,bicubic
mobileone_s4.apple_in1k,79.426,20.574,94.480,5.520,14.95,224,0.900,bilinear
resnet32ts.ra2_in1k,79.388,20.612,94.652,5.348,17.96,288,1.000,bicubic
repvgg_b2g4.rvgg_in1k,79.382,20.618,94.676,5.324,61.76,224,0.875,bilinear
resmlp_24_224.fb_in1k,79.374,20.626,94.546,5.454,30.02,224,0.875,bicubic
dpn68b.ra_in1k,79.360,20.640,94.436,5.564,12.61,288,1.000,bicubic
resnext50_32x4d.gluon_in1k,79.360,20.640,94.430,5.570,25.03,224,0.875,bicubic
convnextv2_femto.fcmae_ft_in1k,79.338,20.662,94.560,5.440,5.23,288,0.950,bicubic
resnet101.gluon_in1k,79.310,20.690,94.522,5.478,44.55,224,0.875,bicubic
resnext101_32x8d.tv_in1k,79.310,20.690,94.520,5.480,88.79,224,0.875,bilinear
nf_regnet_b1.ra2_in1k,79.308,20.692,94.740,5.260,10.22,288,0.900,bicubic
hrnet_w48.ms_in1k,79.306,20.694,94.516,5.484,77.47,224,0.875,bilinear
tf_efficientnet_cc_b1_8e.in1k,79.302,20.698,94.374,5.626,39.72,240,0.882,bicubic
tf_efficientnet_b1.ap_in1k,79.276,20.724,94.312,5.688,7.79,240,0.882,bicubic
eca_botnext26ts_256.c1_in1k,79.268,20.732,94.606,5.394,10.59,256,0.950,bicubic
resnext50_32x4d.a3_in1k,79.268,20.732,94.306,5.694,25.03,224,0.950,bicubic
fastvit_t12.apple_in1k,79.264,20.736,94.562,5.438,7.55,256,0.900,bicubic
botnet26t_256.c1_in1k,79.258,20.742,94.532,5.468,12.49,256,0.950,bicubic
efficientvit_b1.r224_in1k,79.252,20.748,94.304,5.696,9.10,224,0.950,bicubic
efficientnet_em.ra2_in1k,79.244,20.756,94.794,5.206,6.90,240,0.882,bicubic
resnet50.fb_ssl_yfcc100m_ft_in1k,79.230,20.770,94.826,5.174,25.56,224,0.875,bilinear
regnety_040.pycls_in1k,79.220,20.780,94.656,5.344,20.65,224,0.875,bicubic
res2net101_26w_4s.in1k,79.200,20.800,94.436,5.564,45.21,224,0.875,bilinear
regnetx_080.pycls_in1k,79.198,20.802,94.554,5.446,39.57,224,0.875,bicubic
pit_xs_distilled_224.in1k,79.180,20.820,94.366,5.634,11.00,224,0.900,bicubic
tiny_vit_5m_224.in1k,79.170,20.830,94.794,5.206,5.39,224,0.950,bicubic
vit_base_patch16_224.augreg_in1k,79.154,20.846,94.090,5.910,86.57,224,0.900,bicubic
fbnetv3_b.ra2_in1k,79.146,20.854,94.744,5.256,8.60,256,0.950,bilinear
halonet26t.a1h_in1k,79.106,20.894,94.306,5.694,12.48,256,0.950,bicubic
coat_lite_mini.in1k,79.102,20.898,94.608,5.392,11.01,224,0.900,bicubic
lambda_resnet26t.c1_in1k,79.088,20.912,94.590,5.410,10.96,256,0.940,bicubic
resnet50d.gluon_in1k,79.078,20.922,94.466,5.534,25.58,224,0.875,bicubic
legacy_seresnext50_32x4d.in1k,79.076,20.924,94.432,5.568,27.56,224,0.875,bilinear
regnetx_064.pycls_in1k,79.066,20.934,94.460,5.540,26.21,224,0.875,bicubic
repvit_m0_9.dist_450e_in1k,79.066,20.934,94.380,5.620,5.49,224,0.950,bicubic
legacy_xception.tf_in1k,79.040,20.960,94.382,5.618,22.86,299,0.897,bicubic
resnet50.am_in1k,79.002,20.998,94.398,5.602,25.56,224,0.875,bicubic
mixnet_l.ft_in1k,78.966,21.034,94.182,5.818,7.33,224,0.875,bicubic
lambda_resnet26rpt_256.c1_in1k,78.964,21.036,94.436,5.564,10.99,256,0.940,bicubic
res2net50_26w_8s.in1k,78.942,21.058,94.294,5.706,48.40,224,0.875,bilinear
hrnet_w40.ms_in1k,78.932,21.068,94.464,5.536,57.56,224,0.875,bilinear
convnext_femto_ols.d1_in1k,78.924,21.076,94.526,5.474,5.23,288,0.950,bicubic
convnext_tiny.fb_in22k_ft_in1k,78.898,21.102,94.674,5.326,28.59,288,1.000,bicubic
hrnet_w44.ms_in1k,78.894,21.106,94.364,5.636,67.06,224,0.875,bilinear
regnety_032.pycls_in1k,78.876,21.124,94.408,5.592,19.44,224,0.875,bicubic
vit_small_patch16_224.augreg_in1k,78.848,21.152,94.288,5.712,22.05,224,0.900,bicubic
wide_resnet101_2.tv_in1k,78.842,21.158,94.282,5.718,126.89,224,0.875,bilinear
tf_efficientnet_b1.aa_in1k,78.828,21.172,94.200,5.800,7.79,240,0.882,bicubic
seresnext26d_32x4d.bt_in1k,78.814,21.186,94.240,5.760,16.81,288,0.950,bicubic
repghostnet_200.in1k,78.806,21.194,94.330,5.670,9.80,224,0.875,bicubic
inception_v3.gluon_in1k,78.802,21.198,94.376,5.624,23.83,299,0.875,bicubic
efficientnet_b1.ft_in1k,78.800,21.200,94.342,5.658,7.79,256,1.000,bicubic
repvgg_b2.rvgg_in1k,78.792,21.208,94.420,5.580,89.02,224,0.875,bilinear
tf_mixnet_l.in1k,78.776,21.224,94.002,5.998,7.33,224,0.875,bicubic
vit_base_patch32_384.augreg_in1k,78.756,21.244,94.226,5.774,88.30,384,1.000,bicubic
seresnext26t_32x4d.bt_in1k,78.744,21.256,94.312,5.688,16.81,288,0.950,bicubic
resnet50d.a3_in1k,78.720,21.280,94.232,5.768,25.58,224,0.950,bicubic
convnext_femto.d1_in1k,78.716,21.284,94.430,5.570,5.22,288,0.950,bicubic
resnet50s.gluon_in1k,78.714,21.286,94.242,5.758,25.68,224,0.875,bicubic
dla169.in1k,78.708,21.292,94.344,5.656,53.39,224,0.875,bilinear
pvt_v2_b1.in1k,78.704,21.296,94.502,5.498,14.01,224,0.900,bicubic
tf_efficientnet_b0.ns_jft_in1k,78.668,21.332,94.372,5.628,5.29,224,0.875,bicubic
regnety_008_tv.tv2_in1k,78.666,21.334,94.390,5.610,6.43,224,0.965,bicubic
legacy_seresnet152.in1k,78.660,21.340,94.370,5.630,66.82,224,0.875,bilinear
repvit_m0_9.dist_300e_in1k,78.658,21.342,94.116,5.884,5.49,224,0.950,bicubic
xcit_tiny_12_p16_224.fb_dist_in1k,78.574,21.426,94.198,5.802,6.72,224,1.000,bicubic
res2net50_26w_6s.in1k,78.568,21.432,94.122,5.878,37.05,224,0.875,bilinear
tf_efficientnet_b1.in1k,78.562,21.438,94.094,5.906,7.79,240,0.882,bicubic
repvit_m1.dist_in1k,78.538,21.462,94.070,5.930,5.49,224,0.950,bicubic
dla102x.in1k,78.512,21.488,94.236,5.764,26.31,224,0.875,bilinear
xception41.tf_in1k,78.504,21.496,94.276,5.724,26.97,299,0.903,bicubic
levit_conv_128.fb_dist_in1k,78.494,21.506,94.008,5.992,9.21,224,0.900,bicubic
regnetx_040.pycls_in1k,78.492,21.508,94.242,5.758,22.12,224,0.875,bicubic
levit_128.fb_dist_in1k,78.490,21.510,94.012,5.988,9.21,224,0.900,bicubic
resnest26d.gluon_in1k,78.482,21.518,94.294,5.706,17.07,224,0.875,bilinear
wide_resnet50_2.tv_in1k,78.476,21.524,94.088,5.912,68.88,224,0.875,bilinear
dla60_res2net.in1k,78.464,21.536,94.198,5.802,20.85,224,0.875,bilinear
hrnet_w32.ms_in1k,78.442,21.558,94.190,5.810,41.23,224,0.875,bilinear
dla60_res2next.in1k,78.440,21.560,94.144,5.856,17.03,224,0.875,bilinear
resnet34d.ra2_in1k,78.436,21.564,94.344,5.656,21.82,288,0.950,bicubic
coat_tiny.in1k,78.426,21.574,94.048,5.952,5.50,224,0.900,bicubic
vit_tiny_patch16_384.augreg_in21k_ft_in1k,78.424,21.576,94.542,5.458,5.79,384,1.000,bicubic
gcresnext26ts.ch_in1k,78.414,21.586,94.036,5.964,10.48,288,1.000,bicubic
selecsls60b.in1k,78.412,21.588,94.168,5.832,32.77,224,0.875,bicubic
legacy_seresnet101.in1k,78.386,21.614,94.262,5.738,49.33,224,0.875,bilinear
cait_xxs24_224.fb_dist_in1k,78.384,21.616,94.316,5.684,11.96,224,1.000,bicubic
repvgg_b1.rvgg_in1k,78.368,21.632,94.096,5.904,57.42,224,0.875,bilinear
tf_efficientnetv2_b0.in1k,78.358,21.642,94.014,5.986,7.14,224,0.875,bicubic
resnet26t.ra2_in1k,78.328,21.672,94.124,5.876,16.01,320,1.000,bicubic
resnet152.tv_in1k,78.322,21.678,94.046,5.954,60.19,224,0.875,bilinear
mobilevit_s.cvnets_in1k,78.312,21.688,94.148,5.852,5.58,256,0.900,bicubic
seresnext26ts.ch_in1k,78.270,21.730,94.092,5.908,10.39,288,1.000,bicubic
bat_resnext26ts.ch_in1k,78.252,21.748,94.098,5.902,10.73,256,0.900,bicubic
res2next50.in1k,78.242,21.758,93.892,6.108,24.67,224,0.875,bilinear
efficientnet_b1_pruned.in1k,78.240,21.760,93.834,6.166,6.33,240,0.882,bicubic
dla60x.in1k,78.236,21.764,94.026,5.974,17.35,224,0.875,bilinear
hrnet_w30.ms_in1k,78.196,21.804,94.222,5.778,37.71,224,0.875,bilinear
hrnet_w18_small_v2.gluon_in1k,78.190,21.810,93.902,6.098,15.60,224,0.875,bicubic
pit_xs_224.in1k,78.176,21.824,94.162,5.838,10.62,224,0.900,bicubic
regnetx_032.pycls_in1k,78.168,21.832,94.082,5.918,15.30,224,0.875,bicubic
visformer_tiny.in1k,78.160,21.840,94.166,5.834,10.32,224,0.900,bicubic
res2net50_14w_8s.in1k,78.158,21.842,93.846,6.154,25.06,224,0.875,bilinear
tf_efficientnet_em.in1k,78.126,21.874,94.048,5.952,6.90,240,0.882,bicubic
hrnet_w18.ms_aug_in1k,78.122,21.878,94.054,5.946,21.30,224,0.950,bilinear
hardcorenas_f.miil_green_in1k,78.096,21.904,93.802,6.198,8.20,224,0.875,bilinear
mobilevitv2_100.cvnets_in1k,78.080,21.920,94.170,5.830,4.90,256,0.888,bicubic
efficientnet_es.ra_in1k,78.058,21.942,93.926,6.074,5.44,224,0.875,bicubic
resnet50.a3_in1k,78.048,21.952,93.780,6.220,25.56,224,0.950,bicubic
gmixer_24_224.ra3_in1k,78.026,21.974,93.668,6.332,24.72,224,0.875,bicubic
dla102.in1k,78.024,21.976,93.934,6.066,33.27,224,0.875,bilinear
resnet50c.gluon_in1k,78.006,21.994,93.992,6.008,25.58,224,0.875,bicubic
poolformerv2_s12.sail_in1k,78.002,21.998,93.864,6.136,11.89,224,1.000,bicubic
eca_resnext26ts.ch_in1k,78.000,22.000,93.926,6.074,10.30,288,1.000,bicubic
mobileone_s3.apple_in1k,77.992,22.008,93.914,6.086,10.17,224,0.900,bilinear
selecsls60.in1k,77.988,22.012,93.830,6.170,30.67,224,0.875,bicubic
resmlp_12_224.fb_distilled_in1k,77.954,22.046,93.560,6.440,15.35,224,0.875,bicubic
res2net50_26w_4s.in1k,77.950,22.050,93.852,6.148,25.70,224,0.875,bilinear
mobilenetv3_large_100.miil_in21k_ft_in1k,77.920,22.080,92.914,7.086,5.48,224,0.875,bilinear
resnet34.a1_in1k,77.918,22.082,93.764,6.236,21.80,288,1.000,bicubic
tf_efficientnet_cc_b0_8e.in1k,77.904,22.096,93.662,6.338,24.01,224,0.875,bicubic
regnety_016.pycls_in1k,77.868,22.132,93.718,6.282,11.20,224,0.875,bicubic
rexnet_100.nav_in1k,77.856,22.144,93.866,6.134,4.80,224,0.875,bicubic
inception_v3.tf_in1k,77.856,22.144,93.640,6.360,23.83,299,0.875,bicubic
ghostnetv2_160.in1k,77.832,22.168,93.940,6.060,12.39,224,0.875,bicubic
xcit_nano_12_p8_384.fb_dist_in1k,77.820,22.180,94.040,5.960,3.05,384,1.000,bicubic
hardcorenas_e.miil_green_in1k,77.790,22.210,93.700,6.300,8.07,224,0.875,bilinear
convnextv2_atto.fcmae_ft_in1k,77.760,22.240,93.726,6.274,3.71,288,0.950,bicubic
ese_vovnet19b_dw.ra_in1k,77.744,22.256,93.784,6.216,6.54,288,0.950,bicubic
efficientnet_b0.ra_in1k,77.694,22.306,93.532,6.468,5.29,224,0.875,bicubic
tinynet_a.in1k,77.648,22.352,93.540,6.460,6.19,192,0.875,bicubic
legacy_seresnet50.in1k,77.644,22.356,93.758,6.242,28.09,224,0.875,bilinear
cs3darknet_m.c2ns_in1k,77.634,22.366,94.016,5.984,9.31,288,0.950,bicubic
resnext50_32x4d.tv_in1k,77.622,22.378,93.696,6.304,25.03,224,0.875,bilinear
inception_v3.tf_adv_in1k,77.592,22.408,93.730,6.270,23.83,299,0.875,bicubic
repvgg_b1g4.rvgg_in1k,77.588,22.412,93.836,6.164,39.97,224,0.875,bilinear
resnet50.gluon_in1k,77.582,22.418,93.720,6.280,25.56,224,0.875,bicubic
coat_lite_tiny.in1k,77.520,22.480,93.922,6.078,5.72,224,0.900,bicubic
dpn68b.mx_in1k,77.518,22.482,93.852,6.148,12.61,224,0.875,bicubic
mobileone_s2.apple_in1k,77.516,22.484,93.668,6.332,7.88,224,0.900,bilinear
res2net50_48w_2s.in1k,77.514,22.486,93.550,6.450,25.29,224,0.875,bilinear
tf_efficientnet_lite2.in1k,77.462,22.538,93.752,6.248,6.09,260,0.890,bicubic
repghostnet_150.in1k,77.460,22.540,93.510,6.490,6.58,224,0.875,bicubic
hardcorenas_d.miil_green_in1k,77.434,22.566,93.490,6.510,7.50,224,0.875,bilinear
inception_v3.tv_in1k,77.434,22.566,93.474,6.526,23.83,299,0.875,bicubic
resnet26d.bt_in1k,77.408,22.592,93.638,6.362,16.01,288,0.950,bicubic
resnet101.tv_in1k,77.380,22.620,93.546,6.454,44.55,224,0.875,bilinear
densenet161.tv_in1k,77.358,22.642,93.642,6.358,28.68,224,0.875,bicubic
densenetblur121d.ra_in1k,77.322,22.678,93.788,6.212,8.00,288,0.950,bicubic
mobilenetv2_120d.ra_in1k,77.308,22.692,93.502,6.498,5.83,224,0.875,bicubic
regnetx_008.tv2_in1k,77.306,22.694,93.664,6.336,7.26,224,0.965,bicubic
tf_efficientnet_cc_b0_4e.in1k,77.302,22.698,93.336,6.664,13.31,224,0.875,bicubic
densenet201.tv_in1k,77.286,22.714,93.480,6.520,20.01,224,0.875,bicubic
cs3darknet_focus_m.c2ns_in1k,77.284,22.716,93.966,6.034,9.30,288,0.950,bicubic
mixnet_m.ft_in1k,77.260,22.740,93.418,6.582,5.01,224,0.875,bicubic
poolformer_s12.sail_in1k,77.240,22.760,93.532,6.468,11.92,224,0.900,bicubic
convnext_atto_ols.a2_in1k,77.216,22.784,93.676,6.324,3.70,288,0.950,bicubic
resnext26ts.ra2_in1k,77.178,22.822,93.464,6.536,10.30,288,1.000,bicubic
fastvit_t8.apple_dist_in1k,77.176,22.824,93.298,6.702,4.03,256,0.900,bicubic
selecsls42b.in1k,77.170,22.830,93.392,6.608,32.46,224,0.875,bicubic
resnet34.a2_in1k,77.158,22.842,93.274,6.726,21.80,288,1.000,bicubic
xcit_tiny_12_p16_224.fb_in1k,77.140,22.860,93.716,6.284,6.72,224,1.000,bicubic
legacy_seresnext26_32x4d.in1k,77.108,22.892,93.314,6.686,16.79,224,0.875,bicubic
tf_efficientnet_b0.ap_in1k,77.090,22.910,93.262,6.738,5.29,224,0.875,bicubic
hardcorenas_c.miil_green_in1k,77.066,22.934,93.162,6.838,5.52,224,0.875,bilinear
efficientvit_m5.r224_in1k,77.058,22.942,93.184,6.816,12.47,224,0.875,bicubic
dla60.in1k,77.046,22.954,93.318,6.682,22.04,224,0.875,bilinear
seresnet50.a3_in1k,77.026,22.974,93.072,6.928,28.09,224,0.950,bicubic
convnext_atto.d2_in1k,77.008,22.992,93.702,6.298,3.70,288,0.950,bicubic
crossvit_9_dagger_240.in1k,76.978,23.022,93.618,6.382,8.78,240,0.875,bicubic
tf_mixnet_m.in1k,76.954,23.046,93.154,6.846,5.01,224,0.875,bicubic
convmixer_1024_20_ks9_p14.in1k,76.936,23.064,93.350,6.650,24.38,224,0.960,bicubic
regnetx_016.pycls_in1k,76.924,23.076,93.416,6.584,9.19,224,0.875,bicubic
skresnet34.ra_in1k,76.910,23.090,93.316,6.684,22.28,224,0.875,bicubic
gernet_s.idstcv_in1k,76.910,23.090,93.144,6.856,8.17,224,0.875,bilinear
tf_efficientnet_b0.aa_in1k,76.844,23.156,93.218,6.782,5.29,224,0.875,bicubic
ghostnetv2_130.in1k,76.756,23.244,93.362,6.638,8.96,224,0.875,bicubic
hrnet_w18.ms_in1k,76.752,23.248,93.444,6.556,21.30,224,0.875,bilinear
resmlp_12_224.fb_in1k,76.648,23.352,93.178,6.822,15.35,224,0.875,bicubic
tf_efficientnet_lite1.in1k,76.644,23.356,93.224,6.776,5.42,240,0.882,bicubic
mixer_b16_224.goog_in21k_ft_in1k,76.602,23.398,92.224,7.776,59.88,224,0.875,bicubic
tf_efficientnet_es.in1k,76.598,23.402,93.202,6.798,5.44,224,0.875,bicubic
hardcorenas_b.miil_green_in1k,76.548,23.452,92.762,7.238,5.18,224,0.875,bilinear
tf_efficientnet_b0.in1k,76.530,23.470,93.008,6.992,5.29,224,0.875,bicubic
levit_128s.fb_dist_in1k,76.526,23.474,92.872,7.128,7.78,224,0.900,bicubic
levit_conv_128s.fb_dist_in1k,76.520,23.480,92.866,7.134,7.78,224,0.900,bicubic
mobilenetv2_140.ra_in1k,76.516,23.484,92.988,7.012,6.11,224,0.875,bicubic
densenet121.ra_in1k,76.500,23.500,93.368,6.632,7.98,288,0.950,bicubic
resnet34.bt_in1k,76.480,23.520,93.354,6.646,21.80,288,0.950,bicubic
repvgg_a2.rvgg_in1k,76.458,23.542,93.002,6.998,28.21,224,0.875,bilinear
repghostnet_130.in1k,76.376,23.624,92.892,7.108,5.48,224,0.875,bicubic
resnet26.bt_in1k,76.366,23.634,93.180,6.820,16.00,288,0.950,bicubic
dpn68.mx_in1k,76.346,23.654,93.008,6.992,12.61,224,0.875,bicubic
xcit_nano_12_p8_224.fb_dist_in1k,76.332,23.668,93.098,6.902,3.05,224,1.000,bicubic
regnety_008.pycls_in1k,76.302,23.698,93.062,6.938,6.26,224,0.875,bicubic
fastvit_t8.apple_in1k,76.174,23.826,93.052,6.948,4.03,256,0.900,bicubic
resnet50.tv_in1k,76.128,23.872,92.858,7.142,25.56,224,0.875,bilinear
efficientformerv2_s0.snap_dist_in1k,76.114,23.886,92.858,7.142,3.60,224,0.950,bicubic
vit_small_patch32_224.augreg_in21k_ft_in1k,75.994,24.006,93.270,6.730,22.88,224,0.900,bicubic
mixnet_s.ft_in1k,75.994,24.006,92.800,7.200,4.13,224,0.875,bicubic
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,75.960,24.040,93.262,6.738,6.36,384,1.000,bicubic
hardcorenas_a.miil_green_in1k,75.938,24.062,92.508,7.492,5.26,224,0.875,bilinear
densenet169.tv_in1k,75.900,24.100,93.028,6.972,14.15,224,0.875,bicubic
mobileone_s1.apple_in1k,75.786,24.214,92.792,7.208,4.83,224,0.900,bilinear
mobilenetv3_large_100.ra_in1k,75.766,24.234,92.538,7.462,5.48,224,0.875,bicubic
edgenext_x_small.in1k,75.688,24.312,92.766,7.234,2.34,288,1.000,bicubic
tf_mixnet_s.in1k,75.652,24.348,92.640,7.360,4.13,224,0.875,bicubic
mobilenetv3_rw.rmsp_in1k,75.620,24.380,92.704,7.296,5.48,224,0.875,bicubic
mobilevitv2_075.cvnets_in1k,75.608,24.392,92.744,7.256,2.87,256,0.888,bicubic
regnety_004.tv2_in1k,75.594,24.406,92.700,7.300,4.34,224,0.965,bicubic
tf_mobilenetv3_large_100.in1k,75.516,24.484,92.594,7.406,5.48,224,0.875,bilinear
resnest14d.gluon_in1k,75.508,24.492,92.508,7.492,10.61,224,0.875,bilinear
efficientnet_lite0.ra_in1k,75.482,24.518,92.520,7.480,4.65,224,0.875,bicubic
vit_tiny_patch16_224.augreg_in21k_ft_in1k,75.462,24.538,92.844,7.156,5.72,224,0.900,bicubic
xcit_nano_12_p16_384.fb_dist_in1k,75.458,24.542,92.698,7.302,3.05,384,1.000,bicubic
semnasnet_100.rmsp_in1k,75.450,24.550,92.598,7.402,3.89,224,0.875,bicubic
regnety_006.pycls_in1k,75.268,24.732,92.526,7.474,6.06,224,0.875,bicubic
ghostnetv2_100.in1k,75.166,24.834,92.354,7.646,6.16,224,0.875,bicubic
repvgg_b0.rvgg_in1k,75.144,24.856,92.416,7.584,15.82,224,0.875,bilinear
fbnetc_100.rmsp_in1k,75.130,24.870,92.388,7.612,5.57,224,0.875,bilinear
hrnet_w18_small_v2.ms_in1k,75.110,24.890,92.416,7.584,15.60,224,0.875,bilinear
repghostnet_111.in1k,75.056,24.944,92.192,7.808,4.54,224,0.875,bicubic
mobilenetv2_110d.ra_in1k,75.054,24.946,92.184,7.816,4.52,224,0.875,bicubic
regnetx_008.pycls_in1k,75.028,24.972,92.338,7.662,7.26,224,0.875,bicubic
efficientnet_es_pruned.in1k,75.006,24.994,92.444,7.556,5.44,224,0.875,bicubic
tinynet_b.in1k,74.978,25.022,92.186,7.814,3.73,188,0.875,bicubic
vit_base_patch32_224.augreg_in1k,74.894,25.106,91.778,8.222,88.22,224,0.900,bicubic
tf_efficientnet_lite0.in1k,74.832,25.168,92.170,7.830,4.65,224,0.875,bicubic
legacy_seresnet34.in1k,74.802,25.198,92.126,7.874,21.96,224,0.875,bilinear
densenet121.tv_in1k,74.764,25.236,92.154,7.846,7.98,224,0.875,bicubic
mnasnet_100.rmsp_in1k,74.652,25.348,92.122,7.878,4.38,224,0.875,bicubic
dla34.in1k,74.640,25.360,92.066,7.934,15.74,224,0.875,bilinear
mobilevit_xs.cvnets_in1k,74.634,25.366,92.348,7.652,2.32,256,0.900,bicubic
regnetx_004_tv.tv2_in1k,74.600,25.400,92.170,7.830,5.50,224,0.965,bicubic
resnet34.gluon_in1k,74.580,25.420,91.982,8.018,21.80,224,0.875,bicubic
deit_tiny_distilled_patch16_224.fb_in1k,74.504,25.496,91.890,8.110,5.91,224,0.900,bicubic
repvgg_a1.rvgg_in1k,74.462,25.538,91.856,8.144,14.09,224,0.875,bilinear
efficientvit_m4.r224_in1k,74.368,25.632,91.980,8.020,8.80,224,0.875,bicubic
pit_ti_distilled_224.in1k,74.256,25.744,91.952,8.048,5.10,224,0.900,bicubic
vgg19_bn.tv_in1k,74.216,25.784,91.844,8.156,143.68,224,0.875,bilinear
repghostnet_100.in1k,74.206,25.794,91.542,8.458,4.07,224,0.875,bicubic
spnasnet_100.rmsp_in1k,74.094,25.906,91.820,8.180,4.42,224,0.875,bilinear
regnety_004.pycls_in1k,74.026,25.974,91.748,8.252,4.34,224,0.875,bicubic
crossvit_9_240.in1k,73.960,26.040,91.962,8.038,8.55,240,0.875,bicubic
ghostnet_100.in1k,73.958,26.042,91.532,8.468,5.18,224,0.875,bicubic
hrnet_w18_small.gluon_in1k,73.920,26.080,91.194,8.806,13.19,224,0.875,bicubic
xcit_nano_12_p8_224.fb_in1k,73.910,26.090,92.168,7.832,3.05,224,1.000,bicubic
regnetx_006.pycls_in1k,73.868,26.132,91.678,8.322,6.20,224,0.875,bicubic
resnet18d.ra2_in1k,73.794,26.206,91.838,8.162,11.71,288,0.950,bicubic
vit_base_patch32_224.sam_in1k,73.694,26.306,91.014,8.986,88.22,224,0.900,bicubic
tf_mobilenetv3_large_075.in1k,73.430,26.570,91.352,8.648,3.99,224,0.875,bilinear
efficientvit_m3.r224_in1k,73.374,26.626,91.348,8.652,6.90,224,0.875,bicubic
vgg16_bn.tv_in1k,73.370,26.630,91.514,8.486,138.37,224,0.875,bilinear
crossvit_tiny_240.in1k,73.340,26.660,91.908,8.092,7.01,240,0.875,bicubic
resnet34.tv_in1k,73.306,26.694,91.420,8.580,21.80,224,0.875,bilinear
resnet18.fb_swsl_ig1b_ft_in1k,73.288,26.712,91.730,8.270,11.69,224,0.875,bilinear
resnet18.a1_in1k,73.158,26.842,91.026,8.974,11.69,288,1.000,bicubic
convit_tiny.fb_in1k,73.112,26.888,91.712,8.288,5.71,224,0.875,bicubic
skresnet18.ra_in1k,73.034,26.966,91.172,8.828,11.96,224,0.875,bicubic
semnasnet_075.rmsp_in1k,73.004,26.996,91.140,8.860,2.91,224,0.875,bicubic
resnet34.a3_in1k,72.970,27.030,91.106,8.894,21.80,224,0.950,bicubic
mobilenetv2_100.ra_in1k,72.968,27.032,91.016,8.984,3.50,224,0.875,bicubic
pit_ti_224.in1k,72.910,27.090,91.404,8.596,4.85,224,0.900,bicubic
resnet18.fb_ssl_yfcc100m_ft_in1k,72.598,27.402,91.416,8.584,11.69,224,0.875,bilinear
repvgg_a0.rvgg_in1k,72.408,27.592,90.492,9.508,9.11,224,0.875,bilinear
regnetx_004.pycls_in1k,72.402,27.598,90.826,9.174,5.16,224,0.875,bicubic
vgg19.tv_in1k,72.378,27.622,90.874,9.126,143.67,224,0.875,bilinear
resnet18.a2_in1k,72.372,27.628,90.596,9.404,11.69,288,1.000,bicubic
hrnet_w18_small.ms_in1k,72.336,27.664,90.680,9.320,13.19,224,0.875,bilinear
xcit_nano_12_p16_224.fb_dist_in1k,72.310,27.690,90.860,9.140,3.05,224,1.000,bicubic
tf_mobilenetv3_large_minimal_100.in1k,72.264,27.736,90.640,9.360,3.92,224,0.875,bilinear
resnet14t.c3_in1k,72.254,27.746,90.306,9.694,10.08,224,0.950,bicubic
repghostnet_080.in1k,72.212,27.788,90.484,9.516,3.28,224,0.875,bicubic
deit_tiny_patch16_224.fb_in1k,72.170,27.830,91.116,8.884,5.72,224,0.900,bicubic
lcnet_100.ra2_in1k,72.102,27.898,90.354,9.646,2.95,224,0.875,bicubic
mixer_l16_224.goog_in21k_ft_in1k,72.054,27.946,87.674,12.326,208.20,224,0.875,bicubic
edgenext_xx_small.in1k,71.878,28.122,90.552,9.448,1.33,288,1.000,bicubic
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,71.798,28.202,90.824,9.176,6.34,224,0.900,bicubic
legacy_seresnet18.in1k,71.760,28.240,90.332,9.668,11.78,224,0.875,bicubic
vgg16.tv_in1k,71.592,28.408,90.384,9.616,138.36,224,0.875,bilinear
vgg13_bn.tv_in1k,71.588,28.412,90.378,9.622,133.05,224,0.875,bilinear
mobileone_s0.apple_in1k,71.402,28.598,89.842,10.158,5.29,224,0.875,bilinear
efficientvit_b0.r224_in1k,71.398,28.602,89.428,10.572,3.41,224,0.950,bicubic
tinynet_c.in1k,71.242,28.758,89.732,10.268,2.46,184,0.875,bicubic
resnet18.gluon_in1k,70.834,29.166,89.756,10.244,11.69,224,0.875,bicubic
efficientvit_m2.r224_in1k,70.814,29.186,90.142,9.858,4.19,224,0.875,bicubic
pvt_v2_b0.in1k,70.660,29.340,90.196,9.804,3.67,224,0.900,bicubic
vgg11_bn.tv_in1k,70.382,29.618,89.808,10.192,132.87,224,0.875,bilinear
regnety_002.pycls_in1k,70.280,29.720,89.530,10.470,3.16,224,0.875,bicubic
mobilevitv2_050.cvnets_in1k,70.148,29.852,89.918,10.082,1.37,256,0.888,bicubic
xcit_nano_12_p16_224.fb_in1k,69.962,30.038,89.762,10.238,3.05,224,1.000,bicubic
vgg13.tv_in1k,69.932,30.068,89.250,10.750,133.05,224,0.875,bilinear
resnet18.tv_in1k,69.760,30.240,89.070,10.930,11.69,224,0.875,bilinear
vgg11.tv_in1k,69.022,30.978,88.624,11.376,132.86,224,0.875,bilinear
mobilevit_xxs.cvnets_in1k,68.918,31.082,88.946,11.054,1.27,256,0.900,bicubic
repghostnet_058.in1k,68.914,31.086,88.420,11.580,2.55,224,0.875,bicubic
lcnet_075.ra2_in1k,68.782,31.218,88.360,11.640,2.36,224,0.875,bicubic
regnetx_002.pycls_in1k,68.752,31.248,88.542,11.458,2.68,224,0.875,bicubic
resnet10t.c3_in1k,68.364,31.636,88.036,11.964,5.44,224,0.950,bicubic
efficientvit_m1.r224_in1k,68.306,31.694,88.670,11.330,2.98,224,0.875,bicubic
resnet18.a3_in1k,68.252,31.748,88.172,11.828,11.69,224,0.950,bicubic
tf_mobilenetv3_small_100.in1k,67.922,32.078,87.672,12.328,2.54,224,0.875,bilinear
dla60x_c.in1k,67.912,32.088,88.432,11.568,1.32,224,0.875,bilinear
mobilenetv3_small_100.lamb_in1k,67.658,32.342,87.636,12.364,2.54,224,0.875,bicubic
tinynet_d.in1k,66.972,33.028,87.066,12.934,2.34,152,0.875,bicubic
repghostnet_050.in1k,66.966,33.034,86.920,13.080,2.31,224,0.875,bicubic
mnasnet_small.lamb_in1k,66.196,33.804,86.504,13.496,2.03,224,0.875,bicubic
dla46x_c.in1k,65.992,34.008,86.974,13.026,1.07,224,0.875,bilinear
mobilenetv2_050.lamb_in1k,65.948,34.052,86.084,13.916,1.97,224,0.875,bicubic
tf_mobilenetv3_small_075.in1k,65.726,34.274,86.132,13.868,2.04,224,0.875,bilinear
mobilenetv3_small_075.lamb_in1k,65.236,34.764,85.446,14.554,2.04,224,0.875,bicubic
dla46_c.in1k,64.872,35.128,86.298,13.702,1.30,224,0.875,bilinear
efficientvit_m0.r224_in1k,63.270,36.730,85.176,14.824,2.35,224,0.875,bicubic
lcnet_050.ra2_in1k,63.138,36.862,84.382,15.618,1.88,224,0.875,bicubic
tf_mobilenetv3_small_minimal_100.in1k,62.894,37.106,84.238,15.762,2.04,224,0.875,bilinear
tinynet_e.in1k,59.866,40.134,81.762,18.238,2.04,106,0.875,bicubic
mobilenetv3_small_050.lamb_in1k,57.916,42.084,80.180,19.820,1.59,224,0.875,bicubic
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/results/results-sketch.csv | model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff
eva_giant_patch14_336.clip_ft_in1k,71.177,28.823,90.299,9.701,"1,013.01",336,1.000,bicubic,-18.289,-8.527,+6
eva02_large_patch14_448.mim_m38m_ft_in22k_in1k,70.662,29.338,89.856,10.144,305.08,448,1.000,bicubic,-19.390,-9.192,-1
eva02_large_patch14_448.mim_m38m_ft_in1k,70.546,29.454,89.843,10.157,305.08,448,1.000,bicubic,-19.028,-9.081,+2
convnext_xxlarge.clip_laion2b_soup_ft_in1k,70.039,29.961,90.334,9.666,846.47,256,1.000,bicubic,-18.565,-8.374,+10
eva_giant_patch14_224.clip_ft_in1k,70.021,29.979,89.768,10.232,"1,012.56",224,0.900,bicubic,-18.859,-8.912,+4
eva_giant_patch14_336.m30m_ft_in22k_in1k,68.052,31.948,87.819,12.181,"1,013.01",336,1.000,bicubic,-21.514,-11.133,0
eva02_large_patch14_448.mim_in22k_ft_in22k_in1k,67.533,32.467,87.506,12.494,305.08,448,1.000,bicubic,-22.437,-11.506,-5
eva_giant_patch14_560.m30m_ft_in22k_in1k,67.486,32.514,87.473,12.527,"1,014.45",560,1.000,bicubic,-22.300,-11.519,-5
vit_huge_patch14_clip_224.laion2b_ft_in1k,67.396,32.604,87.882,12.118,632.05,224,1.000,bicubic,-20.192,-10.337,+39
eva02_large_patch14_448.mim_in22k_ft_in1k,66.987,33.013,87.443,12.557,305.08,448,1.000,bicubic,-22.635,-11.507,-6
convnext_large_mlp.clip_laion2b_augreg_ft_in1k_384,66.435,33.565,87.349,12.651,200.13,384,1.000,bicubic,-21.413,-11.097,+31
vit_large_patch14_clip_336.laion2b_ft_in1k,65.741,34.259,86.909,13.091,304.53,336,1.000,bicubic,-22.115,-11.459,+28
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384,65.731,34.269,87.017,12.983,200.13,384,1.000,bicubic,-22.575,-11.565,+10
vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,65.323,34.677,86.826,13.174,632.05,224,1.000,bicubic,-22.933,-11.726,+11
vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,65.260,34.740,86.757,13.242,632.46,336,1.000,bicubic,-23.332,-11.905,+1
convnext_large_mlp.clip_laion2b_augreg_ft_in1k,65.091,34.909,86.284,13.716,200.13,256,1.000,bicubic,-22.245,-11.934,+42
convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320,64.918,35.082,86.649,13.351,200.13,320,1.000,bicubic,-23.040,-11.827,+19
vit_large_patch14_clip_224.laion2b_ft_in1k,64.820,35.181,86.575,13.425,304.20,224,1.000,bicubic,-22.466,-11.669,+43
regnety_1280.swag_ft_in1k,64.106,35.894,86.034,13.966,644.81,384,1.000,bicubic,-24.124,-12.652,+9
vit_large_patch14_clip_336.openai_ft_in12k_in1k,64.065,35.935,85.912,14.088,304.53,336,1.000,bicubic,-24.203,-12.614,+4
eva_large_patch14_336.in22k_ft_in1k,63.096,36.904,84.382,15.618,304.53,336,1.000,bicubic,-25.574,-14.340,-8
vit_large_patch14_clip_224.openai_ft_in1k,62.629,37.371,85.109,14.891,304.20,224,1.000,bicubic,-25.225,-13.317,+19
vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,62.065,37.935,84.313,15.687,304.20,224,1.000,bicubic,-25.830,-14.095,+16
vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,61.611,38.389,83.651,16.349,304.53,336,1.000,bicubic,-26.569,-14.921,+8
vit_large_patch14_clip_224.openai_ft_in12k_in1k,61.402,38.598,83.362,16.638,304.20,224,1.000,bicubic,-26.772,-15.184,+8
eva_large_patch14_196.in22k_ft_in1k,61.113,38.887,82.776,17.224,304.14,196,1.000,bicubic,-26.819,-15.722,+11
eva_large_patch14_336.in22k_ft_in22k_in1k,60.944,39.056,82.136,17.864,304.53,336,1.000,bicubic,-28.262,-16.718,-19
convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384,60.795,39.205,83.220,16.780,88.59,384,1.000,bicubic,-26.339,-15.002,+36
convnext_base.clip_laion2b_augreg_ft_in1k,60.276,39.724,82.678,17.322,88.59,256,1.000,bicubic,-25.882,-15.002,+90
regnety_1280.swag_lc_in1k,59.913,40.087,83.161,16.839,644.81,224,0.965,bicubic,-26.069,-14.689,+105
eva_large_patch14_196.in22k_ft_in22k_in1k,59.883,40.117,81.143,18.857,304.14,196,1.000,bicubic,-28.691,-17.515,-14
convnext_base.clip_laion2b_augreg_ft_in12k_in1k,59.836,40.164,82.810,17.190,88.59,256,1.000,bicubic,-26.534,-15.174,+74
convnext_base.clip_laiona_augreg_ft_in1k_384,59.392,40.608,82.242,17.758,88.59,384,1.000,bicubic,-27.110,-15.726,+63
eva02_base_patch14_448.mim_in22k_ft_in22k_in1k,58.512,41.488,80.797,19.203,87.12,448,1.000,bicubic,-30.178,-17.927,-23
resnext101_32x32d.fb_wsl_ig1b_ft_in1k,58.376,41.624,80.398,19.602,468.53,224,0.875,bilinear,-26.722,-17.040,+163
beitv2_large_patch16_224.in1k_ft_in22k_in1k,58.366,41.634,80.226,19.774,304.43,224,0.950,bicubic,-30.028,-18.372,-16
eva02_base_patch14_448.mim_in22k_ft_in1k,58.036,41.964,80.768,19.232,87.12,448,1.000,bicubic,-30.216,-17.796,-11
regnety_320.swag_ft_in1k,57.906,42.094,81.456,18.544,145.05,384,1.000,bicubic,-28.928,-16.906,+42
convnextv2_huge.fcmae_ft_in22k_in1k_384,57.865,42.135,79.671,20.329,660.29,384,1.000,bicubic,-30.805,-19.067,-27
convnextv2_huge.fcmae_ft_in22k_in1k_512,57.851,42.149,79.497,20.503,660.29,512,1.000,bicubic,-31.007,-19.251,-30
resnext101_32x16d.fb_wsl_ig1b_ft_in1k,57.696,42.304,79.907,20.093,194.03,224,0.875,bilinear,-26.470,-17.291,+242
resnext101_32x16d.fb_swsl_ig1b_ft_in1k,57.478,42.522,80.373,19.627,194.03,224,0.875,bilinear,-25.858,-16.473,+333
vit_base_patch16_clip_384.laion2b_ft_in1k,56.879,43.121,80.004,19.997,86.86,384,1.000,bicubic,-29.739,-18.004,+45
beit_large_patch16_384.in22k_ft_in22k_in1k,56.879,43.121,79.221,20.779,305.00,384,1.000,bicubic,-31.523,-19.387,-24
beit_large_patch16_512.in22k_ft_in22k_in1k,56.757,43.243,78.911,21.089,305.67,512,1.000,bicubic,-31.839,-19.745,-30
resnext101_32x8d.fb_swsl_ig1b_ft_in1k,56.438,43.562,78.931,21.069,88.79,224,0.875,bilinear,-27.864,-18.245,+223
maxvit_rmlp_base_rw_384.sw_in12k_ft_in1k,56.327,43.673,77.305,22.695,116.14,384,1.000,bicubic,-31.502,-21.067,-4
maxvit_xlarge_tf_384.in21k_ft_in1k,56.212,43.788,78.742,21.258,475.32,384,1.000,bicubic,-32.101,-19.802,-26
maxvit_xlarge_tf_512.in21k_ft_in1k,56.156,43.844,78.636,21.364,475.77,512,1.000,bicubic,-32.382,-20.008,-31
maxvit_base_tf_512.in21k_ft_in1k,56.083,43.917,78.606,21.394,119.88,512,1.000,bicubic,-32.137,-19.924,-20
deit3_huge_patch14_224.fb_in22k_ft_in1k,55.767,44.233,77.626,22.374,632.13,224,1.000,bicubic,-31.420,-20.634,+12
maxvit_base_tf_384.in21k_ft_in1k,55.639,44.361,78.078,21.922,119.65,384,1.000,bicubic,-32.283,-20.466,-14
vit_base_patch16_clip_224.laion2b_ft_in1k,55.405,44.595,79.050,20.950,86.57,224,1.000,bicubic,-30.065,-18.525,+111
regnety_320.swag_lc_in1k,55.354,44.646,79.703,20.297,145.05,224,0.965,bicubic,-29.194,-17.739,+184
regnety_160.swag_ft_in1k,55.177,44.823,79.316,20.684,83.59,384,1.000,bicubic,-30.843,-18.736,+74
maxvit_large_tf_512.in21k_ft_in1k,55.171,44.829,77.276,22.724,212.33,512,1.000,bicubic,-33.053,-21.322,-27
maxvit_large_tf_384.in21k_ft_in1k,55.077,44.923,77.142,22.858,212.03,384,1.000,bicubic,-32.909,-21.426,-22
beit_large_patch16_224.in22k_ft_in22k_in1k,54.965,45.035,77.608,22.392,304.43,224,0.900,bicubic,-32.513,-20.696,-8
convnext_xlarge.fb_in22k_ft_in1k_384,54.961,45.039,76.824,23.176,350.20,384,1.000,bicubic,-32.791,-21.732,-15
resnext101_32x8d.fb_wsl_ig1b_ft_in1k,54.908,45.092,77.541,22.459,88.79,224,0.875,bilinear,-27.790,-18.603,+374
deit3_large_patch16_384.fb_in22k_ft_in1k,54.886,45.114,77.372,22.628,304.76,384,1.000,bicubic,-32.834,-21.140,-16
maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k,54.747,45.253,76.848,23.152,116.09,384,1.000,bicubic,-32.717,-21.526,-10
caformer_b36.sail_in22k_ft_in1k_384,54.440,45.560,76.830,23.170,98.75,384,1.000,bicubic,-33.618,-21.752,-29
deit3_large_patch16_224.fb_in22k_ft_in1k,54.359,45.641,76.563,23.437,304.37,224,1.000,bicubic,-32.623,-21.673,+9
beitv2_large_patch16_224.in1k_ft_in1k,54.161,45.839,75.562,24.438,304.43,224,0.950,bicubic,-33.251,-22.671,-9
convnextv2_large.fcmae_ft_in22k_in1k_384,53.947,46.053,76.007,23.993,197.96,384,1.000,bicubic,-34.251,-22.521,-35
maxvit_rmlp_base_rw_224.sw_in12k_ft_in1k,53.731,46.269,75.140,24.860,116.14,224,0.950,bicubic,-33.163,-22.874,+9
resnext101_32x4d.fb_swsl_ig1b_ft_in1k,53.587,46.413,76.337,23.663,44.18,224,0.875,bilinear,-29.639,-20.423,+316
vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,53.493,46.507,75.653,24.347,86.86,384,1.000,bicubic,-33.713,-22.381,-7
vit_base_patch16_clip_384.openai_ft_in1k,53.074,46.926,76.655,23.345,86.86,384,1.000,bicubic,-33.132,-21.221,+44
regnety_160.swag_lc_in1k,53.043,46.957,78.090,21.910,83.59,224,0.965,bicubic,-30.739,-19.190,+253
convnextv2_base.fcmae_ft_in22k_in1k_384,52.907,47.093,75.083,24.917,88.72,384,1.000,bicubic,-34.737,-23.333,-26
convformer_b36.sail_in22k_ft_in1k_384,52.874,47.126,74.979,25.021,99.88,384,1.000,bicubic,-34.728,-23.455,-26
convnext_large.fb_in22k_ft_in1k_384,52.774,47.226,74.700,25.300,197.77,384,1.000,bicubic,-34.698,-23.686,-23
vit_large_patch16_384.augreg_in21k_ft_in1k,52.760,47.240,74.706,25.294,304.72,384,1.000,bicubic,-34.324,-23.596,-8
caformer_b36.sail_in22k_ft_in1k,52.756,47.244,75.309,24.691,98.75,224,1.000,bicubic,-34.664,-23.019,-21
convformer_b36.sail_in22k_ft_in1k,52.746,47.254,74.896,25.104,99.88,224,1.000,bicubic,-34.252,-23.276,-6
coatnet_rmlp_2_rw_384.sw_in12k_ft_in1k,52.389,47.611,73.802,26.198,73.88,384,1.000,bicubic,-34.994,-24.510,-21
swinv2_large_window12to24_192to384.ms_in22k_ft_in1k,52.304,47.696,74.415,25.585,196.74,384,1.000,bicubic,-35.160,-23.835,-26
convnext_xlarge.fb_in22k_ft_in1k,52.216,47.784,73.955,26.045,350.20,288,1.000,bicubic,-35.114,-24.373,-21
vit_large_r50_s32_384.augreg_in21k_ft_in1k,52.041,47.959,73.570,26.430,329.09,384,1.000,bicubic,-34.141,-24.352,+35
vit_large_patch16_224.augreg_in21k_ft_in1k,51.819,48.181,73.690,26.310,304.33,224,0.900,bicubic,-34.017,-23.974,+57
vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,51.785,48.215,74.637,25.363,86.57,224,0.950,bicubic,-34.385,-23.119,+34
convnext_base.fb_in22k_ft_in1k_384,51.565,48.435,74.543,25.457,88.59,384,1.000,bicubic,-35.231,-23.721,-1
tf_efficientnet_l2.ns_jft_in1k_475,51.496,48.504,73.931,26.069,480.31,475,0.936,bicubic,-36.738,-24.615,-58
maxxvitv2_rmlp_base_rw_224.sw_in12k_ft_in1k,51.190,48.810,73.126,26.874,116.09,224,0.950,bicubic,-35.452,-24.894,+2
vit_base_patch16_clip_384.openai_ft_in12k_in1k,51.153,48.847,74.323,25.677,86.86,384,0.950,bicubic,-35.873,-23.859,-17
caformer_m36.sail_in22k_ft_in1k_384,51.048,48.952,73.442,26.558,56.20,384,1.000,bicubic,-36.398,-24.866,-34
swinv2_base_window12to24_192to384.ms_in22k_ft_in1k,50.976,49.024,73.295,26.705,87.92,384,1.000,bicubic,-36.120,-24.939,-23
vit_base_patch16_clip_224.openai_ft_in1k,50.936,49.064,74.841,25.159,86.57,224,0.900,bicubic,-34.356,-22.595,+88
seresnextaa201d_32x8d.sw_in12k_ft_in1k_384,50.710,49.290,73.666,26.334,149.39,384,1.000,bicubic,-36.578,-24.668,-31
resnext50_32x4d.fb_swsl_ig1b_ft_in1k,50.465,49.535,73.366,26.634,25.03,224,0.875,bilinear,-31.707,-22.858,+420
swinv2_large_window12to16_192to256.ms_in22k_ft_in1k,50.433,49.567,72.735,27.265,196.74,256,0.900,bicubic,-36.519,-25.371,-19
swin_large_patch4_window12_384.ms_in22k_ft_in1k,50.394,49.606,72.538,27.462,196.74,384,1.000,bicubic,-36.738,-25.696,-29
convnextv2_large.fcmae_ft_in22k_in1k,50.160,49.840,72.399,27.601,197.96,288,1.000,bicubic,-37.324,-25.957,-46
convnext_large.fb_in22k_ft_in1k,49.999,50.001,72.267,27.733,197.77,288,1.000,bicubic,-37.027,-25.937,-27
tf_efficientnetv2_xl.in21k_ft_in1k,49.722,50.278,72.124,27.876,208.12,512,1.000,bicubic,-37.026,-25.890,-12
vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.700,50.300,72.868,27.132,86.57,224,0.950,bicubic,-36.242,-24.860,+37
caformer_m36.sail_in22k_ft_in1k,49.700,50.300,72.141,27.859,56.20,224,1.000,bicubic,-36.894,-25.883,-7
resnet50.fb_swsl_ig1b_ft_in1k,49.531,50.469,72.338,27.662,25.56,224,0.875,bilinear,-31.641,-23.648,+508
beitv2_base_patch16_224.in1k_ft_in22k_in1k,49.516,50.484,72.391,27.609,86.53,224,0.900,bicubic,-36.958,-25.661,-1
coatnet_rmlp_2_rw_224.sw_in12k_ft_in1k,49.394,50.606,70.848,29.152,73.88,224,0.950,bicubic,-37.110,-27.046,-7
convnextv2_base.fcmae_ft_in22k_in1k,49.142,50.858,71.230,28.770,88.72,288,1.000,bicubic,-37.856,-26.939,-31
convformer_m36.sail_in22k_ft_in1k_384,49.132,50.868,71.387,28.613,57.05,384,1.000,bicubic,-37.760,-26.729,-27
convformer_m36.sail_in22k_ft_in1k,49.091,50.909,71.471,28.529,57.05,224,1.000,bicubic,-37.057,-26.379,+15
vit_base_patch32_clip_224.laion2b_ft_in1k,49.062,50.938,72.584,27.416,88.22,224,0.900,bicubic,-33.520,-23.617,+350
swin_large_patch4_window7_224.ms_in22k_ft_in1k,48.993,51.007,71.387,28.613,196.53,224,0.900,bicubic,-37.319,-26.515,+1
convnext_base.fb_in22k_ft_in1k,48.938,51.062,71.748,28.252,88.59,288,1.000,bicubic,-37.336,-26.344,+2
swinv2_base_window12to16_192to256.ms_in22k_ft_in1k,48.781,51.219,71.410,28.590,87.92,256,0.900,bicubic,-37.487,-26.472,+2
tf_efficientnetv2_l.in21k_ft_in1k,48.739,51.261,71.992,28.008,118.52,480,1.000,bicubic,-38.063,-26.144,-29
coatnet_2_rw_224.sw_in12k_ft_in1k,48.678,51.322,70.123,29.877,73.87,224,0.950,bicubic,-37.885,-27.773,-18
beit_base_patch16_384.in22k_ft_in22k_in1k,48.669,51.331,72.102,27.898,86.74,384,1.000,bicubic,-38.131,-26.034,-30
swin_base_patch4_window12_384.ms_in22k_ft_in1k,48.545,51.455,71.819,28.181,87.90,384,1.000,bicubic,-37.893,-26.247,-10
caformer_s36.sail_in22k_ft_in1k_384,48.486,51.514,71.518,28.482,39.30,384,1.000,bicubic,-38.372,-26.694,-36
maxvit_base_tf_512.in1k,48.240,51.760,70.793,29.207,119.88,512,1.000,bicubic,-38.362,-27.125,-25
vit_large_r50_s32_224.augreg_in21k_ft_in1k,48.185,51.815,70.866,29.134,328.99,224,0.900,bicubic,-36.233,-26.306,+144
vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.934,52.066,70.923,29.077,88.30,384,1.000,bicubic,-37.432,-26.737,+58
tf_efficientnet_b7.ns_jft_in1k,47.798,52.202,69.638,30.362,66.35,600,0.949,bicubic,-39.042,-28.454,-39
tf_efficientnet_b6.ns_jft_in1k,47.761,52.239,69.956,30.044,43.04,528,0.942,bicubic,-38.697,-27.934,-17
vit_base_patch8_224.augreg_in21k_ft_in1k,47.727,52.273,70.933,29.067,86.58,224,0.900,bicubic,-38.071,-26.857,+23
deit3_base_patch16_384.fb_in22k_ft_in1k,47.676,52.324,69.752,30.248,86.88,384,1.000,bicubic,-39.064,-28.364,-35
tf_efficientnet_l2.ns_jft_in1k,47.574,52.426,70.019,29.981,480.31,800,0.960,bicubic,-40.778,-28.629,-101
vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,47.570,52.430,70.047,29.953,88.34,448,1.000,bicubic,-38.210,-27.591,+22
vit_base_patch8_224.augreg2_in21k_ft_in1k,47.507,52.493,70.326,29.674,86.58,224,0.900,bicubic,-38.711,-27.506,-11
tf_efficientnetv2_m.in21k_ft_in1k,47.456,52.544,70.945,29.055,54.14,480,1.000,bicubic,-38.536,-26.999,+7
deit3_base_patch16_224.fb_in22k_ft_in1k,47.378,52.622,69.769,30.230,86.59,224,1.000,bicubic,-38.322,-27.977,+25
tiny_vit_21m_512.dist_in22k_ft_in1k,47.254,52.746,70.062,29.938,21.27,512,1.000,bicubic,-39.204,-27.822,-26
convformer_s36.sail_in22k_ft_in1k_384,47.152,52.848,69.498,30.502,40.01,384,1.000,bicubic,-39.226,-28.486,-23
maxvit_large_tf_512.in1k,47.022,52.978,69.506,30.494,212.33,512,1.000,bicubic,-39.504,-28.374,-35
convnext_small.fb_in22k_ft_in1k_384,46.882,53.118,69.528,30.472,50.22,384,1.000,bicubic,-38.896,-28.362,+16
convnextv2_huge.fcmae_ft_in1k,46.880,53.120,67.785,32.215,660.29,288,1.000,bicubic,-39.700,-30.187,-39
convformer_s36.sail_in22k_ft_in1k,46.863,53.137,69.528,30.472,40.01,224,1.000,bicubic,-38.551,-28.040,+37
caformer_s36.sail_in22k_ft_in1k,46.708,53.292,69.744,30.256,39.30,224,1.000,bicubic,-39.083,-28.082,+11
tiny_vit_21m_384.dist_in22k_ft_in1k,46.256,53.744,69.231,30.769,21.23,384,1.000,bicubic,-39.852,-28.479,-12
beit_base_patch16_224.in22k_ft_in22k_in1k,46.254,53.746,69.885,30.115,86.53,224,0.900,bicubic,-38.958,-27.773,+52
vit_base_patch32_clip_384.openai_ft_in12k_in1k,46.234,53.766,69.288,30.712,88.30,384,0.950,bicubic,-38.980,-28.116,+49
maxvit_base_tf_384.in1k,46.234,53.766,68.531,31.468,119.65,384,1.000,bicubic,-40.068,-29.267,-27
hrnet_w48_ssld.paddle_in1k,46.177,53.823,68.056,31.944,77.47,288,1.000,bilinear,-38.303,-29.178,+110
beitv2_base_patch16_224.in1k_ft_in1k,46.002,53.998,67.859,32.141,86.53,224,0.900,bicubic,-39.592,-29.647,+17
vit_base_patch16_384.augreg_in21k_ft_in1k,45.894,54.106,68.557,31.443,86.86,384,1.000,bicubic,-40.100,-29.445,-9
seresnextaa101d_32x8d.sw_in12k_ft_in1k_288,45.863,54.137,68.608,31.392,93.59,320,1.000,bicubic,-40.861,-29.568,-54
tf_efficientnet_b8.ap_in1k,45.780,54.220,67.907,32.093,87.41,672,0.954,bicubic,-39.584,-29.385,+34
maxvit_large_tf_384.in1k,45.760,54.240,68.160,31.840,212.03,384,1.000,bicubic,-40.470,-29.528,-31
vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,45.758,54.242,68.881,31.119,88.22,224,0.900,bicubic,-37.538,-27.647,+236
convnext_small.in12k_ft_in1k_384,45.721,54.279,67.818,32.182,50.22,384,1.000,bicubic,-40.461,-30.104,-30
tf_efficientnet_b5.ns_jft_in1k,45.611,54.389,67.850,32.150,30.39,456,0.934,bicubic,-40.477,-29.906,-22
swin_base_patch4_window7_224.ms_in22k_ft_in1k,45.532,54.468,68.504,31.496,87.77,224,0.900,bicubic,-39.740,-29.060,+33
mvitv2_large.fb_in1k,45.277,54.723,65.183,34.817,217.99,224,0.900,bicubic,-39.967,-32.031,+35
vit_base_patch16_224.augreg2_in21k_ft_in1k,45.114,54.886,67.394,32.606,86.57,224,0.900,bicubic,-39.980,-30.136,+50
vit_base_patch32_clip_224.openai_ft_in1k,45.031,54.969,68.453,31.547,88.22,224,0.900,bicubic,-36.899,-27.513,+388
tiny_vit_21m_224.dist_in22k_ft_in1k,44.851,55.149,67.590,32.410,21.20,224,0.950,bicubic,-40.235,-29.776,+50
seresnextaa101d_32x8d.sw_in12k_ft_in1k,44.801,55.199,67.372,32.628,93.59,288,1.000,bicubic,-41.683,-30.658,-53
convnextv2_large.fcmae_ft_in1k,44.797,55.203,65.853,34.147,197.96,288,1.000,bicubic,-41.320,-31.969,-32
volo_d5_512.sail_in1k,44.577,55.423,65.765,34.235,296.09,512,1.150,bicubic,-42.481,-32.205,-86
convnextv2_tiny.fcmae_ft_in22k_in1k_384,44.322,55.678,66.655,33.345,28.64,384,1.000,bicubic,-40.784,-30.973,+41
cait_m48_448.fb_dist_in1k,44.212,55.788,64.674,35.326,356.46,448,1.000,bicubic,-42.280,-33.078,-58
deit3_large_patch16_384.fb_in1k,44.182,55.818,64.845,35.155,304.76,384,1.000,bicubic,-41.630,-32.753,-15
volo_d5_448.sail_in1k,44.100,55.900,65.065,34.935,295.91,448,1.150,bicubic,-42.852,-32.873,-83
eva02_small_patch14_336.mim_in22k_ft_in1k,43.960,56.040,65.942,34.058,22.13,336,1.000,bicubic,-41.758,-31.692,-9
deit3_huge_patch14_224.fb_in1k,43.807,56.193,64.350,35.650,632.13,224,0.900,bicubic,-41.417,-33.010,+25
convnext_small.fb_in22k_ft_in1k,43.620,56.380,66.464,33.536,50.22,288,1.000,bicubic,-41.642,-31.218,+20
deit3_large_patch16_224.fb_in1k,43.516,56.484,63.572,36.428,304.37,224,0.900,bicubic,-41.258,-33.464,+63
vit_base_r50_s16_384.orig_in21k_ft_in1k,43.501,56.499,66.781,33.219,98.95,384,1.000,bicubic,-41.475,-30.509,+47
tf_efficientnet_b4.ns_jft_in1k,43.447,56.553,65.513,34.487,19.34,380,0.922,bicubic,-41.712,-31.955,+28
deit3_medium_patch16_224.fb_in22k_ft_in1k,43.276,56.724,64.888,35.112,38.85,224,1.000,bicubic,-41.273,-32.300,+72
volo_d5_224.sail_in1k,43.243,56.757,64.079,35.921,295.46,224,0.960,bicubic,-42.827,-33.497,-40
vit_base_patch16_224.augreg_in21k_ft_in1k,43.221,56.779,65.722,34.279,86.57,224,0.900,bicubic,-41.310,-31.572,+73
volo_d4_448.sail_in1k,43.133,56.867,64.108,35.892,193.41,448,1.150,bicubic,-43.659,-33.776,-84
efficientnet_b5.sw_in12k_ft_in1k,42.872,57.128,65.415,34.585,30.39,448,1.000,bicubic,-43.024,-32.321,-32
xcit_large_24_p8_384.fb_dist_in1k,42.838,57.162,63.418,36.582,188.93,384,1.000,bicubic,-43.158,-34.272,-40
regnety_160.lion_in12k_ft_in1k,42.748,57.252,64.203,35.797,83.59,288,1.000,bicubic,-43.240,-33.632,-38
regnety_160.sw_in12k_ft_in1k,42.683,57.317,64.338,35.662,83.59,288,1.000,bicubic,-43.303,-33.496,-38
maxvit_small_tf_512.in1k,42.681,57.319,64.537,35.464,69.13,512,1.000,bicubic,-43.403,-33.227,-48
convnext_small.in12k_ft_in1k,42.669,57.331,64.342,35.658,50.22,288,1.000,bicubic,-42.661,-33.204,+3
xcit_large_24_p8_224.fb_dist_in1k,42.557,57.443,63.098,36.902,188.93,224,1.000,bicubic,-42.845,-34.304,-4
tf_efficientnet_b8.ra_in1k,42.498,57.502,64.873,35.127,87.41,672,0.954,bicubic,-42.870,-32.521,-2
caformer_b36.sail_in1k,42.465,57.535,62.849,37.151,98.75,224,1.000,bicubic,-43.039,-34.461,-15
caformer_b36.sail_in1k_384,42.457,57.543,62.856,37.144,98.75,384,1.000,bicubic,-43.951,-34.958,-74
maxvit_large_tf_224.in1k,42.414,57.586,63.399,36.601,211.79,224,0.950,bicubic,-42.528,-33.571,+33
cait_m36_384.fb_dist_in1k,42.410,57.590,63.324,36.676,271.22,384,1.000,bicubic,-43.648,-34.406,-53
volo_d4_224.sail_in1k,42.304,57.696,63.002,36.998,192.96,224,0.960,bicubic,-43.568,-34.470,-43
caformer_s18.sail_in22k_ft_in1k_384,42.054,57.946,64.774,35.226,26.34,384,1.000,bicubic,-43.360,-32.928,-14
deit3_small_patch16_384.fb_in22k_ft_in1k,41.954,58.046,64.564,35.436,22.21,384,1.000,bicubic,-42.870,-32.922,+38
vit_medium_patch16_gap_384.sw_in12k_ft_in1k,41.891,58.109,63.701,36.299,39.03,384,0.950,bicubic,-43.639,-33.935,-23
maxvit_tiny_tf_512.in1k,41.842,58.158,63.576,36.424,31.05,512,1.000,bicubic,-43.822,-34.008,-32
caformer_s36.sail_in1k_384,41.738,58.262,62.762,37.238,39.30,384,1.000,bicubic,-44.004,-34.910,-38
swin_small_patch4_window7_224.ms_in22k_ft_in1k,41.590,58.410,64.542,35.458,49.61,224,0.900,bicubic,-41.708,-32.422,+192
convformer_s18.sail_in22k_ft_in1k_384,41.573,58.427,63.348,36.652,26.77,384,1.000,bicubic,-43.425,-34.222,+21
regnety_2560.seer_ft_in1k,41.524,58.476,64.896,35.104,"1,282.60",384,1.000,bicubic,-43.626,-32.542,+4
caformer_m36.sail_in1k_384,41.498,58.502,61.524,38.476,56.20,384,1.000,bicubic,-44.668,-36.296,-72
coatnet_rmlp_1_rw2_224.sw_in12k_ft_in1k,41.486,58.514,61.485,38.515,41.72,224,0.950,bicubic,-43.424,-35.473,+25
tf_efficientnet_b7.ra_in1k,41.437,58.563,63.027,36.973,66.35,600,0.949,bicubic,-43.495,-34.181,+21
tf_efficientnet_b7.ap_in1k,41.433,58.567,62.880,37.120,66.35,600,0.949,bicubic,-43.691,-34.372,+1
tf_efficientnet_b5.ap_in1k,41.418,58.582,62.074,37.926,30.39,456,0.934,bicubic,-42.840,-34.900,+79
regnety_120.sw_in12k_ft_in1k,41.331,58.669,63.187,36.813,51.82,288,1.000,bicubic,-44.069,-34.395,-23
dm_nfnet_f3.dm_in1k,41.323,58.677,62.110,37.890,254.92,416,0.940,bicubic,-44.363,-35.460,-44
resnetv2_152x4_bit.goog_in21k_ft_in1k,41.306,58.694,64.311,35.689,936.53,480,1.000,bilinear,-43.610,-33.127,+17
dm_nfnet_f5.dm_in1k,41.290,58.710,62.013,37.987,377.21,544,0.954,bicubic,-44.810,-35.675,-75
caformer_s18.sail_in22k_ft_in1k,41.217,58.783,63.831,36.169,26.34,224,1.000,bicubic,-42.857,-33.367,+92
dm_nfnet_f6.dm_in1k,41.170,58.830,62.843,37.157,438.36,576,0.956,bicubic,-45.192,-35.053,-93
convnext_tiny.in12k_ft_in1k_384,41.113,58.887,62.825,37.175,28.59,384,1.000,bicubic,-44.009,-34.781,-6
tf_efficientnet_b6.ap_in1k,41.095,58.905,62.365,37.635,43.04,528,0.942,bicubic,-43.693,-34.773,+22
xcit_large_24_p16_384.fb_dist_in1k,41.036,58.964,61.237,38.763,189.10,384,1.000,bicubic,-44.718,-36.301,-56
xcit_large_24_p16_224.fb_dist_in1k,40.942,59.058,61.326,38.674,189.10,224,1.000,bicubic,-43.974,-35.802,+11
tf_efficientnetv2_l.in1k,40.928,59.072,62.011,37.989,118.52,480,1.000,bicubic,-44.736,-35.463,-51
tf_efficientnetv2_s.in21k_ft_in1k,40.922,59.078,63.849,36.151,21.46,384,1.000,bicubic,-43.364,-33.403,+64
maxvit_small_tf_384.in1k,40.844,59.156,61.972,38.028,69.02,384,1.000,bicubic,-44.696,-35.490,-47
edgenext_base.in21k_ft_in1k,40.836,59.164,61.776,38.224,18.51,320,1.000,bicubic,-43.218,-35.420,+86
maxvit_base_tf_224.in1k,40.789,59.211,61.196,38.804,119.47,224,0.950,bicubic,-44.071,-35.792,+10
xcit_medium_24_p8_224.fb_dist_in1k,40.498,59.502,60.502,39.498,84.32,224,1.000,bicubic,-44.576,-36.748,-7
vit_small_r26_s32_384.augreg_in21k_ft_in1k,40.474,59.526,62.731,37.269,36.47,384,1.000,bicubic,-43.574,-34.597,+85
tf_efficientnet_b4.ap_in1k,40.474,59.526,61.713,38.287,19.34,380,0.922,bicubic,-42.776,-34.683,+171
deit3_base_patch16_224.fb_in1k,40.382,59.618,60.164,39.836,86.59,224,0.900,bicubic,-43.404,-36.422,+110
inception_next_base.sail_in1k_384,40.333,59.667,60.781,39.219,86.67,384,1.000,bicubic,-44.869,-36.633,-25
convformer_s18.sail_in22k_ft_in1k,40.301,59.699,61.719,38.281,26.77,224,1.000,bicubic,-43.437,-35.329,+117
vit_medium_patch16_gap_256.sw_in12k_ft_in1k,40.278,59.722,61.664,38.336,38.86,256,0.950,bicubic,-44.168,-35.546,+36
flexivit_large.600ep_in1k,40.268,59.732,60.365,39.635,304.36,240,0.950,bicubic,-45.272,-37.123,-58
vit_base_patch16_224_miil.in21k_ft_in1k,40.164,59.836,60.883,39.117,86.54,224,0.875,bilinear,-44.102,-35.921,+54
deit3_small_patch16_224.fb_in22k_ft_in1k,40.152,59.848,61.864,38.136,22.06,224,1.000,bicubic,-42.924,-34.912,+183
regnetz_e8.ra3_in1k,40.136,59.864,61.316,38.684,57.70,320,1.000,bicubic,-44.898,-35.956,-14
maxvit_rmlp_small_rw_224.sw_in1k,40.105,59.895,59.514,40.486,64.90,224,0.900,bicubic,-44.387,-37.496,+26
flexivit_large.1200ep_in1k,40.097,59.903,60.650,39.350,304.36,240,0.950,bicubic,-45.547,-36.890,-67
xcit_medium_24_p8_384.fb_dist_in1k,40.050,59.950,60.451,39.549,84.32,384,1.000,bicubic,-45.766,-37.141,-82
flexivit_large.300ep_in1k,39.991,60.009,59.987,40.013,304.36,240,0.950,bicubic,-45.297,-37.413,-45
maxvit_tiny_tf_384.in1k,39.971,60.029,60.909,39.091,30.98,384,1.000,bicubic,-45.129,-36.469,-28
convnextv2_tiny.fcmae_ft_in22k_in1k,39.938,60.062,61.835,38.165,28.64,288,1.000,bicubic,-44.478,-35.425,+35
dm_nfnet_f4.dm_in1k,39.926,60.074,60.449,39.551,316.07,512,0.951,bicubic,-45.910,-37.369,-87
xcit_medium_24_p16_384.fb_dist_in1k,39.897,60.103,60.097,39.903,84.40,384,1.000,bicubic,-45.527,-37.233,-62
convnext_tiny.fb_in22k_ft_in1k_384,39.787,60.213,61.536,38.464,28.59,384,1.000,bicubic,-44.301,-35.608,+61
cait_s36_384.fb_dist_in1k,39.767,60.233,60.469,39.531,68.37,384,1.000,bicubic,-45.687,-37.009,-65
convnextv2_base.fcmae_ft_in1k,39.755,60.245,59.875,40.125,88.72,288,1.000,bicubic,-45.719,-37.509,-68
volo_d3_448.sail_in1k,39.702,60.298,59.760,40.240,86.63,448,1.000,bicubic,-46.800,-37.950,-135
efficientnetv2_rw_m.agc_in1k,39.675,60.325,59.679,40.321,53.24,416,1.000,bicubic,-45.135,-37.473,-10
xception65.ra3_in1k,39.623,60.377,60.919,39.081,39.92,299,0.940,bicubic,-43.557,-35.673,+153
ecaresnet269d.ra2_in1k,39.604,60.396,60.345,39.655,102.09,352,1.000,bicubic,-45.364,-36.877,-24
tf_efficientnet_b3.ns_jft_in1k,39.586,60.414,61.475,38.525,12.23,300,0.904,bicubic,-44.466,-35.443,+60
caformer_m36.sail_in1k,39.576,60.424,58.692,41.308,56.20,224,1.000,bicubic,-45.656,-38.508,-53
convformer_b36.sail_in1k,39.525,60.475,58.081,41.919,99.88,224,1.000,bicubic,-45.293,-38.865,-16
caformer_s36.sail_in1k,39.519,60.481,59.760,40.240,39.30,224,1.000,bicubic,-44.987,-37.236,+5
volo_d3_224.sail_in1k,39.482,60.518,59.858,40.142,86.33,224,0.960,bicubic,-45.932,-37.418,-70
convnext_large.fb_in1k,39.460,60.540,59.188,40.812,197.77,288,1.000,bicubic,-45.386,-38.026,-21
deit3_base_patch16_384.fb_in1k,39.409,60.591,58.930,41.070,86.88,384,1.000,bicubic,-45.665,-38.344,-38
xcit_small_24_p8_224.fb_dist_in1k,39.327,60.673,59.402,40.598,47.63,224,1.000,bicubic,-45.541,-37.788,-25
inception_next_base.sail_in1k,39.295,60.705,59.245,40.755,86.67,224,0.950,bicubic,-44.797,-37.551,+45
xcit_medium_24_p16_224.fb_dist_in1k,39.270,60.730,59.461,40.539,84.40,224,1.000,bicubic,-45.016,-37.471,+26
convformer_m36.sail_in1k,39.234,60.766,57.631,42.369,57.05,224,1.000,bicubic,-45.260,-39.235,-1
coat_lite_medium_384.in1k,39.175,60.825,59.280,40.720,44.57,384,1.000,bicubic,-45.703,-38.092,-30
tiny_vit_11m_224.dist_in22k_ft_in1k,39.124,60.876,61.035,38.965,11.00,224,0.950,bicubic,-44.104,-35.595,+135
efficientnet_b4.ra2_in1k,39.087,60.913,59.618,40.382,19.34,384,1.000,bicubic,-44.327,-36.980,+115
hrnet_w18_ssld.paddle_in1k,39.067,60.933,60.650,39.350,21.30,288,1.000,bilinear,-42.981,-35.600,+275
tresnet_v2_l.miil_in21k_ft_in1k,39.010,60.990,59.477,40.523,46.17,224,0.875,bilinear,-44.884,-37.013,+57
xcit_small_24_p8_384.fb_dist_in1k,39.010,60.990,59.166,40.834,47.63,384,1.000,bicubic,-46.544,-38.404,-94
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k_384,38.979,61.021,62.428,37.572,236.34,384,1.000,bicubic,-44.857,-34.698,+63
convformer_b36.sail_in1k_384,38.930,61.070,58.413,41.587,99.88,384,1.000,bicubic,-46.810,-39.111,-105
convnext_tiny.in12k_ft_in1k,38.912,61.088,59.862,40.138,28.59,288,1.000,bicubic,-45.538,-37.478,-5
maxvit_small_tf_224.in1k,38.871,61.129,59.162,40.838,68.93,224,0.950,bicubic,-45.555,-37.662,+3
coatnet_rmlp_2_rw_224.sw_in1k,38.869,61.131,58.018,41.982,73.88,224,0.950,bicubic,-45.739,-38.722,-24
vit_base_patch32_384.augreg_in21k_ft_in1k,38.796,61.204,60.329,39.671,88.30,384,1.000,bicubic,-44.556,-36.511,+115
tf_efficientnetv2_m.in1k,38.714,61.286,59.791,40.209,54.14,480,1.000,bicubic,-46.490,-37.573,-71
efficientvit_b3.r288_in1k,38.659,61.341,58.364,41.636,48.65,288,1.000,bicubic,-45.495,-38.372,+25
eca_nfnet_l2.ra3_in1k,38.655,61.345,59.445,40.555,56.72,384,1.000,bicubic,-46.045,-37.821,-32
davit_small.msft_in1k,38.631,61.369,58.203,41.797,49.75,224,0.950,bicubic,-45.621,-38.737,+12
efficientvit_b3.r256_in1k,38.621,61.379,58.641,41.359,48.65,256,1.000,bicubic,-45.181,-37.875,+59
mvitv2_small.fb_in1k,38.578,61.422,58.130,41.870,34.87,224,0.900,bicubic,-45.192,-38.446,+61
xcit_small_12_p8_384.fb_dist_in1k,38.547,61.453,58.795,41.205,26.21,384,1.000,bicubic,-46.531,-38.487,-63
convformer_m36.sail_in1k_384,38.531,61.469,57.736,42.264,57.05,384,1.000,bicubic,-47.049,-39.806,-109
xcit_small_24_p16_384.fb_dist_in1k,38.499,61.501,58.396,41.604,47.67,384,1.000,bicubic,-46.591,-38.916,-67
davit_base.msft_in1k,38.490,61.510,57.535,42.465,87.95,224,0.950,bicubic,-46.152,-39.485,-37
mvitv2_base.fb_in1k,38.458,61.542,57.934,42.066,51.47,224,0.900,bicubic,-45.992,-38.924,-18
rexnetr_300.sw_in12k_ft_in1k,38.431,61.569,60.612,39.388,34.81,288,1.000,bicubic,-46.115,-36.644,-31
convformer_s36.sail_in1k,38.405,61.595,57.710,42.290,40.01,224,1.000,bicubic,-45.655,-39.036,+22
xcit_small_12_p8_224.fb_dist_in1k,38.360,61.640,58.832,41.168,26.21,224,1.000,bicubic,-45.876,-38.038,+5
tf_efficientnet_b5.ra_in1k,38.331,61.669,59.928,40.072,30.39,456,0.934,bicubic,-45.483,-36.823,+46
fastvit_ma36.apple_dist_in1k,38.323,61.677,58.461,41.539,44.07,256,0.950,bicubic,-46.275,-38.541,-39
deit_base_distilled_patch16_384.fb_in1k,38.244,61.756,57.785,42.215,87.63,384,1.000,bicubic,-47.180,-39.621,-108
xcit_large_24_p8_224.fb_in1k,38.106,61.894,57.873,42.127,188.93,224,1.000,bicubic,-46.288,-38.791,-10
vit_base_patch16_384.orig_in21k_ft_in1k,38.105,61.895,60.426,39.574,86.86,384,1.000,bicubic,-46.096,-36.792,+4
resnetv2_152x2_bit.goog_in21k_ft_in1k,38.000,62.000,61.137,38.863,236.34,448,1.000,bilinear,-46.510,-36.297,-36
repvit_m2_3.dist_450e_in1k,37.996,62.004,58.154,41.846,23.69,224,0.950,bicubic,-45.746,-38.490,+51
pvt_v2_b4.in1k,37.953,62.047,58.217,41.783,62.56,224,0.900,bicubic,-45.759,-38.453,+55
coat_lite_medium.in1k,37.880,62.120,57.792,42.208,44.57,224,0.900,bicubic,-45.719,-38.935,+64
cait_s24_384.fb_dist_in1k,37.879,62.121,58.069,41.931,47.06,384,1.000,bicubic,-47.169,-39.277,-77
convnextv2_nano.fcmae_ft_in22k_in1k_384,37.875,62.125,59.443,40.557,15.62,384,1.000,bicubic,-45.499,-37.301,+88
resnet152d.ra2_in1k,37.853,62.147,58.362,41.638,60.21,320,1.000,bicubic,-45.831,-38.376,+56
convformer_s36.sail_in1k_384,37.812,62.188,57.488,42.512,40.01,384,1.000,bicubic,-47.566,-39.988,-112
resnetrs420.tf_in1k,37.745,62.255,58.209,41.791,191.89,416,1.000,bicubic,-47.259,-38.915,-78
deit3_medium_patch16_224.fb_in1k,37.709,62.291,57.087,42.913,38.85,224,0.900,bicubic,-45.377,-39.207,+114
xcit_small_24_p16_224.fb_dist_in1k,37.700,62.300,57.374,42.626,47.67,224,1.000,bicubic,-46.174,-39.362,+23
resnetrs350.tf_in1k,37.664,62.336,58.097,41.903,163.96,384,1.000,bicubic,-47.050,-38.895,-63
caformer_s18.sail_in1k_384,37.660,62.340,57.612,42.388,26.34,384,1.000,bicubic,-47.366,-39.746,-83
regnety_640.seer_ft_in1k,37.584,62.416,59.864,40.136,281.38,384,1.000,bicubic,-46.324,-37.058,+15
xcit_small_12_p16_384.fb_dist_in1k,37.582,62.418,57.761,42.239,26.25,384,1.000,bicubic,-47.130,-39.357,-65
pvt_v2_b5.in1k,37.548,62.452,57.301,42.699,81.96,224,0.900,bicubic,-46.192,-39.335,+38
resnet200d.ra2_in1k,37.503,62.497,58.301,41.699,64.69,320,1.000,bicubic,-46.461,-38.525,+8
maxvit_rmlp_tiny_rw_256.sw_in1k,37.381,62.619,57.193,42.807,29.15,256,0.950,bicubic,-46.843,-39.675,-16
resnetv2_152x2_bit.goog_teacher_in21k_ft_in1k,37.342,62.658,59.404,40.596,236.34,224,0.875,bicubic,-45.534,-37.178,+122
efficientvit_b3.r224_in1k,37.340,62.660,57.122,42.878,48.65,224,0.950,bicubic,-46.120,-39.208,+62
regnety_1280.seer_ft_in1k,37.332,62.668,59.133,40.867,644.81,384,1.000,bicubic,-47.100,-37.959,-42
resnest269e.in1k,37.309,62.691,57.488,42.512,110.93,416,0.928,bicubic,-47.199,-39.502,-56
convnext_base.fb_in1k,37.307,62.693,57.319,42.681,88.59,288,1.000,bicubic,-47.121,-39.649,-43
resmlp_big_24_224.fb_in22k_ft_in1k,37.242,62.758,58.191,41.809,129.14,224,0.875,bicubic,-47.156,-38.921,-36
vit_small_r26_s32_224.augreg_in21k_ft_in1k,37.232,62.768,59.072,40.928,36.43,224,0.900,bicubic,-44.632,-36.950,+239
repvit_m2_3.dist_300e_in1k,37.210,62.790,57.234,42.766,23.69,224,0.950,bicubic,-46.294,-39.270,+49
pit_b_distilled_224.in1k,37.195,62.805,56.507,43.493,74.79,224,0.900,bicubic,-46.571,-39.961,+22
cait_s24_224.fb_dist_in1k,37.153,62.847,56.724,43.276,46.92,224,1.000,bicubic,-46.289,-39.850,+56
dm_nfnet_f2.dm_in1k,37.128,62.872,56.981,43.019,193.78,352,0.920,bicubic,-48.064,-40.365,-115
resnetaa101d.sw_in12k_ft_in1k,37.116,62.884,57.853,42.147,44.57,288,1.000,bicubic,-47.008,-39.253,-20
tiny_vit_21m_224.in1k,37.114,62.886,57.380,42.620,21.20,224,0.950,bicubic,-46.140,-39.212,+73
efficientformer_l7.snap_dist_in1k,37.112,62.888,56.900,43.100,82.23,224,0.950,bicubic,-46.270,-39.636,+61
pvt_v2_b3.in1k,37.108,62.892,57.335,42.665,45.24,224,0.900,bicubic,-46.010,-39.221,+87
fastvit_sa36.apple_dist_in1k,37.106,62.894,57.144,42.856,31.53,256,0.900,bicubic,-46.920,-39.710,-13
vit_base_patch32_224.augreg_in21k_ft_in1k,37.073,62.927,59.307,40.693,88.22,224,0.900,bicubic,-43.643,-36.259,+347
volo_d1_384.sail_in1k,37.069,62.931,57.138,42.862,26.78,384,1.000,bicubic,-48.175,-40.056,-131
efficientnetv2_rw_s.ra2_in1k,37.057,62.943,56.814,43.186,23.94,384,1.000,bicubic,-46.749,-39.918,+6
tf_efficientnet_b3.ap_in1k,37.051,62.949,57.238,42.762,12.23,300,0.904,bicubic,-44.769,-38.388,+230
maxvit_tiny_tf_224.in1k,37.020,62.980,56.904,43.096,30.92,224,0.950,bicubic,-46.382,-39.686,+49
swinv2_base_window16_256.ms_in1k,36.990,63.010,56.140,43.860,87.92,256,0.900,bicubic,-47.610,-40.950,-83
xcit_small_12_p16_224.fb_dist_in1k,36.975,63.025,56.725,43.275,26.25,224,1.000,bicubic,-46.353,-39.691,+60
regnetz_040_h.ra3_in1k,36.973,63.027,57.276,42.724,28.94,320,1.000,bicubic,-47.519,-39.482,-73
inception_next_small.sail_in1k,36.914,63.086,56.749,43.251,49.37,224,0.875,bicubic,-46.664,-39.849,+28
volo_d1_224.sail_in1k,36.888,63.112,56.635,43.365,26.63,224,0.960,bicubic,-47.274,-40.141,-37
seresnet152d.ra2_in1k,36.784,63.216,56.727,43.273,66.84,320,1.000,bicubic,-47.576,-40.313,-55
efficientformerv2_l.snap_dist_in1k,36.764,63.236,56.627,43.373,26.32,224,0.950,bicubic,-46.868,-39.931,+20
maxxvit_rmlp_small_rw_256.sw_in1k,36.707,63.293,56.012,43.988,66.01,256,0.950,bicubic,-47.917,-41.056,-92
seresnext101d_32x8d.ah_in1k,36.633,63.367,56.325,43.675,93.59,288,1.000,bicubic,-47.725,-40.596,-57
volo_d2_224.sail_in1k,36.627,63.373,56.470,43.530,58.68,224,0.960,bicubic,-48.575,-40.720,-136
caformer_s18.sail_in1k,36.578,63.422,55.831,44.169,26.34,224,1.000,bicubic,-47.076,-40.687,+15
xception65p.ra3_in1k,36.570,63.430,56.438,43.562,39.82,299,0.940,bicubic,-46.556,-40.044,+66
fastvit_ma36.apple_in1k,36.568,63.432,56.564,43.436,44.07,256,0.950,bicubic,-47.314,-40.178,-19
fastvit_sa36.apple_in1k,36.556,63.444,56.004,43.996,31.53,256,0.900,bicubic,-46.944,-40.626,+24
seresnextaa101d_32x8d.ah_in1k,36.532,63.468,56.409,43.591,93.59,288,1.000,bicubic,-48.034,-40.667,-95
focalnet_base_srf.ms_in1k,36.460,63.540,56.217,43.783,88.15,224,0.900,bicubic,-47.360,-40.464,-14
regnetz_d32.ra3_in1k,36.452,63.548,57.366,42.634,27.58,320,0.950,bicubic,-47.570,-39.502,-34
cait_xs24_384.fb_dist_in1k,36.416,63.584,56.944,43.056,26.67,384,1.000,bicubic,-47.645,-39.941,-42
efficientnet_b3.ra2_in1k,36.414,63.586,56.830,43.170,12.23,320,1.000,bicubic,-45.831,-39.288,+166
deit_base_distilled_patch16_224.fb_in1k,36.407,63.593,56.615,43.385,87.34,224,0.900,bicubic,-46.983,-39.873,+32
volo_d2_384.sail_in1k,36.407,63.593,56.323,43.677,58.87,384,1.000,bicubic,-49.635,-41.251,-209
resnetv2_101x3_bit.goog_in21k_ft_in1k,36.383,63.617,59.068,40.932,387.93,448,1.000,bilinear,-48.055,-38.314,-83
gcvit_base.in1k,36.381,63.619,55.880,44.120,90.32,224,0.875,bicubic,-48.063,-41.202,-85
dm_nfnet_f1.dm_in1k,36.326,63.674,55.747,44.253,132.63,320,0.910,bicubic,-48.376,-41.435,-112
resnetrs270.tf_in1k,36.310,63.690,56.566,43.434,129.86,352,1.000,bicubic,-48.118,-40.402,-83
tresnet_m.miil_in21k_ft_in1k,36.289,63.711,55.792,44.208,31.39,224,0.875,bilinear,-46.781,-40.318,+61
mixer_b16_224.miil_in21k_ft_in1k,36.267,63.733,55.965,44.035,59.88,224,0.875,bilinear,-46.039,-39.755,+152
convnext_small.fb_in1k,36.248,63.752,55.912,44.088,50.22,288,1.000,bicubic,-47.453,-40.896,-7
convformer_s18.sail_in1k_384,36.204,63.796,56.059,43.941,26.77,384,1.000,bicubic,-48.198,-41.053,-81
deit3_small_patch16_384.fb_in1k,36.193,63.807,55.558,44.442,22.21,384,1.000,bicubic,-47.235,-41.116,+16
tf_efficientnet_b2.ns_jft_in1k,36.167,63.833,57.559,42.441,9.11,260,0.890,bicubic,-46.211,-38.695,+133
mvitv2_tiny.fb_in1k,36.167,63.833,55.132,44.868,24.17,224,0.900,bicubic,-46.243,-41.020,+129
focalnet_base_lrf.ms_in1k,36.118,63.882,55.810,44.190,88.75,224,0.900,bicubic,-47.720,-40.798,-34
regnety_320.seer_ft_in1k,36.069,63.931,58.484,41.516,145.05,384,1.000,bicubic,-47.259,-38.224,+27
regnetz_040.ra3_in1k,36.053,63.947,55.735,44.265,27.12,320,1.000,bicubic,-48.187,-41.197,-75
resnest200e.in1k,35.929,64.071,55.847,44.153,70.20,320,0.909,bicubic,-47.915,-41.037,-39
resnet18.fb_swsl_ig1b_ft_in1k,35.874,64.126,58.447,41.553,11.69,224,0.875,bilinear,-37.414,-33.283,+666
sequencer2d_l.in1k,35.831,64.169,55.715,44.285,54.30,224,0.875,bicubic,-47.563,-40.781,+13
eca_nfnet_l1.ra2_in1k,35.815,64.185,55.953,44.047,41.41,320,1.000,bicubic,-48.197,-41.073,-54
gcvit_small.in1k,35.760,64.240,54.790,45.210,51.09,224,0.875,bicubic,-48.132,-41.868,-47
vit_base_patch16_224.orig_in21k_ft_in1k,35.754,64.246,57.401,42.599,86.57,224,0.900,bicubic,-46.036,-38.725,+191
vit_relpos_medium_patch16_cls_224.sw_in1k,35.725,64.275,54.919,45.081,38.76,224,0.900,bicubic,-46.847,-41.148,+101
xcit_small_24_p8_224.fb_in1k,35.556,64.444,54.780,45.220,47.63,224,1.000,bicubic,-48.278,-41.852,-42
xcit_small_12_p8_224.fb_in1k,35.522,64.478,55.507,44.493,26.21,224,1.000,bicubic,-47.812,-40.975,+15
xcit_large_24_p16_224.fb_in1k,35.522,64.478,54.741,45.259,189.10,224,1.000,bicubic,-47.380,-41.143,+56
coat_small.in1k,35.520,64.480,55.157,44.843,21.69,224,0.900,bicubic,-46.842,-41.051,+121
flexivit_base.1200ep_in1k,35.519,64.481,53.837,46.163,86.59,240,0.950,bicubic,-49.157,-43.157,-133
vit_small_patch16_384.augreg_in21k_ft_in1k,35.467,64.533,57.543,42.457,22.20,384,1.000,bicubic,-48.337,-39.557,-43
regnetz_d8_evos.ch_in1k,35.454,64.546,55.751,44.249,23.46,320,1.000,bicubic,-48.672,-41.261,-79
xcit_medium_24_p8_224.fb_in1k,35.452,64.548,54.823,45.177,84.32,224,1.000,bicubic,-48.294,-41.887,-37
swinv2_base_window8_256.ms_in1k,35.450,64.550,54.607,45.393,87.92,256,0.900,bicubic,-48.800,-42.317,-92
swinv2_small_window16_256.ms_in1k,35.428,64.572,54.623,45.377,49.73,256,0.900,bicubic,-48.796,-42.155,-90
dm_nfnet_f0.dm_in1k,35.407,64.594,55.525,44.475,71.49,256,0.900,bicubic,-48.080,-41.043,-12
resnest101e.in1k,35.373,64.627,55.790,44.210,48.28,256,0.875,bilinear,-47.511,-40.532,+47
resnet152.a1h_in1k,35.357,64.643,54.627,45.373,60.19,288,1.000,bicubic,-48.093,-41.911,-11
tf_efficientnet_b5.aa_in1k,35.316,64.684,56.038,43.962,30.39,456,0.934,bicubic,-48.372,-40.674,-33
convit_base.fb_in1k,35.302,64.698,54.939,45.061,86.54,224,0.875,bicubic,-46.988,-40.997,+123
focalnet_small_lrf.ms_in1k,35.277,64.723,54.888,45.112,50.34,224,0.900,bicubic,-48.217,-41.692,-18
efficientformer_l3.snap_dist_in1k,35.253,64.747,54.501,45.499,31.41,224,0.950,bicubic,-47.295,-41.749,+88
xcit_tiny_24_p8_224.fb_dist_in1k,35.241,64.759,55.267,44.733,12.11,224,1.000,bicubic,-47.325,-40.791,+85
edgenext_base.usi_in1k,35.216,64.784,55.126,44.874,18.51,320,1.000,bicubic,-48.742,-41.644,-74
fastvit_sa24.apple_dist_in1k,35.214,64.786,54.674,45.326,21.55,256,0.900,bicubic,-48.128,-41.878,-4
convformer_s18.sail_in1k,35.208,64.792,54.629,45.371,26.77,224,1.000,bicubic,-47.778,-41.621,+32
flexivit_base.600ep_in1k,35.137,64.863,53.652,46.348,86.59,240,0.950,bicubic,-49.387,-43.284,-139
twins_svt_large.in1k,35.084,64.916,54.719,45.281,99.27,224,0.900,bicubic,-48.594,-41.869,-40
repvgg_b3.rvgg_in1k,35.057,64.943,54.548,45.452,123.09,224,0.875,bilinear,-45.449,-40.706,+294
repvgg_b3g4.rvgg_in1k,35.049,64.951,54.788,45.212,83.83,224,0.875,bilinear,-45.167,-40.304,+327
convnextv2_tiny.fcmae_ft_in1k,35.045,64.955,54.224,45.776,28.64,288,1.000,bicubic,-48.419,-42.494,-26
repvit_m1_5.dist_450e_in1k,35.021,64.979,54.477,45.523,14.64,224,0.950,bicubic,-47.491,-41.635,+82
regnetz_d8.ra3_in1k,35.008,64.992,55.930,44.070,23.37,320,1.000,bicubic,-49.044,-41.066,-91
xcit_tiny_24_p8_384.fb_dist_in1k,34.915,65.085,55.148,44.852,12.11,384,1.000,bicubic,-48.831,-41.253,-59
resnet101d.ra2_in1k,34.876,65.124,54.210,45.790,44.57,320,1.000,bicubic,-48.144,-42.242,+19
rexnetr_200.sw_in12k_ft_in1k,34.870,65.130,55.857,44.143,16.52,288,1.000,bicubic,-48.268,-40.779,+2
repvit_m1_5.dist_300e_in1k,34.868,65.132,54.375,45.625,14.64,224,0.950,bicubic,-47.508,-41.655,+92
coatnet_1_rw_224.sw_in1k,34.850,65.150,53.442,46.558,41.72,224,0.950,bicubic,-48.746,-42.940,-45
coatnet_rmlp_1_rw_224.sw_in1k,34.803,65.197,53.953,46.047,41.69,224,0.950,bicubic,-48.559,-42.497,-20
swin_s3_base_224.ms_in1k,34.803,65.197,53.703,46.297,71.13,224,0.900,bicubic,-49.117,-42.969,-88
flexivit_base.300ep_in1k,34.799,65.201,53.161,46.839,86.59,240,0.950,bicubic,-49.607,-43.723,-131
seresnext101_32x8d.ah_in1k,34.789,65.211,53.452,46.548,93.57,288,1.000,bicubic,-49.397,-43.422,-113
resmlp_big_24_224.fb_distilled_in1k,34.788,65.213,54.642,45.358,129.14,224,0.875,bicubic,-48.804,-42.008,-49
maxvit_tiny_rw_224.sw_in1k,34.780,65.220,53.351,46.649,29.06,224,0.950,bicubic,-48.724,-43.163,-44
repvgg_d2se.rvgg_in1k,34.740,65.260,53.200,46.800,133.33,320,1.000,bilinear,-48.820,-43.458,-49
vit_relpos_base_patch16_clsgap_224.sw_in1k,34.725,65.275,54.214,45.786,86.43,224,0.900,bicubic,-48.035,-41.958,+29
vit_base_patch16_rpn_224.sw_in1k,34.715,65.285,54.658,45.342,86.54,224,0.900,bicubic,-47.487,-41.338,+108
sequencer2d_m.in1k,34.703,65.297,53.996,46.004,38.31,224,0.875,bicubic,-48.109,-42.278,+21
deit3_small_patch16_224.fb_in1k,34.685,65.315,53.173,46.827,22.06,224,0.900,bicubic,-46.685,-42.283,+188
davit_tiny.msft_in1k,34.673,65.326,54.344,45.656,28.36,224,0.950,bicubic,-48.023,-41.930,+33
vit_large_patch32_384.orig_in21k_ft_in1k,34.672,65.329,55.733,44.267,306.63,384,1.000,bicubic,-46.839,-40.357,+168
focalnet_small_srf.ms_in1k,34.670,65.330,54.420,45.580,49.89,224,0.900,bicubic,-48.746,-42.018,-42
ecaresnet101d.miil_in1k,34.644,65.356,54.499,45.501,44.57,288,0.950,bicubic,-48.340,-42.043,+6
resnext101_32x16d.fb_ssl_yfcc100m_ft_in1k,34.615,65.385,55.937,44.063,194.03,224,0.875,bilinear,-47.223,-40.155,+136
vit_relpos_base_patch16_224.sw_in1k,34.607,65.393,54.291,45.709,86.43,224,0.900,bicubic,-47.889,-41.847,+61
repvgg_b2g4.rvgg_in1k,34.593,65.407,54.768,45.232,61.76,224,0.875,bilinear,-44.789,-39.908,+359
gcvit_tiny.in1k,34.554,65.446,53.249,46.751,28.22,224,0.875,bicubic,-48.830,-43.149,-41
resnetrs200.tf_in1k,34.502,65.498,54.281,45.719,93.21,320,1.000,bicubic,-49.942,-42.561,-158
poolformerv2_m48.sail_in1k,34.485,65.515,54.027,45.973,73.35,224,1.000,bicubic,-48.133,-42.045,+35
efficientvit_b2.r256_in1k,34.430,65.570,53.593,46.407,24.33,256,1.000,bicubic,-48.260,-42.501,+24
convnextv2_nano.fcmae_ft_in22k_in1k,34.379,65.621,55.014,44.986,15.62,288,1.000,bicubic,-48.285,-41.506,+26
resnest50d_4s2x40d.in1k,34.369,65.631,54.725,45.275,30.42,224,0.875,bicubic,-46.751,-40.835,+199
resnetrs152.tf_in1k,34.351,65.649,53.564,46.436,86.62,320,1.000,bicubic,-49.351,-43.048,-80
pvt_v2_b2_li.in1k,34.318,65.682,54.104,45.896,22.55,224,0.900,bicubic,-47.876,-41.988,+93
crossvit_18_dagger_408.in1k,34.247,65.753,53.106,46.894,44.61,408,1.000,bicubic,-49.955,-43.712,-138
xcit_medium_24_p16_224.fb_in1k,34.241,65.759,53.157,46.843,84.40,224,1.000,bicubic,-48.399,-42.825,+24
efficientvit_b2.r288_in1k,34.224,65.776,53.546,46.454,24.33,288,1.000,bicubic,-48.876,-42.758,-20
pit_s_distilled_224.in1k,34.161,65.839,53.361,46.639,24.04,224,0.900,bicubic,-47.653,-42.369,+125
efficientnetv2_rw_t.ra2_in1k,34.159,65.841,53.135,46.865,13.65,288,1.000,bicubic,-48.191,-43.057,+65
tf_efficientnet_b1.ns_jft_in1k,34.151,65.849,55.503,44.497,7.79,240,0.882,bicubic,-47.237,-40.235,+164
twins_pcpvt_large.in1k,34.119,65.881,54.136,45.864,60.99,224,0.900,bicubic,-49.011,-42.468,-31
fastvit_sa24.apple_in1k,34.098,65.902,53.782,46.218,21.55,256,0.900,bicubic,-48.580,-42.490,+13
tf_efficientnet_b4.aa_in1k,34.058,65.942,54.212,45.788,19.34,380,0.922,bicubic,-48.960,-42.088,-18
efficientformerv2_s2.snap_dist_in1k,34.056,65.944,53.473,46.527,12.71,224,0.950,bicubic,-48.109,-42.437,+86
resnetv2_101.a1h_in1k,34.056,65.944,52.308,47.692,44.54,288,1.000,bicubic,-48.944,-44.146,-19
resnext101_32x8d.fb_ssl_yfcc100m_ft_in1k,34.033,65.967,55.596,44.404,88.79,224,0.875,bilinear,-47.573,-40.445,+131
xcit_small_24_p16_224.fb_in1k,34.005,65.995,53.281,46.719,47.67,224,1.000,bicubic,-48.571,-42.731,+28
tf_efficientnet_b6.aa_in1k,34.002,65.999,54.542,45.458,43.04,528,0.942,bicubic,-50.110,-42.342,-144
nfnet_l0.ra2_in1k,34.002,65.999,54.377,45.623,35.07,288,1.000,bicubic,-48.748,-42.139,-2
efficientnet_b3_pruned.in1k,33.992,66.008,54.110,45.890,9.86,300,0.904,bicubic,-46.860,-41.134,+213
regnety_160.deit_in1k,33.980,66.020,53.552,46.448,83.59,288,1.000,bicubic,-49.710,-43.228,-96
resnext101_64x4d.tv_in1k,33.966,66.034,52.530,47.470,83.46,224,0.875,bilinear,-49.026,-43.714,-25
gc_efficientnetv2_rw_t.agc_in1k,33.962,66.038,53.228,46.772,13.68,288,1.000,bicubic,-48.494,-43.068,+38
swinv2_cr_small_ns_224.sw_in1k,33.846,66.154,52.640,47.360,49.70,224,0.900,bicubic,-49.652,-43.844,-82
resnext101_64x4d.c1_in1k,33.842,66.158,52.161,47.839,83.46,288,1.000,bicubic,-49.314,-44.213,-48
poolformerv2_s36.sail_in1k,33.825,66.175,53.685,46.315,30.79,224,1.000,bicubic,-47.741,-42.005,+124
repvit_m3.dist_in1k,33.791,66.209,53.123,46.877,10.68,224,0.950,bicubic,-47.711,-42.444,+133
resnet101.a1h_in1k,33.781,66.219,53.104,46.896,44.55,288,1.000,bicubic,-48.997,-43.206,-15
xcit_small_12_p16_224.fb_in1k,33.756,66.244,53.228,46.772,26.25,224,1.000,bicubic,-48.214,-42.584,+91
swin_s3_small_224.ms_in1k,33.697,66.303,52.365,47.635,49.74,224,0.900,bicubic,-50.059,-44.087,-116
resnetv2_50x3_bit.goog_in21k_ft_in1k,33.671,66.329,55.888,44.112,217.32,448,1.000,bilinear,-50.349,-41.238,-144
swinv2_small_window8_256.ms_in1k,33.634,66.366,52.827,47.173,49.73,256,0.900,bicubic,-50.220,-43.817,-133
resnet152.tv2_in1k,33.622,66.378,51.656,48.344,60.19,224,0.965,bilinear,-48.664,-44.348,+51
inception_next_tiny.sail_in1k,33.583,66.417,52.978,47.022,28.06,224,0.875,bicubic,-48.895,-43.044,+25
resnet51q.ra2_in1k,33.555,66.445,53.023,46.977,35.70,288,1.000,bilinear,-48.805,-43.163,+37
xcit_tiny_24_p16_384.fb_dist_in1k,33.508,66.492,52.768,47.232,12.12,384,1.000,bicubic,-49.062,-43.508,+11
vit_relpos_medium_patch16_224.sw_in1k,33.498,66.502,52.620,47.380,38.75,224,0.900,bicubic,-48.964,-43.462,+23
regnety_080.ra3_in1k,33.455,66.545,52.939,47.061,39.18,288,1.000,bicubic,-50.471,-43.951,-148
cs3edgenet_x.c2_in1k,33.455,66.545,52.925,47.075,47.82,288,1.000,bicubic,-49.253,-43.445,-18
convmixer_1536_20.in1k,33.432,66.568,53.029,46.971,51.63,224,0.960,bicubic,-47.930,-42.585,+138
sequencer2d_s.in1k,33.430,66.570,52.383,47.617,27.65,224,0.875,bicubic,-48.910,-43.645,+35
regnety_032.ra_in1k,33.412,66.588,52.770,47.230,19.44,288,1.000,bicubic,-49.314,-43.646,-24
crossvit_18_240.in1k,33.392,66.608,52.245,47.755,43.27,240,0.875,bicubic,-49.008,-43.815,+22
vit_srelpos_medium_patch16_224.sw_in1k,33.373,66.627,52.459,47.541,38.74,224,0.900,bicubic,-48.867,-43.483,+45
gernet_l.idstcv_in1k,33.371,66.629,51.911,48.089,31.08,256,0.875,bilinear,-47.983,-43.619,+135
tf_efficientnetv2_b3.in21k_ft_in1k,33.353,66.647,54.922,45.078,14.36,300,0.900,bicubic,-49.317,-41.705,-20
regnetz_c16.ra3_in1k,33.337,66.663,54.242,45.758,13.46,320,1.000,bicubic,-49.295,-42.076,-15
crossvit_15_dagger_408.in1k,33.335,66.665,52.200,47.800,28.50,408,1.000,bicubic,-50.505,-44.578,-147
tiny_vit_5m_224.dist_in22k_ft_in1k,33.306,66.694,55.028,44.972,5.39,224,0.950,bicubic,-47.570,-40.636,+180
crossvit_18_dagger_240.in1k,33.284,66.716,52.190,47.810,44.27,240,0.875,bicubic,-49.234,-43.878,+3
swin_tiny_patch4_window7_224.ms_in22k_ft_in1k,33.257,66.743,55.295,44.705,28.29,224,0.900,bicubic,-47.711,-40.719,+167
wide_resnet101_2.tv2_in1k,33.257,66.743,51.430,48.570,126.89,224,0.965,bilinear,-49.245,-44.586,+3
tresnet_xl.miil_in1k,33.251,66.749,52.298,47.702,78.44,224,0.875,bilinear,-48.823,-43.630,+56
nest_base_jx.goog_in1k,33.215,66.785,51.825,48.175,67.72,224,0.875,bicubic,-50.319,-44.549,-116
convnext_tiny.fb_in1k,33.168,66.832,52.677,47.323,28.59,288,1.000,bicubic,-49.530,-43.955,-33
resnest50d_1s4x24d.in1k,33.145,66.855,52.840,47.160,25.68,224,0.875,bicubic,-47.843,-42.485,+159
convnext_nano.in12k_ft_in1k,33.119,66.881,53.988,46.012,15.59,288,1.000,bicubic,-49.743,-42.568,-51
vit_relpos_medium_patch16_rpn_224.sw_in1k,33.092,66.908,52.363,47.637,38.73,224,0.900,bicubic,-49.218,-43.609,+23
resnet61q.ra2_in1k,33.090,66.910,51.746,48.254,36.85,288,1.000,bicubic,-49.434,-44.384,-7
maxxvit_rmlp_nano_rw_256.sw_in1k,33.074,66.926,51.852,48.148,16.78,256,0.950,bicubic,-49.968,-44.498,-67
nest_small_jx.goog_in1k,33.052,66.948,51.064,48.936,38.35,224,0.875,bicubic,-50.072,-45.256,-79
rexnet_300.nav_in1k,33.048,66.952,52.365,47.635,34.71,224,0.875,bicubic,-49.726,-43.873,-48
crossvit_base_240.in1k,33.035,66.965,51.390,48.610,105.03,240,0.875,bicubic,-49.179,-44.444,+30
poolformerv2_m36.sail_in1k,33.033,66.967,51.860,48.140,56.08,224,1.000,bicubic,-49.183,-44.064,+28
twins_pcpvt_base.in1k,33.021,66.979,52.502,47.498,43.83,224,0.900,bicubic,-49.693,-43.844,-46
pvt_v2_b2.in1k,33.017,66.983,52.001,47.999,25.36,224,0.900,bicubic,-49.067,-43.955,+42
xcit_tiny_24_p16_224.fb_dist_in1k,32.999,67.001,52.074,47.926,12.12,224,1.000,bicubic,-47.455,-43.144,+206
resnest50d.in1k,32.966,67.034,52.707,47.293,27.48,224,0.875,bilinear,-47.994,-42.675,+151
rexnet_200.nav_in1k,32.962,67.038,52.921,47.079,16.37,224,0.875,bicubic,-48.674,-42.745,+76
crossvit_15_dagger_240.in1k,32.925,67.075,51.807,48.193,28.21,240,0.875,bicubic,-49.405,-44.149,+8
convit_small.fb_in1k,32.923,67.077,52.115,47.885,27.78,224,0.875,bicubic,-48.497,-43.629,+100
tf_efficientnetv2_s.in1k,32.913,67.087,51.728,48.272,21.46,384,1.000,bicubic,-50.985,-44.968,-178
swin_base_patch4_window12_384.ms_in1k,32.905,67.095,51.750,48.250,87.90,384,1.000,bicubic,-51.571,-45.142,-237
vit_small_patch16_224.augreg_in21k_ft_in1k,32.885,67.115,53.923,46.077,22.05,224,0.900,bicubic,-48.501,-42.213,+101
convnext_tiny_hnf.a2h_in1k,32.881,67.119,51.194,48.806,28.59,288,1.000,bicubic,-49.703,-44.814,-33
tf_efficientnet_b3.aa_in1k,32.864,67.136,52.964,47.036,12.23,300,0.904,bicubic,-48.776,-42.758,+68
pnasnet5large.tf_in1k,32.862,67.138,50.516,49.484,86.06,331,0.911,bicubic,-49.920,-45.524,-65
twins_svt_base.in1k,32.832,67.168,51.565,48.435,56.07,224,0.900,bicubic,-50.288,-44.849,-95
regnetv_064.ra3_in1k,32.830,67.170,52.864,47.136,30.58,288,1.000,bicubic,-50.886,-43.878,-158
convnextv2_nano.fcmae_ft_in1k,32.815,67.185,52.656,47.344,15.62,288,1.000,bicubic,-49.671,-43.570,-23
nasnetalarge.tf_in1k,32.781,67.219,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.901,-48
gernet_m.idstcv_in1k,32.758,67.242,51.899,48.101,21.14,224,0.875,bilinear,-47.978,-43.291,+162
inception_resnet_v2.tf_in1k,32.734,67.266,50.640,49.360,55.84,299,0.897,bicubic,-47.724,-44.550,+187
resnet152d.gluon_in1k,32.730,67.270,51.080,48.920,60.21,224,0.875,bicubic,-47.746,-44.122,+183
repvit_m1_1.dist_450e_in1k,32.726,67.274,52.687,47.313,8.80,224,0.950,bicubic,-48.586,-42.849,+96
pit_b_224.in1k,32.722,67.278,49.854,50.146,73.76,224,0.900,bicubic,-49.716,-45.860,-24
tf_efficientnet_b2.ap_in1k,32.685,67.315,52.237,47.763,9.11,260,0.890,bicubic,-47.625,-42.789,+199
swin_base_patch4_window7_224.ms_in1k,32.644,67.356,51.573,48.427,87.77,224,0.900,bicubic,-50.962,-44.879,-157
fbnetv3_g.ra2_in1k,32.624,67.376,52.886,47.114,16.62,288,0.950,bilinear,-49.416,-43.174,+24
regnety_320.tv2_in1k,32.616,67.384,50.296,49.704,145.05,224,0.965,bicubic,-50.546,-46.118,-114
tresnet_l.miil_in1k,32.561,67.439,51.137,48.863,55.99,224,0.875,bilinear,-48.919,-44.487,+73
cait_xxs36_384.fb_dist_in1k,32.553,67.447,52.221,47.779,17.37,384,1.000,bicubic,-49.651,-43.923,+2
resnext101_32x8d.tv2_in1k,32.549,67.451,50.164,49.836,88.79,224,0.965,bilinear,-50.283,-46.068,-86
regnetz_c16_evos.ch_in1k,32.539,67.461,52.921,47.079,13.49,320,0.950,bicubic,-50.097,-43.553,-63
gmlp_s16_224.ra3_in1k,32.420,67.580,51.817,48.183,19.42,224,0.875,bicubic,-47.224,-42.805,+242
inception_resnet_v2.tf_ens_adv_in1k,32.368,67.632,50.429,49.571,55.84,299,0.897,bicubic,-47.609,-44.519,+211
deit_base_patch16_224.fb_in1k,32.363,67.637,50.991,49.009,86.57,224,0.900,bicubic,-49.629,-44.745,+20
maxvit_nano_rw_256.sw_in1k,32.355,67.645,50.622,49.378,15.45,256,0.950,bicubic,-50.573,-45.598,-96
swin_small_patch4_window7_224.ms_in1k,32.347,67.653,50.903,49.097,49.61,224,0.900,bicubic,-50.861,-45.413,-127
resnet152s.gluon_in1k,32.333,67.667,50.541,49.459,60.32,224,0.875,bicubic,-48.675,-44.875,+114
xcit_tiny_24_p8_224.fb_in1k,32.292,67.708,51.895,48.105,12.11,224,1.000,bicubic,-49.600,-44.075,+25
deit_small_distilled_patch16_224.fb_in1k,32.270,67.730,52.109,47.891,22.44,224,0.900,bicubic,-48.946,-43.514,+89
poolformerv2_s24.sail_in1k,32.266,67.734,51.492,48.508,21.34,224,1.000,bicubic,-48.482,-43.818,+140
repvit_m1_1.dist_300e_in1k,32.243,67.757,51.917,48.083,8.80,224,0.950,bicubic,-48.583,-43.253,+132
seresnext101_64x4d.gluon_in1k,32.192,67.808,50.313,49.687,88.23,224,0.875,bicubic,-48.702,-44.983,+121
regnetx_320.tv2_in1k,32.174,67.826,49.349,50.651,107.81,224,0.965,bicubic,-50.636,-46.859,-96
efficientvit_b2.r224_in1k,32.148,67.852,51.001,48.999,24.33,224,0.950,bicubic,-50.000,-44.705,-5
seresnext101_32x4d.gluon_in1k,32.121,67.879,51.243,48.757,48.96,224,0.875,bicubic,-48.771,-44.053,+119
coat_lite_small.in1k,32.113,67.887,49.928,50.072,19.84,224,0.900,bicubic,-50.199,-45.922,-29
flexivit_small.1200ep_in1k,32.095,67.905,50.323,49.677,22.06,240,0.950,bicubic,-50.431,-45.803,-59
tiny_vit_11m_224.in1k,32.072,67.928,51.278,48.722,11.00,224,0.950,bicubic,-49.420,-44.584,+50
coatnext_nano_rw_224.sw_in1k,32.070,67.930,51.017,48.983,14.70,224,0.900,bicubic,-49.872,-44.899,+11
maxxvitv2_nano_rw_256.sw_in1k,32.068,67.932,50.345,49.655,23.70,256,0.950,bicubic,-51.042,-45.979,-128
focalnet_tiny_lrf.ms_in1k,32.052,67.948,51.451,48.549,28.65,224,0.900,bicubic,-50.102,-44.497,-13
gcvit_xtiny.in1k,32.044,67.956,50.991,49.009,19.98,224,0.875,bicubic,-49.910,-44.975,+7
deit_base_patch16_384.fb_in1k,31.991,68.009,50.559,49.441,86.86,384,1.000,bicubic,-51.113,-45.809,-130
maxvit_rmlp_nano_rw_256.sw_in1k,31.976,68.025,50.618,49.382,15.50,256,0.950,bicubic,-50.978,-45.648,-117
xcit_tiny_12_p8_224.fb_dist_in1k,31.940,68.060,51.402,48.598,6.71,224,1.000,bicubic,-49.272,-44.200,+75
coatnet_bn_0_rw_224.sw_in1k,31.905,68.095,51.015,48.985,27.44,224,0.950,bicubic,-50.495,-45.171,-55
tf_efficientnet_b7.aa_in1k,31.877,68.123,51.909,48.091,66.35,600,0.949,bicubic,-52.539,-44.999,-271
levit_384.fb_dist_in1k,31.875,68.125,50.594,49.406,39.13,224,0.900,bicubic,-50.721,-45.424,-83
levit_conv_384.fb_dist_in1k,31.871,68.129,50.596,49.404,39.13,224,0.900,bicubic,-50.719,-45.420,-82
resnetrs101.tf_in1k,31.858,68.142,51.023,48.977,63.62,288,0.940,bicubic,-50.426,-44.991,-38
cs3se_edgenet_x.c2ns_in1k,31.808,68.192,50.769,49.231,50.72,320,1.000,bicubic,-51.738,-45.901,-187
vit_relpos_small_patch16_224.sw_in1k,31.781,68.219,50.620,49.380,21.98,224,0.900,bicubic,-49.681,-45.200,+41
convnext_tiny.fb_in22k_ft_in1k,31.667,68.333,51.785,48.215,28.59,288,1.000,bicubic,-47.231,-42.889,+268
poolformer_m48.sail_in1k,31.665,68.335,49.800,50.200,73.47,224,0.950,bicubic,-50.817,-46.165,-69
flexivit_small.600ep_in1k,31.647,68.353,49.384,50.616,22.06,240,0.950,bicubic,-50.715,-46.700,-57
tnt_s_patch16_224,31.634,68.366,51.153,48.847,23.76,224,0.900,bicubic,-49.902,-44.537,+25
eca_nfnet_l0.ra2_in1k,31.616,68.384,51.587,48.413,24.14,288,1.000,bicubic,-50.962,-44.905,-86
focalnet_tiny_srf.ms_in1k,31.606,68.394,50.864,49.136,28.43,224,0.900,bicubic,-50.532,-45.104,-27
resnetv2_50x1_bit.goog_distilled_in1k,31.581,68.419,51.267,48.733,25.55,224,0.875,bicubic,-51.243,-45.252,-124
coatnet_rmlp_nano_rw_224.sw_in1k,31.549,68.451,50.178,49.822,15.15,224,0.900,bicubic,-50.501,-45.700,-22
mobilevitv2_200.cvnets_in22k_ft_in1k,31.521,68.478,51.777,48.223,18.45,256,0.888,bicubic,-50.810,-44.165,-57
wide_resnet50_2.racm_in1k,31.521,68.478,50.388,49.612,68.88,288,0.950,bicubic,-50.758,-45.676,-49
xception41p.ra3_in1k,31.504,68.496,50.380,49.620,26.91,299,0.940,bicubic,-50.468,-45.404,-18
regnety_064.ra3_in1k,31.463,68.537,50.520,49.480,30.58,288,1.000,bicubic,-52.257,-46.202,-217
poolformer_m36.sail_in1k,31.447,68.553,50.017,49.983,56.17,224,0.950,bicubic,-50.655,-45.681,-31
flexivit_small.300ep_in1k,31.437,68.563,49.215,50.785,22.06,240,0.950,bicubic,-50.741,-46.823,-41
resnet152.a1_in1k,31.433,68.567,48.653,51.347,60.19,288,1.000,bicubic,-51.299,-47.067,-123
resnext101_32x4d.fb_ssl_yfcc100m_ft_in1k,31.429,68.571,52.127,47.873,44.18,224,0.875,bilinear,-49.495,-43.607,+82
repvit_m1_0.dist_450e_in1k,31.413,68.587,50.653,49.347,7.30,224,0.950,bicubic,-49.020,-44.265,+132
inception_v4.tf_in1k,31.386,68.614,49.235,50.765,42.68,299,0.875,bicubic,-48.770,-45.735,+159
rexnet_150.nav_in1k,31.376,68.624,51.284,48.716,9.73,224,0.875,bicubic,-48.948,-43.706,+140
efficientformer_l1.snap_dist_in1k,31.341,68.659,50.441,49.559,12.29,224,0.950,bicubic,-49.157,-44.547,+119
regnety_032.tv2_in1k,31.341,68.659,50.127,49.873,19.44,224,0.965,bicubic,-50.415,-45.717,-9
pit_s_224.in1k,31.339,68.661,49.677,50.323,23.46,224,0.900,bicubic,-49.747,-45.653,+62
resnet101.tv2_in1k,31.339,68.661,49.673,50.327,44.55,224,0.965,bilinear,-50.549,-46.095,-21
crossvit_15_240.in1k,31.319,68.681,50.182,49.818,27.53,240,0.875,bicubic,-50.217,-45.554,+6
swinv2_tiny_window16_256.ms_in1k,31.305,68.695,49.667,50.333,28.35,256,0.900,bicubic,-51.499,-46.569,-139
repvit_m1_0.dist_300e_in1k,31.274,68.726,50.801,49.199,7.30,224,0.950,bicubic,-48.852,-43.943,+152
cspresnet50.ra_in1k,31.272,68.728,51.225,48.775,21.62,256,0.887,bilinear,-48.310,-43.485,+188
cait_xxs36_224.fb_dist_in1k,31.272,68.728,50.614,49.386,17.30,224,1.000,bicubic,-48.474,-44.260,+174
crossvit_small_240.in1k,31.270,68.730,50.192,49.808,26.86,240,0.875,bicubic,-49.748,-45.264,+59
vit_srelpos_small_patch16_224.sw_in1k,31.260,68.740,50.233,49.767,21.97,224,0.900,bicubic,-49.832,-45.337,+52
swinv2_cr_small_224.sw_in1k,31.254,68.746,48.745,51.255,49.70,224,0.900,bicubic,-51.882,-47.363,-177
coatnet_0_rw_224.sw_in1k,31.252,68.748,48.633,51.367,27.44,224,0.950,bicubic,-51.138,-47.203,-91
swin_s3_tiny_224.ms_in1k,31.248,68.752,49.728,50.272,28.33,224,0.900,bicubic,-50.896,-46.226,-55
repvit_m2.dist_in1k,31.239,68.761,50.626,49.374,8.80,224,0.950,bicubic,-49.221,-44.542,+110
cspresnext50.ra_in1k,31.227,68.773,50.871,49.129,20.57,256,0.887,bilinear,-49.327,-44.454,+98
regnetv_040.ra3_in1k,31.225,68.775,50.115,49.885,20.64,288,1.000,bicubic,-51.965,-46.543,-188
convmixer_768_32.in1k,31.219,68.781,50.928,49.072,21.11,224,0.960,bicubic,-48.949,-44.145,+138
coat_mini.in1k,31.191,68.809,49.761,50.239,10.34,224,0.900,bicubic,-50.079,-45.621,+23
xcit_tiny_12_p8_384.fb_dist_in1k,31.182,68.818,50.508,49.492,6.71,384,1.000,bicubic,-51.206,-45.712,-97
fastvit_sa12.apple_dist_in1k,31.138,68.862,49.958,50.042,11.58,256,0.900,bicubic,-50.716,-45.752,-36
resnet101s.gluon_in1k,31.107,68.893,49.791,50.209,44.67,224,0.875,bicubic,-49.197,-45.361,+121
edgenext_small.usi_in1k,31.101,68.899,50.135,49.865,5.59,320,1.000,bicubic,-50.463,-45.577,-16
coatnet_nano_rw_224.sw_in1k,31.099,68.901,49.581,50.419,15.14,224,0.900,bicubic,-50.597,-46.066,-29
tf_efficientnet_cc_b0_8e.in1k,31.091,68.909,50.781,49.219,24.01,224,0.875,bicubic,-46.813,-42.881,+302
resmlp_36_224.fb_distilled_in1k,31.062,68.938,49.696,50.304,44.69,224,0.875,bicubic,-50.086,-45.782,+29
ecaresnet50t.ra2_in1k,31.050,68.950,50.573,49.427,25.57,320,0.950,bicubic,-51.302,-45.567,-98
repvit_m0_9.dist_300e_in1k,31.046,68.954,50.681,49.319,5.49,224,0.950,bicubic,-47.612,-43.435,+244
resnet152c.gluon_in1k,31.018,68.981,48.936,51.064,60.21,224,0.875,bicubic,-48.894,-45.910,+139
cs3sedarknet_x.c2ns_in1k,31.017,68.984,50.131,49.869,35.40,288,1.000,bicubic,-51.642,-46.219,-146
cspdarknet53.ra_in1k,31.001,68.999,50.412,49.588,27.64,256,0.887,bilinear,-49.067,-44.666,+130
resnext101_64x4d.gluon_in1k,30.993,69.007,48.549,51.451,83.46,224,0.875,bicubic,-49.607,-46.443,+81
tresnet_m.miil_in1k,30.987,69.013,48.686,51.314,31.39,224,0.875,bilinear,-49.811,-46.170,+61
twins_svt_small.in1k,30.983,69.017,49.219,50.781,24.06,224,0.900,bicubic,-50.693,-46.439,-38
regnety_160.tv2_in1k,30.942,69.058,49.060,50.940,83.59,224,0.965,bicubic,-51.704,-47.154,-150
gcresnet50t.ra2_in1k,30.936,69.064,50.034,49.966,25.90,288,1.000,bicubic,-50.520,-45.684,-13
tf_efficientnet_cc_b1_8e.in1k,30.903,69.097,50.078,49.922,39.72,240,0.882,bicubic,-48.399,-44.296,+183
resmlp_24_224.fb_distilled_in1k,30.901,69.099,50.174,49.826,30.02,224,0.875,bicubic,-49.855,-45.050,+60
regnety_080_tv.tv2_in1k,30.891,69.109,48.724,51.276,39.38,224,0.965,bicubic,-51.703,-47.524,-144
resnext101_32x4d.gluon_in1k,30.891,69.109,48.539,51.461,44.18,224,0.875,bicubic,-49.449,-46.391,+98
tf_efficientnetv2_b3.in1k,30.853,69.147,49.808,50.192,14.36,300,0.904,bicubic,-51.119,-45.994,-66
repvit_m0_9.dist_450e_in1k,30.846,69.154,50.119,49.881,5.49,224,0.950,bicubic,-48.220,-44.261,+200
tf_efficientnet_lite4.in1k,30.830,69.170,50.390,49.610,13.01,380,0.920,bilinear,-50.700,-45.274,-31
resnetaa50d.sw_in12k_ft_in1k,30.802,69.198,50.569,49.431,25.58,288,1.000,bicubic,-51.798,-45.929,-151
efficientvit_b1.r288_in1k,30.791,69.210,50.009,49.991,9.10,288,1.000,bicubic,-49.533,-45.167,+96
nf_resnet50.ra2_in1k,30.698,69.302,49.944,50.056,25.56,288,0.940,bicubic,-49.942,-45.390,+63
poolformer_s36.sail_in1k,30.690,69.310,49.445,50.555,30.86,224,0.900,bicubic,-50.740,-45.999,-22
xcit_tiny_24_p16_224.fb_in1k,30.671,69.329,50.416,49.584,12.12,224,1.000,bicubic,-48.777,-44.462,+158
resnet50.a1h_in1k,30.631,69.369,49.415,50.585,25.56,224,1.000,bicubic,-50.047,-45.891,+56
dpn107.mx_in1k,30.625,69.374,48.739,51.261,86.92,224,0.875,bicubic,-49.544,-46.203,+105
tresnet_xl.miil_in1k_448,30.622,69.379,49.077,50.923,78.44,448,0.875,bilinear,-52.437,-47.095,-204
resnet152.gluon_in1k,30.614,69.386,48.515,51.485,60.19,224,0.875,bicubic,-49.082,-46.215,+135
resnext50_32x4d.fb_ssl_yfcc100m_ft_in1k,30.602,69.398,50.667,49.333,25.03,224,0.875,bilinear,-49.732,-44.733,+86
haloregnetz_b.ra3_in1k,30.594,69.406,48.987,51.013,11.68,224,0.940,bicubic,-50.452,-46.213,+13
pit_xs_distilled_224.in1k,30.543,69.457,50.180,49.820,11.00,224,0.900,bicubic,-48.637,-44.186,+177
regnetx_080.tv2_in1k,30.521,69.479,48.030,51.970,39.57,224,0.965,bicubic,-51.019,-47.512,-47
resnet101d.gluon_in1k,30.506,69.494,47.973,52.027,44.57,224,0.875,bicubic,-49.920,-47.051,+74
resnest26d.gluon_in1k,30.490,69.510,50.663,49.337,17.07,224,0.875,bilinear,-47.992,-43.631,+223
mobilevitv2_200.cvnets_in22k_ft_in1k_384,30.490,69.510,50.575,49.425,18.45,384,1.000,bicubic,-52.910,-46.007,-249
resnet50.ram_in1k,30.451,69.549,48.997,51.003,25.56,288,0.950,bicubic,-49.525,-46.055,+104
efficientformerv2_s1.snap_dist_in1k,30.445,69.555,49.582,50.418,6.19,224,0.950,bicubic,-49.247,-45.134,+127
efficientnet_b2.ra_in1k,30.427,69.573,49.688,50.312,9.11,288,1.000,bicubic,-50.183,-45.625,+49
tf_efficientnet_b1.ap_in1k,30.423,69.577,49.555,50.445,7.79,240,0.882,bicubic,-48.853,-44.757,+158
cs3darknet_x.c2ns_in1k,30.409,69.591,49.197,50.803,35.05,288,1.000,bicubic,-51.813,-47.033,-117
xcit_tiny_12_p16_384.fb_dist_in1k,30.403,69.597,50.131,49.869,6.72,384,1.000,bicubic,-50.535,-45.283,+12
twins_pcpvt_small.in1k,30.396,69.604,49.384,50.616,24.11,224,0.900,bicubic,-50.696,-46.264,-4
ecaresnetlight.miil_in1k,30.382,69.618,49.146,50.854,30.16,288,0.950,bicubic,-51.026,-46.670,-39
resnet50d.ra2_in1k,30.376,69.624,48.794,51.206,25.58,288,0.950,bicubic,-50.980,-46.944,-33
ecaresnet101d_pruned.miil_in1k,30.356,69.644,48.810,51.190,24.88,288,0.950,bicubic,-51.642,-47.350,-97
visformer_small.in1k,30.339,69.661,48.285,51.715,40.22,224,0.900,bicubic,-51.767,-47.593,-108
resnet101.a1_in1k,30.297,69.703,46.584,53.416,44.55,288,1.000,bicubic,-52.025,-49.048,-136
tf_efficientnet_b5.in1k,30.295,69.705,49.753,50.247,30.39,456,0.934,bicubic,-52.881,-46.783,-241
regnety_040.ra3_in1k,30.266,69.734,48.930,51.070,20.65,288,1.000,bicubic,-52.778,-47.572,-225
regnetx_160.tv2_in1k,30.217,69.783,47.055,52.945,54.28,224,0.965,bicubic,-52.349,-49.117,-169
mobilevitv2_175.cvnets_in22k_ft_in1k,30.209,69.791,49.056,50.944,14.25,256,0.888,bicubic,-51.729,-46.734,-95
vit_relpos_base_patch32_plus_rpn_256.sw_in1k,30.201,69.799,48.688,51.312,119.42,256,0.900,bicubic,-49.283,-45.450,+127
resnet50.b1k_in1k,30.197,69.803,49.187,50.813,25.56,288,1.000,bicubic,-50.509,-46.245,+26
fastvit_s12.apple_dist_in1k,30.187,69.813,48.962,51.038,9.47,256,0.900,bicubic,-50.883,-46.323,-12
seresnext50_32x4d.racm_in1k,30.168,69.832,49.093,50.907,27.56,288,0.950,bicubic,-52.028,-47.055,-127
resnet50.b2k_in1k,30.152,69.848,48.244,51.756,25.56,288,1.000,bicubic,-50.302,-47.074,+48
regnety_016.tv2_in1k,30.138,69.862,49.231,50.769,11.20,224,0.965,bicubic,-50.528,-46.099,+26
wide_resnet50_2.tv2_in1k,30.116,69.883,48.362,51.638,68.88,224,0.965,bilinear,-51.489,-47.398,-78
dpn68b.ra_in1k,30.109,69.891,48.164,51.836,12.61,288,1.000,bicubic,-49.251,-46.272,+130
convmixer_1024_20_ks9_p14.in1k,30.093,69.907,49.934,50.066,24.38,224,0.960,bicubic,-46.843,-43.416,+294
efficientnet_el.ra_in1k,30.028,69.972,48.849,51.151,10.59,300,0.904,bicubic,-51.284,-46.640,-47
tf_efficientnet_b2.aa_in1k,30.012,69.988,49.590,50.410,9.11,260,0.890,bicubic,-50.072,-45.316,+74
xcit_tiny_12_p16_224.fb_dist_in1k,30.010,69.990,49.651,50.349,6.72,224,1.000,bicubic,-48.564,-44.547,+186
legacy_senet154.in1k,29.993,70.007,48.038,51.962,115.09,224,0.875,bilinear,-51.319,-47.522,-49
halo2botnet50ts_256.a1h_in1k,29.987,70.013,48.380,51.620,22.64,256,0.950,bicubic,-52.073,-47.254,-123
dpn98.mx_in1k,29.981,70.019,48.146,51.854,61.57,224,0.875,bicubic,-49.689,-46.508,+102
mobilevitv2_150.cvnets_in22k_ft_in1k,29.971,70.029,49.217,50.783,10.59,256,0.888,bicubic,-51.517,-46.451,-73
resnetv2_50d_gn.ah_in1k,29.957,70.043,48.195,51.805,25.57,288,1.000,bicubic,-52.001,-47.733,-115
dpn92.mx_in1k,29.946,70.054,49.113,50.887,37.67,224,0.875,bicubic,-50.092,-45.747,+69
resnext50_32x4d.a1h_in1k,29.942,70.058,48.234,51.766,25.03,288,1.000,bicubic,-52.072,-47.700,-124
convnextv2_pico.fcmae_ft_in1k,29.940,70.060,48.853,51.147,9.07,288,0.950,bicubic,-51.146,-46.627,-31
dpn131.mx_in1k,29.934,70.066,48.034,51.966,79.25,224,0.875,bicubic,-49.880,-46.666,+82
resnetv2_101x1_bit.goog_in21k_ft_in1k,29.898,70.102,51.109,48.891,44.54,448,1.000,bilinear,-52.444,-45.411,-166
senet154.gluon_in1k,29.887,70.113,47.879,52.121,115.09,224,0.875,bicubic,-51.339,-47.479,-54
tf_efficientnet_b4.in1k,29.861,70.139,49.014,50.986,19.34,380,0.922,bicubic,-52.747,-46.737,-208
legacy_xception.tf_in1k,29.855,70.145,48.684,51.316,22.86,299,0.897,bicubic,-49.185,-45.698,+143
resnet50.tv2_in1k,29.837,70.162,48.012,51.988,25.56,224,0.965,bilinear,-51.011,-47.422,-11
cs3sedarknet_l.c2ns_in1k,29.832,70.168,49.009,50.991,21.91,288,0.950,bicubic,-51.952,-46.955,-110
resnet152.a2_in1k,29.826,70.174,45.961,54.039,60.19,288,1.000,bicubic,-52.782,-50.167,-211
efficientvit_b1.r224_in1k,29.822,70.178,48.289,51.711,9.10,224,0.950,bicubic,-49.430,-46.015,+122
inception_v3.tf_adv_in1k,29.818,70.182,47.843,52.157,23.83,299,0.875,bicubic,-47.774,-45.887,+236
vit_base_patch16_384.augreg_in1k,29.792,70.208,48.327,51.673,86.86,384,1.000,bicubic,-51.310,-46.793,-46
resnetaa50.a1h_in1k,29.790,70.210,48.014,51.986,25.56,288,1.000,bicubic,-51.824,-47.788,-105
lamhalobotnet50ts_256.a1h_in1k,29.755,70.245,48.335,51.665,22.57,256,0.950,bicubic,-51.797,-47.157,-100
fbnetv3_d.ra2_in1k,29.739,70.261,49.472,50.528,10.31,256,0.950,bilinear,-49.943,-45.472,+81
ese_vovnet39b.ra_in1k,29.733,70.267,49.046,50.954,24.57,288,0.950,bicubic,-50.617,-46.320,+26
fastvit_sa12.apple_in1k,29.731,70.269,48.610,51.390,11.58,256,0.900,bicubic,-51.113,-46.730,-20
resmlp_36_224.fb_in1k,29.698,70.302,48.958,51.042,44.69,224,0.875,bicubic,-50.074,-45.926,+69
fastvit_t12.apple_dist_in1k,29.686,70.314,48.517,51.483,7.55,256,0.900,bicubic,-50.666,-46.525,+22
convnext_nano.d1h_in1k,29.684,70.316,47.920,52.080,15.59,288,1.000,bicubic,-51.798,-47.738,-95
resnet50.c1_in1k,29.672,70.328,48.458,51.542,25.56,288,1.000,bicubic,-51.240,-47.094,-35
vit_base_patch32_384.augreg_in1k,29.645,70.355,48.987,51.013,88.30,384,1.000,bicubic,-49.111,-45.239,+146
efficientvit_b1.r256_in1k,29.621,70.379,48.209,51.791,9.10,256,1.000,bicubic,-50.113,-46.571,+66
ecaresnet50d.miil_in1k,29.562,70.438,48.967,51.033,25.58,288,0.950,bicubic,-52.088,-46.915,-119
nest_tiny_jx.goog_in1k,29.558,70.442,46.983,53.017,17.06,224,0.875,bicubic,-51.868,-48.635,-93
resnet152.a3_in1k,29.525,70.475,47.038,52.962,60.19,224,0.950,bicubic,-51.021,-47.962,-5
gcresnext50ts.ch_in1k,29.517,70.483,47.851,52.149,15.67,288,1.000,bicubic,-51.713,-47.691,-78
resnext50_32x4d.tv2_in1k,29.480,70.520,47.232,52.768,25.03,224,0.965,bilinear,-51.702,-48.108,-72
cs3darknet_l.c2ns_in1k,29.466,70.534,48.234,51.766,21.16,288,0.950,bicubic,-51.430,-47.428,-42
efficientnet_em.ra2_in1k,29.464,70.536,48.922,51.078,6.90,240,0.882,bicubic,-49.780,-45.872,+103
resnext101_32x8d.tv_in1k,29.437,70.563,48.488,51.512,88.79,224,0.875,bilinear,-49.873,-46.032,+92
resnet50.fb_ssl_yfcc100m_ft_in1k,29.435,70.565,49.791,50.209,25.56,224,0.875,bilinear,-49.795,-45.035,+101
coat_lite_mini.in1k,29.435,70.565,47.718,52.282,11.01,224,0.900,bicubic,-49.667,-46.890,+111
resnetv2_50.a1h_in1k,29.435,70.565,47.441,52.559,25.55,288,1.000,bicubic,-51.963,-48.285,-99
deit_small_patch16_224.fb_in1k,29.425,70.575,48.250,51.750,22.05,224,0.900,bicubic,-50.423,-46.794,+45
sebotnet33ts_256.a1h_in1k,29.423,70.577,47.160,52.840,13.70,256,0.940,bicubic,-51.745,-48.009,-78
nf_regnet_b1.ra2_in1k,29.411,70.589,49.427,50.573,10.22,288,0.900,bicubic,-49.897,-45.313,+87
repvit_m1.dist_in1k,29.407,70.593,48.541,51.459,5.49,224,0.950,bicubic,-49.131,-45.529,+144
mobileone_s4.apple_in1k,29.405,70.595,47.967,52.033,14.95,224,0.900,bilinear,-50.021,-46.513,+76
cait_xxs24_384.fb_dist_in1k,29.391,70.609,48.751,51.249,12.03,384,1.000,bicubic,-51.581,-46.889,-62
resnetv2_50d_evos.ah_in1k,29.382,70.618,47.226,52.774,25.59,288,1.000,bicubic,-52.620,-48.674,-164
edgenext_small_rw.sw_in1k,29.346,70.654,48.722,51.278,7.83,320,1.000,bicubic,-51.112,-46.586,-9
convnext_nano_ols.d1h_in1k,29.325,70.675,47.478,52.522,15.65,288,1.000,bicubic,-52.275,-48.158,-132
regnetz_b16.ra3_in1k,29.321,70.679,47.885,52.115,9.72,288,1.000,bicubic,-51.407,-47.633,-37
swin_tiny_patch4_window7_224.ms_in1k,29.321,70.679,47.609,52.391,28.29,224,0.900,bicubic,-52.055,-47.934,-107
cait_xxs24_224.fb_dist_in1k,29.295,70.705,48.537,51.463,11.96,224,1.000,bicubic,-49.089,-45.779,+153
eca_resnet33ts.ra2_in1k,29.277,70.723,48.928,51.072,19.68,288,1.000,bicubic,-51.395,-46.436,-35
resnet50.d_in1k,29.250,70.750,47.213,52.787,25.56,288,1.000,bicubic,-51.722,-48.217,-69
resnet50.c2_in1k,29.244,70.756,47.165,52.835,25.56,288,1.000,bicubic,-51.626,-48.369,-56
pvt_v2_b1.in1k,29.242,70.758,48.962,51.038,14.01,224,0.900,bicubic,-49.462,-45.540,+124
maxvit_rmlp_pico_rw_256.sw_in1k,29.238,70.762,47.719,52.281,7.52,256,0.950,bicubic,-51.276,-47.495,-28
gcvit_xxtiny.in1k,29.218,70.781,48.348,51.652,12.00,224,0.875,bicubic,-50.508,-46.732,+38
poolformer_s24.sail_in1k,29.205,70.795,48.079,51.921,21.39,224,0.900,bicubic,-51.089,-46.995,-1
tresnet_l.miil_in1k_448,29.162,70.838,47.224,52.776,55.99,448,0.875,bilinear,-53.114,-48.754,-205
seresnet50.ra2_in1k,29.146,70.854,47.749,52.251,28.09,288,0.950,bicubic,-52.138,-47.903,-108
inception_v3.gluon_in1k,29.112,70.888,46.943,53.057,23.83,299,0.875,bicubic,-49.690,-47.433,+108
lambda_resnet50ts.a1h_in1k,29.108,70.891,46.961,53.039,21.54,256,0.950,bicubic,-52.050,-48.137,-97
resnet101.a2_in1k,29.091,70.909,45.762,54.238,44.55,288,1.000,bicubic,-53.145,-49.968,-206
xception71.tf_in1k,29.030,70.970,47.391,52.609,42.34,299,0.903,bicubic,-50.844,-47.537,+17
resnet34d.ra2_in1k,29.020,70.980,48.052,51.948,21.82,288,0.950,bicubic,-49.416,-46.292,+133
hrnet_w64.ms_in1k,28.987,71.013,47.138,52.862,128.06,224,0.875,bilinear,-50.489,-47.514,+49
xcit_tiny_12_p8_224.fb_in1k,28.979,71.021,47.501,52.499,6.71,224,1.000,bicubic,-50.709,-47.553,+33
regnetx_032.tv2_in1k,28.975,71.025,47.069,52.931,15.30,224,0.965,bicubic,-51.951,-48.209,-79
cs3darknet_focus_l.c2ns_in1k,28.939,71.061,47.639,52.361,21.15,288,0.950,bicubic,-51.937,-48.043,-74
tf_efficientnet_b0.ns_jft_in1k,28.902,71.098,49.007,50.993,5.29,224,0.875,bicubic,-49.766,-45.365,+110
xception65.tf_in1k,28.894,71.106,47.154,52.846,39.92,299,0.903,bicubic,-50.662,-47.504,+38
tf_efficientnet_b1.aa_in1k,28.886,71.114,47.523,52.477,7.79,240,0.882,bicubic,-49.942,-46.677,+94
vit_small_patch32_384.augreg_in21k_ft_in1k,28.875,71.125,48.889,51.111,22.92,384,1.000,bicubic,-51.611,-46.711,-41
mobilevitv2_150.cvnets_in22k_ft_in1k_384,28.871,71.129,47.932,52.068,10.59,384,1.000,bicubic,-53.715,-48.382,-266
resnet101.gluon_in1k,28.853,71.147,46.381,53.619,44.55,224,0.875,bicubic,-50.457,-48.141,+52
skresnext50_32x4d.ra_in1k,28.810,71.190,46.507,53.493,27.48,224,0.875,bicubic,-51.354,-48.133,-8
sehalonet33ts.ra2_in1k,28.780,71.220,46.574,53.426,13.69,256,0.940,bicubic,-52.178,-48.698,-90
levit_256.fb_dist_in1k,28.743,71.257,46.729,53.271,18.89,224,0.900,bicubic,-52.781,-48.765,-154
levit_conv_256.fb_dist_in1k,28.739,71.261,46.723,53.277,18.89,224,0.900,bicubic,-52.783,-48.767,-154
resnet50.ra_in1k,28.694,71.306,47.366,52.634,25.56,288,0.950,bicubic,-51.142,-47.600,+8
mobileone_s3.apple_in1k,28.676,71.324,47.582,52.418,10.17,224,0.900,bilinear,-49.316,-46.332,+151
resnetblur50.bt_in1k,28.662,71.338,46.908,53.092,25.56,288,0.950,bicubic,-51.572,-48.326,-20
tf_efficientnet_lite3.in1k,28.651,71.349,47.360,52.640,8.20,300,0.904,bilinear,-51.155,-47.554,+8
darknetaa53.c2ns_in1k,28.647,71.353,46.949,53.051,36.02,288,1.000,bilinear,-51.859,-48.373,-55
hrnet_w40.ms_in1k,28.645,71.355,47.452,52.548,57.56,224,0.875,bilinear,-50.287,-47.012,+74
skresnet34.ra_in1k,28.631,71.369,47.961,52.039,22.28,224,0.875,bicubic,-48.279,-45.183,+205
swinv2_tiny_window8_256.ms_in1k,28.627,71.373,46.189,53.811,28.35,256,0.900,bicubic,-53.193,-49.805,-189
seresnext50_32x4d.gluon_in1k,28.619,71.381,46.438,53.562,27.56,224,0.875,bicubic,-51.305,-48.386,-11
mobilevitv2_175.cvnets_in22k_ft_in1k_384,28.598,71.403,47.118,52.882,14.25,384,1.000,bicubic,-54.341,-49.308,-321
tf_efficientnet_b3.in1k,28.582,71.418,47.981,52.019,12.23,300,0.904,bicubic,-52.292,-47.319,-93
halonet50ts.a1h_in1k,28.580,71.420,46.183,53.817,22.73,256,0.940,bicubic,-53.082,-49.427,-183
tf_efficientnetv2_b0.in1k,28.570,71.430,47.077,52.923,7.14,224,0.875,bicubic,-49.788,-46.937,+114
poolformerv2_s12.sail_in1k,28.556,71.444,47.399,52.601,11.89,224,1.000,bicubic,-49.446,-46.465,+137
resnet152.tv_in1k,28.543,71.457,47.106,52.894,60.19,224,0.875,bilinear,-49.779,-46.940,+114
xcit_tiny_12_p16_224.fb_in1k,28.515,71.485,47.403,52.597,6.72,224,1.000,bicubic,-48.625,-46.313,+184
eva02_tiny_patch14_336.mim_in22k_ft_in1k,28.507,71.493,47.539,52.461,5.76,336,1.000,bicubic,-52.123,-47.987,-75
ecaresnet50t.a1_in1k,28.456,71.544,45.576,54.424,25.57,288,1.000,bicubic,-53.672,-50.066,-225
repvgg_b2.rvgg_in1k,28.421,71.579,47.044,52.956,89.02,224,0.875,bilinear,-50.371,-47.376,+73
hrnet_w48.ms_in1k,28.409,71.591,47.576,52.424,77.47,224,0.875,bilinear,-50.897,-46.940,+31
swinv2_cr_tiny_ns_224.sw_in1k,28.379,71.621,45.902,54.098,28.33,224,0.900,bicubic,-53.423,-49.916,-199
resnext50_32x4d.gluon_in1k,28.372,71.628,45.332,54.668,25.03,224,0.875,bicubic,-50.988,-49.098,+24
efficientnet_b2_pruned.in1k,28.360,71.640,47.057,52.943,8.31,260,0.890,bicubic,-51.560,-47.795,-24
tf_efficientnet_b0.ap_in1k,28.354,71.646,47.535,52.465,5.29,224,0.875,bicubic,-48.736,-45.727,+178
darknet53.c2ns_in1k,28.328,71.672,46.880,53.120,41.61,288,1.000,bicubic,-52.204,-48.552,-77
dla169.in1k,28.320,71.680,47.388,52.612,53.39,224,0.875,bilinear,-50.388,-46.956,+73
tf_efficientnet_cc_b0_4e.in1k,28.313,71.687,47.360,52.640,13.31,224,0.875,bicubic,-48.989,-45.976,+162
dla102x2.in1k,28.313,71.687,46.774,53.226,41.28,224,0.875,bilinear,-51.133,-47.858,+12
resnext50_32x4d.ra_in1k,28.303,71.697,46.081,53.919,25.03,288,0.950,bicubic,-52.395,-49.311,-93
mixnet_xl.ra_in1k,28.291,71.709,46.708,53.292,11.90,224,0.875,bicubic,-52.191,-48.229,-76
seresnet33ts.ra2_in1k,28.236,71.764,47.578,52.422,19.78,288,1.000,bicubic,-52.548,-47.784,-103
resnet50d.gluon_in1k,28.234,71.766,45.880,54.120,25.58,224,0.875,bicubic,-50.844,-48.586,+39
resnet50.a1_in1k,28.220,71.780,44.937,55.063,25.56,288,1.000,bicubic,-52.994,-50.165,-153
fastvit_s12.apple_in1k,28.126,71.874,46.649,53.351,9.47,256,0.900,bicubic,-51.816,-48.145,-37
densenet161.tv_in1k,28.108,71.892,46.653,53.347,28.68,224,0.875,bicubic,-49.250,-46.989,+151
resnet101c.gluon_in1k,28.104,71.896,45.953,54.047,44.57,224,0.875,bicubic,-51.434,-48.631,-4
resnet34.a1_in1k,28.100,71.900,45.707,54.293,21.80,288,1.000,bicubic,-49.818,-48.057,+121
wide_resnet101_2.tv_in1k,28.095,71.906,46.426,53.574,126.89,224,0.875,bilinear,-50.748,-47.855,+48
regnetx_320.pycls_in1k,28.079,71.921,45.118,54.882,107.81,224,0.875,bicubic,-52.167,-49.904,-58
regnety_320.pycls_in1k,28.075,71.925,45.460,54.540,145.05,224,0.875,bicubic,-52.735,-49.778,-115
resnext50_32x4d.a1_in1k,28.075,71.925,44.815,55.185,25.03,288,1.000,bicubic,-53.391,-50.359,-188
gernet_s.idstcv_in1k,28.051,71.949,46.727,53.273,8.17,224,0.875,bilinear,-48.859,-46.589,+171
ecaresnet50d_pruned.miil_in1k,28.043,71.957,47.038,52.962,19.94,288,0.950,bicubic,-52.747,-48.532,-116
levit_conv_192.fb_dist_in1k,28.032,71.968,45.874,54.126,10.95,224,0.900,bicubic,-51.806,-48.904,-37
mobilevitv2_175.cvnets_in1k,28.028,71.972,46.098,53.902,14.25,256,0.888,bicubic,-52.832,-49.158,-126
levit_192.fb_dist_in1k,28.028,71.972,45.870,54.130,10.95,224,0.900,bicubic,-51.810,-48.914,-37
efficientnet_el_pruned.in1k,28.004,71.996,46.804,53.196,10.59,300,0.904,bicubic,-52.294,-48.418,-71
vit_base_patch16_224.augreg_in1k,27.971,72.029,45.737,54.263,86.57,224,0.900,bicubic,-51.183,-48.353,+18
fastvit_t12.apple_in1k,27.935,72.065,46.393,53.607,7.55,256,0.900,bicubic,-51.329,-48.169,+7
resnet101.a3_in1k,27.925,72.075,45.014,54.986,44.55,224,0.950,bicubic,-51.888,-49.600,-39
resnet50_gn.a1h_in1k,27.916,72.084,46.075,53.925,25.56,288,0.950,bicubic,-53.300,-49.309,-173
xception41.tf_in1k,27.880,72.120,45.888,54.112,26.97,299,0.903,bicubic,-50.624,-48.388,+58
regnetx_160.pycls_in1k,27.827,72.173,45.631,54.369,54.28,224,0.875,bicubic,-52.039,-49.197,-49
dpn68b.mx_in1k,27.814,72.186,47.415,52.585,12.61,224,0.875,bicubic,-49.704,-46.437,+123
resnet50d.a1_in1k,27.790,72.210,44.377,55.623,25.58,288,1.000,bicubic,-53.660,-50.841,-199
inception_v3.tf_in1k,27.780,72.220,45.727,54.273,23.83,299,0.875,bicubic,-50.076,-48.139,+106
res2net101_26w_4s.in1k,27.778,72.222,45.159,54.841,45.21,224,0.875,bilinear,-51.422,-49.277,+5
tf_efficientnetv2_b1.in1k,27.745,72.255,46.580,53.420,8.14,240,0.882,bicubic,-51.715,-48.142,-21
repghostnet_200.in1k,27.719,72.281,46.322,53.678,9.80,224,0.875,bicubic,-51.087,-48.008,+30
vit_base_patch16_224.sam_in1k,27.703,72.297,45.100,54.900,86.57,224,0.900,bicubic,-52.535,-49.656,-78
fbnetv3_b.ra2_in1k,27.670,72.330,46.987,53.013,8.60,256,0.950,bilinear,-51.476,-47.757,+6
regnety_160.pycls_in1k,27.641,72.359,45.531,54.469,83.59,224,0.875,bicubic,-52.657,-49.433,-85
mobilevitv2_200.cvnets_in1k,27.637,72.363,45.784,54.216,18.45,256,0.888,bicubic,-53.497,-49.578,-175
repvgg_b1.rvgg_in1k,27.631,72.369,46.533,53.467,57.42,224,0.875,bilinear,-50.737,-47.563,+62
hrnet_w44.ms_in1k,27.627,72.373,45.837,54.163,67.06,224,0.875,bilinear,-51.267,-48.527,+18
resnet50.am_in1k,27.574,72.426,45.369,54.631,25.56,224,0.875,bicubic,-51.428,-49.029,+10
inception_v3.tv_in1k,27.556,72.444,45.265,54.735,23.83,299,0.875,bicubic,-49.878,-48.209,+116
resmlp_24_224.fb_in1k,27.517,72.483,45.690,54.310,30.02,224,0.875,bicubic,-51.857,-48.856,-24
pit_xs_224.in1k,27.485,72.515,45.910,54.090,10.62,224,0.900,bicubic,-50.691,-48.252,+69
tiny_vit_5m_224.in1k,27.483,72.517,45.855,54.145,5.39,224,0.950,bicubic,-51.687,-48.939,-5
gcresnet33ts.ra2_in1k,27.401,72.599,46.155,53.845,19.88,288,1.000,bicubic,-53.199,-49.167,-127
regnetx_080.pycls_in1k,27.397,72.603,45.012,54.988,39.57,224,0.875,bicubic,-51.801,-49.542,-9
hrnet_w30.ms_in1k,27.389,72.611,46.554,53.446,37.71,224,0.875,bilinear,-50.807,-47.668,+63
hrnet_w32.ms_in1k,27.381,72.619,46.006,53.994,41.23,224,0.875,bilinear,-51.061,-48.184,+43
convnext_pico.d1_in1k,27.358,72.642,45.660,54.340,9.05,288,0.950,bicubic,-53.058,-49.388,-111
vit_small_patch16_384.augreg_in1k,27.328,72.672,46.118,53.882,22.20,384,1.000,bicubic,-53.788,-49.456,-186
resnet50s.gluon_in1k,27.324,72.676,45.214,54.786,25.68,224,0.875,bicubic,-51.390,-49.028,+21
res2net50_26w_8s.in1k,27.306,72.694,44.815,55.185,48.40,224,0.875,bilinear,-51.635,-49.479,+1
convnext_pico_ols.d1_in1k,27.297,72.703,45.644,54.356,9.06,288,1.000,bicubic,-53.165,-49.608,-123
densenet201.tv_in1k,27.271,72.729,46.210,53.790,20.01,224,0.875,bicubic,-50.015,-47.270,+111
resnet33ts.ra2_in1k,27.257,72.743,45.183,54.817,19.68,288,1.000,bicubic,-52.469,-49.645,-64
ecaresnet50t.a2_in1k,27.246,72.754,44.047,55.953,25.57,288,1.000,bicubic,-54.412,-51.503,-252
regnety_064.pycls_in1k,27.238,72.762,44.866,55.134,30.58,224,0.875,bicubic,-52.478,-49.900,-65
ghostnetv2_160.in1k,27.232,72.768,46.366,53.634,12.39,224,0.875,bicubic,-50.600,-47.574,+79
efficientnet_b1_pruned.in1k,27.196,72.804,45.861,54.139,6.33,240,0.882,bicubic,-51.044,-47.973,+49
tf_efficientnetv2_b2.in1k,27.163,72.837,44.568,55.432,10.10,260,0.890,bicubic,-53.033,-50.474,-100
vit_base_patch32_224.augreg_in1k,27.140,72.861,45.175,54.825,88.22,224,0.900,bicubic,-47.755,-46.603,+178
seresnet50.a1_in1k,27.124,72.876,43.563,56.437,28.09,288,1.000,bicubic,-53.978,-51.765,-195
resnet50d.a2_in1k,27.120,72.880,43.811,56.189,25.58,288,1.000,bicubic,-54.044,-51.269,-204
resnetrs50.tf_in1k,27.098,72.902,45.027,54.973,35.69,224,0.910,bicubic,-52.796,-49.947,-89
rexnet_130.nav_in1k,27.081,72.919,45.957,54.043,7.56,224,0.875,bicubic,-52.425,-48.721,-58
gmixer_24_224.ra3_in1k,27.031,72.969,44.353,55.647,24.72,224,0.875,bicubic,-50.995,-49.315,+56
dla102x.in1k,27.022,72.978,45.505,54.495,26.31,224,0.875,bilinear,-51.490,-48.731,+16
resnet101.tv_in1k,26.963,73.037,45.234,54.766,44.55,224,0.875,bilinear,-50.417,-48.312,+91
regnetx_120.pycls_in1k,26.866,73.134,44.688,55.312,46.11,224,0.875,bicubic,-52.722,-50.055,-67
resnet32ts.ra2_in1k,26.849,73.151,45.041,54.959,17.96,288,1.000,bicubic,-52.539,-49.611,-54
rexnet_100.nav_in1k,26.841,73.159,45.379,54.621,4.80,224,0.875,bicubic,-51.015,-48.261,+64
legacy_seresnext101_32x4d.in1k,26.821,73.179,43.505,56.495,48.96,224,0.875,bilinear,-53.411,-51.515,-114
densenet169.tv_in1k,26.819,73.181,45.381,54.619,14.15,224,0.875,bicubic,-49.081,-47.647,+142
tinynet_a.in1k,26.815,73.185,45.082,54.918,6.19,192,0.875,bicubic,-50.833,-48.458,+69
regnetx_064.pycls_in1k,26.803,73.197,44.913,55.087,26.21,224,0.875,bicubic,-52.263,-49.547,-28
regnety_120.pycls_in1k,26.780,73.220,44.442,55.558,51.82,224,0.875,bicubic,-53.600,-50.684,-137
regnetx_032.pycls_in1k,26.717,73.283,45.230,54.770,15.30,224,0.875,bicubic,-51.451,-48.852,+36
res2net101d.in1k,26.711,73.289,44.336,55.664,45.23,224,0.875,bilinear,-54.507,-51.014,-227
resnext50_32x4d.a2_in1k,26.682,73.318,42.768,57.232,25.03,288,1.000,bicubic,-54.622,-52.328,-233
efficientvit_m5.r224_in1k,26.674,73.326,44.923,55.077,12.47,224,0.875,bicubic,-50.384,-48.261,+98
legacy_seresnet152.in1k,26.666,73.334,43.949,56.051,66.82,224,0.875,bilinear,-51.994,-50.421,-4
efficientnet_es.ra_in1k,26.619,73.381,45.096,54.904,5.44,224,0.875,bicubic,-51.439,-48.830,+38
res2net50_26w_6s.in1k,26.591,73.409,44.004,55.996,37.05,224,0.875,bilinear,-51.977,-50.118,-3
repvgg_b1g4.rvgg_in1k,26.579,73.421,45.100,54.900,39.97,224,0.875,bilinear,-51.009,-48.736,+64
dla60x.in1k,26.566,73.434,45.031,54.969,17.35,224,0.875,bilinear,-51.670,-48.995,+24
coat_lite_tiny.in1k,26.517,73.484,44.646,55.354,5.72,224,0.900,bicubic,-51.003,-49.276,+64
regnety_080.pycls_in1k,26.515,73.485,44.351,55.649,39.18,224,0.875,bicubic,-53.353,-50.481,-110
mobilenetv3_large_100.miil_in21k_ft_in1k,26.493,73.507,44.491,55.509,5.48,224,0.875,bilinear,-51.427,-48.423,+43
tf_efficientnet_b0.aa_in1k,26.483,73.517,45.642,54.358,5.29,224,0.875,bicubic,-50.361,-47.576,+99
res2net50_14w_8s.in1k,26.477,73.523,44.371,55.629,25.06,224,0.875,bilinear,-51.681,-49.475,+25
tf_efficientnet_b2.in1k,26.462,73.538,44.788,55.212,9.11,260,0.890,bicubic,-53.147,-49.926,-90
mobileone_s2.apple_in1k,26.456,73.544,44.566,55.434,7.88,224,0.900,bilinear,-51.060,-49.102,+60
resnet50.gluon_in1k,26.428,73.572,44.039,55.961,25.56,224,0.875,bicubic,-51.154,-49.681,+56
ecaresnet50t.a3_in1k,26.420,73.580,43.507,56.493,25.57,224,0.950,bicubic,-53.132,-51.188,-89
tf_efficientnet_el.in1k,26.353,73.647,44.173,55.827,10.59,300,0.904,bicubic,-53.895,-50.947,-141
levit_conv_128.fb_dist_in1k,26.330,73.670,44.120,55.880,9.21,224,0.900,bicubic,-52.164,-49.888,-11
levit_128.fb_dist_in1k,26.328,73.672,44.116,55.884,9.21,224,0.900,bicubic,-52.162,-49.896,-10
lambda_resnet26t.c1_in1k,26.326,73.674,44.430,55.570,10.96,256,0.940,bicubic,-52.762,-50.160,-54
resmlp_big_24_224.fb_in1k,26.318,73.682,43.554,56.446,129.14,224,0.875,bicubic,-54.718,-51.464,-225
resmlp_12_224.fb_distilled_in1k,26.316,73.684,44.874,55.126,15.35,224,0.875,bicubic,-51.638,-48.686,+29
visformer_tiny.in1k,26.257,73.743,44.182,55.818,10.32,224,0.900,bicubic,-51.903,-49.984,+13
regnetx_040.pycls_in1k,26.243,73.757,44.424,55.576,22.12,224,0.875,bicubic,-52.249,-49.818,-16
mobilevitv2_150.cvnets_in1k,26.178,73.822,43.762,56.238,10.59,256,0.888,bicubic,-54.192,-51.312,-163
crossvit_9_dagger_240.in1k,26.177,73.823,44.542,55.458,8.78,240,0.875,bicubic,-50.801,-49.076,+78
vit_small_patch32_224.augreg_in21k_ft_in1k,26.165,73.835,45.106,54.894,22.88,224,0.900,bicubic,-49.829,-47.694,+105
seresnet50.a2_in1k,26.165,73.835,42.675,57.325,28.09,288,1.000,bicubic,-54.941,-52.547,-240
densenetblur121d.ra_in1k,26.135,73.865,45.037,54.963,8.00,288,0.950,bicubic,-51.187,-48.751,+54
resnet50.a2_in1k,26.094,73.906,42.583,57.417,25.56,288,1.000,bicubic,-54.678,-52.405,-205
resnet50d.a3_in1k,26.090,73.910,42.970,57.030,25.58,224,0.950,bicubic,-52.630,-51.262,-39
resnext50_32x4d.a3_in1k,26.082,73.918,42.946,57.054,25.03,224,0.950,bicubic,-53.186,-51.360,-81
resnet34.a2_in1k,26.080,73.920,43.109,56.891,21.80,288,1.000,bicubic,-51.078,-50.165,+62
efficientnet_b1.ft_in1k,26.055,73.945,44.080,55.920,7.79,256,1.000,bicubic,-52.745,-50.262,-47
convnextv2_femto.fcmae_ft_in1k,26.039,73.961,44.302,55.698,5.23,288,0.950,bicubic,-53.299,-50.258,-92
fastvit_t8.apple_dist_in1k,26.033,73.967,44.397,55.603,4.03,256,0.900,bicubic,-51.143,-48.901,+57
lambda_resnet26rpt_256.c1_in1k,26.033,73.967,44.190,55.810,10.99,256,0.940,bicubic,-52.931,-50.246,-63
mobilevitv2_125.cvnets_in1k,26.021,73.979,43.668,56.332,7.48,256,0.888,bicubic,-53.659,-51.190,-119
dpn68.mx_in1k,26.006,73.994,44.084,55.916,12.61,224,0.875,bicubic,-50.340,-48.924,+88
hrnet_w18.ms_in1k,25.982,74.018,44.803,55.197,21.30,224,0.875,bilinear,-50.770,-48.641,+72
hardcorenas_f.miil_green_in1k,25.939,74.061,44.204,55.796,8.20,224,0.875,bilinear,-52.157,-49.598,-1
vit_small_patch16_224.augreg_in1k,25.935,74.065,43.964,56.036,22.05,224,0.900,bicubic,-52.913,-50.324,-61
repghostnet_150.in1k,25.923,74.077,44.328,55.672,6.58,224,0.875,bicubic,-51.537,-49.182,+34
regnety_040.pycls_in1k,25.909,74.091,43.854,56.146,20.65,224,0.875,bicubic,-53.311,-50.802,-87
hrnet_w18_small_v2.gluon_in1k,25.884,74.116,43.815,56.185,15.60,224,0.875,bicubic,-52.306,-50.087,-12
regnetx_016.tv2_in1k,25.878,74.122,43.353,56.647,9.19,224,0.965,bicubic,-53.558,-51.415,-110
fastvit_t8.apple_in1k,25.876,74.124,44.153,55.847,4.03,256,0.900,bicubic,-50.298,-48.899,+83
resnext50d_32x4d.bt_in1k,25.876,74.124,42.956,57.044,25.05,288,0.950,bicubic,-54.788,-52.464,-212
res2net50_26w_4s.in1k,25.872,74.128,43.163,56.837,25.70,224,0.875,bilinear,-52.078,-50.689,+3
tresnet_m.miil_in1k_448,25.860,74.140,42.868,57.132,31.39,448,0.875,bilinear,-55.850,-52.706,-328
coat_tiny.in1k,25.858,74.142,43.275,56.725,5.50,224,0.900,bicubic,-52.568,-50.773,-35
hardcorenas_c.miil_green_in1k,25.821,74.179,44.764,55.236,5.52,224,0.875,bilinear,-51.245,-48.398,+47
densenet121.ra_in1k,25.815,74.185,44.866,55.134,7.98,288,0.950,bicubic,-50.685,-48.502,+69
resnet50c.gluon_in1k,25.793,74.207,43.019,56.981,25.58,224,0.875,bicubic,-52.213,-50.973,-8
halonet26t.a1h_in1k,25.776,74.224,43.220,56.780,12.48,256,0.950,bicubic,-53.330,-51.086,-91
selecsls60.in1k,25.729,74.272,44.065,55.935,30.67,224,0.875,bicubic,-52.260,-49.765,-6
hardcorenas_e.miil_green_in1k,25.658,74.342,43.408,56.592,8.07,224,0.875,bilinear,-52.132,-50.292,+4
poolformer_s12.sail_in1k,25.654,74.346,44.167,55.833,11.92,224,0.900,bicubic,-51.586,-49.365,+31
dla60_res2next.in1k,25.654,74.346,43.675,56.325,17.03,224,0.875,bilinear,-52.786,-50.469,-44
dla60_res2net.in1k,25.648,74.352,43.583,56.417,20.85,224,0.875,bilinear,-52.816,-50.615,-48
ecaresnet26t.ra2_in1k,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.312,-51.430,-160
resmlp_12_224.fb_in1k,25.528,74.472,44.330,55.670,15.35,224,0.875,bicubic,-51.120,-48.848,+51
mixnet_l.ft_in1k,25.520,74.480,43.471,56.529,7.33,224,0.875,bicubic,-53.446,-50.711,-90
convnext_femto.d1_in1k,25.510,74.490,43.672,56.328,5.22,288,0.950,bicubic,-53.206,-50.759,-71
tf_efficientnet_lite1.in1k,25.509,74.492,43.573,56.427,5.42,240,0.882,bicubic,-51.136,-49.651,+49
res2net50d.in1k,25.497,74.503,43.041,56.959,25.72,224,0.875,bilinear,-54.757,-51.995,-191
cs3darknet_focus_m.c2ns_in1k,25.493,74.507,43.762,56.238,9.30,288,0.950,bicubic,-51.791,-50.204,+21
resnext50_32x4d.tv_in1k,25.467,74.533,42.787,57.213,25.03,224,0.875,bilinear,-52.155,-50.909,0
bat_resnext26ts.ch_in1k,25.455,74.545,43.194,56.806,10.73,256,0.900,bicubic,-52.797,-50.904,-41
botnet26t_256.c1_in1k,25.451,74.549,42.660,57.340,12.49,256,0.950,bicubic,-53.806,-51.872,-117
eca_halonext26ts.c1_in1k,25.442,74.558,43.182,56.818,10.76,256,0.940,bicubic,-54.044,-51.418,-141
repvgg_a2.rvgg_in1k,25.436,74.564,43.945,56.055,28.21,224,0.875,bilinear,-51.022,-49.057,+52
tf_mixnet_l.in1k,25.414,74.586,42.538,57.462,7.33,224,0.875,bicubic,-53.362,-51.464,-84
regnety_008_tv.tv2_in1k,25.406,74.594,43.434,56.566,6.43,224,0.965,bicubic,-53.260,-50.956,-76
hardcorenas_b.miil_green_in1k,25.396,74.603,44.188,55.812,5.18,224,0.875,bilinear,-51.151,-48.574,+42
res2next50.in1k,25.392,74.608,42.492,57.508,24.67,224,0.875,bilinear,-52.850,-51.399,-47
convnext_femto_ols.d1_in1k,25.387,74.613,43.137,56.863,5.23,288,0.950,bicubic,-53.537,-51.389,-100
efficientformerv2_s0.snap_dist_in1k,25.349,74.651,43.929,56.071,3.60,224,0.950,bicubic,-50.765,-48.929,+54
legacy_seresnet101.in1k,25.332,74.668,42.832,57.168,49.33,224,0.875,bilinear,-53.054,-51.430,-59
selecsls60b.in1k,25.326,74.674,43.556,56.444,32.77,224,0.875,bicubic,-53.086,-50.612,-62
hardcorenas_d.miil_green_in1k,25.326,74.674,43.137,56.863,7.50,224,0.875,bilinear,-52.108,-50.353,-2
resnetv2_50x1_bit.goog_in21k_ft_in1k,25.322,74.678,45.348,54.652,25.55,448,1.000,bilinear,-55.020,-50.334,-217
regnety_032.pycls_in1k,25.318,74.682,42.923,57.077,19.44,224,0.875,bicubic,-53.558,-51.485,-103
wide_resnet50_2.tv_in1k,25.310,74.690,42.182,57.818,68.88,224,0.875,bilinear,-53.166,-51.906,-73
dla102.in1k,25.294,74.706,43.846,56.154,33.27,224,0.875,bilinear,-52.730,-50.088,-40
resnest14d.gluon_in1k,25.280,74.719,44.106,55.894,10.61,224,0.875,bilinear,-50.227,-48.402,+60
legacy_seresnext50_32x4d.in1k,25.212,74.788,41.950,58.050,27.56,224,0.875,bilinear,-53.864,-52.482,-119
ghostnetv2_130.in1k,25.149,74.851,43.271,56.729,8.96,224,0.875,bicubic,-51.607,-50.091,+23
mixer_b16_224.goog_in21k_ft_in1k,25.111,74.888,41.225,58.775,59.88,224,0.875,bicubic,-51.491,-50.999,+26
res2net50_48w_2s.in1k,25.025,74.975,42.206,57.794,25.29,224,0.875,bilinear,-52.489,-51.344,-15
efficientnet_b0.ra_in1k,25.015,74.985,42.797,57.203,5.29,224,0.875,bicubic,-52.679,-50.735,-27
resnet50.a3_in1k,25.000,75.001,41.889,58.111,25.56,224,0.950,bicubic,-53.049,-51.891,-49
dla60.in1k,24.937,75.063,43.322,56.678,22.04,224,0.875,bilinear,-52.109,-49.996,+8
resnet34.gluon_in1k,24.937,75.063,42.237,57.763,21.80,224,0.875,bicubic,-49.643,-49.745,+75
mobilenetv2_120d.ra_in1k,24.915,75.085,43.039,56.961,5.83,224,0.875,bicubic,-52.393,-50.463,-11
convnextv2_atto.fcmae_ft_in1k,24.890,75.111,42.469,57.531,3.71,288,0.950,bicubic,-52.871,-51.257,-34
eca_botnext26ts_256.c1_in1k,24.868,75.132,42.943,57.057,10.59,256,0.950,bicubic,-54.400,-51.663,-147
resnet34.bt_in1k,24.828,75.171,42.080,57.920,21.80,288,0.950,bicubic,-51.652,-51.274,+25
regnety_016.pycls_in1k,24.823,75.177,42.610,57.390,11.20,224,0.875,bicubic,-53.045,-51.108,-43
xcit_nano_12_p8_224.fb_dist_in1k,24.801,75.199,43.072,56.928,3.05,224,1.000,bicubic,-51.531,-50.026,+28
seresnet50.a3_in1k,24.793,75.207,42.094,57.906,28.09,224,0.950,bicubic,-52.233,-50.978,+1
pit_ti_distilled_224.in1k,24.711,75.289,43.225,56.775,5.10,224,0.900,bicubic,-49.545,-48.727,+71
cs3darknet_m.c2ns_in1k,24.626,75.374,42.970,57.030,9.31,288,0.950,bicubic,-53.008,-51.046,-36
eca_resnext26ts.ch_in1k,24.559,75.441,42.536,57.464,10.30,288,1.000,bicubic,-53.441,-51.390,-56
resnet50.bt_in1k,24.559,75.441,41.445,58.555,25.56,288,0.950,bicubic,-55.081,-53.447,-183
mobilevitv2_100.cvnets_in1k,24.553,75.447,42.919,57.081,4.90,256,0.888,bicubic,-53.527,-51.251,-65
tf_efficientnet_lite2.in1k,24.532,75.468,42.290,57.710,6.09,260,0.890,bicubic,-52.930,-51.462,-31
seresnext26ts.ch_in1k,24.506,75.494,42.665,57.335,10.39,288,1.000,bicubic,-53.764,-51.427,-81
skresnet18.ra_in1k,24.497,75.504,42.534,57.466,11.96,224,0.875,bicubic,-48.538,-48.638,+84
regnetx_016.pycls_in1k,24.477,75.523,42.490,57.510,9.19,224,0.875,bicubic,-52.447,-50.925,-3
tf_efficientnet_lite0.in1k,24.373,75.627,42.510,57.490,4.65,224,0.875,bicubic,-50.459,-49.660,+51
hardcorenas_a.miil_green_in1k,24.367,75.633,43.316,56.684,5.26,224,0.875,bilinear,-51.571,-49.192,+24
hrnet_w18.ms_aug_in1k,24.341,75.659,42.897,57.103,21.30,224,0.950,bilinear,-53.781,-51.157,-74
efficientvit_m4.r224_in1k,24.286,75.714,41.758,58.242,8.80,224,0.875,bicubic,-50.082,-50.222,+58
gcresnext26ts.ch_in1k,24.154,75.846,41.306,58.694,10.48,288,1.000,bicubic,-54.260,-52.730,-97
resnet50.tv_in1k,24.096,75.904,41.323,58.677,25.56,224,0.875,bilinear,-52.032,-51.535,+15
tf_efficientnet_b1.in1k,24.070,75.930,41.512,58.488,7.79,240,0.882,bicubic,-54.492,-52.582,-114
levit_128s.fb_dist_in1k,24.060,75.940,41.001,58.999,7.78,224,0.900,bicubic,-52.466,-51.871,+1
levit_conv_128s.fb_dist_in1k,24.052,75.948,41.003,58.997,7.78,224,0.900,bicubic,-52.468,-51.863,+1
legacy_seresnet34.in1k,24.021,75.979,41.901,58.099,21.96,224,0.875,bilinear,-50.781,-50.225,+43
xcit_nano_12_p16_384.fb_dist_in1k,24.005,75.995,42.306,57.694,3.05,384,1.000,bicubic,-51.453,-50.392,+28
xcit_nano_12_p8_384.fb_dist_in1k,23.956,76.044,41.954,58.046,3.05,384,1.000,bicubic,-53.864,-52.086,-62
efficientnet_lite0.ra_in1k,23.905,76.095,42.109,57.891,4.65,224,0.875,bicubic,-51.577,-50.411,+24
repghostnet_130.in1k,23.852,76.148,41.569,58.431,5.48,224,0.875,bicubic,-52.524,-51.323,+1
densenet121.tv_in1k,23.840,76.160,41.928,58.072,7.98,224,0.875,bicubic,-50.924,-50.225,+39
efficientnet_es_pruned.in1k,23.815,76.185,41.991,58.009,5.44,224,0.875,bicubic,-51.191,-50.453,+33
regnetx_008.tv2_in1k,23.779,76.221,40.698,59.302,7.26,224,0.965,bicubic,-53.527,-52.966,-42
mixnet_m.ft_in1k,23.716,76.284,41.146,58.854,5.01,224,0.875,bicubic,-53.544,-52.272,-39
resnet26t.ra2_in1k,23.712,76.288,41.321,58.679,16.01,320,1.000,bicubic,-54.616,-52.803,-105
mobilenetv2_140.ra_in1k,23.695,76.305,41.469,58.531,6.11,224,0.875,bicubic,-52.821,-51.519,-9
dla34.in1k,23.685,76.315,41.538,58.462,15.74,224,0.875,bilinear,-50.955,-50.529,+35
legacy_seresnet50.in1k,23.640,76.360,40.079,59.921,28.09,224,0.875,bilinear,-54.004,-53.679,-66
convnext_atto.d2_in1k,23.589,76.411,41.087,58.913,3.70,288,0.950,bicubic,-53.419,-52.614,-30
resnext26ts.ra2_in1k,23.589,76.411,40.891,59.109,10.30,288,1.000,bicubic,-53.589,-52.573,-42
tf_mixnet_m.in1k,23.484,76.516,40.997,59.003,5.01,224,0.875,bicubic,-53.470,-52.157,-30
resnet34.tv_in1k,23.473,76.527,41.361,58.639,21.80,224,0.875,bilinear,-49.833,-50.059,+53
efficientvit_m3.r224_in1k,23.412,76.588,40.531,59.469,6.90,224,0.875,bicubic,-49.962,-50.817,+49
selecsls42b.in1k,23.369,76.632,40.681,59.319,32.46,224,0.875,bicubic,-53.802,-52.711,-44
tf_efficientnet_em.in1k,23.369,76.632,40.398,59.602,6.90,240,0.882,bicubic,-54.758,-53.650,-101
resnet34.a3_in1k,23.366,76.633,40.068,59.932,21.80,224,0.950,bicubic,-49.603,-51.038,+55
repvgg_b0.rvgg_in1k,23.319,76.681,41.164,58.836,15.82,224,0.875,bilinear,-51.825,-51.252,+12
regnety_004.tv2_in1k,23.292,76.708,40.987,59.013,4.34,224,0.965,bicubic,-52.302,-51.713,+2
xcit_nano_12_p16_224.fb_dist_in1k,23.249,76.751,41.368,58.632,3.05,224,1.000,bicubic,-49.061,-49.492,+61
convnext_atto_ols.a2_in1k,23.129,76.871,40.881,59.119,3.70,288,0.950,bicubic,-54.087,-52.795,-53
mobilenetv2_110d.ra_in1k,23.070,76.930,40.749,59.251,4.52,224,0.875,bicubic,-51.984,-51.434,+12
resnet18.a1_in1k,23.056,76.944,39.551,60.449,11.69,288,1.000,bicubic,-50.102,-51.475,+45
vit_base_patch32_224.sam_in1k,23.046,76.954,39.565,60.435,88.22,224,0.900,bicubic,-50.648,-51.449,+37
tinynet_b.in1k,23.017,76.983,40.979,59.021,3.73,188,0.875,bicubic,-51.961,-51.207,+12
resnet18d.ra2_in1k,23.001,76.999,41.197,58.803,11.71,288,0.950,bicubic,-50.793,-50.640,+34
repghostnet_111.in1k,22.881,77.119,40.439,59.561,4.54,224,0.875,bicubic,-52.175,-51.753,+6
mobileone_s1.apple_in1k,22.803,77.197,39.851,60.149,4.83,224,0.900,bilinear,-52.983,-52.941,-13
deit_tiny_distilled_patch16_224.fb_in1k,22.722,77.278,40.777,59.223,5.91,224,0.900,bicubic,-51.782,-51.113,+18
mobilenetv3_large_100.ra_in1k,22.657,77.343,40.763,59.237,5.48,224,0.875,bicubic,-53.109,-51.775,-14
repvgg_a1.rvgg_in1k,22.640,77.361,39.869,60.131,14.09,224,0.875,bilinear,-51.823,-51.987,+17
mobilenetv3_rw.rmsp_in1k,22.630,77.370,40.362,59.638,5.48,224,0.875,bicubic,-52.990,-52.342,-13
ghostnetv2_100.in1k,22.604,77.396,40.022,59.978,6.16,224,0.875,bicubic,-52.562,-52.332,-4
edgenext_x_small.in1k,22.590,77.410,39.500,60.500,2.34,288,1.000,bicubic,-53.098,-53.266,-17
tf_mobilenetv3_large_100.in1k,22.579,77.421,39.777,60.223,5.48,224,0.875,bilinear,-52.937,-52.817,-13
tf_efficientnet_b0.in1k,22.559,77.441,39.570,60.430,5.29,224,0.875,bicubic,-53.971,-53.438,-41
mobilevit_s.cvnets_in1k,22.478,77.522,38.657,61.343,5.58,256,0.900,bicubic,-55.834,-55.491,-134
xcit_nano_12_p8_224.fb_in1k,22.408,77.592,40.626,59.374,3.05,224,1.000,bicubic,-51.502,-51.542,+20
tf_efficientnet_es.in1k,22.406,77.594,39.089,60.911,5.44,224,0.875,bicubic,-54.192,-54.113,-46
hrnet_w18_small_v2.ms_in1k,22.341,77.659,39.857,60.143,15.60,224,0.875,bilinear,-52.769,-52.559,-8
convit_tiny.fb_in1k,22.268,77.732,39.675,60.325,5.71,224,0.875,bicubic,-50.844,-52.037,+28
regnetx_004_tv.tv2_in1k,22.213,77.787,39.126,60.874,5.50,224,0.965,bicubic,-52.387,-53.044,+3
regnety_008.pycls_in1k,22.109,77.891,38.902,61.098,6.26,224,0.875,bicubic,-54.193,-54.160,-37
ese_vovnet19b_dw.ra_in1k,22.072,77.928,39.464,60.536,6.54,288,0.950,bicubic,-55.672,-54.320,-104
regnety_006.pycls_in1k,21.981,78.019,38.959,61.041,6.06,224,0.875,bicubic,-53.287,-53.567,-17
vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,21.948,78.052,39.405,60.595,6.36,384,1.000,bicubic,-54.012,-53.857,-35
regnetx_008.pycls_in1k,21.948,78.052,38.928,61.072,7.26,224,0.875,bicubic,-53.080,-53.410,-11
semnasnet_100.rmsp_in1k,21.889,78.111,38.604,61.396,3.89,224,0.875,bicubic,-53.561,-53.994,-21
pit_ti_224.in1k,21.871,78.129,39.537,60.463,4.85,224,0.900,bicubic,-51.039,-51.867,+25
pvt_v2_b0.in1k,21.836,78.164,40.152,59.848,3.67,224,0.900,bicubic,-48.824,-50.044,+48
regnetx_006.pycls_in1k,21.747,78.253,38.914,61.086,6.20,224,0.875,bicubic,-52.121,-52.764,+8
vit_tiny_patch16_384.augreg_in21k_ft_in1k,21.720,78.280,39.319,60.681,5.79,384,1.000,bicubic,-56.704,-55.223,-158
crossvit_9_240.in1k,21.696,78.304,39.268,60.732,8.55,240,0.875,bicubic,-52.264,-52.694,+2
vgg19_bn.tv_in1k,21.623,78.376,39.276,60.724,143.68,224,0.875,bilinear,-52.592,-52.568,-3
semnasnet_075.rmsp_in1k,21.582,78.418,38.918,61.082,2.91,224,0.875,bicubic,-51.422,-52.222,+16
resnet18.gluon_in1k,21.545,78.455,38.875,61.125,11.69,224,0.875,bicubic,-49.289,-50.881,+40
mobilevitv2_075.cvnets_in1k,21.535,78.465,38.631,61.369,2.87,256,0.888,bicubic,-54.073,-54.113,-37
fbnetc_100.rmsp_in1k,21.492,78.508,38.179,61.821,5.57,224,0.875,bilinear,-53.638,-54.209,-27
repghostnet_100.in1k,21.459,78.541,38.682,61.318,4.07,224,0.875,bicubic,-52.748,-52.860,-7
xcit_nano_12_p16_224.fb_in1k,21.437,78.563,39.791,60.209,3.05,224,1.000,bicubic,-48.525,-49.971,+42
ghostnet_100.in1k,21.384,78.616,38.158,61.842,5.18,224,0.875,bicubic,-52.574,-53.374,-5
mnasnet_100.rmsp_in1k,21.346,78.653,37.709,62.291,4.38,224,0.875,bicubic,-53.306,-54.413,-20
resnet18.fb_ssl_yfcc100m_ft_in1k,21.293,78.707,39.114,60.886,11.69,224,0.875,bilinear,-51.305,-52.301,+11
lcnet_100.ra2_in1k,21.293,78.707,38.867,61.133,2.95,224,0.875,bicubic,-50.809,-51.487,+23
mixnet_s.ft_in1k,21.276,78.724,38.199,61.801,4.13,224,0.875,bicubic,-54.718,-55.071,-54
legacy_seresnext26_32x4d.in1k,21.089,78.911,37.627,62.373,16.79,224,0.875,bicubic,-56.019,-55.687,-92
efficientvit_m2.r224_in1k,21.081,78.919,37.690,62.310,4.19,224,0.875,bicubic,-49.733,-52.452,+30
crossvit_tiny_240.in1k,21.048,78.952,38.061,61.939,7.01,240,0.875,bicubic,-52.292,-53.847,-3
resnet18.a2_in1k,20.944,79.056,36.851,63.149,11.69,288,1.000,bicubic,-51.428,-53.745,+10
repvgg_a0.rvgg_in1k,20.922,79.078,37.539,62.461,9.11,224,0.875,bilinear,-51.486,-52.953,+6
regnetx_004.pycls_in1k,20.887,79.113,37.541,62.459,5.16,224,0.875,bicubic,-51.515,-53.285,+6
spnasnet_100.rmsp_in1k,20.867,79.133,37.896,62.104,4.42,224,0.875,bilinear,-53.227,-53.924,-19
seresnext26t_32x4d.bt_in1k,20.847,79.153,36.344,63.656,16.81,288,0.950,bicubic,-57.897,-57.968,-205
legacy_seresnet18.in1k,20.835,79.165,37.639,62.361,11.78,224,0.875,bicubic,-50.925,-52.693,+16
mobilenetv2_100.ra_in1k,20.761,79.239,37.757,62.243,3.50,224,0.875,bicubic,-52.207,-53.259,-2
tf_mixnet_s.in1k,20.474,79.526,36.621,63.379,4.13,224,0.875,bicubic,-55.178,-56.019,-58
vit_tiny_patch16_224.augreg_in21k_ft_in1k,20.460,79.540,37.601,62.399,5.72,224,0.900,bicubic,-55.002,-55.243,-52
regnety_004.pycls_in1k,20.411,79.589,37.014,62.986,4.34,224,0.875,bicubic,-53.615,-54.734,-24
tf_mobilenetv3_large_075.in1k,20.378,79.622,36.782,63.218,3.99,224,0.875,bilinear,-53.052,-54.570,-17
hrnet_w18_small.ms_in1k,20.364,79.636,37.093,62.907,13.19,224,0.875,bilinear,-51.972,-53.588,0
hrnet_w18_small.gluon_in1k,20.362,79.638,36.973,63.027,13.19,224,0.875,bicubic,-53.558,-54.221,-24
resnet26d.bt_in1k,20.266,79.734,36.348,63.652,16.01,288,0.950,bicubic,-57.142,-57.290,-125
resnet18.tv_in1k,20.230,79.770,37.258,62.742,11.69,224,0.875,bilinear,-49.530,-51.812,+21
mixer_l16_224.goog_in21k_ft_in1k,20.175,79.825,32.938,67.062,208.20,224,0.875,bicubic,-51.879,-54.736,+3
deit_tiny_patch16_224.fb_in1k,20.148,79.852,37.537,62.463,5.72,224,0.900,bicubic,-52.022,-53.579,0
tf_mobilenetv3_large_minimal_100.in1k,20.103,79.897,36.894,63.106,3.92,224,0.875,bilinear,-52.161,-53.746,-4
seresnext26d_32x4d.bt_in1k,20.067,79.933,35.233,64.766,16.81,288,0.950,bicubic,-58.747,-59.006,-226
vgg16_bn.tv_in1k,19.945,80.055,36.314,63.686,138.37,224,0.875,bilinear,-53.425,-55.200,-24
efficientvit_m1.r224_in1k,19.938,80.062,36.403,63.597,2.98,224,0.875,bicubic,-48.368,-52.267,+22
resnet26.bt_in1k,19.739,80.261,35.839,64.161,16.00,288,0.950,bicubic,-56.627,-57.341,-87
repghostnet_080.in1k,19.454,80.546,35.953,64.047,3.28,224,0.875,bicubic,-52.758,-54.531,-7
vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,19.334,80.666,36.059,63.941,6.34,224,0.900,bicubic,-52.464,-54.765,-3
mobileone_s0.apple_in1k,19.309,80.691,35.342,64.658,5.29,224,0.875,bilinear,-52.093,-54.500,0
tinynet_c.in1k,19.258,80.742,35.982,64.018,2.46,184,0.875,bicubic,-51.984,-53.750,+1
edgenext_xx_small.in1k,18.863,81.137,35.159,64.841,1.33,288,1.000,bicubic,-53.015,-55.393,-7
efficientvit_b0.r224_in1k,18.464,81.536,33.190,66.810,3.41,224,0.950,bicubic,-52.934,-56.238,-2
resnet18.a3_in1k,18.442,81.558,33.487,66.513,11.69,224,0.950,bicubic,-49.810,-54.685,+15
mobilevit_xs.cvnets_in1k,18.312,81.688,33.206,66.794,2.32,256,0.900,bicubic,-56.322,-59.142,-54
lcnet_075.ra2_in1k,18.128,81.872,34.371,65.629,2.36,224,0.875,bicubic,-50.654,-53.989,+9
vgg19.tv_in1k,17.941,82.059,33.058,66.942,143.67,224,0.875,bilinear,-54.437,-57.816,-22
vgg13_bn.tv_in1k,17.798,82.202,34.043,65.957,133.05,224,0.875,bilinear,-53.790,-56.335,-9
vgg16.tv_in1k,17.538,82.462,32.779,67.221,138.36,224,0.875,bilinear,-54.054,-57.605,-11
regnety_002.pycls_in1k,17.456,82.544,32.435,67.565,3.16,224,0.875,bicubic,-52.824,-57.095,-3
vgg11_bn.tv_in1k,17.397,82.603,33.001,66.999,132.87,224,0.875,bilinear,-52.985,-56.807,-5
mobilevitv2_050.cvnets_in1k,17.306,82.694,33.007,66.993,1.37,256,0.888,bicubic,-52.842,-56.911,-4
repghostnet_058.in1k,17.161,82.839,32.596,67.404,2.55,224,0.875,bicubic,-51.753,-55.824,+1
regnetx_002.pycls_in1k,16.959,83.041,32.225,67.775,2.68,224,0.875,bicubic,-51.793,-56.317,+2
mobilenetv3_small_100.lamb_in1k,16.803,83.197,32.518,67.482,2.54,224,0.875,bicubic,-50.855,-55.118,+7
resnet10t.c3_in1k,16.699,83.301,32.123,67.877,5.44,224,0.950,bicubic,-51.665,-55.913,+1
efficientvit_m0.r224_in1k,16.670,83.330,31.948,68.052,2.35,224,0.875,bicubic,-46.600,-53.228,+14
mobilenetv2_050.lamb_in1k,16.668,83.332,31.950,68.050,1.97,224,0.875,bicubic,-49.280,-54.134,+9
tinynet_d.in1k,16.658,83.342,32.449,67.551,2.34,152,0.875,bicubic,-50.314,-54.617,+4
mnasnet_small.lamb_in1k,16.638,83.362,31.909,68.091,2.03,224,0.875,bicubic,-49.558,-54.595,+5
dla60x_c.in1k,16.336,83.664,31.757,68.243,1.32,224,0.875,bilinear,-51.576,-56.675,0
tf_mobilenetv3_small_100.in1k,16.216,83.784,31.205,68.795,2.54,224,0.875,bilinear,-51.706,-56.467,-2
vgg13.tv_in1k,16.096,83.904,30.985,69.015,133.05,224,0.875,bilinear,-53.836,-58.265,-13
resnet14t.c3_in1k,15.925,84.075,30.003,69.997,10.08,224,0.950,bicubic,-56.329,-60.303,-34
vgg11.tv_in1k,15.723,84.278,30.451,69.549,132.86,224,0.875,bilinear,-53.300,-58.173,-13
repghostnet_050.in1k,15.589,84.411,30.189,69.811,2.31,224,0.875,bicubic,-51.377,-56.731,-2
mobilenetv3_small_075.lamb_in1k,14.948,85.052,29.733,70.267,2.04,224,0.875,bicubic,-50.288,-55.713,+2
tf_mobilenetv3_small_075.in1k,14.932,85.067,29.562,70.438,2.04,224,0.875,bilinear,-50.794,-56.570,0
dla46_c.in1k,14.665,85.335,29.397,70.603,1.30,224,0.875,bilinear,-50.207,-56.901,+1
mobilevit_xxs.cvnets_in1k,14.490,85.510,28.654,71.346,1.27,256,0.900,bicubic,-54.428,-60.291,-17
dla46x_c.in1k,14.380,85.620,29.197,70.803,1.07,224,0.875,bilinear,-51.612,-57.777,-5
lcnet_050.ra2_in1k,14.290,85.710,28.659,71.341,1.88,224,0.875,bicubic,-48.848,-55.724,0
tf_mobilenetv3_small_minimal_100.in1k,13.962,86.038,27.990,72.010,2.04,224,0.875,bilinear,-48.932,-56.248,0
tinynet_e.in1k,12.671,87.329,26.383,73.617,2.04,106,0.875,bicubic,-47.195,-55.379,0
mobilenetv3_small_050.lamb_in1k,11.038,88.962,23.477,76.523,1.59,224,0.875,bicubic,-46.878,-56.703,0
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/timm/version.py | __version__ = '0.9.13dev0'
| 0 |
hf_public_repos/pytorch-image-models | hf_public_repos/pytorch-image-models/timm/__init__.py | from .version import __version__
from .layers import is_scriptable, is_exportable, set_scriptable, set_exportable
from .models import create_model, list_models, list_pretrained, is_model, list_modules, model_entrypoint, \
is_model_pretrained, get_pretrained_cfg, get_pretrained_cfg_value
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/ml_decoder.py | from typing import Optional
import torch
from torch import nn
from torch import nn, Tensor
from torch.nn.modules.transformer import _get_activation_fn
def add_ml_decoder_head(model):
if hasattr(model, 'global_pool') and hasattr(model, 'fc'): # most CNN models, like Resnet50
model.global_pool = nn.Identity()
del model.fc
num_classes = model.num_classes
num_features = model.num_features
model.fc = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
elif hasattr(model, 'global_pool') and hasattr(model, 'classifier'): # EfficientNet
model.global_pool = nn.Identity()
del model.classifier
num_classes = model.num_classes
num_features = model.num_features
model.classifier = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
elif 'RegNet' in model._get_name() or 'TResNet' in model._get_name(): # hasattr(model, 'head')
del model.head
num_classes = model.num_classes
num_features = model.num_features
model.head = MLDecoder(num_classes=num_classes, initial_num_features=num_features)
else:
print("Model code-writing is not aligned currently with ml-decoder")
exit(-1)
if hasattr(model, 'drop_rate'): # Ml-Decoder has inner dropout
model.drop_rate = 0
return model
class TransformerDecoderLayerOptimal(nn.Module):
def __init__(self, d_model, nhead=8, dim_feedforward=2048, dropout=0.1, activation="relu",
layer_norm_eps=1e-5) -> None:
super(TransformerDecoderLayerOptimal, self).__init__()
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.dropout = nn.Dropout(dropout)
self.dropout1 = nn.Dropout(dropout)
self.dropout2 = nn.Dropout(dropout)
self.dropout3 = nn.Dropout(dropout)
self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
# Implementation of Feedforward model
self.linear1 = nn.Linear(d_model, dim_feedforward)
self.linear2 = nn.Linear(dim_feedforward, d_model)
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
self.activation = _get_activation_fn(activation)
def __setstate__(self, state):
if 'activation' not in state:
state['activation'] = torch.nn.functional.relu
super(TransformerDecoderLayerOptimal, self).__setstate__(state)
def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
tgt = tgt + self.dropout1(tgt)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(tgt, memory, memory)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
# @torch.jit.script
# class ExtrapClasses(object):
# def __init__(self, num_queries: int, group_size: int):
# self.num_queries = num_queries
# self.group_size = group_size
#
# def __call__(self, h: torch.Tensor, class_embed_w: torch.Tensor, class_embed_b: torch.Tensor, out_extrap:
# torch.Tensor):
# # h = h.unsqueeze(-1).expand(-1, -1, -1, self.group_size)
# h = h[..., None].repeat(1, 1, 1, self.group_size) # torch.Size([bs, 5, 768, groups])
# w = class_embed_w.view((self.num_queries, h.shape[2], self.group_size))
# out = (h * w).sum(dim=2) + class_embed_b
# out = out.view((h.shape[0], self.group_size * self.num_queries))
# return out
@torch.jit.script
class GroupFC(object):
def __init__(self, embed_len_decoder: int):
self.embed_len_decoder = embed_len_decoder
def __call__(self, h: torch.Tensor, duplicate_pooling: torch.Tensor, out_extrap: torch.Tensor):
for i in range(self.embed_len_decoder):
h_i = h[:, i, :]
w_i = duplicate_pooling[i, :, :]
out_extrap[:, i, :] = torch.matmul(h_i, w_i)
class MLDecoder(nn.Module):
def __init__(self, num_classes, num_of_groups=-1, decoder_embedding=768, initial_num_features=2048):
super(MLDecoder, self).__init__()
embed_len_decoder = 100 if num_of_groups < 0 else num_of_groups
if embed_len_decoder > num_classes:
embed_len_decoder = num_classes
# switching to 768 initial embeddings
decoder_embedding = 768 if decoder_embedding < 0 else decoder_embedding
self.embed_standart = nn.Linear(initial_num_features, decoder_embedding)
# decoder
decoder_dropout = 0.1
num_layers_decoder = 1
dim_feedforward = 2048
layer_decode = TransformerDecoderLayerOptimal(d_model=decoder_embedding,
dim_feedforward=dim_feedforward, dropout=decoder_dropout)
self.decoder = nn.TransformerDecoder(layer_decode, num_layers=num_layers_decoder)
# non-learnable queries
self.query_embed = nn.Embedding(embed_len_decoder, decoder_embedding)
self.query_embed.requires_grad_(False)
# group fully-connected
self.num_classes = num_classes
self.duplicate_factor = int(num_classes / embed_len_decoder + 0.999)
self.duplicate_pooling = torch.nn.Parameter(
torch.Tensor(embed_len_decoder, decoder_embedding, self.duplicate_factor))
self.duplicate_pooling_bias = torch.nn.Parameter(torch.Tensor(num_classes))
torch.nn.init.xavier_normal_(self.duplicate_pooling)
torch.nn.init.constant_(self.duplicate_pooling_bias, 0)
self.group_fc = GroupFC(embed_len_decoder)
def forward(self, x):
if len(x.shape) == 4: # [bs,2048, 7,7]
embedding_spatial = x.flatten(2).transpose(1, 2)
else: # [bs, 197,468]
embedding_spatial = x
embedding_spatial_786 = self.embed_standart(embedding_spatial)
embedding_spatial_786 = torch.nn.functional.relu(embedding_spatial_786, inplace=True)
bs = embedding_spatial_786.shape[0]
query_embed = self.query_embed.weight
# tgt = query_embed.unsqueeze(1).repeat(1, bs, 1)
tgt = query_embed.unsqueeze(1).expand(-1, bs, -1) # no allocation of memory with expand
h = self.decoder(tgt, embedding_spatial_786.transpose(0, 1)) # [embed_len_decoder, batch, 768]
h = h.transpose(0, 1)
out_extrap = torch.zeros(h.shape[0], h.shape[1], self.duplicate_factor, device=h.device, dtype=h.dtype)
self.group_fc(h, self.duplicate_pooling, out_extrap)
h_out = out_extrap.flatten(1)[:, :self.num_classes]
h_out += self.duplicate_pooling_bias
logits = h_out
return logits
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/std_conv.py | """ Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code: https://github.com/joe-siyuan-qiao/WeightStandardization
ScaledStdConv:
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
Hacked together by / copyright Ross Wightman, 2021.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from .padding import get_padding, get_padding_value, pad_same
class StdConv2d(nn.Conv2d):
"""Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.
Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=False, eps=1e-6):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
self.eps = eps
def forward(self, x):
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
class StdConv2dSame(nn.Conv2d):
"""Conv2d with Weight Standardization. TF compatible SAME padding. Used for ViT Hybrid model.
Paper: `Micro-Batch Training with Batch-Channel Normalization and Weight Standardization` -
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=False, eps=1e-6):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.same_pad = is_dynamic
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
class ScaledStdConv2d(nn.Conv2d):
"""Conv2d layer with Scaled Weight Standardization.
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
https://arxiv.org/abs/2101.08692
NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5 # gamma * 1 / sqrt(fan-in)
self.eps = eps
def forward(self, x):
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class ScaledStdConv2dSame(nn.Conv2d):
"""Conv2d layer with Scaled Weight Standardization and Tensorflow-like SAME padding support
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets` -
https://arxiv.org/abs/2101.08692
NOTE: the operations used in this impl differ slightly from the DeepMind Haiku impl. The impact is minor.
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5
self.same_pad = is_dynamic
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
weight = F.batch_norm(
self.weight.reshape(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/squeeze_excite.py | """ Squeeze-and-Excitation Channel Attention
An SE implementation originally based on PyTorch SE-Net impl.
Has since evolved with additional functionality / configuration.
Paper: `Squeeze-and-Excitation Networks` - https://arxiv.org/abs/1709.01507
Also included is Effective Squeeze-Excitation (ESE).
Paper: `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
from .create_act import create_act_layer
from .helpers import make_divisible
class SEModule(nn.Module):
""" SE Module as defined in original SE-Nets with a few additions
Additions include:
* divisor can be specified to keep channels % div == 0 (default: 8)
* reduction channels can be specified directly by arg (if rd_channels is set)
* reduction channels can be specified by float rd_ratio (default: 1/16)
* global max pooling can be added to the squeeze aggregation
* customizable activation, normalization, and gate layer
"""
def __init__(
self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8, add_maxpool=False,
bias=True, act_layer=nn.ReLU, norm_layer=None, gate_layer='sigmoid'):
super(SEModule, self).__init__()
self.add_maxpool = add_maxpool
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.fc1 = nn.Conv2d(channels, rd_channels, kernel_size=1, bias=bias)
self.bn = norm_layer(rd_channels) if norm_layer else nn.Identity()
self.act = create_act_layer(act_layer, inplace=True)
self.fc2 = nn.Conv2d(rd_channels, channels, kernel_size=1, bias=bias)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_se = x.mean((2, 3), keepdim=True)
if self.add_maxpool:
# experimental codepath, may remove or change
x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
x_se = self.fc1(x_se)
x_se = self.act(self.bn(x_se))
x_se = self.fc2(x_se)
return x * self.gate(x_se)
SqueezeExcite = SEModule # alias
class EffectiveSEModule(nn.Module):
""" 'Effective Squeeze-Excitation
From `CenterMask : Real-Time Anchor-Free Instance Segmentation` - https://arxiv.org/abs/1911.06667
"""
def __init__(self, channels, add_maxpool=False, gate_layer='hard_sigmoid', **_):
super(EffectiveSEModule, self).__init__()
self.add_maxpool = add_maxpool
self.fc = nn.Conv2d(channels, channels, kernel_size=1, padding=0)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_se = x.mean((2, 3), keepdim=True)
if self.add_maxpool:
# experimental codepath, may remove or change
x_se = 0.5 * x_se + 0.5 * x.amax((2, 3), keepdim=True)
x_se = self.fc(x_se)
return x * self.gate(x_se)
EffectiveSqueezeExcite = EffectiveSEModule # alias
class SqueezeExciteCl(nn.Module):
""" SE Module as defined in original SE-Nets with a few additions
Additions include:
* divisor can be specified to keep channels % div == 0 (default: 8)
* reduction channels can be specified directly by arg (if rd_channels is set)
* reduction channels can be specified by float rd_ratio (default: 1/16)
* global max pooling can be added to the squeeze aggregation
* customizable activation, normalization, and gate layer
"""
def __init__(
self, channels, rd_ratio=1. / 16, rd_channels=None, rd_divisor=8,
bias=True, act_layer=nn.ReLU, gate_layer='sigmoid'):
super().__init__()
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.fc1 = nn.Linear(channels, rd_channels, bias=bias)
self.act = create_act_layer(act_layer, inplace=True)
self.fc2 = nn.Linear(rd_channels, channels, bias=bias)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_se = x.mean((1, 2), keepdims=True) # FIXME avg dim [1:n-1], don't assume 2D NHWC
x_se = self.fc1(x_se)
x_se = self.act(x_se)
x_se = self.fc2(x_se)
return x * self.gate(x_se) | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/conv2d_same.py | """ Conv2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Tuple, Optional
from .config import is_exportable, is_scriptable
from .padding import pad_same, pad_same_arg, get_padding_value
_USE_EXPORT_CONV = False
def conv2d_same(
x,
weight: torch.Tensor,
bias: Optional[torch.Tensor] = None,
stride: Tuple[int, int] = (1, 1),
padding: Tuple[int, int] = (0, 0),
dilation: Tuple[int, int] = (1, 1),
groups: int = 1,
):
x = pad_same(x, weight.shape[-2:], stride, dilation)
return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
class Conv2dSame(nn.Conv2d):
""" Tensorflow like 'SAME' convolution wrapper for 2D convolutions
"""
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
):
super(Conv2dSame, self).__init__(
in_channels, out_channels, kernel_size,
stride, 0, dilation, groups, bias,
)
def forward(self, x):
return conv2d_same(
x, self.weight, self.bias,
self.stride, self.padding, self.dilation, self.groups,
)
class Conv2dSameExport(nn.Conv2d):
""" ONNX export friendly Tensorflow like 'SAME' convolution wrapper for 2D convolutions
NOTE: This does not currently work with torch.jit.script
"""
# pylint: disable=unused-argument
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
):
super(Conv2dSameExport, self).__init__(
in_channels, out_channels, kernel_size,
stride, 0, dilation, groups, bias,
)
self.pad = None
self.pad_input_size = (0, 0)
def forward(self, x):
input_size = x.size()[-2:]
if self.pad is None:
pad_arg = pad_same_arg(input_size, self.weight.size()[-2:], self.stride, self.dilation)
self.pad = nn.ZeroPad2d(pad_arg)
self.pad_input_size = input_size
x = self.pad(x)
return F.conv2d(
x, self.weight, self.bias,
self.stride, self.padding, self.dilation, self.groups,
)
def create_conv2d_pad(in_chs, out_chs, kernel_size, **kwargs):
padding = kwargs.pop('padding', '')
kwargs.setdefault('bias', False)
padding, is_dynamic = get_padding_value(padding, kernel_size, **kwargs)
if is_dynamic:
if _USE_EXPORT_CONV and is_exportable():
# older PyTorch ver needed this to export same padding reasonably
assert not is_scriptable() # Conv2DSameExport does not work with jit
return Conv2dSameExport(in_chs, out_chs, kernel_size, **kwargs)
else:
return Conv2dSame(in_chs, out_chs, kernel_size, **kwargs)
else:
return nn.Conv2d(in_chs, out_chs, kernel_size, padding=padding, **kwargs)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/classifier.py | """ Classifier head and layer factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from collections import OrderedDict
from functools import partial
from typing import Optional, Union, Callable
import torch
import torch.nn as nn
from torch.nn import functional as F
from .adaptive_avgmax_pool import SelectAdaptivePool2d
from .create_act import get_act_layer
from .create_norm import get_norm_layer
def _create_pool(
num_features: int,
num_classes: int,
pool_type: str = 'avg',
use_conv: bool = False,
input_fmt: Optional[str] = None,
):
flatten_in_pool = not use_conv # flatten when we use a Linear layer after pooling
if not pool_type:
assert num_classes == 0 or use_conv,\
'Pooling can only be disabled if classifier is also removed or conv classifier is used'
flatten_in_pool = False # disable flattening if pooling is pass-through (no pooling)
global_pool = SelectAdaptivePool2d(
pool_type=pool_type,
flatten=flatten_in_pool,
input_fmt=input_fmt,
)
num_pooled_features = num_features * global_pool.feat_mult()
return global_pool, num_pooled_features
def _create_fc(num_features, num_classes, use_conv=False):
if num_classes <= 0:
fc = nn.Identity() # pass-through (no classifier)
elif use_conv:
fc = nn.Conv2d(num_features, num_classes, 1, bias=True)
else:
fc = nn.Linear(num_features, num_classes, bias=True)
return fc
def create_classifier(
num_features: int,
num_classes: int,
pool_type: str = 'avg',
use_conv: bool = False,
input_fmt: str = 'NCHW',
drop_rate: Optional[float] = None,
):
global_pool, num_pooled_features = _create_pool(
num_features,
num_classes,
pool_type,
use_conv=use_conv,
input_fmt=input_fmt,
)
fc = _create_fc(
num_pooled_features,
num_classes,
use_conv=use_conv,
)
if drop_rate is not None:
dropout = nn.Dropout(drop_rate)
return global_pool, dropout, fc
return global_pool, fc
class ClassifierHead(nn.Module):
"""Classifier head w/ configurable global pooling and dropout."""
def __init__(
self,
in_features: int,
num_classes: int,
pool_type: str = 'avg',
drop_rate: float = 0.,
use_conv: bool = False,
input_fmt: str = 'NCHW',
):
"""
Args:
in_features: The number of input features.
num_classes: The number of classes for the final classifier layer (output).
pool_type: Global pooling type, pooling disabled if empty string ('').
drop_rate: Pre-classifier dropout rate.
"""
super(ClassifierHead, self).__init__()
self.in_features = in_features
self.use_conv = use_conv
self.input_fmt = input_fmt
global_pool, fc = create_classifier(
in_features,
num_classes,
pool_type,
use_conv=use_conv,
input_fmt=input_fmt,
)
self.global_pool = global_pool
self.drop = nn.Dropout(drop_rate)
self.fc = fc
self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity()
def reset(self, num_classes, pool_type=None):
if pool_type is not None and pool_type != self.global_pool.pool_type:
self.global_pool, self.fc = create_classifier(
self.in_features,
num_classes,
pool_type=pool_type,
use_conv=self.use_conv,
input_fmt=self.input_fmt,
)
self.flatten = nn.Flatten(1) if self.use_conv and pool_type else nn.Identity()
else:
num_pooled_features = self.in_features * self.global_pool.feat_mult()
self.fc = _create_fc(
num_pooled_features,
num_classes,
use_conv=self.use_conv,
)
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.drop(x)
if pre_logits:
return self.flatten(x)
x = self.fc(x)
return self.flatten(x)
class NormMlpClassifierHead(nn.Module):
def __init__(
self,
in_features: int,
num_classes: int,
hidden_size: Optional[int] = None,
pool_type: str = 'avg',
drop_rate: float = 0.,
norm_layer: Union[str, Callable] = 'layernorm2d',
act_layer: Union[str, Callable] = 'tanh',
):
"""
Args:
in_features: The number of input features.
num_classes: The number of classes for the final classifier layer (output).
hidden_size: The hidden size of the MLP (pre-logits FC layer) if not None.
pool_type: Global pooling type, pooling disabled if empty string ('').
drop_rate: Pre-classifier dropout rate.
norm_layer: Normalization layer type.
act_layer: MLP activation layer type (only used if hidden_size is not None).
"""
super().__init__()
self.in_features = in_features
self.hidden_size = hidden_size
self.num_features = in_features
self.use_conv = not pool_type
norm_layer = get_norm_layer(norm_layer)
act_layer = get_act_layer(act_layer)
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
self.global_pool = SelectAdaptivePool2d(pool_type=pool_type)
self.norm = norm_layer(in_features)
self.flatten = nn.Flatten(1) if pool_type else nn.Identity()
if hidden_size:
self.pre_logits = nn.Sequential(OrderedDict([
('fc', linear_layer(in_features, hidden_size)),
('act', act_layer()),
]))
self.num_features = hidden_size
else:
self.pre_logits = nn.Identity()
self.drop = nn.Dropout(drop_rate)
self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def reset(self, num_classes, global_pool=None):
if global_pool is not None:
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
self.use_conv = self.global_pool.is_identity()
linear_layer = partial(nn.Conv2d, kernel_size=1) if self.use_conv else nn.Linear
if self.hidden_size:
if ((isinstance(self.pre_logits.fc, nn.Conv2d) and not self.use_conv) or
(isinstance(self.pre_logits.fc, nn.Linear) and self.use_conv)):
with torch.no_grad():
new_fc = linear_layer(self.in_features, self.hidden_size)
new_fc.weight.copy_(self.pre_logits.fc.weight.reshape(new_fc.weight.shape))
new_fc.bias.copy_(self.pre_logits.fc.bias)
self.pre_logits.fc = new_fc
self.fc = linear_layer(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x, pre_logits: bool = False):
x = self.global_pool(x)
x = self.norm(x)
x = self.flatten(x)
x = self.pre_logits(x)
x = self.drop(x)
if pre_logits:
return x
x = self.fc(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/config.py | """ Model / Layer Config singleton state
"""
import os
import warnings
from typing import Any, Optional
import torch
__all__ = [
'is_exportable', 'is_scriptable', 'is_no_jit', 'use_fused_attn',
'set_exportable', 'set_scriptable', 'set_no_jit', 'set_layer_config', 'set_fused_attn'
]
# Set to True if prefer to have layers with no jit optimization (includes activations)
_NO_JIT = False
# Set to True if prefer to have activation layers with no jit optimization
# NOTE not currently used as no difference between no_jit and no_activation jit as only layers obeying
# the jit flags so far are activations. This will change as more layers are updated and/or added.
_NO_ACTIVATION_JIT = False
# Set to True if exporting a model with Same padding via ONNX
_EXPORTABLE = False
# Set to True if wanting to use torch.jit.script on a model
_SCRIPTABLE = False
# use torch.scaled_dot_product_attention where possible
_HAS_FUSED_ATTN = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
if 'TIMM_FUSED_ATTN' in os.environ:
_USE_FUSED_ATTN = int(os.environ['TIMM_FUSED_ATTN'])
else:
_USE_FUSED_ATTN = 1 # 0 == off, 1 == on (for tested use), 2 == on (for experimental use)
def is_no_jit():
return _NO_JIT
class set_no_jit:
def __init__(self, mode: bool) -> None:
global _NO_JIT
self.prev = _NO_JIT
_NO_JIT = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _NO_JIT
_NO_JIT = self.prev
return False
def is_exportable():
return _EXPORTABLE
class set_exportable:
def __init__(self, mode: bool) -> None:
global _EXPORTABLE
self.prev = _EXPORTABLE
_EXPORTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _EXPORTABLE
_EXPORTABLE = self.prev
return False
def is_scriptable():
return _SCRIPTABLE
class set_scriptable:
def __init__(self, mode: bool) -> None:
global _SCRIPTABLE
self.prev = _SCRIPTABLE
_SCRIPTABLE = mode
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
_SCRIPTABLE = self.prev
return False
class set_layer_config:
""" Layer config context manager that allows setting all layer config flags at once.
If a flag arg is None, it will not change the current value.
"""
def __init__(
self,
scriptable: Optional[bool] = None,
exportable: Optional[bool] = None,
no_jit: Optional[bool] = None,
no_activation_jit: Optional[bool] = None):
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
self.prev = _SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT
if scriptable is not None:
_SCRIPTABLE = scriptable
if exportable is not None:
_EXPORTABLE = exportable
if no_jit is not None:
_NO_JIT = no_jit
if no_activation_jit is not None:
_NO_ACTIVATION_JIT = no_activation_jit
def __enter__(self) -> None:
pass
def __exit__(self, *args: Any) -> bool:
global _SCRIPTABLE
global _EXPORTABLE
global _NO_JIT
global _NO_ACTIVATION_JIT
_SCRIPTABLE, _EXPORTABLE, _NO_JIT, _NO_ACTIVATION_JIT = self.prev
return False
def use_fused_attn(experimental: bool = False) -> bool:
# NOTE: ONNX export cannot handle F.scaled_dot_product_attention as of pytorch 2.0
if not _HAS_FUSED_ATTN or _EXPORTABLE:
return False
if experimental:
return _USE_FUSED_ATTN > 1
return _USE_FUSED_ATTN > 0
def set_fused_attn(enable: bool = True, experimental: bool = False):
global _USE_FUSED_ATTN
if not _HAS_FUSED_ATTN:
warnings.warn('This version of pytorch does not have F.scaled_dot_product_attention, fused_attn flag ignored.')
return
if experimental and enable:
_USE_FUSED_ATTN = 2
elif enable:
_USE_FUSED_ATTN = 1
else:
_USE_FUSED_ATTN = 0
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/mixed_conv2d.py | """ PyTorch Mixed Convolution
Paper: MixConv: Mixed Depthwise Convolutional Kernels (https://arxiv.org/abs/1907.09595)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv2d_same import create_conv2d_pad
def _split_channels(num_chan, num_groups):
split = [num_chan // num_groups for _ in range(num_groups)]
split[0] += num_chan - sum(split)
return split
class MixedConv2d(nn.ModuleDict):
""" Mixed Grouped Convolution
Based on MDConv and GroupedConv in MixNet impl:
https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mixnet/custom_layers.py
"""
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, depthwise=False, **kwargs):
super(MixedConv2d, self).__init__()
kernel_size = kernel_size if isinstance(kernel_size, list) else [kernel_size]
num_groups = len(kernel_size)
in_splits = _split_channels(in_channels, num_groups)
out_splits = _split_channels(out_channels, num_groups)
self.in_channels = sum(in_splits)
self.out_channels = sum(out_splits)
for idx, (k, in_ch, out_ch) in enumerate(zip(kernel_size, in_splits, out_splits)):
conv_groups = in_ch if depthwise else 1
# use add_module to keep key space clean
self.add_module(
str(idx),
create_conv2d_pad(
in_ch, out_ch, k, stride=stride,
padding=padding, dilation=dilation, groups=conv_groups, **kwargs)
)
self.splits = in_splits
def forward(self, x):
x_split = torch.split(x, self.splits, 1)
x_out = [c(x_split[i]) for i, c in enumerate(self.values())]
x = torch.cat(x_out, 1)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/attention_pool2d.py | """ Attention Pool 2D
Implementations of 2D spatial feature pooling using multi-head attention instead of average pool.
Based on idea in CLIP by OpenAI, licensed Apache 2.0
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import Union, Tuple
import torch
import torch.nn as nn
from .helpers import to_2tuple
from .pos_embed_sincos import apply_rot_embed, RotaryEmbedding
from .weight_init import trunc_normal_
class RotAttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ rotary (relative) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
Adapted from the AttentionPool2d in CLIP w/ rotary embedding instead of learned embed.
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
NOTE: While this impl does not require a fixed feature size, performance at differeing resolutions from
train varies widely and falls off dramatically. I'm not sure if there is a way around this... -RW
"""
def __init__(
self,
in_features: int,
out_features: int = None,
embed_dim: int = None,
num_heads: int = 4,
qkv_bias: bool = True,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dim, out_features)
self.num_heads = num_heads
assert embed_dim % num_heads == 0
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
self.pos_embed = RotaryEmbedding(self.head_dim)
trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
nn.init.zeros_(self.qkv.bias)
def forward(self, x):
B, _, H, W = x.shape
N = H * W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
qc, q = q[:, :, :1], q[:, :, 1:]
sin_emb, cos_emb = self.pos_embed.get_embed((H, W))
q = apply_rot_embed(q, sin_emb, cos_emb)
q = torch.cat([qc, q], dim=2)
kc, k = k[:, :, :1], k[:, :, 1:]
k = apply_rot_embed(k, sin_emb, cos_emb)
k = torch.cat([kc, k], dim=2)
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N + 1, -1)
x = self.proj(x)
return x[:, 0]
class AttentionPool2d(nn.Module):
""" Attention based 2D feature pooling w/ learned (absolute) pos embedding.
This is a multi-head attention based replacement for (spatial) average pooling in NN architectures.
It was based on impl in CLIP by OpenAI
https://github.com/openai/CLIP/blob/3b473b0e682c091a9e53623eebc1ca1657385717/clip/model.py
NOTE: This requires feature size upon construction and well prevent adaptive sizing of the network.
"""
def __init__(
self,
in_features: int,
feat_size: Union[int, Tuple[int, int]],
out_features: int = None,
embed_dim: int = None,
num_heads: int = 4,
qkv_bias: bool = True,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
assert embed_dim % num_heads == 0
self.feat_size = to_2tuple(feat_size)
self.qkv = nn.Linear(in_features, embed_dim * 3, bias=qkv_bias)
self.proj = nn.Linear(embed_dim, out_features)
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
spatial_dim = self.feat_size[0] * self.feat_size[1]
self.pos_embed = nn.Parameter(torch.zeros(spatial_dim + 1, in_features))
trunc_normal_(self.pos_embed, std=in_features ** -0.5)
trunc_normal_(self.qkv.weight, std=in_features ** -0.5)
nn.init.zeros_(self.qkv.bias)
def forward(self, x):
B, _, H, W = x.shape
N = H * W
assert self.feat_size[0] == H
assert self.feat_size[1] == W
x = x.reshape(B, -1, N).permute(0, 2, 1)
x = torch.cat([x.mean(1, keepdim=True), x], dim=1)
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
x = self.qkv(x).reshape(B, N + 1, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = x[0], x[1], x[2]
attn = (q @ k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = (attn @ v).transpose(1, 2).reshape(B, N + 1, -1)
x = self.proj(x)
return x[:, 0]
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/space_to_depth.py | import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
bs: torch.jit.Final[int]
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, self.bs, W // self.bs, self.bs) # (N, C, H//bs, bs, W//bs, bs)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs)
x = x.view(N, C * self.bs * self.bs, H // self.bs, W // self.bs) # (N, C*bs^2, H//bs, W//bs)
return x
@torch.jit.script
class SpaceToDepthJit:
def __call__(self, x: torch.Tensor):
# assuming hard-coded that block_size==4 for acceleration
N, C, H, W = x.size()
x = x.view(N, C, H // 4, 4, W // 4, 4) # (N, C, H//bs, bs, W//bs, bs)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # (N, bs, bs, C, H//bs, W//bs)
x = x.view(N, C * 16, H // 4, W // 4) # (N, C*bs^2, H//bs, W//bs)
return x
class SpaceToDepthModule(nn.Module):
def __init__(self, no_jit=False):
super().__init__()
if not no_jit:
self.op = SpaceToDepthJit()
else:
self.op = SpaceToDepth()
def forward(self, x):
return self.op(x)
class DepthToSpace(nn.Module):
def __init__(self, block_size):
super().__init__()
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, self.bs, self.bs, C // (self.bs ** 2), H, W) # (N, bs, bs, C//bs^2, H, W)
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # (N, C//bs^2, H, bs, W, bs)
x = x.view(N, C // (self.bs ** 2), H * self.bs, W * self.bs) # (N, C//bs^2, H * bs, W * bs)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/grn.py | """ Global Response Normalization Module
Based on the GRN layer presented in
`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808
This implementation
* works for both NCHW and NHWC tensor layouts
* uses affine param names matching existing torch norm layers
* slightly improves eager mode performance via fused addcmul
Hacked together by / Copyright 2023 Ross Wightman
"""
import torch
from torch import nn as nn
class GlobalResponseNorm(nn.Module):
""" Global Response Normalization layer
"""
def __init__(self, dim, eps=1e-6, channels_last=True):
super().__init__()
self.eps = eps
if channels_last:
self.spatial_dim = (1, 2)
self.channel_dim = -1
self.wb_shape = (1, 1, 1, -1)
else:
self.spatial_dim = (2, 3)
self.channel_dim = 1
self.wb_shape = (1, -1, 1, 1)
self.weight = nn.Parameter(torch.zeros(dim))
self.bias = nn.Parameter(torch.zeros(dim))
def forward(self, x):
x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True)
x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps)
return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_act.py | """ Activation Factory
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Union, Callable, Type
from .activations import *
from .activations_jit import *
from .activations_me import *
from .config import is_exportable, is_scriptable, is_no_jit
# PyTorch has an optimized, native 'silu' (aka 'swish') operator as of PyTorch 1.7.
# Also hardsigmoid, hardswish, and soon mish. This code will use native version if present.
# Eventually, the custom SiLU, Mish, Hard*, layers will be removed and only native variants will be used.
_has_silu = 'silu' in dir(torch.nn.functional)
_has_hardswish = 'hardswish' in dir(torch.nn.functional)
_has_hardsigmoid = 'hardsigmoid' in dir(torch.nn.functional)
_has_mish = 'mish' in dir(torch.nn.functional)
_ACT_FN_DEFAULT = dict(
silu=F.silu if _has_silu else swish,
swish=F.silu if _has_silu else swish,
mish=F.mish if _has_mish else mish,
relu=F.relu,
relu6=F.relu6,
leaky_relu=F.leaky_relu,
elu=F.elu,
celu=F.celu,
selu=F.selu,
gelu=gelu,
gelu_tanh=gelu_tanh,
quick_gelu=quick_gelu,
sigmoid=sigmoid,
tanh=tanh,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid,
hard_swish=F.hardswish if _has_hardswish else hard_swish,
hard_mish=hard_mish,
)
_ACT_FN_JIT = dict(
silu=F.silu if _has_silu else swish_jit,
swish=F.silu if _has_silu else swish_jit,
mish=F.mish if _has_mish else mish_jit,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_jit,
hard_swish=F.hardswish if _has_hardswish else hard_swish_jit,
hard_mish=hard_mish_jit,
)
_ACT_FN_ME = dict(
silu=F.silu if _has_silu else swish_me,
swish=F.silu if _has_silu else swish_me,
mish=F.mish if _has_mish else mish_me,
hard_sigmoid=F.hardsigmoid if _has_hardsigmoid else hard_sigmoid_me,
hard_swish=F.hardswish if _has_hardswish else hard_swish_me,
hard_mish=hard_mish_me,
)
_ACT_FNS = (_ACT_FN_ME, _ACT_FN_JIT, _ACT_FN_DEFAULT)
for a in _ACT_FNS:
a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
a.setdefault('hardswish', a.get('hard_swish'))
_ACT_LAYER_DEFAULT = dict(
silu=nn.SiLU if _has_silu else Swish,
swish=nn.SiLU if _has_silu else Swish,
mish=nn.Mish if _has_mish else Mish,
relu=nn.ReLU,
relu6=nn.ReLU6,
leaky_relu=nn.LeakyReLU,
elu=nn.ELU,
prelu=PReLU,
celu=nn.CELU,
selu=nn.SELU,
gelu=GELU,
gelu_tanh=GELUTanh,
quick_gelu=QuickGELU,
sigmoid=Sigmoid,
tanh=Tanh,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoid,
hard_swish=nn.Hardswish if _has_hardswish else HardSwish,
hard_mish=HardMish,
identity=nn.Identity,
)
_ACT_LAYER_JIT = dict(
silu=nn.SiLU if _has_silu else SwishJit,
swish=nn.SiLU if _has_silu else SwishJit,
mish=nn.Mish if _has_mish else MishJit,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidJit,
hard_swish=nn.Hardswish if _has_hardswish else HardSwishJit,
hard_mish=HardMishJit,
)
_ACT_LAYER_ME = dict(
silu=nn.SiLU if _has_silu else SwishMe,
swish=nn.SiLU if _has_silu else SwishMe,
mish=nn.Mish if _has_mish else MishMe,
hard_sigmoid=nn.Hardsigmoid if _has_hardsigmoid else HardSigmoidMe,
hard_swish=nn.Hardswish if _has_hardswish else HardSwishMe,
hard_mish=HardMishMe,
)
_ACT_LAYERS = (_ACT_LAYER_ME, _ACT_LAYER_JIT, _ACT_LAYER_DEFAULT)
for a in _ACT_LAYERS:
a.setdefault('hardsigmoid', a.get('hard_sigmoid'))
a.setdefault('hardswish', a.get('hard_swish'))
def get_act_fn(name: Union[Callable, str] = 'relu'):
""" Activation Function Factory
Fetching activation fns by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if not name:
return None
if isinstance(name, Callable):
return name
if not (is_no_jit() or is_exportable() or is_scriptable()):
# If not exporting or scripting the model, first look for a memory-efficient version with
# custom autograd, then fallback
if name in _ACT_FN_ME:
return _ACT_FN_ME[name]
if not (is_no_jit() or is_exportable()):
if name in _ACT_FN_JIT:
return _ACT_FN_JIT[name]
return _ACT_FN_DEFAULT[name]
def get_act_layer(name: Union[Type[nn.Module], str] = 'relu'):
""" Activation Layer Factory
Fetching activation layers by name with this function allows export or torch script friendly
functions to be returned dynamically based on current config.
"""
if name is None:
return None
if not isinstance(name, str):
# callable, module, etc
return name
if not name:
return None
if not (is_no_jit() or is_exportable() or is_scriptable()):
if name in _ACT_LAYER_ME:
return _ACT_LAYER_ME[name]
if not (is_no_jit() or is_exportable()):
if name in _ACT_LAYER_JIT:
return _ACT_LAYER_JIT[name]
return _ACT_LAYER_DEFAULT[name]
def create_act_layer(name: Union[nn.Module, str], inplace=None, **kwargs):
act_layer = get_act_layer(name)
if act_layer is None:
return None
if inplace is None:
return act_layer(**kwargs)
try:
return act_layer(inplace=inplace, **kwargs)
except TypeError:
# recover if act layer doesn't have inplace arg
return act_layer(**kwargs)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/activations_me.py | """ Activations (memory-efficient w/ custom autograd)
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
These activations are not compatible with jit scripting or ONNX export of the model, please use either
the JIT or basic versions of the activations.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
@torch.jit.script
def swish_jit_fwd(x):
return x.mul(torch.sigmoid(x))
@torch.jit.script
def swish_jit_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid)))
class SwishJitAutoFn(torch.autograd.Function):
""" torch.jit.script optimised Swish w/ memory-efficient checkpoint
Inspired by conversation btw Jeremy Howard & Adam Pazske
https://twitter.com/jeremyphoward/status/1188251041835315200
"""
@staticmethod
def symbolic(g, x):
return g.op("Mul", x, g.op("Sigmoid", x))
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return swish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return swish_jit_bwd(x, grad_output)
def swish_me(x, inplace=False):
return SwishJitAutoFn.apply(x)
class SwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishMe, self).__init__()
def forward(self, x):
return SwishJitAutoFn.apply(x)
@torch.jit.script
def mish_jit_fwd(x):
return x.mul(torch.tanh(F.softplus(x)))
@torch.jit.script
def mish_jit_bwd(x, grad_output):
x_sigmoid = torch.sigmoid(x)
x_tanh_sp = F.softplus(x).tanh()
return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))
class MishJitAutoFn(torch.autograd.Function):
""" Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
A memory efficient, jit scripted variant of Mish
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return mish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return mish_jit_bwd(x, grad_output)
def mish_me(x, inplace=False):
return MishJitAutoFn.apply(x)
class MishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(MishMe, self).__init__()
def forward(self, x):
return MishJitAutoFn.apply(x)
@torch.jit.script
def hard_sigmoid_jit_fwd(x, inplace: bool = False):
return (x + 3).clamp(min=0, max=6).div(6.)
@torch.jit.script
def hard_sigmoid_jit_bwd(x, grad_output):
m = torch.ones_like(x) * ((x >= -3.) & (x <= 3.)) / 6.
return grad_output * m
class HardSigmoidJitAutoFn(torch.autograd.Function):
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_sigmoid_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_sigmoid_jit_bwd(x, grad_output)
def hard_sigmoid_me(x, inplace: bool = False):
return HardSigmoidJitAutoFn.apply(x)
class HardSigmoidMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidMe, self).__init__()
def forward(self, x):
return HardSigmoidJitAutoFn.apply(x)
@torch.jit.script
def hard_swish_jit_fwd(x):
return x * (x + 3).clamp(min=0, max=6).div(6.)
@torch.jit.script
def hard_swish_jit_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= 3.)
m = torch.where((x >= -3.) & (x <= 3.), x / 3. + .5, m)
return grad_output * m
class HardSwishJitAutoFn(torch.autograd.Function):
"""A memory efficient, jit-scripted HardSwish activation"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_swish_jit_bwd(x, grad_output)
@staticmethod
def symbolic(g, self):
input = g.op("Add", self, g.op('Constant', value_t=torch.tensor(3, dtype=torch.float)))
hardtanh_ = g.op("Clip", input, g.op('Constant', value_t=torch.tensor(0, dtype=torch.float)), g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
hardtanh_ = g.op("Div", hardtanh_, g.op('Constant', value_t=torch.tensor(6, dtype=torch.float)))
return g.op("Mul", self, hardtanh_)
def hard_swish_me(x, inplace=False):
return HardSwishJitAutoFn.apply(x)
class HardSwishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishMe, self).__init__()
def forward(self, x):
return HardSwishJitAutoFn.apply(x)
@torch.jit.script
def hard_mish_jit_fwd(x):
return 0.5 * x * (x + 2).clamp(min=0, max=2)
@torch.jit.script
def hard_mish_jit_bwd(x, grad_output):
m = torch.ones_like(x) * (x >= -2.)
m = torch.where((x >= -2.) & (x <= 0.), x + 1., m)
return grad_output * m
class HardMishJitAutoFn(torch.autograd.Function):
""" A memory efficient, jit scripted variant of Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
@staticmethod
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_mish_jit_fwd(x)
@staticmethod
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_mish_jit_bwd(x, grad_output)
def hard_mish_me(x, inplace: bool = False):
return HardMishJitAutoFn.apply(x)
class HardMishMe(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishMe, self).__init__()
def forward(self, x):
return HardMishJitAutoFn.apply(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pos_embed_sincos.py | """ Sin-cos, fourier, rotary position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
from typing import List, Tuple, Optional, Union
import torch
from torch import nn as nn
from .trace_utils import _assert
def pixel_freq_bands(
num_bands: int,
max_freq: float = 224.,
linear_bands: bool = True,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
):
if linear_bands:
bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=dtype, device=device)
else:
bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=dtype, device=device)
return bands * torch.pi
def freq_bands(
num_bands: int,
temperature: float = 10000.,
step: int = 2,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
) -> torch.Tensor:
bands = 1. / (temperature ** (torch.arange(0, num_bands, step, dtype=dtype, device=device) / num_bands))
return bands
def build_sincos2d_pos_embed(
feat_shape: List[int],
dim: int = 64,
temperature: float = 10000.,
reverse_coord: bool = False,
interleave_sin_cos: bool = False,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None
) -> torch.Tensor:
"""
Args:
feat_shape:
dim:
temperature:
reverse_coord: stack grid order W, H instead of H, W
interleave_sin_cos: sin, cos, sin, cos stack instead of sin, sin, cos, cos
dtype:
device:
Returns:
"""
assert dim % 4 == 0, 'Embed dimension must be divisible by 4 for sin-cos 2D position embedding'
pos_dim = dim // 4
bands = freq_bands(pos_dim, temperature=temperature, step=1, dtype=dtype, device=device)
if reverse_coord:
feat_shape = feat_shape[::-1] # stack W, H instead of H, W
grid = torch.stack(torch.meshgrid(
[torch.arange(s, device=device, dtype=dtype) for s in feat_shape])).flatten(1).transpose(0, 1)
pos2 = grid.unsqueeze(-1) * bands.unsqueeze(0)
# FIXME add support for unflattened spatial dim?
stack_dim = 2 if interleave_sin_cos else 1 # stack sin, cos, sin, cos instead of sin sin cos cos
pos_emb = torch.stack([torch.sin(pos2), torch.cos(pos2)], dim=stack_dim).flatten(1)
return pos_emb
def build_fourier_pos_embed(
feat_shape: List[int],
bands: Optional[torch.Tensor] = None,
num_bands: int = 64,
max_res: int = 224,
temperature: float = 10000.,
linear_bands: bool = False,
include_grid: bool = False,
in_pixels: bool = True,
ref_feat_shape: Optional[List[int]] = None,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
) -> List[torch.Tensor]:
"""
Args:
feat_shape: Feature shape for embedding.
bands: Pre-calculated frequency bands.
num_bands: Number of frequency bands (determines output dim).
max_res: Maximum resolution for pixel based freq.
temperature: Temperature for non-pixel freq.
linear_bands: Linear band spacing for pixel based freq.
include_grid: Include the spatial grid in output.
in_pixels: Output in pixel freq.
ref_feat_shape: Reference feature shape for resize / fine-tune.
dtype: Output dtype.
device: Output device.
Returns:
"""
if bands is None:
if in_pixels:
bands = pixel_freq_bands(
num_bands,
float(max_res),
linear_bands=linear_bands,
dtype=dtype,
device=device,
)
else:
bands = freq_bands(
num_bands,
temperature=temperature,
step=1,
dtype=dtype,
device=device,
)
else:
if device is None:
device = bands.device
if dtype is None:
dtype = bands.dtype
if in_pixels:
t = [torch.linspace(-1., 1., steps=s, device=device, dtype=dtype) for s in feat_shape]
else:
t = [torch.arange(s, device=device, dtype=dtype) for s in feat_shape]
if ref_feat_shape is not None:
# eva's scheme for resizing rope embeddings (ref shape = pretrain)
t = [x / f * r for x, f, r in zip(t, feat_shape, ref_feat_shape)]
grid = torch.stack(torch.meshgrid(t), dim=-1)
grid = grid.unsqueeze(-1)
pos = grid * bands
pos_sin, pos_cos = pos.sin(), pos.cos()
out = [grid, pos_sin, pos_cos] if include_grid else [pos_sin, pos_cos]
return out
class FourierEmbed(nn.Module):
def __init__(
self,
max_res: int = 224,
num_bands: int = 64,
concat_grid=True,
keep_spatial=False,
):
super().__init__()
self.max_res = max_res
self.num_bands = num_bands
self.concat_grid = concat_grid
self.keep_spatial = keep_spatial
self.register_buffer(
'bands',
pixel_freq_bands(max_res, num_bands),
persistent=False,
)
def forward(self, x):
B, C = x.shape[:2]
feat_shape = x.shape[2:]
emb = build_fourier_pos_embed(
feat_shape,
self.bands,
include_grid=self.concat_grid,
dtype=x.dtype,
device=x.device,
)
emb = torch.cat(emb, dim=-1)
emb = emb.transpose(-1, -2).flatten(len(feat_shape))
batch_expand = (B,) + (-1,) * (x.ndim - 1)
# FIXME support nD
if self.keep_spatial:
x = torch.cat([x, emb.unsqueeze(0).expand(batch_expand).permute(0, 3, 1, 2)], dim=1)
else:
x = torch.cat([x.permute(0, 2, 3, 1), emb.unsqueeze(0).expand(batch_expand)], dim=-1)
x = x.reshape(B, feat_shape.numel(), -1)
return x
def rot(x):
return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape)
def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb):
if sin_emb.ndim == 3:
return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
return x * cos_emb + rot(x) * sin_emb
def apply_rot_embed_list(x: List[torch.Tensor], sin_emb, cos_emb):
if isinstance(x, torch.Tensor):
x = [x]
return [t * cos_emb + rot(t) * sin_emb for t in x]
def apply_rot_embed_cat(x: torch.Tensor, emb):
sin_emb, cos_emb = emb.tensor_split(2, -1)
if sin_emb.ndim == 3:
return x * cos_emb.unsqueeze(1).expand_as(x) + rot(x) * sin_emb.unsqueeze(1).expand_as(x)
return x * cos_emb + rot(x) * sin_emb
def apply_keep_indices_nlc(x, pos_embed, keep_indices):
pos_embed = pos_embed.unsqueeze(0).expand(x.shape[0], -1, -1)
pos_embed = pos_embed.gather(1, keep_indices.unsqueeze(-1).expand(-1, -1, pos_embed.shape[-1]))
return pos_embed
def build_rotary_pos_embed(
feat_shape: List[int],
bands: Optional[torch.Tensor] = None,
dim: int = 64,
max_res: int = 224,
temperature: float = 10000.,
linear_bands: bool = False,
in_pixels: bool = True,
ref_feat_shape: Optional[List[int]] = None,
dtype: torch.dtype = torch.float32,
device: Optional[torch.device] = None,
):
"""
Args:
feat_shape: Spatial shape of the target tensor for embedding.
bands: Optional pre-generated frequency bands
dim: Output dimension of embedding tensor.
max_res: Maximum resolution for pixel mode.
temperature: Temperature (inv freq) for non-pixel mode
linear_bands: Linearly (instead of log) spaced bands for pixel mode
in_pixels: Pixel vs language (inv freq) mode.
dtype: Output dtype.
device: Output device.
Returns:
"""
sin_emb, cos_emb = build_fourier_pos_embed(
feat_shape,
bands=bands,
num_bands=dim // 4,
max_res=max_res,
temperature=temperature,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=ref_feat_shape,
device=device,
dtype=dtype,
)
num_spatial_dim = 1
# this would be much nicer as a .numel() call to torch.Size(), but torchscript sucks
for x in feat_shape:
num_spatial_dim *= x
sin_emb = sin_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
cos_emb = cos_emb.reshape(num_spatial_dim, -1).repeat_interleave(2, -1)
return sin_emb, cos_emb
class RotaryEmbedding(nn.Module):
""" Rotary position embedding
NOTE: This is my initial attempt at impl rotary embedding for spatial use, it has not
been well tested, and will likely change. It will be moved to its own file.
The following impl/resources were referenced for this impl:
* https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
* https://blog.eleuther.ai/rotary-embeddings/
"""
def __init__(
self,
dim,
max_res=224,
temperature=10000,
in_pixels=True,
linear_bands: bool = False,
feat_shape: Optional[List[int]] = None,
ref_feat_shape: Optional[List[int]] = None,
):
super().__init__()
self.dim = dim
self.max_res = max_res
self.temperature = temperature
self.in_pixels = in_pixels
self.feat_shape = feat_shape
self.ref_feat_shape = ref_feat_shape
if feat_shape is None:
# only cache bands
if in_pixels:
bands = pixel_freq_bands(
dim // 4,
float(max_res),
linear_bands=linear_bands,
)
else:
bands = freq_bands(
dim // 4,
temperature=temperature,
step=1,
)
print(bands)
self.register_buffer(
'bands',
bands,
persistent=False,
)
self.pos_embed_sin = None
self.pos_embed_cos = None
else:
# cache full sin/cos embeddings if shape provided up front
emb_sin, emb_cos = build_rotary_pos_embed(
feat_shape=feat_shape,
dim=dim,
max_res=max_res,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
self.bands = None
self.register_buffer(
'pos_embed_sin',
emb_sin,
persistent=False,
)
self.register_buffer(
'pos_embed_cos',
emb_cos,
persistent=False,
)
def get_embed(self, shape: Optional[List[int]] = None):
if self.bands is not None:
# rebuild embeddings every call, use if target shape changes
assert shape is not None
return build_rotary_pos_embed(
shape,
self.bands,
in_pixels=self.in_pixels,
)
else:
return self.pos_embed_sin, self.pos_embed_cos
def forward(self, x):
# assuming channel-first tensor where spatial dim are >= 2
sin_emb, cos_emb = self.get_embed(x.shape[2:])
return apply_rot_embed(x, sin_emb, cos_emb)
class RotaryEmbeddingCat(nn.Module):
""" Rotary position embedding w/ concatenatd sin & cos
The following impl/resources were referenced for this impl:
* https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py
* https://blog.eleuther.ai/rotary-embeddings/
"""
def __init__(
self,
dim,
max_res=224,
temperature=10000,
in_pixels=True,
linear_bands: bool = False,
feat_shape: Optional[List[int]] = None,
ref_feat_shape: Optional[List[int]] = None,
):
super().__init__()
self.dim = dim
self.max_res = max_res
self.temperature = temperature
self.in_pixels = in_pixels
self.feat_shape = feat_shape
self.ref_feat_shape = ref_feat_shape
if feat_shape is None:
# only cache bands
if in_pixels:
bands = pixel_freq_bands(
dim // 4,
float(max_res),
linear_bands=linear_bands,
)
else:
bands = freq_bands(
dim // 4,
temperature=temperature,
step=1,
)
self.register_buffer(
'bands',
bands,
persistent=False,
)
self.pos_embed = None
else:
# cache full sin/cos embeddings if shape provided up front
embeds = build_rotary_pos_embed(
feat_shape=feat_shape,
dim=dim,
max_res=max_res,
linear_bands=linear_bands,
in_pixels=in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
self.bands = None
self.register_buffer(
'pos_embed',
torch.cat(embeds, -1),
persistent=False,
)
def get_embed(self, shape: Optional[List[int]] = None):
if self.bands is not None and shape is not None:
# rebuild embeddings every call, use if target shape changes
embeds = build_rotary_pos_embed(
shape,
self.bands,
in_pixels=self.in_pixels,
ref_feat_shape=self.ref_feat_shape,
)
return torch.cat(embeds, -1)
elif self.pos_embed is not None:
return self.pos_embed
else:
assert False, "get_embed() requires pre-computed pos_embed or valid shape w/ pre-computed bands"
def forward(self, x):
# assuming channel-first tensor where spatial dim are >= 2
pos_embed = self.get_embed(x.shape[2:])
return apply_rot_embed_cat(x, pos_embed)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/activations.py | """ Activations
A collection of activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
def swish(x, inplace: bool = False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul_(x.sigmoid()) if inplace else x.mul(x.sigmoid())
class Swish(nn.Module):
def __init__(self, inplace: bool = False):
super(Swish, self).__init__()
self.inplace = inplace
def forward(self, x):
return swish(x, self.inplace)
def mish(x, inplace: bool = False):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
NOTE: I don't have a working inplace variant
"""
return x.mul(F.softplus(x).tanh())
class Mish(nn.Module):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
def __init__(self, inplace: bool = False):
super(Mish, self).__init__()
def forward(self, x):
return mish(x)
def sigmoid(x, inplace: bool = False):
return x.sigmoid_() if inplace else x.sigmoid()
# PyTorch has this, but not with a consistent inplace argmument interface
class Sigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(Sigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.sigmoid_() if self.inplace else x.sigmoid()
def tanh(x, inplace: bool = False):
return x.tanh_() if inplace else x.tanh()
# PyTorch has this, but not with a consistent inplace argmument interface
class Tanh(nn.Module):
def __init__(self, inplace: bool = False):
super(Tanh, self).__init__()
self.inplace = inplace
def forward(self, x):
return x.tanh_() if self.inplace else x.tanh()
def hard_swish(x, inplace: bool = False):
inner = F.relu6(x + 3.).div_(6.)
return x.mul_(inner) if inplace else x.mul(inner)
class HardSwish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwish, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_swish(x, self.inplace)
def hard_sigmoid(x, inplace: bool = False):
if inplace:
return x.add_(3.).clamp_(0., 6.).div_(6.)
else:
return F.relu6(x + 3.) / 6.
class HardSigmoid(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoid, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_sigmoid(x, self.inplace)
def hard_mish(x, inplace: bool = False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
if inplace:
return x.mul_(0.5 * (x + 2).clamp(min=0, max=2))
else:
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMish(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMish, self).__init__()
self.inplace = inplace
def forward(self, x):
return hard_mish(x, self.inplace)
class PReLU(nn.PReLU):
"""Applies PReLU (w/ dummy inplace arg)
"""
def __init__(self, num_parameters: int = 1, init: float = 0.25, inplace: bool = False) -> None:
super(PReLU, self).__init__(num_parameters=num_parameters, init=init)
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.prelu(input, self.weight)
def gelu(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
return F.gelu(x)
class GELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.gelu(input)
def gelu_tanh(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
return F.gelu(x, approximate='tanh')
class GELUTanh(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(GELUTanh, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
return F.gelu(input, approximate='tanh')
def quick_gelu(x: torch.Tensor, inplace: bool = False) -> torch.Tensor:
return x * torch.sigmoid(1.702 * x)
class QuickGELU(nn.Module):
"""Applies the Gaussian Error Linear Units function (w/ dummy inplace arg)
"""
def __init__(self, inplace: bool = False):
super(QuickGELU, self).__init__()
def forward(self, input: torch.Tensor) -> torch.Tensor:
return quick_gelu(input)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_norm.py | """ Norm Layer Factory
Create norm modules by string (to mirror create_act and creat_norm-act fns)
Copyright 2022 Ross Wightman
"""
import functools
import types
from typing import Type
import torch.nn as nn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from torchvision.ops.misc import FrozenBatchNorm2d
_NORM_MAP = dict(
batchnorm=nn.BatchNorm2d,
batchnorm2d=nn.BatchNorm2d,
batchnorm1d=nn.BatchNorm1d,
groupnorm=GroupNorm,
groupnorm1=GroupNorm1,
layernorm=LayerNorm,
layernorm2d=LayerNorm2d,
rmsnorm=RmsNorm,
frozenbatchnorm2d=FrozenBatchNorm2d,
)
_NORM_TYPES = {m for n, m in _NORM_MAP.items()}
def create_norm_layer(layer_name, num_features, **kwargs):
layer = get_norm_layer(layer_name)
layer_instance = layer(num_features, **kwargs)
return layer_instance
def get_norm_layer(norm_layer):
if norm_layer is None:
return None
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
norm_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_kwargs.update(norm_layer.keywords)
norm_layer = norm_layer.func
if isinstance(norm_layer, str):
if not norm_layer:
return None
layer_name = norm_layer.replace('_', '')
norm_layer = _NORM_MAP[layer_name]
else:
norm_layer = norm_layer
if norm_kwargs:
norm_layer = functools.partial(norm_layer, **norm_kwargs) # bind/rebind args
return norm_layer
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/linear.py | """ Linear layer (alternate definition)
"""
import torch
import torch.nn.functional as F
from torch import nn as nn
class Linear(nn.Linear):
r"""Applies a linear transformation to the incoming data: :math:`y = xA^T + b`
Wraps torch.nn.Linear to support AMP + torchscript usage by manually casting
weight & bias to input.dtype to work around an issue w/ torch.addmm in this use case.
"""
def forward(self, input: torch.Tensor) -> torch.Tensor:
if torch.jit.is_scripting():
bias = self.bias.to(dtype=input.dtype) if self.bias is not None else None
return F.linear(input, self.weight.to(dtype=input.dtype), bias=bias)
else:
return F.linear(input, self.weight, self.bias)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/separable_conv.py | """ Depthwise Separable Conv Modules
Basic DWS convs. Other variations of DWS exist with batch norm or activations between the
DW and PW convs such as the Depthwise modules in MobileNetV2 / EfficientNet and Xception.
Hacked together by / Copyright 2020 Ross Wightman
"""
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class SeparableConvNormAct(nn.Module):
""" Separable Conv w/ trailing Norm and Activation
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1, norm_layer=nn.BatchNorm2d, act_layer=nn.ReLU,
apply_act=True, drop_layer=None):
super(SeparableConvNormAct, self).__init__()
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
norm_kwargs = dict(drop_layer=drop_layer) if drop_layer is not None else {}
self.bn = norm_act_layer(out_channels, apply_act=apply_act, **norm_kwargs)
@property
def in_channels(self):
return self.conv_dw.in_channels
@property
def out_channels(self):
return self.conv_pw.out_channels
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
x = self.bn(x)
return x
SeparableConvBnAct = SeparableConvNormAct
class SeparableConv2d(nn.Module):
""" Separable Conv
"""
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, padding='', bias=False,
channel_multiplier=1.0, pw_kernel_size=1):
super(SeparableConv2d, self).__init__()
self.conv_dw = create_conv2d(
in_channels, int(in_channels * channel_multiplier), kernel_size,
stride=stride, dilation=dilation, padding=padding, depthwise=True)
self.conv_pw = create_conv2d(
int(in_channels * channel_multiplier), out_channels, pw_kernel_size, padding=padding, bias=bias)
@property
def in_channels(self):
return self.conv_dw.in_channels
@property
def out_channels(self):
return self.conv_pw.out_channels
def forward(self, x):
x = self.conv_dw(x)
x = self.conv_pw(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/trace_utils.py | try:
from torch import _assert
except ImportError:
def _assert(condition: bool, message: str):
assert condition, message
def _float_to_int(x: float) -> int:
"""
Symbolic tracing helper to substitute for inbuilt `int`.
Hint: Inbuilt `int` can't accept an argument of type `Proxy`
"""
return int(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/norm.py | """ Normalization layers and wrappers
Norm layer definitions that support fast norm and consistent channel arg order (always first arg).
Hacked together by / Copyright 2022 Ross Wightman
"""
import numbers
from typing import Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm, fast_rms_norm
class GroupNorm(nn.GroupNorm):
def __init__(self, num_channels, num_groups=32, eps=1e-5, affine=True):
# NOTE num_channels is swapped to first arg for consistency in swapping norm layers with BN
super().__init__(num_groups, num_channels, eps=eps, affine=affine)
self.fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x):
if self.fast_norm:
return fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
class GroupNorm1(nn.GroupNorm):
""" Group Normalization with 1 group.
Input: tensor in shape [B, C, *]
"""
def __init__(self, num_channels, **kwargs):
super().__init__(1, num_channels, **kwargs)
self.fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.fast_norm:
return fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
else:
return F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps)
class LayerNorm(nn.LayerNorm):
""" LayerNorm w/ fast norm option
"""
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channels, eps=eps, elementwise_affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
return x
class LayerNorm2d(nn.LayerNorm):
""" LayerNorm for channels of '2D' spatial NCHW tensors """
def __init__(self, num_channels, eps=1e-6, affine=True):
super().__init__(num_channels, eps=eps, elementwise_affine=affine)
self._fast_norm = is_fast_norm() # can't script unless we have these flags here (no globals)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = x.permute(0, 2, 3, 1)
if self._fast_norm:
x = fast_layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
else:
x = F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
x = x.permute(0, 3, 1, 2)
return x
def _is_contiguous(tensor: torch.Tensor) -> bool:
# jit is oh so lovely :/
if torch.jit.is_scripting():
return tensor.is_contiguous()
else:
return tensor.is_contiguous(memory_format=torch.contiguous_format)
@torch.jit.script
def _layer_norm_cf(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
s, u = torch.var_mean(x, dim=1, unbiased=False, keepdim=True)
x = (x - u) * torch.rsqrt(s + eps)
x = x * weight[:, None, None] + bias[:, None, None]
return x
def _layer_norm_cf_sqm(x: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, eps: float):
u = x.mean(dim=1, keepdim=True)
s = ((x * x).mean(dim=1, keepdim=True) - (u * u)).clamp(0)
x = (x - u) * torch.rsqrt(s + eps)
x = x * weight.view(1, -1, 1, 1) + bias.view(1, -1, 1, 1)
return x
class LayerNormExp2d(nn.LayerNorm):
""" LayerNorm for channels_first tensors with 2d spatial dimensions (ie N, C, H, W).
Experimental implementation w/ manual norm for tensors non-contiguous tensors.
This improves throughput in some scenarios (tested on Ampere GPU), esp w/ channels_last
layout. However, benefits are not always clear and can perform worse on other GPUs.
"""
def __init__(self, num_channels, eps=1e-6):
super().__init__(num_channels, eps=eps)
def forward(self, x) -> torch.Tensor:
if _is_contiguous(x):
x = F.layer_norm(
x.permute(0, 2, 3, 1), self.normalized_shape, self.weight, self.bias, self.eps).permute(0, 3, 1, 2)
else:
x = _layer_norm_cf(x, self.weight, self.bias, self.eps)
return x
class RmsNorm(nn.Module):
""" RmsNorm w/ fast (apex) norm if available
"""
__constants__ = ['normalized_shape', 'eps', 'elementwise_affine']
normalized_shape: Tuple[int, ...]
eps: float
elementwise_affine: bool
def __init__(self, channels, eps=1e-6, affine=True, device=None, dtype=None) -> None:
factory_kwargs = {'device': device, 'dtype': dtype}
super().__init__()
normalized_shape = channels
if isinstance(normalized_shape, numbers.Integral):
# mypy error: incompatible types in assignment
normalized_shape = (normalized_shape,) # type: ignore[assignment]
self.normalized_shape = tuple(normalized_shape) # type: ignore[arg-type]
self.eps = eps
self.elementwise_affine = affine
if self.elementwise_affine:
self.weight = nn.Parameter(torch.empty(self.normalized_shape, **factory_kwargs))
else:
self.register_parameter('weight', None)
self.reset_parameters()
def reset_parameters(self) -> None:
if self.elementwise_affine:
nn.init.ones_(self.weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
# NOTE fast norm fallback needs our rms norm impl, so both paths through here.
# Since there is no built-in PyTorch impl, always use APEX RmsNorm if is installed.
x = fast_rms_norm(x, self.normalized_shape, self.weight, self.eps)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/gather_excite.py | """ Gather-Excite Attention Block
Paper: `Gather-Excite: Exploiting Feature Context in CNNs` - https://arxiv.org/abs/1810.12348
Official code here, but it's only partial impl in Caffe: https://github.com/hujie-frank/GENet
I've tried to support all of the extent both w/ and w/o params. I don't believe I've seen another
impl that covers all of the cases.
NOTE: extent=0 + extra_params=False is equivalent to Squeeze-and-Excitation
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
from torch import nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer, get_act_layer
from .create_conv2d import create_conv2d
from .helpers import make_divisible
from .mlp import ConvMlp
class GatherExcite(nn.Module):
""" Gather-Excite Attention Module
"""
def __init__(
self, channels, feat_size=None, extra_params=False, extent=0, use_mlp=True,
rd_ratio=1./16, rd_channels=None, rd_divisor=1, add_maxpool=False,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, gate_layer='sigmoid'):
super(GatherExcite, self).__init__()
self.add_maxpool = add_maxpool
act_layer = get_act_layer(act_layer)
self.extent = extent
if extra_params:
self.gather = nn.Sequential()
if extent == 0:
assert feat_size is not None, 'spatial feature size must be specified for global extent w/ params'
self.gather.add_module(
'conv1', create_conv2d(channels, channels, kernel_size=feat_size, stride=1, depthwise=True))
if norm_layer:
self.gather.add_module(f'norm1', nn.BatchNorm2d(channels))
else:
assert extent % 2 == 0
num_conv = int(math.log2(extent))
for i in range(num_conv):
self.gather.add_module(
f'conv{i + 1}',
create_conv2d(channels, channels, kernel_size=3, stride=2, depthwise=True))
if norm_layer:
self.gather.add_module(f'norm{i + 1}', nn.BatchNorm2d(channels))
if i != num_conv - 1:
self.gather.add_module(f'act{i + 1}', act_layer(inplace=True))
else:
self.gather = None
if self.extent == 0:
self.gk = 0
self.gs = 0
else:
assert extent % 2 == 0
self.gk = self.extent * 2 - 1
self.gs = self.extent
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.mlp = ConvMlp(channels, rd_channels, act_layer=act_layer) if use_mlp else nn.Identity()
self.gate = create_act_layer(gate_layer)
def forward(self, x):
size = x.shape[-2:]
if self.gather is not None:
x_ge = self.gather(x)
else:
if self.extent == 0:
# global extent
x_ge = x.mean(dim=(2, 3), keepdims=True)
if self.add_maxpool:
# experimental codepath, may remove or change
x_ge = 0.5 * x_ge + 0.5 * x.amax((2, 3), keepdim=True)
else:
x_ge = F.avg_pool2d(
x, kernel_size=self.gk, stride=self.gs, padding=self.gk // 2, count_include_pad=False)
if self.add_maxpool:
# experimental codepath, may remove or change
x_ge = 0.5 * x_ge + 0.5 * F.max_pool2d(x, kernel_size=self.gk, stride=self.gs, padding=self.gk // 2)
x_ge = self.mlp(x_ge)
if x_ge.shape[-1] != 1 or x_ge.shape[-2] != 1:
x_ge = F.interpolate(x_ge, size=size)
return x * self.gate(x_ge)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_conv2d.py | """ Create Conv2d Factory Method
Hacked together by / Copyright 2020 Ross Wightman
"""
from .mixed_conv2d import MixedConv2d
from .cond_conv2d import CondConv2d
from .conv2d_same import create_conv2d_pad
def create_conv2d(in_channels, out_channels, kernel_size, **kwargs):
""" Select a 2d convolution implementation based on arguments
Creates and returns one of torch.nn.Conv2d, Conv2dSame, MixedConv2d, or CondConv2d.
Used extensively by EfficientNet, MobileNetv3 and related networks.
"""
if isinstance(kernel_size, list):
assert 'num_experts' not in kwargs # MixNet + CondConv combo not supported currently
if 'groups' in kwargs:
groups = kwargs.pop('groups')
if groups == in_channels:
kwargs['depthwise'] = True
else:
assert groups == 1
# We're going to use only lists for defining the MixedConv2d kernel groups,
# ints, tuples, other iterables will continue to pass to normal conv and specify h, w.
m = MixedConv2d(in_channels, out_channels, kernel_size, **kwargs)
else:
depthwise = kwargs.pop('depthwise', False)
# for DW out_channels must be multiple of in_channels as must have out_channels % groups == 0
groups = in_channels if depthwise else kwargs.pop('groups', 1)
if 'num_experts' in kwargs and kwargs['num_experts'] > 0:
m = CondConv2d(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
else:
m = create_conv2d_pad(in_channels, out_channels, kernel_size, groups=groups, **kwargs)
return m
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/non_local_attn.py | """ Bilinear-Attention-Transform and Non-Local Attention
Paper: `Non-Local Neural Networks With Grouped Bilinear Attentional Transforms`
- https://openaccess.thecvf.com/content_CVPR_2020/html/Chi_Non-Local_Neural_Networks_With_Grouped_Bilinear_Attentional_Transforms_CVPR_2020_paper.html
Adapted from original code: https://github.com/BA-Transform/BAT-Image-Classification
"""
import torch
from torch import nn
from torch.nn import functional as F
from .conv_bn_act import ConvNormAct
from .helpers import make_divisible
from .trace_utils import _assert
class NonLocalAttn(nn.Module):
"""Spatial NL block for image classification.
This was adapted from https://github.com/BA-Transform/BAT-Image-Classification
Their NonLocal impl inspired by https://github.com/facebookresearch/video-nonlocal-net.
"""
def __init__(self, in_channels, use_scale=True, rd_ratio=1/8, rd_channels=None, rd_divisor=8, **kwargs):
super(NonLocalAttn, self).__init__()
if rd_channels is None:
rd_channels = make_divisible(in_channels * rd_ratio, divisor=rd_divisor)
self.scale = in_channels ** -0.5 if use_scale else 1.0
self.t = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True)
self.p = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True)
self.g = nn.Conv2d(in_channels, rd_channels, kernel_size=1, stride=1, bias=True)
self.z = nn.Conv2d(rd_channels, in_channels, kernel_size=1, stride=1, bias=True)
self.norm = nn.BatchNorm2d(in_channels)
self.reset_parameters()
def forward(self, x):
shortcut = x
t = self.t(x)
p = self.p(x)
g = self.g(x)
B, C, H, W = t.size()
t = t.view(B, C, -1).permute(0, 2, 1)
p = p.view(B, C, -1)
g = g.view(B, C, -1).permute(0, 2, 1)
att = torch.bmm(t, p) * self.scale
att = F.softmax(att, dim=2)
x = torch.bmm(att, g)
x = x.permute(0, 2, 1).reshape(B, C, H, W)
x = self.z(x)
x = self.norm(x) + shortcut
return x
def reset_parameters(self):
for name, m in self.named_modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(
m.weight, mode='fan_out', nonlinearity='relu')
if len(list(m.parameters())) > 1:
nn.init.constant_(m.bias, 0.0)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 0)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.GroupNorm):
nn.init.constant_(m.weight, 0)
nn.init.constant_(m.bias, 0)
class BilinearAttnTransform(nn.Module):
def __init__(self, in_channels, block_size, groups, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
super(BilinearAttnTransform, self).__init__()
self.conv1 = ConvNormAct(in_channels, groups, 1, act_layer=act_layer, norm_layer=norm_layer)
self.conv_p = nn.Conv2d(groups, block_size * block_size * groups, kernel_size=(block_size, 1))
self.conv_q = nn.Conv2d(groups, block_size * block_size * groups, kernel_size=(1, block_size))
self.conv2 = ConvNormAct(in_channels, in_channels, 1, act_layer=act_layer, norm_layer=norm_layer)
self.block_size = block_size
self.groups = groups
self.in_channels = in_channels
def resize_mat(self, x, t: int):
B, C, block_size, block_size1 = x.shape
_assert(block_size == block_size1, '')
if t <= 1:
return x
x = x.view(B * C, -1, 1, 1)
x = x * torch.eye(t, t, dtype=x.dtype, device=x.device)
x = x.view(B * C, block_size, block_size, t, t)
x = torch.cat(torch.split(x, 1, dim=1), dim=3)
x = torch.cat(torch.split(x, 1, dim=2), dim=4)
x = x.view(B, C, block_size * t, block_size * t)
return x
def forward(self, x):
_assert(x.shape[-1] % self.block_size == 0, '')
_assert(x.shape[-2] % self.block_size == 0, '')
B, C, H, W = x.shape
out = self.conv1(x)
rp = F.adaptive_max_pool2d(out, (self.block_size, 1))
cp = F.adaptive_max_pool2d(out, (1, self.block_size))
p = self.conv_p(rp).view(B, self.groups, self.block_size, self.block_size).sigmoid()
q = self.conv_q(cp).view(B, self.groups, self.block_size, self.block_size).sigmoid()
p = p / p.sum(dim=3, keepdim=True)
q = q / q.sum(dim=2, keepdim=True)
p = p.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size(
0), self.groups, C // self.groups, self.block_size, self.block_size).contiguous()
p = p.view(B, C, self.block_size, self.block_size)
q = q.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size(
0), self.groups, C // self.groups, self.block_size, self.block_size).contiguous()
q = q.view(B, C, self.block_size, self.block_size)
p = self.resize_mat(p, H // self.block_size)
q = self.resize_mat(q, W // self.block_size)
y = p.matmul(x)
y = y.matmul(q)
y = self.conv2(y)
return y
class BatNonLocalAttn(nn.Module):
""" BAT
Adapted from: https://github.com/BA-Transform/BAT-Image-Classification
"""
def __init__(
self, in_channels, block_size=7, groups=2, rd_ratio=0.25, rd_channels=None, rd_divisor=8,
drop_rate=0.2, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, **_):
super().__init__()
if rd_channels is None:
rd_channels = make_divisible(in_channels * rd_ratio, divisor=rd_divisor)
self.conv1 = ConvNormAct(in_channels, rd_channels, 1, act_layer=act_layer, norm_layer=norm_layer)
self.ba = BilinearAttnTransform(rd_channels, block_size, groups, act_layer=act_layer, norm_layer=norm_layer)
self.conv2 = ConvNormAct(rd_channels, in_channels, 1, act_layer=act_layer, norm_layer=norm_layer)
self.dropout = nn.Dropout2d(p=drop_rate)
def forward(self, x):
xl = self.conv1(x)
y = self.ba(xl)
y = self.conv2(y)
y = self.dropout(y)
return y + x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/__init__.py | from .activations import *
from .adaptive_avgmax_pool import \
adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d
from .attention_pool import AttentionPoolLatent
from .attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding
from .blur_pool import BlurPool2d
from .classifier import ClassifierHead, create_classifier, NormMlpClassifierHead
from .cond_conv2d import CondConv2d, get_condconv_initializer
from .config import is_exportable, is_scriptable, is_no_jit, use_fused_attn, \
set_exportable, set_scriptable, set_no_jit, set_layer_config, set_fused_attn
from .conv2d_same import Conv2dSame, conv2d_same
from .conv_bn_act import ConvNormAct, ConvNormActAa, ConvBnAct
from .create_act import create_act_layer, get_act_layer, get_act_fn
from .create_attn import get_attn, create_attn
from .create_conv2d import create_conv2d
from .create_norm import get_norm_layer, create_norm_layer
from .create_norm_act import get_norm_act_layer, create_norm_act_layer, get_norm_act_layer
from .drop import DropBlock2d, DropPath, drop_block_2d, drop_path
from .eca import EcaModule, CecaModule, EfficientChannelAttn, CircularEfficientChannelAttn
from .evo_norm import EvoNorm2dB0, EvoNorm2dB1, EvoNorm2dB2,\
EvoNorm2dS0, EvoNorm2dS0a, EvoNorm2dS1, EvoNorm2dS1a, EvoNorm2dS2, EvoNorm2dS2a
from .fast_norm import is_fast_norm, set_fast_norm, fast_group_norm, fast_layer_norm
from .filter_response_norm import FilterResponseNormTlu2d, FilterResponseNormAct2d
from .format import Format, get_channel_dim, get_spatial_dim, nchw_to, nhwc_to
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, extend_tuple
from .inplace_abn import InplaceAbn
from .linear import Linear
from .mixed_conv2d import MixedConv2d
from .mlp import Mlp, GluMlp, GatedMlp, SwiGLU, SwiGLUPacked, ConvMlp, GlobalResponseNormMlp
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d, RmsNorm
from .norm_act import BatchNormAct2d, GroupNormAct, GroupNorm1Act, LayerNormAct, LayerNormAct2d,\
SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d
from .padding import get_padding, get_same_padding, pad_same
from .patch_dropout import PatchDropout
from .patch_embed import PatchEmbed, PatchEmbedWithSize, resample_patch_embed
from .pool2d_same import AvgPool2dSame, create_pool2d
from .pos_embed import resample_abs_pos_embed, resample_abs_pos_embed_nhwc
from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords, \
resize_rel_pos_bias_table, resize_rel_pos_bias_table_simple, resize_rel_pos_bias_table_levit
from .pos_embed_sincos import pixel_freq_bands, freq_bands, build_sincos2d_pos_embed, build_fourier_pos_embed, \
build_rotary_pos_embed, apply_rot_embed, apply_rot_embed_cat, apply_rot_embed_list, apply_keep_indices_nlc, \
FourierEmbed, RotaryEmbedding, RotaryEmbeddingCat
from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite
from .selective_kernel import SelectiveKernel
from .separable_conv import SeparableConv2d, SeparableConvNormAct
from .space_to_depth import SpaceToDepthModule, SpaceToDepth, DepthToSpace
from .split_attn import SplitAttn
from .split_batchnorm import SplitBatchNorm2d, convert_splitbn_model
from .std_conv import StdConv2d, StdConv2dSame, ScaledStdConv2d, ScaledStdConv2dSame
from .test_time_pool import TestTimePoolHead, apply_test_time_pool
from .trace_utils import _assert, _float_to_int
from .typing import LayerType, PadType
from .weight_init import trunc_normal_, trunc_normal_tf_, variance_scaling_, lecun_normal_
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/format.py | from enum import Enum
from typing import Union
import torch
class Format(str, Enum):
NCHW = 'NCHW'
NHWC = 'NHWC'
NCL = 'NCL'
NLC = 'NLC'
FormatT = Union[str, Format]
def get_spatial_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NLC:
dim = (1,)
elif fmt is Format.NCL:
dim = (2,)
elif fmt is Format.NHWC:
dim = (1, 2)
else:
dim = (2, 3)
return dim
def get_channel_dim(fmt: FormatT):
fmt = Format(fmt)
if fmt is Format.NHWC:
dim = 3
elif fmt is Format.NLC:
dim = 2
else:
dim = 1
return dim
def nchw_to(x: torch.Tensor, fmt: Format):
if fmt == Format.NHWC:
x = x.permute(0, 2, 3, 1)
elif fmt == Format.NLC:
x = x.flatten(2).transpose(1, 2)
elif fmt == Format.NCL:
x = x.flatten(2)
return x
def nhwc_to(x: torch.Tensor, fmt: Format):
if fmt == Format.NCHW:
x = x.permute(0, 3, 1, 2)
elif fmt == Format.NLC:
x = x.flatten(1, 2)
elif fmt == Format.NCL:
x = x.flatten(1, 2).transpose(1, 2)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pool2d_same.py | """ AvgPool2d w/ Same Padding
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Tuple, Optional
from .helpers import to_2tuple
from .padding import pad_same, get_padding_value
def avg_pool2d_same(x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
ceil_mode: bool = False, count_include_pad: bool = True):
# FIXME how to deal with count_include_pad vs not for external padding?
x = pad_same(x, kernel_size, stride)
return F.avg_pool2d(x, kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
class AvgPool2dSame(nn.AvgPool2d):
""" Tensorflow like 'SAME' wrapper for 2D average pooling
"""
def __init__(self, kernel_size: int, stride=None, padding=0, ceil_mode=False, count_include_pad=True):
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
super(AvgPool2dSame, self).__init__(kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
def forward(self, x):
x = pad_same(x, self.kernel_size, self.stride)
return F.avg_pool2d(
x, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad)
def max_pool2d_same(
x, kernel_size: List[int], stride: List[int], padding: List[int] = (0, 0),
dilation: List[int] = (1, 1), ceil_mode: bool = False):
x = pad_same(x, kernel_size, stride, value=-float('inf'))
return F.max_pool2d(x, kernel_size, stride, (0, 0), dilation, ceil_mode)
class MaxPool2dSame(nn.MaxPool2d):
""" Tensorflow like 'SAME' wrapper for 2D max pooling
"""
def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False):
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
dilation = to_2tuple(dilation)
super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode)
def forward(self, x):
x = pad_same(x, self.kernel_size, self.stride, value=-float('inf'))
return F.max_pool2d(x, self.kernel_size, self.stride, (0, 0), self.dilation, self.ceil_mode)
def create_pool2d(pool_type, kernel_size, stride=None, **kwargs):
stride = stride or kernel_size
padding = kwargs.pop('padding', '')
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, **kwargs)
if is_dynamic:
if pool_type == 'avg':
return AvgPool2dSame(kernel_size, stride=stride, **kwargs)
elif pool_type == 'max':
return MaxPool2dSame(kernel_size, stride=stride, **kwargs)
else:
assert False, f'Unsupported pool type {pool_type}'
else:
if pool_type == 'avg':
return nn.AvgPool2d(kernel_size, stride=stride, padding=padding, **kwargs)
elif pool_type == 'max':
return nn.MaxPool2d(kernel_size, stride=stride, padding=padding, **kwargs)
else:
assert False, f'Unsupported pool type {pool_type}'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/bottleneck_attn.py | """ Bottleneck Self Attention (Bottleneck Transformers)
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
@misc{2101.11605,
Author = {Aravind Srinivas and Tsung-Yi Lin and Niki Parmar and Jonathon Shlens and Pieter Abbeel and Ashish Vaswani},
Title = {Bottleneck Transformers for Visual Recognition},
Year = {2021},
}
Based on ref gist at: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
This impl is a WIP but given that it is based on the ref gist likely not too far off.
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, make_divisible
from .weight_init import trunc_normal_
from .trace_utils import _assert
def rel_logits_1d(q, rel_k, permute_mask: List[int]):
""" Compute relative logits along one dimension
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
Args:
q: (batch, heads, height, width, dim)
rel_k: (2 * width - 1, dim)
permute_mask: permute output dim according to this
"""
B, H, W, dim = q.shape
x = (q @ rel_k.transpose(-1, -2))
x = x.reshape(-1, W, 2 * W -1)
# pad to shift from relative to absolute indexing
x_pad = F.pad(x, [0, 1]).flatten(1)
x_pad = F.pad(x_pad, [0, W - 1])
# reshape and slice out the padded elements
x_pad = x_pad.reshape(-1, W + 1, 2 * W - 1)
x = x_pad[:, :W, W - 1:]
# reshape and tile
x = x.reshape(B, H, 1, W, W).expand(-1, -1, H, -1, -1)
return x.permute(permute_mask)
class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, feat_size, dim_head, scale):
super().__init__()
self.height, self.width = to_2tuple(feat_size)
self.dim_head = dim_head
self.height_rel = nn.Parameter(torch.randn(self.height * 2 - 1, dim_head) * scale)
self.width_rel = nn.Parameter(torch.randn(self.width * 2 - 1, dim_head) * scale)
def forward(self, q):
B, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(B, self.height, self.width, -1)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
rel_logits = rel_logits_h + rel_logits_w
rel_logits = rel_logits.reshape(B, HW, HW)
return rel_logits
class BottleneckAttn(nn.Module):
""" Bottleneck Attention
Paper: `Bottleneck Transformers for Visual Recognition` - https://arxiv.org/abs/2101.11605
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query and key (qk) dimensions are determined by
* num_heads * dim_head if dim_head is not None
* num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
stride (int): output stride of the module, avg pool used if stride == 2 (default: 1).
num_heads (int): parallel attention heads (default: 4)
dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool): add bias to q, k, and v projections
scale_pos_embed (bool): scale the position embedding as well as Q @ K
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=None,
qk_ratio=1.0, qkv_bias=False, scale_pos_embed=False):
super().__init__()
assert feat_size is not None, 'A concrete feature size matching expected input (H, W) is required'
dim_out = dim_out or dim
assert dim_out % num_heads == 0
self.num_heads = num_heads
self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.dim_head_v = dim_out // self.num_heads
self.dim_out_qk = num_heads * self.dim_head_qk
self.dim_out_v = num_heads * self.dim_head_v
self.scale = self.dim_head_qk ** -0.5
self.scale_pos_embed = scale_pos_embed
self.qkv = nn.Conv2d(dim, self.dim_out_qk * 2 + self.dim_out_v, 1, bias=qkv_bias)
# NOTE I'm only supporting relative pos embedding for now
self.pos_embed = PosEmbedRel(feat_size, dim_head=self.dim_head_qk, scale=self.scale)
self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) # fan-in
trunc_normal_(self.pos_embed.height_rel, std=self.scale)
trunc_normal_(self.pos_embed.width_rel, std=self.scale)
def forward(self, x):
B, C, H, W = x.shape
_assert(H == self.pos_embed.height, '')
_assert(W == self.pos_embed.width, '')
x = self.qkv(x) # B, (2 * dim_head_qk + dim_head_v) * num_heads, H, W
# NOTE head vs channel split ordering in qkv projection was decided before I allowed qk to differ from v
# So, this is more verbose than if heads were before qkv splits, but throughput is not impacted.
q, k, v = torch.split(x, [self.dim_out_qk, self.dim_out_qk, self.dim_out_v], dim=1)
q = q.reshape(B * self.num_heads, self.dim_head_qk, -1).transpose(-1, -2)
k = k.reshape(B * self.num_heads, self.dim_head_qk, -1) # no transpose, for q @ k
v = v.reshape(B * self.num_heads, self.dim_head_v, -1).transpose(-1, -2)
if self.scale_pos_embed:
attn = (q @ k + self.pos_embed(q)) * self.scale # B * num_heads, H * W, H * W
else:
attn = (q @ k) * self.scale + self.pos_embed(q)
attn = attn.softmax(dim=-1)
out = (attn @ v).transpose(-1, -2).reshape(B, self.dim_out_v, H, W) # B, dim_out, H, W
out = self.pool(out)
return out
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/lambda_layer.py | """ Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
@misc{2102.08602,
Author = {Irwan Bello},
Title = {LambdaNetworks: Modeling Long-Range Interactions Without Attention},
Year = {2021},
}
Status:
This impl is a WIP. Code snippets in the paper were used as reference but
good chance some details are missing/wrong.
I've only implemented local lambda conv based pos embeddings.
For a PyTorch impl that includes other embedding options checkout
https://github.com/lucidrains/lambda-networks
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from torch import nn
import torch.nn.functional as F
from .helpers import to_2tuple, make_divisible
from .weight_init import trunc_normal_
def rel_pos_indices(size):
size = to_2tuple(size)
pos = torch.stack(torch.meshgrid(torch.arange(size[0]), torch.arange(size[1]))).flatten(1)
rel_pos = pos[:, None, :] - pos[:, :, None]
rel_pos[0] += size[0] - 1
rel_pos[1] += size[1] - 1
return rel_pos # 2, H * W, H * W
class LambdaLayer(nn.Module):
"""Lambda Layer
Paper: `LambdaNetworks: Modeling Long-Range Interactions Without Attention`
- https://arxiv.org/abs/2102.08602
NOTE: intra-depth parameter 'u' is fixed at 1. It did not appear worth the complexity to add.
The internal dimensions of the lambda module are controlled via the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query (q) and key (k) dimension are determined by
* dim_head = (dim_out * attn_ratio // num_heads) if dim_head is None
* q = num_heads * dim_head, k = dim_head
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not set
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
feat_size (Tuple[int, int]): size of input feature_map for relative pos variant H, W
stride (int): output stride of the module, avg pool used if stride == 2
num_heads (int): parallel attention heads.
dim_head (int): dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
r (int): local lambda convolution radius. Use lambda conv if set, else relative pos if not. (default: 9)
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool): add bias to q, k, and v projections
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=4, dim_head=16, r=9,
qk_ratio=1.0, qkv_bias=False):
super().__init__()
dim_out = dim_out or dim
assert dim_out % num_heads == 0, ' should be divided by num_heads'
self.dim_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.num_heads = num_heads
self.dim_v = dim_out // num_heads
self.qkv = nn.Conv2d(
dim,
num_heads * self.dim_qk + self.dim_qk + self.dim_v,
kernel_size=1, bias=qkv_bias)
self.norm_q = nn.BatchNorm2d(num_heads * self.dim_qk)
self.norm_v = nn.BatchNorm2d(self.dim_v)
if r is not None:
# local lambda convolution for pos
self.conv_lambda = nn.Conv3d(1, self.dim_qk, (r, r, 1), padding=(r // 2, r // 2, 0))
self.pos_emb = None
self.rel_pos_indices = None
else:
# relative pos embedding
assert feat_size is not None
feat_size = to_2tuple(feat_size)
rel_size = [2 * s - 1 for s in feat_size]
self.conv_lambda = None
self.pos_emb = nn.Parameter(torch.zeros(rel_size[0], rel_size[1], self.dim_qk))
self.register_buffer('rel_pos_indices', rel_pos_indices(feat_size), persistent=False)
self.pool = nn.AvgPool2d(2, 2) if stride == 2 else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
trunc_normal_(self.qkv.weight, std=self.qkv.weight.shape[1] ** -0.5) # fan-in
if self.conv_lambda is not None:
trunc_normal_(self.conv_lambda.weight, std=self.dim_qk ** -0.5)
if self.pos_emb is not None:
trunc_normal_(self.pos_emb, std=.02)
def forward(self, x):
B, C, H, W = x.shape
M = H * W
qkv = self.qkv(x)
q, k, v = torch.split(qkv, [
self.num_heads * self.dim_qk, self.dim_qk, self.dim_v], dim=1)
q = self.norm_q(q).reshape(B, self.num_heads, self.dim_qk, M).transpose(-1, -2) # B, num_heads, M, K
v = self.norm_v(v).reshape(B, self.dim_v, M).transpose(-1, -2) # B, M, V
k = F.softmax(k.reshape(B, self.dim_qk, M), dim=-1) # B, K, M
content_lam = k @ v # B, K, V
content_out = q @ content_lam.unsqueeze(1) # B, num_heads, M, V
if self.pos_emb is None:
position_lam = self.conv_lambda(v.reshape(B, 1, H, W, self.dim_v)) # B, H, W, V, K
position_lam = position_lam.reshape(B, 1, self.dim_qk, H * W, self.dim_v).transpose(2, 3) # B, 1, M, K, V
else:
# FIXME relative pos embedding path not fully verified
pos_emb = self.pos_emb[self.rel_pos_indices[0], self.rel_pos_indices[1]].expand(B, -1, -1, -1)
position_lam = (pos_emb.transpose(-1, -2) @ v.unsqueeze(1)).unsqueeze(1) # B, 1, M, K, V
position_out = (q.unsqueeze(-2) @ position_lam).squeeze(-2) # B, num_heads, M, V
out = (content_out + position_out).transpose(-1, -2).reshape(B, C, H, W) # B, C (num_heads * V), H, W
out = self.pool(out)
return out
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/attention_pool.py | from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
from .config import use_fused_attn
from .mlp import Mlp
from .weight_init import trunc_normal_tf_
class AttentionPoolLatent(nn.Module):
""" Attention pooling w/ latent query
"""
fused_attn: torch.jit.Final[bool]
def __init__(
self,
in_features: int,
out_features: int = None,
embed_dim: int = None,
num_heads: int = 8,
mlp_ratio: float = 4.0,
qkv_bias: bool = True,
qk_norm: bool = False,
latent_len: int = 1,
latent_dim: int = None,
pos_embed: str = '',
pool_type: str = 'token',
norm_layer: Optional[nn.Module] = None,
drop: float = 0.0,
):
super().__init__()
embed_dim = embed_dim or in_features
out_features = out_features or in_features
assert embed_dim % num_heads == 0
self.num_heads = num_heads
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim ** -0.5
self.pool = pool_type
self.fused_attn = use_fused_attn()
if pos_embed == 'abs':
spatial_len = self.feat_size
self.pos_embed = nn.Parameter(torch.zeros(spatial_len, in_features))
else:
self.pos_embed = None
self.latent_dim = latent_dim or embed_dim
self.latent_len = latent_len
self.latent = nn.Parameter(torch.zeros(1, self.latent_len, embed_dim))
self.q = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
self.kv = nn.Linear(embed_dim, embed_dim * 2, bias=qkv_bias)
self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
self.proj = nn.Linear(embed_dim, embed_dim)
self.proj_drop = nn.Dropout(drop)
self.norm = norm_layer(out_features) if norm_layer is not None else nn.Identity()
self.mlp = Mlp(embed_dim, int(embed_dim * mlp_ratio))
self.init_weights()
def init_weights(self):
if self.pos_embed is not None:
trunc_normal_tf_(self.pos_embed, std=self.pos_embed.shape[1] ** -0.5)
trunc_normal_tf_(self.latent, std=self.latent_dim ** -0.5)
def forward(self, x):
B, N, C = x.shape
if self.pos_embed is not None:
# FIXME interpolate
x = x + self.pos_embed.unsqueeze(0).to(x.dtype)
q_latent = self.latent.expand(B, -1, -1)
q = self.q(q_latent).reshape(B, self.latent_len, self.num_heads, self.head_dim).transpose(1, 2)
kv = self.kv(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
k, v = kv.unbind(0)
q, k = self.q_norm(q), self.k_norm(k)
if self.fused_attn:
x = F.scaled_dot_product_attention(q, k, v)
else:
q = q * self.scale
attn = q @ k.transpose(-2, -1)
attn = attn.softmax(dim=-1)
x = attn @ v
x = x.transpose(1, 2).reshape(B, self.latent_len, C)
x = self.proj(x)
x = self.proj_drop(x)
x = x + self.mlp(self.norm(x))
# optional pool if latent seq_len > 1 and pooled output is desired
if self.pool == 'token':
x = x[:, 0]
elif self.pool == 'avg':
x = x.mean(1)
return x | 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pos_embed_rel.py | """ Relative position embedding modules and functions
Hacked together by / Copyright 2022 Ross Wightman
"""
import math
import os
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
from .interpolate import RegularGridInterpolator
from .mlp import Mlp
from .weight_init import trunc_normal_
_USE_SCIPY = int(os.environ.get('TIMM_USE_SCIPY_INTERP', 0)) > 0
def gen_relative_position_index(
q_size: Tuple[int, int],
k_size: Optional[Tuple[int, int]] = None,
class_token: bool = False,
) -> torch.Tensor:
# Adapted with significant modifications from Swin / BeiT codebases
# get pair-wise relative position index for each token inside the window
assert k_size is None, 'Different q & k sizes not currently supported' # FIXME
coords = torch.stack(
torch.meshgrid([
torch.arange(q_size[0]),
torch.arange(q_size[1])
])
).flatten(1) # 2, Wh, Ww
relative_coords = coords[:, :, None] - coords[:, None, :] # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0) # Qh*Qw, Kh*Kw, 2
relative_coords[:, :, 0] += q_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += q_size[1] - 1
relative_coords[:, :, 0] *= 2 * q_size[1] - 1
num_relative_distance = (2 * q_size[0] - 1) * (2 * q_size[1] - 1)
# else:
# # FIXME different q vs k sizes is a WIP, need to better offset the two grids?
# q_coords = torch.stack(
# torch.meshgrid([
# torch.arange(q_size[0]),
# torch.arange(q_size[1])
# ])
# ).flatten(1) # 2, Wh, Ww
# k_coords = torch.stack(
# torch.meshgrid([
# torch.arange(k_size[0]),
# torch.arange(k_size[1])
# ])
# ).flatten(1)
# relative_coords = q_coords[:, :, None] - k_coords[:, None, :] # 2, Wh*Ww, Wh*Ww
# relative_coords = relative_coords.permute(1, 2, 0) # Qh*Qw, Kh*Kw, 2
# relative_coords[:, :, 0] += max(q_size[0], k_size[0]) - 1 # shift to start from 0
# relative_coords[:, :, 1] += max(q_size[1], k_size[1]) - 1
# relative_coords[:, :, 0] *= k_size[1] + q_size[1] - 1
# relative_position_index = relative_coords.sum(-1) # Qh*Qw, Kh*Kw
# num_relative_distance = (q_size[0] + k_size[0] - 1) * (q_size[1] + k_size[1] - 1) + 3
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
if class_token:
# handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias
# NOTE not intended or tested with MLP log-coords
relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0])
relative_position_index[0, 0:] = num_relative_distance
relative_position_index[0:, 0] = num_relative_distance + 1
relative_position_index[0, 0] = num_relative_distance + 2
return relative_position_index.contiguous()
def resize_rel_pos_bias_table_simple(
rel_pos_bias,
new_window_size: Tuple[int, int],
new_bias_shape: Tuple[int, ...],
):
dst_size = (new_window_size[0] * 2 - 1, new_window_size[1] * 2 - 1)
if rel_pos_bias.ndim == 3:
# TF maxvit style (num_heads, H, W) bias shape, no extra tokens currently supported
_, dst_h, dst_w = new_bias_shape
num_attn_heads, src_h, src_w = rel_pos_bias.shape
assert dst_h == dst_size[0] and dst_w == dst_size[1]
if src_h != dst_h or src_w != dst_w:
rel_pos_bias = torch.nn.functional.interpolate(
rel_pos_bias.unsqueeze(0),
size=dst_size,
mode="bicubic",
align_corners=False,
).squeeze(0)
else:
assert rel_pos_bias.ndim == 2
# (num_pos, num_heads) (aka flat) bias shape
dst_num_pos, _ = new_bias_shape
src_num_pos, num_attn_heads = rel_pos_bias.shape
num_extra_tokens = dst_num_pos - (dst_size[0] * dst_size[1])
src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
src_size = (src_size, src_size) # FIXME could support non-equal src if argument passed
if src_size[0] != dst_size[0] or src_size[1] != dst_size[1]:
if num_extra_tokens:
extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
else:
extra_tokens = None
rel_pos_bias = torch.nn.functional.interpolate(
rel_pos_bias.transpose(1, 0).reshape((1, -1, src_size[0], src_size[1])),
size=dst_size,
mode="bicubic",
align_corners=False,
).view(-1, dst_num_pos - num_extra_tokens).transpose(0, 1)
if extra_tokens is not None:
rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
return rel_pos_bias
def resize_rel_pos_bias_table_levit(
position_bias_table,
new_size,
interpolation: str = 'bicubic',
antialias: bool = True,
):
"""
Resample relative position bias table suggested in LeVit
Adapted from: https://github.com/microsoft/Cream/blob/main/TinyViT/utils.py
"""
L1, nH1 = position_bias_table.size()
L2, nH2 = new_size
assert nH1 == nH2
if L1 != L2:
orig_dtype = position_bias_table.dtype
position_bias_table = position_bias_table.float()
# bicubic interpolate relative_position_bias_table if not match
S1 = int(L1 ** 0.5)
S2 = int(L2 ** 0.5)
relative_position_bias_table_resized = F.interpolate(
position_bias_table.permute(1, 0).view(1, nH1, S1, S1),
size=(S2, S2),
mode=interpolation,
antialias=antialias)
relative_position_bias_table_resized = \
relative_position_bias_table_resized.view(nH2, L2).permute(1, 0)
relative_position_bias_table_resized.to(orig_dtype)
return relative_position_bias_table_resized
else:
return position_bias_table
def resize_rel_pos_bias_table(
rel_pos_bias,
new_window_size: Tuple[int, int],
new_bias_shape: Tuple[int, ...],
):
""" Resize relative position bias table using more advanced interpolation.
Modified from code in Microsoft Unilm (https://github.com/microsoft/unilm) repo (BeiT, BeiT-v2, etc).
https://github.com/microsoft/unilm/blob/5255d52de86dad642810f5849dd357769346c1d7/beit/run_class_finetuning.py#L351
Args:
rel_pos_bias:
new_window_size:
new_bias_shape:
Returns:
"""
if _USE_SCIPY:
from scipy import interpolate
dst_size = (new_window_size[0] * 2 - 1, new_window_size[1] * 2 - 1)
if rel_pos_bias.ndim == 3:
# TF maxvit style (num_heads, H, W) bias shape, no extra tokens currently supported
num_extra_tokens = 0
_, dst_h, dst_w = new_bias_shape
assert dst_h == dst_size[0] and dst_w == dst_size[1]
num_attn_heads, src_h, src_w = rel_pos_bias.shape
src_size = (src_h, src_w)
has_flat_shape = False
else:
assert rel_pos_bias.ndim == 2
# (num_pos, num_heads) (aka flat) bias shape
dst_num_pos, _ = new_bias_shape
src_num_pos, num_attn_heads = rel_pos_bias.shape
num_extra_tokens = dst_num_pos - (dst_size[0] * dst_size[1])
src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
src_size = (src_size, src_size)
has_flat_shape = True
if src_size[0] != dst_size[0] or src_size[1] != dst_size[1]:
# print("Interpolating position from %dx%d to %dx%d" % (src_size[0], src_size[1], dst_size[0], dst_size[1]))
if num_extra_tokens:
extra_tokens = rel_pos_bias[-num_extra_tokens:, :]
rel_pos_bias = rel_pos_bias[:-num_extra_tokens, :]
else:
extra_tokens = None
def geometric_progression(a, r, n):
return a * (1.0 - r ** n) / (1.0 - r)
def _calc(src, dst):
left, right = 1.01, 1.5
while right - left > 1e-6:
q = (left + right) / 2.0
gp = geometric_progression(1, q, src // 2)
if gp > dst // 2:
right = q
else:
left = q
dis = []
cur = 1
for i in range(src // 2):
dis.append(cur)
cur += q ** (i + 1)
r_ids = [-_ for _ in reversed(dis)]
return r_ids + [0] + dis
y = _calc(src_size[0], dst_size[0])
x = _calc(src_size[1], dst_size[1])
yx = [torch.tensor(y), torch.tensor(x)]
# print("Original positions = %s" % str(x))
ty = dst_size[0] // 2.0
tx = dst_size[1] // 2.0
dy = torch.arange(-ty, ty + 0.1, 1.0)
dx = torch.arange(-tx, tx + 0.1, 1.0)
dyx = torch.meshgrid([dy, dx])
# print("Target positions = %s" % str(dx))
all_rel_pos_bias = []
for i in range(num_attn_heads):
if has_flat_shape:
z = rel_pos_bias[:, i].view(src_size[0], src_size[1]).float()
else:
z = rel_pos_bias[i, :, :].float()
if _USE_SCIPY:
# Original beit code uses scipy w/ cubic interpolation
f = interpolate.interp2d(x, y, z.numpy(), kind='cubic')
r = torch.Tensor(f(dx, dy)).contiguous().to(rel_pos_bias.device)
else:
# Without scipy dependency, I've found a reasonably simple impl
# that supports uneven spaced interpolation pts with 'linear' interp.
# Results are comparable to scipy for model accuracy in most cases.
f = RegularGridInterpolator(yx, z)
r = f(dyx).contiguous().to(rel_pos_bias.device)
if has_flat_shape:
r = r.view(-1, 1)
all_rel_pos_bias.append(r)
if has_flat_shape:
rel_pos_bias = torch.cat(all_rel_pos_bias, dim=-1)
else:
rel_pos_bias = torch.cat(all_rel_pos_bias, dim=0)
if extra_tokens is not None:
assert has_flat_shape
rel_pos_bias = torch.cat((rel_pos_bias, extra_tokens), dim=0)
return rel_pos_bias
class RelPosBias(nn.Module):
""" Relative Position Bias
Adapted from Swin-V1 relative position bias impl, modularized.
"""
def __init__(self, window_size, num_heads, prefix_tokens=0):
super().__init__()
assert prefix_tokens <= 1
self.window_size = window_size
self.window_area = window_size[0] * window_size[1]
self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,)
num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens
self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads))
self.register_buffer(
"relative_position_index",
gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0).view(-1),
persistent=False,
)
self.init_weights()
def init_weights(self):
trunc_normal_(self.relative_position_bias_table, std=.02)
def get_bias(self) -> torch.Tensor:
relative_position_bias = self.relative_position_bias_table[self.relative_position_index]
# win_h * win_w, win_h * win_w, num_heads
relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1)
return relative_position_bias.unsqueeze(0).contiguous()
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
return attn + self.get_bias()
def gen_relative_log_coords(
win_size: Tuple[int, int],
pretrained_win_size: Tuple[int, int] = (0, 0),
mode='swin',
):
assert mode in ('swin', 'cr')
# as per official swin-v2 impl, supporting timm specific 'cr' log coords as well
relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32)
relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32)
relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2
if mode == 'swin':
if pretrained_win_size[0] > 0:
relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1)
relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1)
else:
relative_coords_table[:, :, 0] /= (win_size[0] - 1)
relative_coords_table[:, :, 1] /= (win_size[1] - 1)
relative_coords_table *= 8 # normalize to -8, 8
relative_coords_table = torch.sign(relative_coords_table) * torch.log2(
1.0 + relative_coords_table.abs()) / math.log2(8)
else:
# mode == 'cr'
relative_coords_table = torch.sign(relative_coords_table) * torch.log(
1.0 + relative_coords_table.abs())
return relative_coords_table
class RelPosMlp(nn.Module):
""" Log-Coordinate Relative Position MLP
Based on ideas presented in Swin-V2 paper (https://arxiv.org/abs/2111.09883)
This impl covers the 'swin' implementation as well as two timm specific modes ('cr', and 'rw')
"""
def __init__(
self,
window_size,
num_heads=8,
hidden_dim=128,
prefix_tokens=0,
mode='cr',
pretrained_window_size=(0, 0)
):
super().__init__()
self.window_size = window_size
self.window_area = self.window_size[0] * self.window_size[1]
self.prefix_tokens = prefix_tokens
self.num_heads = num_heads
self.bias_shape = (self.window_area,) * 2 + (num_heads,)
if mode == 'swin':
self.bias_act = nn.Sigmoid()
self.bias_gain = 16
mlp_bias = (True, False)
else:
self.bias_act = nn.Identity()
self.bias_gain = None
mlp_bias = True
self.mlp = Mlp(
2, # x, y
hidden_features=hidden_dim,
out_features=num_heads,
act_layer=nn.ReLU,
bias=mlp_bias,
drop=(0.125, 0.)
)
self.register_buffer(
"relative_position_index",
gen_relative_position_index(window_size).view(-1),
persistent=False)
# get relative_coords_table
self.register_buffer(
"rel_coords_log",
gen_relative_log_coords(window_size, pretrained_window_size, mode=mode),
persistent=False)
def get_bias(self) -> torch.Tensor:
relative_position_bias = self.mlp(self.rel_coords_log)
if self.relative_position_index is not None:
relative_position_bias = relative_position_bias.view(-1, self.num_heads)[self.relative_position_index]
relative_position_bias = relative_position_bias.view(self.bias_shape)
relative_position_bias = relative_position_bias.permute(2, 0, 1)
relative_position_bias = self.bias_act(relative_position_bias)
if self.bias_gain is not None:
relative_position_bias = self.bias_gain * relative_position_bias
if self.prefix_tokens:
relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0])
return relative_position_bias.unsqueeze(0).contiguous()
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
return attn + self.get_bias()
def generate_lookup_tensor(
length: int,
max_relative_position: Optional[int] = None,
):
"""Generate a one_hot lookup tensor to reindex embeddings along one dimension.
Args:
length: the length to reindex to.
max_relative_position: the maximum relative position to consider.
Relative position embeddings for distances above this threshold
are zeroed out.
Returns:
a lookup Tensor of size [length, length, vocab_size] that satisfies
ret[n,m,v] = 1{m - n + max_relative_position = v}.
"""
if max_relative_position is None:
max_relative_position = length - 1
# Return the cached lookup tensor, otherwise compute it and cache it.
vocab_size = 2 * max_relative_position + 1
ret = torch.zeros(length, length, vocab_size)
for i in range(length):
for x in range(length):
v = x - i + max_relative_position
if abs(x - i) > max_relative_position:
continue
ret[i, x, v] = 1
return ret
def reindex_2d_einsum_lookup(
relative_position_tensor,
height: int,
width: int,
height_lookup: torch.Tensor,
width_lookup: torch.Tensor,
) -> torch.Tensor:
"""Reindex 2d relative position bias with 2 independent einsum lookups.
Adapted from:
https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py
Args:
relative_position_tensor: tensor of shape
[..., vocab_height, vocab_width, ...].
height: height to reindex to.
width: width to reindex to.
height_lookup: one-hot height lookup
width_lookup: one-hot width lookup
Returns:
reindexed_tensor: a Tensor of shape
[..., height * width, height * width, ...]
"""
reindexed_tensor = torch.einsum('nhw,ixh->nixw', relative_position_tensor, height_lookup)
reindexed_tensor = torch.einsum('nixw,jyw->nijxy', reindexed_tensor, width_lookup)
area = height * width
return reindexed_tensor.reshape(relative_position_tensor.shape[0], area, area)
class RelPosBiasTf(nn.Module):
""" Relative Position Bias Impl (Compatible with Tensorflow MaxViT models)
Adapted from:
https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py
"""
def __init__(self, window_size, num_heads, prefix_tokens=0):
super().__init__()
assert prefix_tokens <= 1
self.window_size = window_size
self.window_area = window_size[0] * window_size[1]
self.num_heads = num_heads
vocab_height = 2 * window_size[0] - 1
vocab_width = 2 * window_size[1] - 1
self.bias_shape = (self.num_heads, vocab_height, vocab_width)
self.relative_position_bias_table = nn.Parameter(torch.zeros(self.bias_shape))
self.register_buffer('height_lookup', generate_lookup_tensor(window_size[0]), persistent=False)
self.register_buffer('width_lookup', generate_lookup_tensor(window_size[1]), persistent=False)
self.init_weights()
def init_weights(self):
nn.init.normal_(self.relative_position_bias_table, std=.02)
def get_bias(self) -> torch.Tensor:
# FIXME change to not use one-hot/einsum?
return reindex_2d_einsum_lookup(
self.relative_position_bias_table,
self.window_size[0],
self.window_size[1],
self.height_lookup,
self.width_lookup
)
def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None):
return attn + self.get_bias()
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/fast_norm.py | """ 'Fast' Normalization Functions
For GroupNorm and LayerNorm these functions bypass typical AMP upcast to float32.
Additionally, for LayerNorm, the APEX fused LN is used if available (which also does not upcast)
Hacked together by / Copyright 2022 Ross Wightman
"""
from typing import List, Optional
import torch
from torch.nn import functional as F
try:
from apex.normalization.fused_layer_norm import fused_layer_norm_affine
has_apex = True
except ImportError:
has_apex = False
try:
from apex.normalization.fused_layer_norm import fused_rms_norm_affine, fused_rms_norm
has_apex_rmsnorm = True
except ImportError:
has_apex_rmsnorm = False
# fast (ie lower precision LN) can be disabled with this flag if issues crop up
_USE_FAST_NORM = False # defaulting to False for now
def is_fast_norm():
return _USE_FAST_NORM
def set_fast_norm(enable=True):
global _USE_FAST_NORM
_USE_FAST_NORM = enable
def fast_group_norm(
x: torch.Tensor,
num_groups: int,
weight: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
eps: float = 1e-5
) -> torch.Tensor:
if torch.jit.is_scripting():
# currently cannot use is_autocast_enabled within torchscript
return F.group_norm(x, num_groups, weight, bias, eps)
if torch.is_autocast_enabled():
# normally native AMP casts GN inputs to float32
# here we use the low precision autocast dtype
# FIXME what to do re CPU autocast?
dt = torch.get_autocast_gpu_dtype()
x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
with torch.cuda.amp.autocast(enabled=False):
return F.group_norm(x, num_groups, weight, bias, eps)
def fast_layer_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
eps: float = 1e-5
) -> torch.Tensor:
if torch.jit.is_scripting():
# currently cannot use is_autocast_enabled within torchscript
return F.layer_norm(x, normalized_shape, weight, bias, eps)
if has_apex:
return fused_layer_norm_affine(x, weight, bias, normalized_shape, eps)
if torch.is_autocast_enabled():
# normally native AMP casts LN inputs to float32
# apex LN does not, this is behaving like Apex
dt = torch.get_autocast_gpu_dtype()
# FIXME what to do re CPU autocast?
x, weight, bias = x.to(dt), weight.to(dt), bias.to(dt) if bias is not None else None
with torch.cuda.amp.autocast(enabled=False):
return F.layer_norm(x, normalized_shape, weight, bias, eps)
def rms_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
eps: float = 1e-5,
):
norm_ndim = len(normalized_shape)
if torch.jit.is_scripting():
# ndim = len(x.shape)
# dims = list(range(ndim - norm_ndim, ndim)) # this doesn't work on pytorch <= 1.13.x
# NOTE -ve dims cause torchscript to crash in some cases, out of options to work around
assert norm_ndim == 1
v = torch.var(x, dim=-1).unsqueeze(-1) # ts crashes with -ve dim + keepdim=True
else:
dims = tuple(range(-1, -norm_ndim - 1, -1))
v = torch.var(x, dim=dims, keepdim=True)
x = x * torch.rsqrt(v + eps)
if weight is not None:
x = x * weight
return x
def fast_rms_norm(
x: torch.Tensor,
normalized_shape: List[int],
weight: Optional[torch.Tensor] = None,
eps: float = 1e-5,
) -> torch.Tensor:
if torch.jit.is_scripting():
# this must be by itself, cannot merge with has_apex_rmsnorm
return rms_norm(x, normalized_shape, weight, eps)
if has_apex_rmsnorm:
if weight is None:
return fused_rms_norm(x, normalized_shape, eps)
else:
return fused_rms_norm_affine(x, weight, normalized_shape, eps)
# fallback
return rms_norm(x, normalized_shape, weight, eps)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/mlp.py | """ MLP module w/ dropout and configurable activation layer
Hacked together by / Copyright 2020 Ross Wightman
"""
from functools import partial
from torch import nn as nn
from .grn import GlobalResponseNorm
from .helpers import to_2tuple
class Mlp(nn.Module):
""" MLP as used in Vision Transformer, MLP-Mixer and related networks
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class GluMlp(nn.Module):
""" MLP w/ GLU style gating
See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.Sigmoid,
norm_layer=None,
bias=True,
drop=0.,
use_conv=False,
gate_last=True,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
assert hidden_features % 2 == 0
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.chunk_dim = 1 if use_conv else -1
self.gate_last = gate_last # use second half of width for gate
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features // 2) if norm_layer is not None else nn.Identity()
self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
fc1_mid = self.fc1.bias.shape[0] // 2
nn.init.ones_(self.fc1.bias[fc1_mid:])
nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6)
def forward(self, x):
x = self.fc1(x)
x1, x2 = x.chunk(2, dim=self.chunk_dim)
x = x1 * self.act(x2) if self.gate_last else self.act(x1) * x2
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
SwiGLUPacked = partial(GluMlp, act_layer=nn.SiLU, gate_last=False)
class SwiGLU(nn.Module):
""" SwiGLU
NOTE: GluMLP above can implement SwiGLU, but this impl has split fc1 and
better matches some other common impl which makes mapping checkpoints simpler.
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.SiLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1_g = nn.Linear(in_features, hidden_features, bias=bias[0])
self.fc1_x = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
nn.init.ones_(self.fc1_g.bias)
nn.init.normal_(self.fc1_g.weight, std=1e-6)
def forward(self, x):
x_gate = self.fc1_g(x)
x = self.fc1_x(x)
x = self.act(x_gate) * x
x = self.drop1(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class GatedMlp(nn.Module):
""" MLP as used in gMLP
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
norm_layer=None,
gate_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
if gate_layer is not None:
assert hidden_features % 2 == 0
self.gate = gate_layer(hidden_features)
hidden_features = hidden_features // 2 # FIXME base reduction on gate property?
else:
self.gate = nn.Identity()
self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.gate(x)
x = self.norm(x)
x = self.fc2(x)
x = self.drop2(x)
return x
class ConvMlp(nn.Module):
""" MLP using 1x1 convs that keeps spatial dims
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.ReLU,
norm_layer=None,
bias=True,
drop=0.,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
self.fc1 = nn.Conv2d(in_features, hidden_features, kernel_size=1, bias=bias[0])
self.norm = norm_layer(hidden_features) if norm_layer else nn.Identity()
self.act = act_layer()
self.drop = nn.Dropout(drop)
self.fc2 = nn.Conv2d(hidden_features, out_features, kernel_size=1, bias=bias[1])
def forward(self, x):
x = self.fc1(x)
x = self.norm(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
return x
class GlobalResponseNormMlp(nn.Module):
""" MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d
"""
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
bias=True,
drop=0.,
use_conv=False,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
bias = to_2tuple(bias)
drop_probs = to_2tuple(drop)
linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0])
self.act = act_layer()
self.drop1 = nn.Dropout(drop_probs[0])
self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv)
self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1])
self.drop2 = nn.Dropout(drop_probs[1])
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop1(x)
x = self.grn(x)
x = self.fc2(x)
x = self.drop2(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/adaptive_avgmax_pool.py | """ PyTorch selectable adaptive pooling
Adaptive pooling with the ability to select the type of pooling from:
* 'avg' - Average pooling
* 'max' - Max pooling
* 'avgmax' - Sum of average and max pooling re-scaled by 0.5
* 'avgmaxc' - Concatenation of average and max pooling along feature dim, doubles feature dim
Both a functional and a nn.Module version of the pooling is provided.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .format import get_spatial_dim, get_channel_dim
_int_tuple_2_t = Union[int, Tuple[int, int]]
def adaptive_pool_feat_mult(pool_type='avg'):
if pool_type.endswith('catavgmax'):
return 2
else:
return 1
def adaptive_avgmax_pool2d(x, output_size: _int_tuple_2_t = 1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return 0.5 * (x_avg + x_max)
def adaptive_catavgmax_pool2d(x, output_size: _int_tuple_2_t = 1):
x_avg = F.adaptive_avg_pool2d(x, output_size)
x_max = F.adaptive_max_pool2d(x, output_size)
return torch.cat((x_avg, x_max), 1)
def select_adaptive_pool2d(x, pool_type='avg', output_size: _int_tuple_2_t = 1):
"""Selectable global pooling function with dynamic input kernel size
"""
if pool_type == 'avg':
x = F.adaptive_avg_pool2d(x, output_size)
elif pool_type == 'avgmax':
x = adaptive_avgmax_pool2d(x, output_size)
elif pool_type == 'catavgmax':
x = adaptive_catavgmax_pool2d(x, output_size)
elif pool_type == 'max':
x = F.adaptive_max_pool2d(x, output_size)
else:
assert False, 'Invalid pool type: %s' % pool_type
return x
class FastAdaptiveAvgPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: F = 'NCHW'):
super(FastAdaptiveAvgPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
return x.mean(self.dim, keepdim=not self.flatten)
class FastAdaptiveMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
return x.amax(self.dim, keepdim=not self.flatten)
class FastAdaptiveAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim = get_spatial_dim(input_fmt)
def forward(self, x):
x_avg = x.mean(self.dim, keepdim=not self.flatten)
x_max = x.amax(self.dim, keepdim=not self.flatten)
return 0.5 * x_avg + 0.5 * x_max
class FastAdaptiveCatAvgMaxPool(nn.Module):
def __init__(self, flatten: bool = False, input_fmt: str = 'NCHW'):
super(FastAdaptiveCatAvgMaxPool, self).__init__()
self.flatten = flatten
self.dim_reduce = get_spatial_dim(input_fmt)
if flatten:
self.dim_cat = 1
else:
self.dim_cat = get_channel_dim(input_fmt)
def forward(self, x):
x_avg = x.mean(self.dim_reduce, keepdim=not self.flatten)
x_max = x.amax(self.dim_reduce, keepdim=not self.flatten)
return torch.cat((x_avg, x_max), self.dim_cat)
class AdaptiveAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_avgmax_pool2d(x, self.output_size)
class AdaptiveCatAvgMaxPool2d(nn.Module):
def __init__(self, output_size: _int_tuple_2_t = 1):
super(AdaptiveCatAvgMaxPool2d, self).__init__()
self.output_size = output_size
def forward(self, x):
return adaptive_catavgmax_pool2d(x, self.output_size)
class SelectAdaptivePool2d(nn.Module):
"""Selectable global pooling layer with dynamic input kernel size
"""
def __init__(
self,
output_size: _int_tuple_2_t = 1,
pool_type: str = 'fast',
flatten: bool = False,
input_fmt: str = 'NCHW',
):
super(SelectAdaptivePool2d, self).__init__()
assert input_fmt in ('NCHW', 'NHWC')
self.pool_type = pool_type or '' # convert other falsy values to empty string for consistent TS typing
if not pool_type:
self.pool = nn.Identity() # pass through
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
elif pool_type.startswith('fast') or input_fmt != 'NCHW':
assert output_size == 1, 'Fast pooling and non NCHW input formats require output_size == 1.'
if pool_type.endswith('avgmax'):
self.pool = FastAdaptiveAvgMaxPool(flatten, input_fmt=input_fmt)
elif pool_type.endswith('catavgmax'):
self.pool = FastAdaptiveCatAvgMaxPool(flatten, input_fmt=input_fmt)
elif pool_type.endswith('max'):
self.pool = FastAdaptiveMaxPool(flatten, input_fmt=input_fmt)
else:
self.pool = FastAdaptiveAvgPool(flatten, input_fmt=input_fmt)
self.flatten = nn.Identity()
else:
assert input_fmt == 'NCHW'
if pool_type == 'avgmax':
self.pool = AdaptiveAvgMaxPool2d(output_size)
elif pool_type == 'catavgmax':
self.pool = AdaptiveCatAvgMaxPool2d(output_size)
elif pool_type == 'max':
self.pool = nn.AdaptiveMaxPool2d(output_size)
else:
self.pool = nn.AdaptiveAvgPool2d(output_size)
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
def is_identity(self):
return not self.pool_type
def forward(self, x):
x = self.pool(x)
x = self.flatten(x)
return x
def feat_mult(self):
return adaptive_pool_feat_mult(self.pool_type)
def __repr__(self):
return self.__class__.__name__ + '(' \
+ 'pool_type=' + self.pool_type \
+ ', flatten=' + str(self.flatten) + ')'
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/inplace_abn.py | import torch
from torch import nn as nn
try:
from inplace_abn.functions import inplace_abn, inplace_abn_sync
has_iabn = True
except ImportError:
has_iabn = False
def inplace_abn(x, weight, bias, running_mean, running_var,
training=True, momentum=0.1, eps=1e-05, activation="leaky_relu", activation_param=0.01):
raise ImportError(
"Please install InplaceABN:'pip install git+https://github.com/mapillary/inplace_abn.git@v1.0.12'")
def inplace_abn_sync(**kwargs):
inplace_abn(**kwargs)
class InplaceAbn(nn.Module):
"""Activated Batch Normalization
This gathers a BatchNorm and an activation function in a single module
Parameters
----------
num_features : int
Number of feature channels in the input and output.
eps : float
Small constant to prevent numerical issues.
momentum : float
Momentum factor applied to compute running statistics.
affine : bool
If `True` apply learned scale and shift transformation after normalization.
act_layer : str or nn.Module type
Name or type of the activation functions, one of: `leaky_relu`, `elu`
act_param : float
Negative slope for the `leaky_relu` activation.
"""
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, apply_act=True,
act_layer="leaky_relu", act_param=0.01, drop_layer=None):
super(InplaceAbn, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
self.momentum = momentum
if apply_act:
if isinstance(act_layer, str):
assert act_layer in ('leaky_relu', 'elu', 'identity', '')
self.act_name = act_layer if act_layer else 'identity'
else:
# convert act layer passed as type to string
if act_layer == nn.ELU:
self.act_name = 'elu'
elif act_layer == nn.LeakyReLU:
self.act_name = 'leaky_relu'
elif act_layer is None or act_layer == nn.Identity:
self.act_name = 'identity'
else:
assert False, f'Invalid act layer {act_layer.__name__} for IABN'
else:
self.act_name = 'identity'
self.act_param = act_param
if self.affine:
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
else:
self.register_parameter('weight', None)
self.register_parameter('bias', None)
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.constant_(self.running_mean, 0)
nn.init.constant_(self.running_var, 1)
if self.affine:
nn.init.constant_(self.weight, 1)
nn.init.constant_(self.bias, 0)
def forward(self, x):
output = inplace_abn(
x, self.weight, self.bias, self.running_mean, self.running_var,
self.training, self.momentum, self.eps, self.act_name, self.act_param)
if isinstance(output, tuple):
output = output[0]
return output
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/pos_embed.py | """ Position Embedding Utilities
Hacked together by / Copyright 2022 Ross Wightman
"""
import logging
import math
from typing import List, Tuple, Optional, Union
import torch
import torch.nn.functional as F
from .helpers import to_2tuple
_logger = logging.getLogger(__name__)
def resample_abs_pos_embed(
posemb,
new_size: List[int],
old_size: Optional[List[int]] = None,
num_prefix_tokens: int = 1,
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
# sort out sizes, assume square if old size not provided
num_pos_tokens = posemb.shape[1]
num_new_tokens = new_size[0] * new_size[1] + num_prefix_tokens
if num_new_tokens == num_pos_tokens and new_size[0] == new_size[1]:
return posemb
if old_size is None:
hw = int(math.sqrt(num_pos_tokens - num_prefix_tokens))
old_size = hw, hw
if num_prefix_tokens:
posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:]
else:
posemb_prefix, posemb = None, posemb
# do the interpolation
embed_dim = posemb.shape[-1]
orig_dtype = posemb.dtype
posemb = posemb.float() # interpolate needs float32
posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
posemb = posemb.permute(0, 2, 3, 1).reshape(1, -1, embed_dim)
posemb = posemb.to(orig_dtype)
# add back extra (class, etc) prefix tokens
if posemb_prefix is not None:
posemb = torch.cat([posemb_prefix, posemb], dim=1)
if not torch.jit.is_scripting() and verbose:
_logger.info(f'Resized position embedding: {old_size} to {new_size}.')
return posemb
def resample_abs_pos_embed_nhwc(
posemb,
new_size: List[int],
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
if new_size[0] == posemb.shape[-3] and new_size[1] == posemb.shape[-2]:
return posemb
orig_dtype = posemb.dtype
posemb = posemb.float()
# do the interpolation
posemb = posemb.reshape(1, posemb.shape[-3], posemb.shape[-2], posemb.shape[-1]).permute(0, 3, 1, 2)
posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias)
posemb = posemb.permute(0, 2, 3, 1).to(orig_dtype)
if not torch.jit.is_scripting() and verbose:
_logger.info(f'Resized position embedding: {posemb.shape[-3:-1]} to {new_size}.')
return posemb
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/conv_bn_act.py | """ Conv2d + BN + Act
Hacked together by / Copyright 2020 Ross Wightman
"""
import functools
from torch import nn as nn
from .create_conv2d import create_conv2d
from .create_norm_act import get_norm_act_layer
class ConvNormAct(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.BatchNorm2d,
norm_kwargs=None,
act_layer=nn.ReLU,
act_kwargs=None,
drop_layer=None,
):
super(ConvNormAct, self).__init__()
norm_kwargs = norm_kwargs or {}
act_kwargs = act_kwargs or {}
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
# NOTE for backwards compatibility with models that use separate norm and act layer definitions
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
if drop_layer:
norm_kwargs['drop_layer'] = drop_layer
self.bn = norm_act_layer(
out_channels,
apply_act=apply_act,
act_kwargs=act_kwargs,
**norm_kwargs,
)
@property
def in_channels(self):
return self.conv.in_channels
@property
def out_channels(self):
return self.conv.out_channels
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
return x
ConvBnAct = ConvNormAct
def create_aa(aa_layer, channels, stride=2, enable=True):
if not aa_layer or not enable:
return nn.Identity()
if isinstance(aa_layer, functools.partial):
if issubclass(aa_layer.func, nn.AvgPool2d):
return aa_layer()
else:
return aa_layer(channels)
elif issubclass(aa_layer, nn.AvgPool2d):
return aa_layer(stride)
else:
return aa_layer(channels=channels, stride=stride)
class ConvNormActAa(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size=1,
stride=1,
padding='',
dilation=1,
groups=1,
bias=False,
apply_act=True,
norm_layer=nn.BatchNorm2d,
norm_kwargs=None,
act_layer=nn.ReLU,
act_kwargs=None,
aa_layer=None,
drop_layer=None,
):
super(ConvNormActAa, self).__init__()
use_aa = aa_layer is not None and stride == 2
norm_kwargs = norm_kwargs or {}
act_kwargs = act_kwargs or {}
self.conv = create_conv2d(
in_channels, out_channels, kernel_size, stride=1 if use_aa else stride,
padding=padding, dilation=dilation, groups=groups, bias=bias)
# NOTE for backwards compatibility with models that use separate norm and act layer definitions
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
# NOTE for backwards (weight) compatibility, norm layer name remains `.bn`
if drop_layer:
norm_kwargs['drop_layer'] = drop_layer
self.bn = norm_act_layer(out_channels, apply_act=apply_act, act_kwargs=act_kwargs, **norm_kwargs)
self.aa = create_aa(aa_layer, out_channels, stride=stride, enable=use_aa)
@property
def in_channels(self):
return self.conv.in_channels
@property
def out_channels(self):
return self.conv.out_channels
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
x = self.aa(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/halo_attn.py | """ Halo Self Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
@misc{2103.12731,
Author = {Ashish Vaswani and Prajit Ramachandran and Aravind Srinivas and Niki Parmar and Blake Hechtman and
Jonathon Shlens},
Title = {Scaling Local Self-Attention for Parameter Efficient Visual Backbones},
Year = {2021},
}
Status:
This impl is a WIP, there is no official ref impl and some details in paper weren't clear to me.
The attention mechanism works but it's slow as implemented.
Hacked together by / Copyright 2021 Ross Wightman
"""
from typing import List
import torch
from torch import nn
import torch.nn.functional as F
from .helpers import make_divisible
from .weight_init import trunc_normal_
from .trace_utils import _assert
def rel_logits_1d(q, rel_k, permute_mask: List[int]):
""" Compute relative logits along one dimension
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
Args:
q: (batch, height, width, dim)
rel_k: (2 * window - 1, dim)
permute_mask: permute output dim according to this
"""
B, H, W, dim = q.shape
rel_size = rel_k.shape[0]
win_size = (rel_size + 1) // 2
x = (q @ rel_k.transpose(-1, -2))
x = x.reshape(-1, W, rel_size)
# pad to shift from relative to absolute indexing
x_pad = F.pad(x, [0, 1]).flatten(1)
x_pad = F.pad(x_pad, [0, rel_size - W])
# reshape and slice out the padded elements
x_pad = x_pad.reshape(-1, W + 1, rel_size)
x = x_pad[:, :W, win_size - 1:]
# reshape and tile
x = x.reshape(B, H, 1, W, win_size).expand(-1, -1, win_size, -1, -1)
return x.permute(permute_mask)
class PosEmbedRel(nn.Module):
""" Relative Position Embedding
As per: https://gist.github.com/aravindsrinivas/56359b79f0ce4449bcb04ab4b56a57a2
Originally from: `Attention Augmented Convolutional Networks` - https://arxiv.org/abs/1904.09925
"""
def __init__(self, block_size, win_size, dim_head, scale):
"""
Args:
block_size (int): block size
win_size (int): neighbourhood window size
dim_head (int): attention head dim
scale (float): scale factor (for init)
"""
super().__init__()
self.block_size = block_size
self.dim_head = dim_head
self.height_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
self.width_rel = nn.Parameter(torch.randn(win_size * 2 - 1, dim_head) * scale)
def forward(self, q):
B, BB, HW, _ = q.shape
# relative logits in width dimension.
q = q.reshape(-1, self.block_size, self.block_size, self.dim_head)
rel_logits_w = rel_logits_1d(q, self.width_rel, permute_mask=(0, 1, 3, 2, 4))
# relative logits in height dimension.
q = q.transpose(1, 2)
rel_logits_h = rel_logits_1d(q, self.height_rel, permute_mask=(0, 3, 1, 4, 2))
rel_logits = rel_logits_h + rel_logits_w
rel_logits = rel_logits.reshape(B, BB, HW, -1)
return rel_logits
class HaloAttn(nn.Module):
""" Halo Attention
Paper: `Scaling Local Self-Attention for Parameter Efficient Visual Backbones`
- https://arxiv.org/abs/2103.12731
The internal dimensions of the attention module are controlled by the interaction of several arguments.
* the output dimension of the module is specified by dim_out, which falls back to input dim if not set
* the value (v) dimension is set to dim_out // num_heads, the v projection determines the output dim
* the query and key (qk) dimensions are determined by
* num_heads * dim_head if dim_head is not None
* num_heads * (dim_out * attn_ratio // num_heads) if dim_head is None
* as seen above, attn_ratio determines the ratio of q and k relative to the output if dim_head not used
Args:
dim (int): input dimension to the module
dim_out (int): output dimension of the module, same as dim if not set
feat_size (Tuple[int, int]): size of input feature_map (not used, for arg compat with bottle/lambda)
stride: output stride of the module, query downscaled if > 1 (default: 1).
num_heads: parallel attention heads (default: 8).
dim_head: dimension of query and key heads, calculated from dim_out * attn_ratio // num_heads if not set
block_size (int): size of blocks. (default: 8)
halo_size (int): size of halo overlap. (default: 3)
qk_ratio (float): ratio of q and k dimensions to output dimension when dim_head not set. (default: 1.0)
qkv_bias (bool) : add bias to q, k, and v projections
avg_down (bool): use average pool downsample instead of strided query blocks
scale_pos_embed (bool): scale the position embedding as well as Q @ K
"""
def __init__(
self, dim, dim_out=None, feat_size=None, stride=1, num_heads=8, dim_head=None, block_size=8, halo_size=3,
qk_ratio=1.0, qkv_bias=False, avg_down=False, scale_pos_embed=False):
super().__init__()
dim_out = dim_out or dim
assert dim_out % num_heads == 0
assert stride in (1, 2)
self.num_heads = num_heads
self.dim_head_qk = dim_head or make_divisible(dim_out * qk_ratio, divisor=8) // num_heads
self.dim_head_v = dim_out // self.num_heads
self.dim_out_qk = num_heads * self.dim_head_qk
self.dim_out_v = num_heads * self.dim_head_v
self.scale = self.dim_head_qk ** -0.5
self.scale_pos_embed = scale_pos_embed
self.block_size = self.block_size_ds = block_size
self.halo_size = halo_size
self.win_size = block_size + halo_size * 2 # neighbourhood window size
self.block_stride = 1
use_avg_pool = False
if stride > 1:
use_avg_pool = avg_down or block_size % stride != 0
self.block_stride = 1 if use_avg_pool else stride
self.block_size_ds = self.block_size // self.block_stride
# FIXME not clear if this stride behaviour is what the paper intended
# Also, the paper mentions using a 3D conv for dealing with the blocking/gather, and leaving
# data in unfolded block form. I haven't wrapped my head around how that'd look.
self.q = nn.Conv2d(dim, self.dim_out_qk, 1, stride=self.block_stride, bias=qkv_bias)
self.kv = nn.Conv2d(dim, self.dim_out_qk + self.dim_out_v, 1, bias=qkv_bias)
self.pos_embed = PosEmbedRel(
block_size=self.block_size_ds, win_size=self.win_size, dim_head=self.dim_head_qk, scale=self.scale)
self.pool = nn.AvgPool2d(2, 2) if use_avg_pool else nn.Identity()
self.reset_parameters()
def reset_parameters(self):
std = self.q.weight.shape[1] ** -0.5 # fan-in
trunc_normal_(self.q.weight, std=std)
trunc_normal_(self.kv.weight, std=std)
trunc_normal_(self.pos_embed.height_rel, std=self.scale)
trunc_normal_(self.pos_embed.width_rel, std=self.scale)
def forward(self, x):
B, C, H, W = x.shape
_assert(H % self.block_size == 0, '')
_assert(W % self.block_size == 0, '')
num_h_blocks = H // self.block_size
num_w_blocks = W // self.block_size
num_blocks = num_h_blocks * num_w_blocks
q = self.q(x)
# unfold
q = q.reshape(
-1, self.dim_head_qk,
num_h_blocks, self.block_size_ds, num_w_blocks, self.block_size_ds).permute(0, 1, 3, 5, 2, 4)
# B, num_heads * dim_head * block_size ** 2, num_blocks
q = q.reshape(B * self.num_heads, self.dim_head_qk, -1, num_blocks).transpose(1, 3)
# B * num_heads, num_blocks, block_size ** 2, dim_head
kv = self.kv(x)
# Generate overlapping windows for kv. This approach is good for GPU and CPU. However, unfold() is not
# lowered for PyTorch XLA so it will be very slow. See code at bottom of file for XLA friendly approach.
# FIXME figure out how to switch impl between this and conv2d if XLA being used.
kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size])
kv = kv.unfold(2, self.win_size, self.block_size).unfold(3, self.win_size, self.block_size).reshape(
B * self.num_heads, self.dim_head_qk + self.dim_head_v, num_blocks, -1).permute(0, 2, 3, 1)
k, v = torch.split(kv, [self.dim_head_qk, self.dim_head_v], dim=-1)
# B * num_heads, num_blocks, win_size ** 2, dim_head_qk or dim_head_v
if self.scale_pos_embed:
attn = (q @ k.transpose(-1, -2) + self.pos_embed(q)) * self.scale
else:
attn = (q @ k.transpose(-1, -2)) * self.scale + self.pos_embed(q)
# B * num_heads, num_blocks, block_size ** 2, win_size ** 2
attn = attn.softmax(dim=-1)
out = (attn @ v).transpose(1, 3) # B * num_heads, dim_head_v, block_size ** 2, num_blocks
# fold
out = out.reshape(-1, self.block_size_ds, self.block_size_ds, num_h_blocks, num_w_blocks)
out = out.permute(0, 3, 1, 4, 2).contiguous().view(
B, self.dim_out_v, H // self.block_stride, W // self.block_stride)
# B, dim_out, H // block_stride, W // block_stride
out = self.pool(out)
return out
""" Three alternatives for overlapping windows.
`.unfold().unfold()` is same speed as stride tricks with similar clarity as F.unfold()
if is_xla:
# This code achieves haloing on PyTorch XLA with reasonable runtime trade-off, it is
# EXTREMELY slow for backward on a GPU though so I need a way of selecting based on environment.
WW = self.win_size ** 2
pw = torch.eye(WW, dtype=x.dtype, device=x.device).reshape(WW, 1, self.win_size, self.win_size)
kv = F.conv2d(kv.reshape(-1, 1, H, W), pw, stride=self.block_size, padding=self.halo_size)
elif self.stride_tricks:
kv = F.pad(kv, [self.halo_size, self.halo_size, self.halo_size, self.halo_size]).contiguous()
kv = kv.as_strided((
B, self.dim_out_qk + self.dim_out_v, self.win_size, self.win_size, num_h_blocks, num_w_blocks),
stride=(kv.stride(0), kv.stride(1), kv.shape[-1], 1, self.block_size * kv.shape[-1], self.block_size))
else:
kv = F.unfold(kv, kernel_size=self.win_size, stride=self.block_size, padding=self.halo_size)
kv = kv.reshape(
B * self.num_heads, self.dim_head_qk + self.dim_head_v, -1, num_blocks).transpose(1, 3)
"""
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/evo_norm.py | """ EvoNorm in PyTorch
Based on `Evolving Normalization-Activation Layers` - https://arxiv.org/abs/2004.02967
@inproceedings{NEURIPS2020,
author = {Liu, Hanxiao and Brock, Andy and Simonyan, Karen and Le, Quoc},
booktitle = {Advances in Neural Information Processing Systems},
editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin},
pages = {13539--13550},
publisher = {Curran Associates, Inc.},
title = {Evolving Normalization-Activation Layers},
url = {https://proceedings.neurips.cc/paper/2020/file/9d4c03631b8b0c85ae08bf05eda37d0f-Paper.pdf},
volume = {33},
year = {2020}
}
An attempt at getting decent performing EvoNorms running in PyTorch.
While faster than other PyTorch impl, still quite a ways off the built-in BatchNorm
in terms of memory usage and throughput on GPUs.
I'm testing these modules on TPU w/ PyTorch XLA. Promising start but
currently working around some issues with builtin torch/tensor.var/std. Unlike
GPU, similar train speeds for EvoNormS variants and BatchNorm.
Hacked together by / Copyright 2020 Ross Wightman
"""
from typing import Sequence, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer
from .trace_utils import _assert
def instance_std(x, eps: float = 1e-5):
std = x.float().var(dim=(2, 3), unbiased=False, keepdim=True).add(eps).sqrt().to(x.dtype)
return std.expand(x.shape)
def instance_std_tpu(x, eps: float = 1e-5):
std = manual_var(x, dim=(2, 3)).add(eps).sqrt()
return std.expand(x.shape)
# instance_std = instance_std_tpu
def instance_rms(x, eps: float = 1e-5):
rms = x.float().square().mean(dim=(2, 3), keepdim=True).add(eps).sqrt().to(x.dtype)
return rms.expand(x.shape)
def manual_var(x, dim: Union[int, Sequence[int]], diff_sqm: bool = False):
xm = x.mean(dim=dim, keepdim=True)
if diff_sqm:
# difference of squared mean and mean squared, faster on TPU can be less stable
var = ((x * x).mean(dim=dim, keepdim=True) - (xm * xm)).clamp(0)
else:
var = ((x - xm) * (x - xm)).mean(dim=dim, keepdim=True)
return var
def group_std(x, groups: int = 32, eps: float = 1e-5, flatten: bool = False):
B, C, H, W = x.shape
x_dtype = x.dtype
_assert(C % groups == 0, '')
if flatten:
x = x.reshape(B, groups, -1) # FIXME simpler shape causing TPU / XLA issues
std = x.float().var(dim=2, unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
else:
x = x.reshape(B, groups, C // groups, H, W)
std = x.float().var(dim=(2, 3, 4), unbiased=False, keepdim=True).add(eps).sqrt().to(x_dtype)
return std.expand(x.shape).reshape(B, C, H, W)
def group_std_tpu(x, groups: int = 32, eps: float = 1e-5, diff_sqm: bool = False, flatten: bool = False):
# This is a workaround for some stability / odd behaviour of .var and .std
# running on PyTorch XLA w/ TPUs. These manual var impl are producing much better results
B, C, H, W = x.shape
_assert(C % groups == 0, '')
if flatten:
x = x.reshape(B, groups, -1) # FIXME simpler shape causing TPU / XLA issues
var = manual_var(x, dim=-1, diff_sqm=diff_sqm)
else:
x = x.reshape(B, groups, C // groups, H, W)
var = manual_var(x, dim=(2, 3, 4), diff_sqm=diff_sqm)
return var.add(eps).sqrt().expand(x.shape).reshape(B, C, H, W)
#group_std = group_std_tpu # FIXME TPU temporary
def group_rms(x, groups: int = 32, eps: float = 1e-5):
B, C, H, W = x.shape
_assert(C % groups == 0, '')
x_dtype = x.dtype
x = x.reshape(B, groups, C // groups, H, W)
rms = x.float().square().mean(dim=(2, 3, 4), keepdim=True).add(eps).sqrt_().to(x_dtype)
return rms.expand(x.shape).reshape(B, C, H, W)
class EvoNorm2dB0(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-3, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.v is not None:
nn.init.ones_(self.v)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.v is not None:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
# var = manual_var(x, dim=(0, 2, 3)).squeeze()
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach() * self.momentum * (n / (n - 1)))
else:
var = self.running_var
left = var.add(self.eps).sqrt_().to(x_dtype).view(v_shape).expand_as(x)
v = self.v.to(x_dtype).view(v_shape)
right = x * v + instance_std(x, self.eps)
x = x / left.max(right)
return x * self.weight.to(x_dtype).view(v_shape) + self.bias.to(x_dtype).view(v_shape)
class EvoNorm2dB1(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
else:
var = self.running_var
var = var.to(x_dtype).view(v_shape)
left = var.add(self.eps).sqrt_()
right = (x + 1) * instance_rms(x, self.eps)
x = x / left.max(right)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dB2(nn.Module):
def __init__(self, num_features, apply_act=True, momentum=0.1, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.momentum = momentum
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
if self.training:
var = x.float().var(dim=(0, 2, 3), unbiased=False)
n = x.numel() / x.shape[1]
self.running_var.copy_(
self.running_var * (1 - self.momentum) +
var.detach().to(self.running_var.dtype) * self.momentum * (n / (n - 1)))
else:
var = self.running_var
var = var.to(x_dtype).view(v_shape)
left = var.add(self.eps).sqrt_()
right = instance_rms(x, self.eps) - x
x = x / left.max(right)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS0(nn.Module):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-5, **_):
super().__init__()
self.apply_act = apply_act # apply activation (non-linearity)
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.v = nn.Parameter(torch.ones(num_features)) if apply_act else None
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.v is not None:
nn.init.ones_(self.v)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.v is not None:
v = self.v.view(v_shape).to(x_dtype)
x = x * (x * v).sigmoid() / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS0a(EvoNorm2dS0):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
d = group_std(x, self.groups, self.eps)
if self.v is not None:
v = self.v.view(v_shape).to(x_dtype)
x = x * (x * v).sigmoid()
x = x / d
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS1(nn.Module):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-5, **_):
super().__init__()
act_layer = act_layer or nn.SiLU
self.apply_act = apply_act # apply activation (non-linearity)
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer)
else:
self.act = nn.Identity()
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.pre_act_norm = False
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
x = self.act(x) / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS1a(EvoNorm2dS1):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = self.act(x) / group_std(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS2(nn.Module):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-5, **_):
super().__init__()
act_layer = act_layer or nn.SiLU
self.apply_act = apply_act # apply activation (non-linearity)
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer)
else:
self.act = nn.Identity()
if group_size:
assert num_features % group_size == 0
self.groups = num_features // group_size
else:
self.groups = groups
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
if self.apply_act:
x = self.act(x) / group_rms(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
class EvoNorm2dS2a(EvoNorm2dS2):
def __init__(
self, num_features, groups=32, group_size=None,
apply_act=True, act_layer=None, eps=1e-3, **_):
super().__init__(
num_features, groups=groups, group_size=group_size, apply_act=apply_act, act_layer=act_layer, eps=eps)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = self.act(x) / group_rms(x, self.groups, self.eps)
return x * self.weight.view(v_shape).to(x_dtype) + self.bias.view(v_shape).to(x_dtype)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/split_batchnorm.py | """ Split BatchNorm
A PyTorch BatchNorm layer that splits input batch into N equal parts and passes each through
a separate BN layer. The first split is passed through the parent BN layers with weight/bias
keys the same as the original BN. All other splits pass through BN sub-layers under the '.aux_bn'
namespace.
This allows easily removing the auxiliary BN layers after training to efficiently
achieve the 'Auxiliary BatchNorm' as described in the AdvProp Paper, section 4.2,
'Disentangled Learning via An Auxiliary BN'
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
class SplitBatchNorm2d(torch.nn.BatchNorm2d):
def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True,
track_running_stats=True, num_splits=2):
super().__init__(num_features, eps, momentum, affine, track_running_stats)
assert num_splits > 1, 'Should have at least one aux BN layer (num_splits at least 2)'
self.num_splits = num_splits
self.aux_bn = nn.ModuleList([
nn.BatchNorm2d(num_features, eps, momentum, affine, track_running_stats) for _ in range(num_splits - 1)])
def forward(self, input: torch.Tensor):
if self.training: # aux BN only relevant while training
split_size = input.shape[0] // self.num_splits
assert input.shape[0] == split_size * self.num_splits, "batch size must be evenly divisible by num_splits"
split_input = input.split(split_size)
x = [super().forward(split_input[0])]
for i, a in enumerate(self.aux_bn):
x.append(a(split_input[i + 1]))
return torch.cat(x, dim=0)
else:
return super().forward(input)
def convert_splitbn_model(module, num_splits=2):
"""
Recursively traverse module and its children to replace all instances of
``torch.nn.modules.batchnorm._BatchNorm`` with `SplitBatchnorm2d`.
Args:
module (torch.nn.Module): input module
num_splits: number of separate batchnorm layers to split input across
Example::
>>> # model is an instance of torch.nn.Module
>>> model = timm.models.convert_splitbn_model(model, num_splits=2)
"""
mod = module
if isinstance(module, torch.nn.modules.instancenorm._InstanceNorm):
return module
if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
mod = SplitBatchNorm2d(
module.num_features, module.eps, module.momentum, module.affine,
module.track_running_stats, num_splits=num_splits)
mod.running_mean = module.running_mean
mod.running_var = module.running_var
mod.num_batches_tracked = module.num_batches_tracked
if module.affine:
mod.weight.data = module.weight.data.clone().detach()
mod.bias.data = module.bias.data.clone().detach()
for aux in mod.aux_bn:
aux.running_mean = module.running_mean.clone()
aux.running_var = module.running_var.clone()
aux.num_batches_tracked = module.num_batches_tracked.clone()
if module.affine:
aux.weight.data = module.weight.data.clone().detach()
aux.bias.data = module.bias.data.clone().detach()
for name, child in module.named_children():
mod.add_module(name, convert_splitbn_model(child, num_splits=num_splits))
del module
return mod
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_norm_act.py | """ NormAct (Normalizaiton + Activation Layer) Factory
Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
isntances in models. Where these are used it will be possible to swap separate BN + act layers with
combined modules like IABN or EvoNorms.
Hacked together by / Copyright 2020 Ross Wightman
"""
import types
import functools
from .evo_norm import *
from .filter_response_norm import FilterResponseNormAct2d, FilterResponseNormTlu2d
from .norm_act import BatchNormAct2d, GroupNormAct, LayerNormAct, LayerNormAct2d
from .inplace_abn import InplaceAbn
_NORM_ACT_MAP = dict(
batchnorm=BatchNormAct2d,
batchnorm2d=BatchNormAct2d,
groupnorm=GroupNormAct,
groupnorm1=functools.partial(GroupNormAct, num_groups=1),
layernorm=LayerNormAct,
layernorm2d=LayerNormAct2d,
evonormb0=EvoNorm2dB0,
evonormb1=EvoNorm2dB1,
evonormb2=EvoNorm2dB2,
evonorms0=EvoNorm2dS0,
evonorms0a=EvoNorm2dS0a,
evonorms1=EvoNorm2dS1,
evonorms1a=EvoNorm2dS1a,
evonorms2=EvoNorm2dS2,
evonorms2a=EvoNorm2dS2a,
frn=FilterResponseNormAct2d,
frntlu=FilterResponseNormTlu2d,
inplaceabn=InplaceAbn,
iabn=InplaceAbn,
)
_NORM_ACT_TYPES = {m for n, m in _NORM_ACT_MAP.items()}
# has act_layer arg to define act type
_NORM_ACT_REQUIRES_ARG = {
BatchNormAct2d, GroupNormAct, LayerNormAct, LayerNormAct2d, FilterResponseNormAct2d, InplaceAbn}
def create_norm_act_layer(layer_name, num_features, act_layer=None, apply_act=True, jit=False, **kwargs):
layer = get_norm_act_layer(layer_name, act_layer=act_layer)
layer_instance = layer(num_features, apply_act=apply_act, **kwargs)
if jit:
layer_instance = torch.jit.script(layer_instance)
return layer_instance
def get_norm_act_layer(norm_layer, act_layer=None):
if norm_layer is None:
return None
assert isinstance(norm_layer, (type, str, types.FunctionType, functools.partial))
assert act_layer is None or isinstance(act_layer, (type, str, types.FunctionType, functools.partial))
norm_act_kwargs = {}
# unbind partial fn, so args can be rebound later
if isinstance(norm_layer, functools.partial):
norm_act_kwargs.update(norm_layer.keywords)
norm_layer = norm_layer.func
if isinstance(norm_layer, str):
if not norm_layer:
return None
layer_name = norm_layer.replace('_', '').lower().split('-')[0]
norm_act_layer = _NORM_ACT_MAP[layer_name]
elif norm_layer in _NORM_ACT_TYPES:
norm_act_layer = norm_layer
elif isinstance(norm_layer, types.FunctionType):
# if function type, must be a lambda/fn that creates a norm_act layer
norm_act_layer = norm_layer
else:
type_name = norm_layer.__name__.lower()
if type_name.startswith('batchnorm'):
norm_act_layer = BatchNormAct2d
elif type_name.startswith('groupnorm'):
norm_act_layer = GroupNormAct
elif type_name.startswith('groupnorm1'):
norm_act_layer = functools.partial(GroupNormAct, num_groups=1)
elif type_name.startswith('layernorm2d'):
norm_act_layer = LayerNormAct2d
elif type_name.startswith('layernorm'):
norm_act_layer = LayerNormAct
else:
assert False, f"No equivalent norm_act layer for {type_name}"
if norm_act_layer in _NORM_ACT_REQUIRES_ARG:
# pass `act_layer` through for backwards compat where `act_layer=None` implies no activation.
# In the future, may force use of `apply_act` with `act_layer` arg bound to relevant NormAct types
norm_act_kwargs.setdefault('act_layer', act_layer)
if norm_act_kwargs:
norm_act_layer = functools.partial(norm_act_layer, **norm_act_kwargs) # bind/rebind args
return norm_act_layer
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/filter_response_norm.py | """ Filter Response Norm in PyTorch
Based on `Filter Response Normalization Layer` - https://arxiv.org/abs/1911.09737
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
import torch.nn as nn
from .create_act import create_act_layer
from .trace_utils import _assert
def inv_instance_rms(x, eps: float = 1e-5):
rms = x.square().float().mean(dim=(2, 3), keepdim=True).add(eps).rsqrt().to(x.dtype)
return rms.expand(x.shape)
class FilterResponseNormTlu2d(nn.Module):
def __init__(self, num_features, apply_act=True, eps=1e-5, rms=True, **_):
super(FilterResponseNormTlu2d, self).__init__()
self.apply_act = apply_act # apply activation (non-linearity)
self.rms = rms
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.tau = nn.Parameter(torch.zeros(num_features)) if apply_act else None
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
if self.tau is not None:
nn.init.zeros_(self.tau)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = x * inv_instance_rms(x, self.eps)
x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
return torch.maximum(x, self.tau.reshape(v_shape).to(dtype=x_dtype)) if self.tau is not None else x
class FilterResponseNormAct2d(nn.Module):
def __init__(self, num_features, apply_act=True, act_layer=nn.ReLU, inplace=None, rms=True, eps=1e-5, **_):
super(FilterResponseNormAct2d, self).__init__()
if act_layer is not None and apply_act:
self.act = create_act_layer(act_layer, inplace=inplace)
else:
self.act = nn.Identity()
self.rms = rms
self.eps = eps
self.weight = nn.Parameter(torch.ones(num_features))
self.bias = nn.Parameter(torch.zeros(num_features))
self.reset_parameters()
def reset_parameters(self):
nn.init.ones_(self.weight)
nn.init.zeros_(self.bias)
def forward(self, x):
_assert(x.dim() == 4, 'expected 4D input')
x_dtype = x.dtype
v_shape = (1, -1, 1, 1)
x = x * inv_instance_rms(x, self.eps)
x = x * self.weight.view(v_shape).to(dtype=x_dtype) + self.bias.view(v_shape).to(dtype=x_dtype)
return self.act(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/patch_embed.py | """ Image to Patch Embedding using Conv2d
A convolution based approach to patchifying a 2D image w/ embedding projection.
Based on code in:
* https://github.com/google-research/vision_transformer
* https://github.com/google-research/big_vision/tree/main/big_vision
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch import nn as nn
import torch.nn.functional as F
from .format import Format, nchw_to
from .helpers import to_2tuple
from .trace_utils import _assert
_logger = logging.getLogger(__name__)
class PatchEmbed(nn.Module):
""" 2D Image to Patch Embedding
"""
output_fmt: Format
dynamic_img_pad: torch.jit.Final[bool]
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
output_fmt: Optional[str] = None,
bias: bool = True,
strict_img_size: bool = True,
dynamic_img_pad: bool = False,
):
super().__init__()
self.patch_size = to_2tuple(patch_size)
if img_size is not None:
self.img_size = to_2tuple(img_size)
self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
self.num_patches = self.grid_size[0] * self.grid_size[1]
else:
self.img_size = None
self.grid_size = None
self.num_patches = None
if output_fmt is not None:
self.flatten = False
self.output_fmt = Format(output_fmt)
else:
# flatten spatial dim and transpose to channels last, kept for bwd compat
self.flatten = flatten
self.output_fmt = Format.NCHW
self.strict_img_size = strict_img_size
self.dynamic_img_pad = dynamic_img_pad
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x):
B, C, H, W = x.shape
if self.img_size is not None:
if self.strict_img_size:
_assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
_assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
elif not self.dynamic_img_pad:
_assert(
H % self.patch_size[0] == 0,
f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
)
_assert(
W % self.patch_size[1] == 0,
f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
)
if self.dynamic_img_pad:
pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
x = F.pad(x, (0, pad_w, 0, pad_h))
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
x = self.norm(x)
return x
class PatchEmbedWithSize(PatchEmbed):
""" 2D Image to Patch Embedding
"""
output_fmt: Format
def __init__(
self,
img_size: Optional[int] = 224,
patch_size: int = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten: bool = True,
output_fmt: Optional[str] = None,
bias: bool = True,
):
super().__init__(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer,
flatten=flatten,
output_fmt=output_fmt,
bias=bias,
)
def forward(self, x) -> Tuple[torch.Tensor, List[int]]:
B, C, H, W = x.shape
if self.img_size is not None:
_assert(H % self.patch_size[0] == 0, f"Input image height ({H}) must be divisible by patch size ({self.patch_size[0]}).")
_assert(W % self.patch_size[1] == 0, f"Input image width ({W}) must be divisible by patch size ({self.patch_size[1]}).")
x = self.proj(x)
grid_size = x.shape[-2:]
if self.flatten:
x = x.flatten(2).transpose(1, 2) # NCHW -> NLC
elif self.output_fmt != Format.NCHW:
x = nchw_to(x, self.output_fmt)
x = self.norm(x)
return x, grid_size
def resample_patch_embed(
patch_embed,
new_size: List[int],
interpolation: str = 'bicubic',
antialias: bool = True,
verbose: bool = False,
):
"""Resample the weights of the patch embedding kernel to target resolution.
We resample the patch embedding kernel by approximately inverting the effect
of patch resizing.
Code based on:
https://github.com/google-research/big_vision/blob/b00544b81f8694488d5f36295aeb7972f3755ffe/big_vision/models/proj/flexi/vit.py
With this resizing, we can for example load a B/8 filter into a B/16 model
and, on 2x larger input image, the result will match.
Args:
patch_embed: original parameter to be resized.
new_size (tuple(int, int): target shape (height, width)-only.
interpolation (str): interpolation for resize
antialias (bool): use anti-aliasing filter in resize
verbose (bool): log operation
Returns:
Resized patch embedding kernel.
"""
import numpy as np
try:
import functorch
vmap = functorch.vmap
except ImportError:
if hasattr(torch, 'vmap'):
vmap = torch.vmap
else:
assert False, "functorch or a version of torch with vmap is required for FlexiViT resizing."
assert len(patch_embed.shape) == 4, "Four dimensions expected"
assert len(new_size) == 2, "New shape should only be hw"
old_size = patch_embed.shape[-2:]
if tuple(old_size) == tuple(new_size):
return patch_embed
if verbose:
_logger.info(f"Resize patch embedding {patch_embed.shape} to {new_size}, w/ {interpolation} interpolation.")
def resize(x_np, _new_size):
x_tf = torch.Tensor(x_np)[None, None, ...]
x_upsampled = F.interpolate(
x_tf, size=_new_size, mode=interpolation, antialias=antialias)[0, 0, ...].numpy()
return x_upsampled
def get_resize_mat(_old_size, _new_size):
mat = []
for i in range(np.prod(_old_size)):
basis_vec = np.zeros(_old_size)
basis_vec[np.unravel_index(i, _old_size)] = 1.
mat.append(resize(basis_vec, _new_size).reshape(-1))
return np.stack(mat).T
resize_mat = get_resize_mat(old_size, new_size)
resize_mat_pinv = torch.tensor(np.linalg.pinv(resize_mat.T), device=patch_embed.device)
def resample_kernel(kernel):
resampled_kernel = resize_mat_pinv @ kernel.reshape(-1)
return resampled_kernel.reshape(new_size)
v_resample_kernel = vmap(vmap(resample_kernel, 0, 0), 1, 1)
orig_dtype = patch_embed.dtype
patch_embed = patch_embed.float()
patch_embed = v_resample_kernel(patch_embed)
patch_embed = patch_embed.to(orig_dtype)
return patch_embed
# def divs(n, m=None):
# m = m or n // 2
# if m == 1:
# return [1]
# if n % m == 0:
# return [m] + divs(n, m - 1)
# return divs(n, m - 1)
#
#
# class FlexiPatchEmbed(nn.Module):
# """ 2D Image to Patch Embedding w/ Flexible Patch sizes (FlexiViT)
# FIXME WIP
# """
# def __init__(
# self,
# img_size=240,
# patch_size=16,
# in_chans=3,
# embed_dim=768,
# base_img_size=240,
# base_patch_size=32,
# norm_layer=None,
# flatten=True,
# bias=True,
# ):
# super().__init__()
# self.img_size = to_2tuple(img_size)
# self.patch_size = to_2tuple(patch_size)
# self.num_patches = 0
#
# # full range for 240 = (5, 6, 8, 10, 12, 14, 15, 16, 20, 24, 30, 40, 48)
# self.seqhw = (6, 8, 10, 12, 14, 15, 16, 20, 24, 30)
#
# self.base_img_size = to_2tuple(base_img_size)
# self.base_patch_size = to_2tuple(base_patch_size)
# self.base_grid_size = tuple([i // p for i, p in zip(self.base_img_size, self.base_patch_size)])
# self.base_num_patches = self.base_grid_size[0] * self.base_grid_size[1]
#
# self.flatten = flatten
# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=bias)
# self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
#
# def forward(self, x):
# B, C, H, W = x.shape
#
# if self.patch_size == self.base_patch_size:
# weight = self.proj.weight
# else:
# weight = resample_patch_embed(self.proj.weight, self.patch_size)
# patch_size = self.patch_size
# x = F.conv2d(x, weight, bias=self.proj.bias, stride=patch_size)
# if self.flatten:
# x = x.flatten(2).transpose(1, 2) # BCHW -> BNC
# x = self.norm(x)
# return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/patch_dropout.py | from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
class PatchDropout(nn.Module):
"""
https://arxiv.org/abs/2212.00794
"""
return_indices: torch.jit.Final[bool]
def __init__(
self,
prob: float = 0.5,
num_prefix_tokens: int = 1,
ordered: bool = False,
return_indices: bool = False,
):
super().__init__()
assert 0 <= prob < 1.
self.prob = prob
self.num_prefix_tokens = num_prefix_tokens # exclude CLS token (or other prefix tokens)
self.ordered = ordered
self.return_indices = return_indices
def forward(self, x) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
if not self.training or self.prob == 0.:
if self.return_indices:
return x, None
return x
if self.num_prefix_tokens:
prefix_tokens, x = x[:, :self.num_prefix_tokens], x[:, self.num_prefix_tokens:]
else:
prefix_tokens = None
B = x.shape[0]
L = x.shape[1]
num_keep = max(1, int(L * (1. - self.prob)))
keep_indices = torch.argsort(torch.randn(B, L, device=x.device), dim=-1)[:, :num_keep]
if self.ordered:
# NOTE does not need to maintain patch order in typical transformer use,
# but possibly useful for debug / visualization
keep_indices = keep_indices.sort(dim=-1)[0]
x = x.gather(1, keep_indices.unsqueeze(-1).expand((-1, -1) + x.shape[2:]))
if prefix_tokens is not None:
x = torch.cat((prefix_tokens, x), dim=1)
if self.return_indices:
return x, keep_indices
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/blur_pool.py | """
BlurPool layer inspired by
- Kornia's Max_BlurPool2d
- Making Convolutional Networks Shift-Invariant Again :cite:`zhang2019shiftinvar`
Hacked together by Chris Ha and Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .padding import get_padding
class BlurPool2d(nn.Module):
r"""Creates a module that computes blurs and downsample a given feature map.
See :cite:`zhang2019shiftinvar` for more details.
Corresponds to the Downsample class, which does blurring and subsampling
Args:
channels = Number of input channels
filt_size (int): binomial filter size for blurring. currently supports 3 (default) and 5.
stride (int): downsampling filter stride
Returns:
torch.Tensor: the transformed tensor.
"""
def __init__(self, channels, filt_size=3, stride=2) -> None:
super(BlurPool2d, self).__init__()
assert filt_size > 1
self.channels = channels
self.filt_size = filt_size
self.stride = stride
self.padding = [get_padding(filt_size, stride, dilation=1)] * 4
coeffs = torch.tensor((np.poly1d((0.5, 0.5)) ** (self.filt_size - 1)).coeffs.astype(np.float32))
blur_filter = (coeffs[:, None] * coeffs[None, :])[None, None, :, :].repeat(self.channels, 1, 1, 1)
self.register_buffer('filt', blur_filter, persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
x = F.pad(x, self.padding, 'reflect')
return F.conv2d(x, self.filt, stride=self.stride, groups=self.channels)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/interpolate.py | """ Interpolation helpers for timm layers
RegularGridInterpolator from https://github.com/sbarratt/torch_interpolations
Copyright Shane Barratt, Apache 2.0 license
"""
import torch
from itertools import product
class RegularGridInterpolator:
""" Interpolate data defined on a rectilinear grid with even or uneven spacing.
Produces similar results to scipy RegularGridInterpolator or interp2d
in 'linear' mode.
Taken from https://github.com/sbarratt/torch_interpolations
"""
def __init__(self, points, values):
self.points = points
self.values = values
assert isinstance(self.points, tuple) or isinstance(self.points, list)
assert isinstance(self.values, torch.Tensor)
self.ms = list(self.values.shape)
self.n = len(self.points)
assert len(self.ms) == self.n
for i, p in enumerate(self.points):
assert isinstance(p, torch.Tensor)
assert p.shape[0] == self.values.shape[i]
def __call__(self, points_to_interp):
assert self.points is not None
assert self.values is not None
assert len(points_to_interp) == len(self.points)
K = points_to_interp[0].shape[0]
for x in points_to_interp:
assert x.shape[0] == K
idxs = []
dists = []
overalls = []
for p, x in zip(self.points, points_to_interp):
idx_right = torch.bucketize(x, p)
idx_right[idx_right >= p.shape[0]] = p.shape[0] - 1
idx_left = (idx_right - 1).clamp(0, p.shape[0] - 1)
dist_left = x - p[idx_left]
dist_right = p[idx_right] - x
dist_left[dist_left < 0] = 0.
dist_right[dist_right < 0] = 0.
both_zero = (dist_left == 0) & (dist_right == 0)
dist_left[both_zero] = dist_right[both_zero] = 1.
idxs.append((idx_left, idx_right))
dists.append((dist_left, dist_right))
overalls.append(dist_left + dist_right)
numerator = 0.
for indexer in product([0, 1], repeat=self.n):
as_s = [idx[onoff] for onoff, idx in zip(indexer, idxs)]
bs_s = [dist[1 - onoff] for onoff, dist in zip(indexer, dists)]
numerator += self.values[as_s] * \
torch.prod(torch.stack(bs_s), dim=0)
denominator = torch.prod(torch.stack(overalls), dim=0)
return numerator / denominator
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/eca.py | """
ECA module from ECAnet
paper: ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
https://arxiv.org/abs/1910.03151
Original ECA model borrowed from https://github.com/BangguWu/ECANet
Modified circular ECA implementation and adaption for use in timm package
by Chris Ha https://github.com/VRandme
Original License:
MIT License
Copyright (c) 2019 BangguWu, Qilong Wang
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""
import math
from torch import nn
import torch.nn.functional as F
from .create_act import create_act_layer
from .helpers import make_divisible
class EcaModule(nn.Module):
"""Constructs an ECA module.
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, beta: when channel is given parameters of mapping function
refer to original paper https://arxiv.org/pdf/1910.03151.pdf
(default=None. if channel size not given, use k_size given for kernel size.)
kernel_size: Adaptive selection of kernel size (default=3)
gamm: used in kernel_size calc, see above
beta: used in kernel_size calc, see above
act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
gate_layer: gating non-linearity to use
"""
def __init__(
self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid',
rd_ratio=1/8, rd_channels=None, rd_divisor=8, use_mlp=False):
super(EcaModule, self).__init__()
if channels is not None:
t = int(abs(math.log(channels, 2) + beta) / gamma)
kernel_size = max(t if t % 2 else t + 1, 3)
assert kernel_size % 2 == 1
padding = (kernel_size - 1) // 2
if use_mlp:
# NOTE 'mlp' mode is a timm experiment, not in paper
assert channels is not None
if rd_channels is None:
rd_channels = make_divisible(channels * rd_ratio, divisor=rd_divisor)
act_layer = act_layer or nn.ReLU
self.conv = nn.Conv1d(1, rd_channels, kernel_size=1, padding=0, bias=True)
self.act = create_act_layer(act_layer)
self.conv2 = nn.Conv1d(rd_channels, 1, kernel_size=kernel_size, padding=padding, bias=True)
else:
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)
self.act = None
self.conv2 = None
self.gate = create_act_layer(gate_layer)
def forward(self, x):
y = x.mean((2, 3)).view(x.shape[0], 1, -1) # view for 1d conv
y = self.conv(y)
if self.conv2 is not None:
y = self.act(y)
y = self.conv2(y)
y = self.gate(y).view(x.shape[0], -1, 1, 1)
return x * y.expand_as(x)
EfficientChannelAttn = EcaModule # alias
class CecaModule(nn.Module):
"""Constructs a circular ECA module.
ECA module where the conv uses circular padding rather than zero padding.
Unlike the spatial dimension, the channels do not have inherent ordering nor
locality. Although this module in essence, applies such an assumption, it is unnecessary
to limit the channels on either "edge" from being circularly adapted to each other.
This will fundamentally increase connectivity and possibly increase performance metrics
(accuracy, robustness), without significantly impacting resource metrics
(parameter size, throughput,latency, etc)
Args:
channels: Number of channels of the input feature map for use in adaptive kernel sizes
for actual calculations according to channel.
gamma, beta: when channel is given parameters of mapping function
refer to original paper https://arxiv.org/pdf/1910.03151.pdf
(default=None. if channel size not given, use k_size given for kernel size.)
kernel_size: Adaptive selection of kernel size (default=3)
gamm: used in kernel_size calc, see above
beta: used in kernel_size calc, see above
act_layer: optional non-linearity after conv, enables conv bias, this is an experiment
gate_layer: gating non-linearity to use
"""
def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid'):
super(CecaModule, self).__init__()
if channels is not None:
t = int(abs(math.log(channels, 2) + beta) / gamma)
kernel_size = max(t if t % 2 else t + 1, 3)
has_act = act_layer is not None
assert kernel_size % 2 == 1
# PyTorch circular padding mode is buggy as of pytorch 1.4
# see https://github.com/pytorch/pytorch/pull/17240
# implement manual circular padding
self.padding = (kernel_size - 1) // 2
self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=0, bias=has_act)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
y = x.mean((2, 3)).view(x.shape[0], 1, -1)
# Manually implement circular padding, F.pad does not seemed to be bugged
y = F.pad(y, (self.padding, self.padding), mode='circular')
y = self.conv(y)
y = self.gate(y).view(x.shape[0], -1, 1, 1)
return x * y.expand_as(x)
CircularEfficientChannelAttn = CecaModule
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/global_context.py | """ Global Context Attention Block
Paper: `GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond`
- https://arxiv.org/abs/1904.11492
Official code consulted as reference: https://github.com/xvjiarui/GCNet
Hacked together by / Copyright 2021 Ross Wightman
"""
from torch import nn as nn
import torch.nn.functional as F
from .create_act import create_act_layer, get_act_layer
from .helpers import make_divisible
from .mlp import ConvMlp
from .norm import LayerNorm2d
class GlobalContext(nn.Module):
def __init__(self, channels, use_attn=True, fuse_add=False, fuse_scale=True, init_last_zero=False,
rd_ratio=1./8, rd_channels=None, rd_divisor=1, act_layer=nn.ReLU, gate_layer='sigmoid'):
super(GlobalContext, self).__init__()
act_layer = get_act_layer(act_layer)
self.conv_attn = nn.Conv2d(channels, 1, kernel_size=1, bias=True) if use_attn else None
if rd_channels is None:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
if fuse_add:
self.mlp_add = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
else:
self.mlp_add = None
if fuse_scale:
self.mlp_scale = ConvMlp(channels, rd_channels, act_layer=act_layer, norm_layer=LayerNorm2d)
else:
self.mlp_scale = None
self.gate = create_act_layer(gate_layer)
self.init_last_zero = init_last_zero
self.reset_parameters()
def reset_parameters(self):
if self.conv_attn is not None:
nn.init.kaiming_normal_(self.conv_attn.weight, mode='fan_in', nonlinearity='relu')
if self.mlp_add is not None:
nn.init.zeros_(self.mlp_add.fc2.weight)
def forward(self, x):
B, C, H, W = x.shape
if self.conv_attn is not None:
attn = self.conv_attn(x).reshape(B, 1, H * W) # (B, 1, H * W)
attn = F.softmax(attn, dim=-1).unsqueeze(3) # (B, 1, H * W, 1)
context = x.reshape(B, C, H * W).unsqueeze(1) @ attn
context = context.view(B, C, 1, 1)
else:
context = x.mean(dim=(2, 3), keepdim=True)
if self.mlp_scale is not None:
mlp_x = self.mlp_scale(context)
x = x * self.gate(mlp_x)
if self.mlp_add is not None:
mlp_x = self.mlp_add(context)
x = x + mlp_x
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/typing.py | from typing import Callable, Tuple, Type, Union
import torch
LayerType = Union[str, Callable, Type[torch.nn.Module]]
PadType = Union[str, int, Tuple[int, int]]
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/median_pool.py | """ Median Pool
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch.nn as nn
import torch.nn.functional as F
from .helpers import to_2tuple, to_4tuple
class MedianPool2d(nn.Module):
""" Median pool (usable as median filter when stride=1) module.
Args:
kernel_size: size of pooling kernel, int or 2-tuple
stride: pool stride, int or 2-tuple
padding: pool padding, int or 4-tuple (l, r, t, b) as in pytorch F.pad
same: override padding and enforce same padding, boolean
"""
def __init__(self, kernel_size=3, stride=1, padding=0, same=False):
super(MedianPool2d, self).__init__()
self.k = to_2tuple(kernel_size)
self.stride = to_2tuple(stride)
self.padding = to_4tuple(padding) # convert to l, r, t, b
self.same = same
def _padding(self, x):
if self.same:
ih, iw = x.size()[2:]
if ih % self.stride[0] == 0:
ph = max(self.k[0] - self.stride[0], 0)
else:
ph = max(self.k[0] - (ih % self.stride[0]), 0)
if iw % self.stride[1] == 0:
pw = max(self.k[1] - self.stride[1], 0)
else:
pw = max(self.k[1] - (iw % self.stride[1]), 0)
pl = pw // 2
pr = pw - pl
pt = ph // 2
pb = ph - pt
padding = (pl, pr, pt, pb)
else:
padding = self.padding
return padding
def forward(self, x):
x = F.pad(x, self._padding(x), mode='reflect')
x = x.unfold(2, self.k[0], self.stride[0]).unfold(3, self.k[1], self.stride[1])
x = x.contiguous().view(x.size()[:4] + (-1,)).median(dim=-1)[0]
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/split_attn.py | """ Split Attention Conv2d (for ResNeSt Models)
Paper: `ResNeSt: Split-Attention Networks` - /https://arxiv.org/abs/2004.08955
Adapted from original PyTorch impl at https://github.com/zhanghang1989/ResNeSt
Modified for torchscript compat, performance, and consistency with timm by Ross Wightman
"""
import torch
import torch.nn.functional as F
from torch import nn
from .helpers import make_divisible
class RadixSoftmax(nn.Module):
def __init__(self, radix, cardinality):
super(RadixSoftmax, self).__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
x = F.softmax(x, dim=1)
x = x.reshape(batch, -1)
else:
x = torch.sigmoid(x)
return x
class SplitAttn(nn.Module):
"""Split-Attention (aka Splat)
"""
def __init__(self, in_channels, out_channels=None, kernel_size=3, stride=1, padding=None,
dilation=1, groups=1, bias=False, radix=2, rd_ratio=0.25, rd_channels=None, rd_divisor=8,
act_layer=nn.ReLU, norm_layer=None, drop_layer=None, **kwargs):
super(SplitAttn, self).__init__()
out_channels = out_channels or in_channels
self.radix = radix
mid_chs = out_channels * radix
if rd_channels is None:
attn_chs = make_divisible(in_channels * radix * rd_ratio, min_value=32, divisor=rd_divisor)
else:
attn_chs = rd_channels * radix
padding = kernel_size // 2 if padding is None else padding
self.conv = nn.Conv2d(
in_channels, mid_chs, kernel_size, stride, padding, dilation,
groups=groups * radix, bias=bias, **kwargs)
self.bn0 = norm_layer(mid_chs) if norm_layer else nn.Identity()
self.drop = drop_layer() if drop_layer is not None else nn.Identity()
self.act0 = act_layer(inplace=True)
self.fc1 = nn.Conv2d(out_channels, attn_chs, 1, groups=groups)
self.bn1 = norm_layer(attn_chs) if norm_layer else nn.Identity()
self.act1 = act_layer(inplace=True)
self.fc2 = nn.Conv2d(attn_chs, mid_chs, 1, groups=groups)
self.rsoftmax = RadixSoftmax(radix, groups)
def forward(self, x):
x = self.conv(x)
x = self.bn0(x)
x = self.drop(x)
x = self.act0(x)
B, RC, H, W = x.shape
if self.radix > 1:
x = x.reshape((B, self.radix, RC // self.radix, H, W))
x_gap = x.sum(dim=1)
else:
x_gap = x
x_gap = x_gap.mean((2, 3), keepdim=True)
x_gap = self.fc1(x_gap)
x_gap = self.bn1(x_gap)
x_gap = self.act1(x_gap)
x_attn = self.fc2(x_gap)
x_attn = self.rsoftmax(x_attn).view(B, -1, 1, 1)
if self.radix > 1:
out = (x * x_attn.reshape((B, self.radix, RC // self.radix, 1, 1))).sum(dim=1)
else:
out = x * x_attn
return out.contiguous()
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/selective_kernel.py | """ Selective Kernel Convolution/Attention
Paper: Selective Kernel Networks (https://arxiv.org/abs/1903.06586)
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from .conv_bn_act import ConvNormActAa
from .helpers import make_divisible
from .trace_utils import _assert
def _kernel_valid(k):
if isinstance(k, (list, tuple)):
for ki in k:
return _kernel_valid(ki)
assert k >= 3 and k % 2
class SelectiveKernelAttn(nn.Module):
def __init__(self, channels, num_paths=2, attn_channels=32, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
""" Selective Kernel Attention Module
Selective Kernel attention mechanism factored out into its own module.
"""
super(SelectiveKernelAttn, self).__init__()
self.num_paths = num_paths
self.fc_reduce = nn.Conv2d(channels, attn_channels, kernel_size=1, bias=False)
self.bn = norm_layer(attn_channels)
self.act = act_layer(inplace=True)
self.fc_select = nn.Conv2d(attn_channels, channels * num_paths, kernel_size=1, bias=False)
def forward(self, x):
_assert(x.shape[1] == self.num_paths, '')
x = x.sum(1).mean((2, 3), keepdim=True)
x = self.fc_reduce(x)
x = self.bn(x)
x = self.act(x)
x = self.fc_select(x)
B, C, H, W = x.shape
x = x.view(B, self.num_paths, C // self.num_paths, H, W)
x = torch.softmax(x, dim=1)
return x
class SelectiveKernel(nn.Module):
def __init__(self, in_channels, out_channels=None, kernel_size=None, stride=1, dilation=1, groups=1,
rd_ratio=1./16, rd_channels=None, rd_divisor=8, keep_3x3=True, split_input=True,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, drop_layer=None):
""" Selective Kernel Convolution Module
As described in Selective Kernel Networks (https://arxiv.org/abs/1903.06586) with some modifications.
Largest change is the input split, which divides the input channels across each convolution path, this can
be viewed as a grouping of sorts, but the output channel counts expand to the module level value. This keeps
the parameter count from ballooning when the convolutions themselves don't have groups, but still provides
a noteworthy increase in performance over similar param count models without this attention layer. -Ross W
Args:
in_channels (int): module input (feature) channel count
out_channels (int): module output (feature) channel count
kernel_size (int, list): kernel size for each convolution branch
stride (int): stride for convolutions
dilation (int): dilation for module as a whole, impacts dilation of each branch
groups (int): number of groups for each branch
rd_ratio (int, float): reduction factor for attention features
keep_3x3 (bool): keep all branch convolution kernels as 3x3, changing larger kernels for dilations
split_input (bool): split input channels evenly across each convolution branch, keeps param count lower,
can be viewed as grouping by path, output expands to module out_channels count
act_layer (nn.Module): activation layer to use
norm_layer (nn.Module): batchnorm/norm layer to use
aa_layer (nn.Module): anti-aliasing module
drop_layer (nn.Module): spatial drop module in convs (drop block, etc)
"""
super(SelectiveKernel, self).__init__()
out_channels = out_channels or in_channels
kernel_size = kernel_size or [3, 5] # default to one 3x3 and one 5x5 branch. 5x5 -> 3x3 + dilation
_kernel_valid(kernel_size)
if not isinstance(kernel_size, list):
kernel_size = [kernel_size] * 2
if keep_3x3:
dilation = [dilation * (k - 1) // 2 for k in kernel_size]
kernel_size = [3] * len(kernel_size)
else:
dilation = [dilation] * len(kernel_size)
self.num_paths = len(kernel_size)
self.in_channels = in_channels
self.out_channels = out_channels
self.split_input = split_input
if self.split_input:
assert in_channels % self.num_paths == 0
in_channels = in_channels // self.num_paths
groups = min(out_channels, groups)
conv_kwargs = dict(
stride=stride, groups=groups, act_layer=act_layer, norm_layer=norm_layer,
aa_layer=aa_layer, drop_layer=drop_layer)
self.paths = nn.ModuleList([
ConvNormActAa(in_channels, out_channels, kernel_size=k, dilation=d, **conv_kwargs)
for k, d in zip(kernel_size, dilation)])
attn_channels = rd_channels or make_divisible(out_channels * rd_ratio, divisor=rd_divisor)
self.attn = SelectiveKernelAttn(out_channels, self.num_paths, attn_channels)
def forward(self, x):
if self.split_input:
x_split = torch.split(x, self.in_channels // self.num_paths, 1)
x_paths = [op(x_split[i]) for i, op in enumerate(self.paths)]
else:
x_paths = [op(x) for op in self.paths]
x = torch.stack(x_paths, dim=1)
x_attn = self.attn(x)
x = x * x_attn
x = torch.sum(x, dim=1)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/helpers.py | """ Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse
to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
to_3tuple = _ntuple(3)
to_4tuple = _ntuple(4)
to_ntuple = _ntuple
def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
min_value = min_value or divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < round_limit * v:
new_v += divisor
return new_v
def extend_tuple(x, n):
# pads a tuple to specified n by padding with last value
if not isinstance(x, (tuple, list)):
x = (x,)
else:
x = tuple(x)
pad_n = n - len(x)
if pad_n <= 0:
return x[:n]
return x + (x[-1],) * pad_n
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/activations_jit.py | """ Activations
A collection of jit-scripted activations fn and modules with a common interface so that they can
easily be swapped. All have an `inplace` arg even if not used.
All jit scripted activations are lacking in-place variations on purpose, scripted kernel fusion does not
currently work across in-place op boundaries, thus performance is equal to or less than the non-scripted
versions if they contain in-place ops.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
@torch.jit.script
def swish_jit(x, inplace: bool = False):
"""Swish - Described in: https://arxiv.org/abs/1710.05941
"""
return x.mul(x.sigmoid())
@torch.jit.script
def mish_jit(x, _inplace: bool = False):
"""Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
"""
return x.mul(F.softplus(x).tanh())
class SwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(SwishJit, self).__init__()
def forward(self, x):
return swish_jit(x)
class MishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(MishJit, self).__init__()
def forward(self, x):
return mish_jit(x)
@torch.jit.script
def hard_sigmoid_jit(x, inplace: bool = False):
# return F.relu6(x + 3.) / 6.
return (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
class HardSigmoidJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSigmoidJit, self).__init__()
def forward(self, x):
return hard_sigmoid_jit(x)
@torch.jit.script
def hard_swish_jit(x, inplace: bool = False):
# return x * (F.relu6(x + 3.) / 6)
return x * (x + 3).clamp(min=0, max=6).div(6.) # clamp seems ever so slightly faster?
class HardSwishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardSwishJit, self).__init__()
def forward(self, x):
return hard_swish_jit(x)
@torch.jit.script
def hard_mish_jit(x, inplace: bool = False):
""" Hard Mish
Experimental, based on notes by Mish author Diganta Misra at
https://github.com/digantamisra98/H-Mish/blob/0da20d4bc58e696b6803f2523c58d3c8a82782d0/README.md
"""
return 0.5 * x * (x + 2).clamp(min=0, max=2)
class HardMishJit(nn.Module):
def __init__(self, inplace: bool = False):
super(HardMishJit, self).__init__()
def forward(self, x):
return hard_mish_jit(x)
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/create_attn.py | """ Attention Factory
Hacked together by / Copyright 2021 Ross Wightman
"""
import torch
from functools import partial
from .bottleneck_attn import BottleneckAttn
from .cbam import CbamModule, LightCbamModule
from .eca import EcaModule, CecaModule
from .gather_excite import GatherExcite
from .global_context import GlobalContext
from .halo_attn import HaloAttn
from .lambda_layer import LambdaLayer
from .non_local_attn import NonLocalAttn, BatNonLocalAttn
from .selective_kernel import SelectiveKernel
from .split_attn import SplitAttn
from .squeeze_excite import SEModule, EffectiveSEModule
def get_attn(attn_type):
if isinstance(attn_type, torch.nn.Module):
return attn_type
module_cls = None
if attn_type:
if isinstance(attn_type, str):
attn_type = attn_type.lower()
# Lightweight attention modules (channel and/or coarse spatial).
# Typically added to existing network architecture blocks in addition to existing convolutions.
if attn_type == 'se':
module_cls = SEModule
elif attn_type == 'ese':
module_cls = EffectiveSEModule
elif attn_type == 'eca':
module_cls = EcaModule
elif attn_type == 'ecam':
module_cls = partial(EcaModule, use_mlp=True)
elif attn_type == 'ceca':
module_cls = CecaModule
elif attn_type == 'ge':
module_cls = GatherExcite
elif attn_type == 'gc':
module_cls = GlobalContext
elif attn_type == 'gca':
module_cls = partial(GlobalContext, fuse_add=True, fuse_scale=False)
elif attn_type == 'cbam':
module_cls = CbamModule
elif attn_type == 'lcbam':
module_cls = LightCbamModule
# Attention / attention-like modules w/ significant params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'sk':
module_cls = SelectiveKernel
elif attn_type == 'splat':
module_cls = SplitAttn
# Self-attention / attention-like modules w/ significant compute and/or params
# Typically replace some of the existing workhorse convs in a network architecture.
# All of these accept a stride argument and can spatially downsample the input.
elif attn_type == 'lambda':
return LambdaLayer
elif attn_type == 'bottleneck':
return BottleneckAttn
elif attn_type == 'halo':
return HaloAttn
elif attn_type == 'nl':
module_cls = NonLocalAttn
elif attn_type == 'bat':
module_cls = BatNonLocalAttn
# Woops!
else:
assert False, "Invalid attn module (%s)" % attn_type
elif isinstance(attn_type, bool):
if attn_type:
module_cls = SEModule
else:
module_cls = attn_type
return module_cls
def create_attn(attn_type, channels, **kwargs):
module_cls = get_attn(attn_type)
if module_cls is not None:
# NOTE: it's expected the first (positional) argument of all attention layers is the # input channels
return module_cls(channels, **kwargs)
return None
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/padding.py | """ Padding Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from typing import List, Tuple
import torch
import torch.nn.functional as F
# Calculate symmetric padding for a convolution
def get_padding(kernel_size: int, stride: int = 1, dilation: int = 1, **_) -> int:
padding = ((stride - 1) + dilation * (kernel_size - 1)) // 2
return padding
# Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
def get_same_padding(x: int, kernel_size: int, stride: int, dilation: int):
if isinstance(x, torch.Tensor):
return torch.clamp(((x / stride).ceil() - 1) * stride + (kernel_size - 1) * dilation + 1 - x, min=0)
else:
return max((math.ceil(x / stride) - 1) * stride + (kernel_size - 1) * dilation + 1 - x, 0)
# Can SAME padding for given args be done statically?
def is_static_pad(kernel_size: int, stride: int = 1, dilation: int = 1, **_):
return stride == 1 and (dilation * (kernel_size - 1)) % 2 == 0
def pad_same_arg(
input_size: List[int],
kernel_size: List[int],
stride: List[int],
dilation: List[int] = (1, 1),
) -> List[int]:
ih, iw = input_size
kh, kw = kernel_size
pad_h = get_same_padding(ih, kh, stride[0], dilation[0])
pad_w = get_same_padding(iw, kw, stride[1], dilation[1])
return [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]
# Dynamically pad input x with 'SAME' padding for conv with specified args
def pad_same(
x,
kernel_size: List[int],
stride: List[int],
dilation: List[int] = (1, 1),
value: float = 0,
):
ih, iw = x.size()[-2:]
pad_h = get_same_padding(ih, kernel_size[0], stride[0], dilation[0])
pad_w = get_same_padding(iw, kernel_size[1], stride[1], dilation[1])
x = F.pad(x, (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2), value=value)
return x
def get_padding_value(padding, kernel_size, **kwargs) -> Tuple[Tuple, bool]:
dynamic = False
if isinstance(padding, str):
# for any string padding, the padding will be calculated for you, one of three ways
padding = padding.lower()
if padding == 'same':
# TF compatible 'SAME' padding, has a performance and GPU memory allocation impact
if is_static_pad(kernel_size, **kwargs):
# static case, no extra overhead
padding = get_padding(kernel_size, **kwargs)
else:
# dynamic 'SAME' padding, has runtime/GPU memory overhead
padding = 0
dynamic = True
elif padding == 'valid':
# 'VALID' padding, same as padding=0
padding = 0
else:
# Default to PyTorch style 'same'-ish symmetric padding
padding = get_padding(kernel_size, **kwargs)
return padding, dynamic
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/cbam.py | """ CBAM (sort-of) Attention
Experimental impl of CBAM: Convolutional Block Attention Module: https://arxiv.org/abs/1807.06521
WARNING: Results with these attention layers have been mixed. They can significantly reduce performance on
some tasks, especially fine-grained it seems. I may end up removing this impl.
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
from torch import nn as nn
import torch.nn.functional as F
from .conv_bn_act import ConvNormAct
from .create_act import create_act_layer, get_act_layer
from .helpers import make_divisible
class ChannelAttn(nn.Module):
""" Original CBAM channel attention module, currently avg + max pool variant only.
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(ChannelAttn, self).__init__()
if not rd_channels:
rd_channels = make_divisible(channels * rd_ratio, rd_divisor, round_limit=0.)
self.fc1 = nn.Conv2d(channels, rd_channels, 1, bias=mlp_bias)
self.act = act_layer(inplace=True)
self.fc2 = nn.Conv2d(rd_channels, channels, 1, bias=mlp_bias)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_avg = self.fc2(self.act(self.fc1(x.mean((2, 3), keepdim=True))))
x_max = self.fc2(self.act(self.fc1(x.amax((2, 3), keepdim=True))))
return x * self.gate(x_avg + x_max)
class LightChannelAttn(ChannelAttn):
"""An experimental 'lightweight' that sums avg + max pool first
"""
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightChannelAttn, self).__init__(
channels, rd_ratio, rd_channels, rd_divisor, act_layer, gate_layer, mlp_bias)
def forward(self, x):
x_pool = 0.5 * x.mean((2, 3), keepdim=True) + 0.5 * x.amax((2, 3), keepdim=True)
x_attn = self.fc2(self.act(self.fc1(x_pool)))
return x * F.sigmoid(x_attn)
class SpatialAttn(nn.Module):
""" Original CBAM spatial attention module
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(SpatialAttn, self).__init__()
self.conv = ConvNormAct(2, 1, kernel_size, apply_act=False)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_attn = torch.cat([x.mean(dim=1, keepdim=True), x.amax(dim=1, keepdim=True)], dim=1)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
class LightSpatialAttn(nn.Module):
"""An experimental 'lightweight' variant that sums avg_pool and max_pool results.
"""
def __init__(self, kernel_size=7, gate_layer='sigmoid'):
super(LightSpatialAttn, self).__init__()
self.conv = ConvNormAct(1, 1, kernel_size, apply_act=False)
self.gate = create_act_layer(gate_layer)
def forward(self, x):
x_attn = 0.5 * x.mean(dim=1, keepdim=True) + 0.5 * x.amax(dim=1, keepdim=True)
x_attn = self.conv(x_attn)
return x * self.gate(x_attn)
class CbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(CbamModule, self).__init__()
self.channel = ChannelAttn(
channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
self.spatial = SpatialAttn(spatial_kernel_size, gate_layer=gate_layer)
def forward(self, x):
x = self.channel(x)
x = self.spatial(x)
return x
class LightCbamModule(nn.Module):
def __init__(
self, channels, rd_ratio=1./16, rd_channels=None, rd_divisor=1,
spatial_kernel_size=7, act_layer=nn.ReLU, gate_layer='sigmoid', mlp_bias=False):
super(LightCbamModule, self).__init__()
self.channel = LightChannelAttn(
channels, rd_ratio=rd_ratio, rd_channels=rd_channels,
rd_divisor=rd_divisor, act_layer=act_layer, gate_layer=gate_layer, mlp_bias=mlp_bias)
self.spatial = LightSpatialAttn(spatial_kernel_size)
def forward(self, x):
x = self.channel(x)
x = self.spatial(x)
return x
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/cond_conv2d.py | """ PyTorch Conditionally Parameterized Convolution (CondConv)
Paper: CondConv: Conditionally Parameterized Convolutions for Efficient Inference
(https://arxiv.org/abs/1904.04971)
Hacked together by / Copyright 2020 Ross Wightman
"""
import math
from functools import partial
import numpy as np
import torch
from torch import nn as nn
from torch.nn import functional as F
from .helpers import to_2tuple
from .conv2d_same import conv2d_same
from .padding import get_padding_value
def get_condconv_initializer(initializer, num_experts, expert_shape):
def condconv_initializer(weight):
"""CondConv initializer function."""
num_params = np.prod(expert_shape)
if (len(weight.shape) != 2 or weight.shape[0] != num_experts or
weight.shape[1] != num_params):
raise (ValueError(
'CondConv variables must have shape [num_experts, num_params]'))
for i in range(num_experts):
initializer(weight[i].view(expert_shape))
return condconv_initializer
class CondConv2d(nn.Module):
""" Conditionally Parameterized Convolution
Inspired by: https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/condconv/condconv_layers.py
Grouped convolution hackery for parallel execution of the per-sample kernel filters inspired by this discussion:
https://github.com/pytorch/pytorch/issues/17983
"""
__constants__ = ['in_channels', 'out_channels', 'dynamic_padding']
def __init__(self, in_channels, out_channels, kernel_size=3,
stride=1, padding='', dilation=1, groups=1, bias=False, num_experts=4):
super(CondConv2d, self).__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = to_2tuple(kernel_size)
self.stride = to_2tuple(stride)
padding_val, is_padding_dynamic = get_padding_value(
padding, kernel_size, stride=stride, dilation=dilation)
self.dynamic_padding = is_padding_dynamic # if in forward to work with torchscript
self.padding = to_2tuple(padding_val)
self.dilation = to_2tuple(dilation)
self.groups = groups
self.num_experts = num_experts
self.weight_shape = (self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight_num_param = 1
for wd in self.weight_shape:
weight_num_param *= wd
self.weight = torch.nn.Parameter(torch.Tensor(self.num_experts, weight_num_param))
if bias:
self.bias_shape = (self.out_channels,)
self.bias = torch.nn.Parameter(torch.Tensor(self.num_experts, self.out_channels))
else:
self.register_parameter('bias', None)
self.reset_parameters()
def reset_parameters(self):
init_weight = get_condconv_initializer(
partial(nn.init.kaiming_uniform_, a=math.sqrt(5)), self.num_experts, self.weight_shape)
init_weight(self.weight)
if self.bias is not None:
fan_in = np.prod(self.weight_shape[1:])
bound = 1 / math.sqrt(fan_in)
init_bias = get_condconv_initializer(
partial(nn.init.uniform_, a=-bound, b=bound), self.num_experts, self.bias_shape)
init_bias(self.bias)
def forward(self, x, routing_weights):
B, C, H, W = x.shape
weight = torch.matmul(routing_weights, self.weight)
new_weight_shape = (B * self.out_channels, self.in_channels // self.groups) + self.kernel_size
weight = weight.view(new_weight_shape)
bias = None
if self.bias is not None:
bias = torch.matmul(routing_weights, self.bias)
bias = bias.view(B * self.out_channels)
# move batch elements with channels so each batch element can be efficiently convolved with separate kernel
# reshape instead of view to work with channels_last input
x = x.reshape(1, B * C, H, W)
if self.dynamic_padding:
out = conv2d_same(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
else:
out = F.conv2d(
x, weight, bias, stride=self.stride, padding=self.padding,
dilation=self.dilation, groups=self.groups * B)
out = out.permute([1, 0, 2, 3]).view(B, self.out_channels, out.shape[-2], out.shape[-1])
# Literal port (from TF definition)
# x = torch.split(x, 1, 0)
# weight = torch.split(weight, 1, 0)
# if self.bias is not None:
# bias = torch.matmul(routing_weights, self.bias)
# bias = torch.split(bias, 1, 0)
# else:
# bias = [None] * B
# out = []
# for xi, wi, bi in zip(x, weight, bias):
# wi = wi.view(*self.weight_shape)
# if bi is not None:
# bi = bi.view(*self.bias_shape)
# out.append(self.conv_fn(
# xi, wi, bi, stride=self.stride, padding=self.padding,
# dilation=self.dilation, groups=self.groups))
# out = torch.cat(out, 0)
return out
| 0 |
hf_public_repos/pytorch-image-models/timm | hf_public_repos/pytorch-image-models/timm/layers/drop.py | """ DropBlock, DropPath
PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
Papers:
DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
Code:
DropBlock impl inspired by two Tensorflow impl that I liked:
- https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
Hacked together by / Copyright 2020 Ross Wightman
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
def drop_block_2d(
x, drop_prob: float = 0.1, block_size: int = 7, gamma_scale: float = 1.0,
with_noise: bool = False, inplace: bool = False, batchwise: bool = False):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
runs with success, but needs further validation and possibly optimization for lower runtime impact.
"""
B, C, H, W = x.shape
total_size = W * H
clipped_block_size = min(block_size, min(W, H))
# seed_drop_rate, the gamma parameter
gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
(W - block_size + 1) * (H - block_size + 1))
# Forces the block to be inside the feature map.
w_i, h_i = torch.meshgrid(torch.arange(W).to(x.device), torch.arange(H).to(x.device))
valid_block = ((w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)) & \
((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
if batchwise:
# one mask for whole batch, quite a bit faster
uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
else:
uniform_noise = torch.rand_like(x)
block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
block_mask = -F.max_pool2d(
-block_mask,
kernel_size=clipped_block_size, # block_size,
stride=1,
padding=clipped_block_size // 2)
if with_noise:
normal_noise = torch.randn((1, C, H, W), dtype=x.dtype, device=x.device) if batchwise else torch.randn_like(x)
if inplace:
x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
else:
x = x * block_mask + normal_noise * (1 - block_mask)
else:
normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)).to(x.dtype)
if inplace:
x.mul_(block_mask * normalize_scale)
else:
x = x * block_mask * normalize_scale
return x
def drop_block_fast_2d(
x: torch.Tensor, drop_prob: float = 0.1, block_size: int = 7,
gamma_scale: float = 1.0, with_noise: bool = False, inplace: bool = False):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
block mask at edges.
"""
B, C, H, W = x.shape
total_size = W * H
clipped_block_size = min(block_size, min(W, H))
gamma = gamma_scale * drop_prob * total_size / clipped_block_size ** 2 / (
(W - block_size + 1) * (H - block_size + 1))
block_mask = torch.empty_like(x).bernoulli_(gamma)
block_mask = F.max_pool2d(
block_mask.to(x.dtype), kernel_size=clipped_block_size, stride=1, padding=clipped_block_size // 2)
if with_noise:
normal_noise = torch.empty_like(x).normal_()
if inplace:
x.mul_(1. - block_mask).add_(normal_noise * block_mask)
else:
x = x * (1. - block_mask) + normal_noise * block_mask
else:
block_mask = 1 - block_mask
normalize_scale = (block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)).to(dtype=x.dtype)
if inplace:
x.mul_(block_mask * normalize_scale)
else:
x = x * block_mask * normalize_scale
return x
class DropBlock2d(nn.Module):
""" DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
"""
def __init__(
self,
drop_prob: float = 0.1,
block_size: int = 7,
gamma_scale: float = 1.0,
with_noise: bool = False,
inplace: bool = False,
batchwise: bool = False,
fast: bool = True):
super(DropBlock2d, self).__init__()
self.drop_prob = drop_prob
self.gamma_scale = gamma_scale
self.block_size = block_size
self.with_noise = with_noise
self.inplace = inplace
self.batchwise = batchwise
self.fast = fast # FIXME finish comparisons of fast vs not
def forward(self, x):
if not self.training or not self.drop_prob:
return x
if self.fast:
return drop_block_fast_2d(
x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace)
else:
return drop_block_2d(
x, self.drop_prob, self.block_size, self.gamma_scale, self.with_noise, self.inplace, self.batchwise)
def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep: bool = True):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
if keep_prob > 0.0 and scale_by_keep:
random_tensor.div_(keep_prob)
return x * random_tensor
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
self.scale_by_keep = scale_by_keep
def forward(self, x):
return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
def extra_repr(self):
return f'drop_prob={round(self.drop_prob,3):0.3f}'
| 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.