Model Zoo Summary¶
In this page, we list all algorithms we support. You can click the link to jump to the corresponding model pages.
And we also list all checkpoints for different tasks we provide. You can sort or search checkpoints in the table and click the corresponding link to model pages for more details.
All supported algorithms¶
Number of papers: 84
Algorithm: 84
Number of checkpoints: 519
[Algorithm] MobileNetV2: Inverted Residuals and Linear Bottlenecks (1 ckpts)
[Algorithm] Searching for MobileNetV3 (6 ckpts)
[Algorithm] Deep Residual Learning for Image Recognition (22 ckpts)
[Algorithm] Res2Net: A New Multi-scale Backbone Architecture (3 ckpts)
[Algorithm] Aggregated Residual Transformations for Deep Neural Networks (4 ckpts)
[Algorithm] Squeeze-and-Excitation Networks (2 ckpts)
[Algorithm] ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices (1 ckpts)
[Algorithm] ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (1 ckpts)
[Algorithm] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows (12 ckpts)
[Algorithm] Very Deep Convolutional Networks for Large-Scale Image Recognition (8 ckpts)
[Algorithm] RepVGG: Making VGG-style ConvNets Great Again (12 ckpts)
[Algorithm] Transformer in Transformer (1 ckpts)
[Algorithm] An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (4 ckpts)
[Algorithm] Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet (3 ckpts)
[Algorithm] TinyViT: Fast Pretraining Distillation for Small Vision Transformers (8 ckpts)
[Algorithm] MLP-Mixer: An all-MLP Architecture for Vision (2 ckpts)
[Algorithm] Conformer: Local Features Coupling Global Representations for Visual Recognition (4 ckpts)
[Algorithm] Designing Network Design Spaces (8 ckpts)
[Algorithm] Training data-efficient image transformers & distillation through attention (9 ckpts)
[Algorithm] Twins: Revisiting the Design of Spatial Attention in Vision Transformers (6 ckpts)
[Algorithm] EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (33 ckpts)
[Algorithm] A ConvNet for the 2020s (24 ckpts)
[Algorithm] Deep High-Resolution Representation Learning for Visual Recognition (9 ckpts)
[Algorithm] RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition (2 ckpts)
[Algorithm] Wide Residual Networks (3 ckpts)
[Algorithm] Visual Attention Network (4 ckpts)
[Algorithm] CSPNet: A New Backbone that can Enhance Learning Capability of CNN (3 ckpts)
[Algorithm] Patches Are All You Need? (3 ckpts)
[Algorithm] Densely Connected Convolutional Networks (4 ckpts)
[Algorithm] MetaFormer is Actually What You Need for Vision (5 ckpts)
[Algorithm] Rethinking the Inception Architecture for Computer Vision (1 ckpts)
[Algorithm] MViTv2: Improved Multiscale Vision Transformers for Classification and Detection (4 ckpts)
[Algorithm] EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications (6 ckpts)
[Algorithm] An Improved One millisecond Mobile Backbone (5 ckpts)
[Algorithm] EfficientFormer: Vision Transformers at MobileNet Speed (3 ckpts)
[Algorithm] Swin Transformer V2: Scaling Up Capacity and Resolution (12 ckpts)
[Algorithm] DeiT III: Revenge of the ViT (16 ckpts)
[Algorithm] HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions (6 ckpts)
[Algorithm] MobileViT Light-weight, General-purpose, and Mobile-friendly Vision Transformer (3 ckpts)
[Algorithm] DaViT: Dual Attention Vision Transformers (3 ckpts)
[Algorithm] Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs (6 ckpts)
[Algorithm] Residual Attention: A Simple but Effective Method for Multi-Label Recognition (1 ckpts)
[Algorithm] BEiT: BERT Pre-Training of Image Transformers (3 ckpts)
[Algorithm] BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers (3 ckpts)
[Algorithm] EVA: Exploring the Limits of Masked Visual Representation Learning at Scale (14 ckpts)
[Algorithm] Reversible Vision Transformers (2 ckpts)
[Algorithm] Learning Transferable Visual Models From Natural Language Supervision (14 ckpts)
[Algorithm] MixMIM: Mixed and Masked Image Modeling for Efficient Visual Representation Learning (2 ckpts)
[Algorithm] EfficientNetV2: Smaller Models and Faster Training (15 ckpts)
[Algorithm] Co-designing and Scaling ConvNets with Masked Autoencoders (26 ckpts)
[Algorithm] LeViT: a Vision Transformer in ConvNet’s Clothing for Faster Inference (5 ckpts)
[Algorithm] Vision GNN: An Image is Worth Graph of Nodes (7 ckpts)
[Algorithm] ArcFace: Additive Angular Margin Loss for Deep Face Recognition (1 ckpts)
[Algorithm] XCiT: Cross-Covariance Image Transformers (42 ckpts)
[Algorithm] Bootstrap your own latent: A new approach to self-supervised Learning (2 ckpts)
[Algorithm] Dense contrastive learning for self-supervised visual pre-training (2 ckpts)
[Algorithm] Improved Baselines with Momentum Contrastive Learning (2 ckpts)
[Algorithm] An Empirical Study of Training Self-Supervised Vision Transformers (13 ckpts)
[Algorithm] A simple framework for contrastive learning of visual representations (4 ckpts)
[Algorithm] Exploring simple siamese representation learning (4 ckpts)
[Algorithm] Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (2 ckpts)
[Algorithm] Masked Autoencoders Are Scalable Vision Learners (11 ckpts)
[Algorithm] SimMIM: A Simple Framework for Masked Image Modeling (6 ckpts)
[Algorithm] Barlow Twins: Self-Supervised Learning via Redundancy Reduction (2 ckpts)
[Algorithm] Context Autoencoder for Self-Supervised Representation Learning (2 ckpts)
[Algorithm] Masked Feature Prediction for Self-Supervised Visual Pre-Training (2 ckpts)
[Algorithm] MILAN: Masked Image Pretraining on Language Assisted Representation (3 ckpts)
[Algorithm] OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (4 ckpts)
[Algorithm] RIFormer: Keep Your Vision Backbone Effective But Removing Token Mixer (10 ckpts)
[Algorithm] Segment Anything (3 ckpts)
[Algorithm] Grounded Language-Image Pre-training (2 ckpts)
[Algorithm] EVA-02: A Visual Representation for Neon Genesis (11 ckpts)
[Algorithm] DINOv2: Learning Robust Visual Features without Supervision (4 ckpts)
[Algorithm] BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (5 ckpts)
[Algorithm] Flamingo: a Visual Language Model for Few-Shot Learning (1 ckpts)
[Algorithm] BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models (2 ckpts)
[Algorithm] Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese (4 ckpts)
[Algorithm] Integrally Pre-Trained Transformer Pyramid Networks (0 ckpts)
[Algorithm] HiViT: A Simple and More Efficient Design of Hierarchical Vision Transformer (0 ckpts)
[Algorithm] Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling (4 ckpts)
[Algorithm] MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models (1 ckpts)
[Algorithm] Visual Instruction Tuning (0 ckpts)
[Algorithm] Otter: A Multi-Modal Model with In-Context Instruction Tuning (1 ckpts)
[Algorithm] Improving Pixel-based MIM by Reducing Wasted Modeling Capability (5 ckpts)
Pretrained Models¶
Model |
Params (M) |
Flops (G) |
Readme |
|---|---|---|---|
convnext-base_3rdparty_in21k |
88.59 |
15.36 |
|
convnext-large_3rdparty_in21k |
197.77 |
34.37 |
|
convnext-xlarge_3rdparty_in21k |
350.20 |
60.93 |
|
swinv2-base-w12_3rdparty_in21k-192px |
87.92 |
8.51 |
|
swinv2-large-w12_3rdparty_in21k-192px |
196.74 |
19.04 |
|
beit_beit-base-p16_8xb256-amp-coslr-300e_in1k |
86.53 |
17.58 |
|
beitv2_beit-base-p16_8xb256-amp-coslr-300e_in1k |
192.81 |
17.58 |
|
eva-mae-style_vit-base-p16_16xb256-coslr-400e_in1k |
111.78 |
17.58 |
|
beit-l-p14_3rdparty-eva_in21k |
303.18 |
81.08 |
|
beit-l-p14_eva-pre_3rdparty_in21k |
303.18 |
81.08 |
|
beit-g-p16_3rdparty-eva_30m |
1011.32 |
203.52 |
|
beit-g-p14_3rdparty-eva_30m |
1011.60 |
267.17 |
|
beit-g-p14_eva-30m-pre_3rdparty_in21k |
1011.60 |
267.17 |
|
vit-large-p14_clip-openai-pre_3rdparty |
303.30 |
59.70 |
|
mixmim_mixmim-base_16xb128-coslr-300e_in1k |
114.67 |
16.35 |
|
efficientnetv2-s_3rdparty_in21k |
48.16 |
3.31 |
|
efficientnetv2-m_3rdparty_in21k |
80.84 |
5.86 |
|
efficientnetv2-l_3rdparty_in21k |
145.22 |
13.11 |
|
efficientnetv2-xl_3rdparty_in21k |
234.82 |
18.86 |
|
convnext-v2-atto_3rdparty-fcmae_in1k |
3.71 |
0.55 |
|
convnext-v2-femto_3rdparty-fcmae_in1k |
5.23 |
0.78 |
|
convnext-v2-pico_3rdparty-fcmae_in1k |
9.07 |
1.37 |
|
convnext-v2-nano_3rdparty-fcmae_in1k |
15.62 |
2.45 |
|
convnext-v2-tiny_3rdparty-fcmae_in1k |
28.64 |
4.47 |
|
convnext-v2-base_3rdparty-fcmae_in1k |
88.72 |
15.38 |
|
convnext-v2-large_3rdparty-fcmae_in1k |
197.96 |
34.40 |
|
convnext-v2-huge_3rdparty-fcmae_in1k |
660.29 |
115.00 |
|
byol_resnet50_16xb256-coslr-200e_in1k |
68.02 |
4.11 |
|
densecl_resnet50_8xb32-coslr-200e_in1k |
64.85 |
4.11 |
|
mocov2_resnet50_8xb32-coslr-200e_in1k |
55.93 |
4.11 |
|
mocov3_resnet50_8xb512-amp-coslr-100e_in1k |
68.01 |
4.11 |
|
mocov3_resnet50_8xb512-amp-coslr-300e_in1k |
68.01 |
4.11 |
|
mocov3_resnet50_8xb512-amp-coslr-800e_in1k |
68.01 |
4.11 |
|
mocov3_vit-small-p16_16xb256-amp-coslr-300e_in1k |
84.27 |
4.61 |
|
mocov3_vit-base-p16_16xb256-amp-coslr-300e_in1k |
215.68 |
17.58 |
|
mocov3_vit-large-p16_64xb64-amp-coslr-300e_in1k |
652.78 |
61.60 |
|
simclr_resnet50_16xb256-coslr-200e_in1k |
27.97 |
4.11 |
|
simclr_resnet50_16xb256-coslr-800e_in1k |
27.97 |
4.11 |
|
simsiam_resnet50_8xb32-coslr-100e_in1k |
38.20 |
4.11 |
|
simsiam_resnet50_8xb32-coslr-200e_in1k |
38.20 |
4.11 |
|
swav_resnet50_8xb32-mcrop-coslr-200e_in1k-224px-96px |
28.35 |
4.11 |
|
mae_vit-base-p16_8xb512-amp-coslr-300e_in1k |
111.91 |
17.58 |
|
mae_vit-base-p16_8xb512-amp-coslr-400e_in1k |
111.91 |
17.58 |
|
mae_vit-base-p16_8xb512-amp-coslr-800e_in1k |
111.91 |
17.58 |
|
mae_vit-base-p16_8xb512-amp-coslr-1600e_in1k |
111.91 |
17.58 |
|
mae_vit-large-p16_8xb512-amp-coslr-400e_in1k |
329.54 |
61.60 |
|
mae_vit-large-p16_8xb512-amp-coslr-800e_in1k |
329.54 |
61.60 |
|
mae_vit-large-p16_8xb512-amp-coslr-1600e_in1k |
329.54 |
61.60 |
|
mae_vit-huge-p16_8xb512-amp-coslr-1600e_in1k |
657.07 |
167.40 |
|
simmim_swin-base-w6_8xb256-amp-coslr-100e_in1k-192px |
89.87 |
18.83 |
|
simmim_swin-base-w6_16xb128-amp-coslr-800e_in1k-192px |
89.87 |
18.83 |
|
simmim_swin-large-w12_16xb128-amp-coslr-800e_in1k-192px |
199.92 |
55.85 |
|
barlowtwins_resnet50_8xb256-coslr-300e_in1k |
174.54 |
4.11 |
|
cae_beit-base-p16_8xb256-amp-coslr-300e_in1k |
288.43 |
17.58 |
|
maskfeat_vit-base-p16_8xb256-amp-coslr-300e_in1k |
85.88 |
17.58 |
|
milan_vit-base-p16_16xb256-amp-coslr-400e_in1k |
111.91 |
17.58 |
|
vit-base-p16_sam-pre_3rdparty_sa1b-1024px |
89.67 |
486.00 |
|
vit-large-p16_sam-pre_3rdparty_sa1b-1024px |
308.00 |
1494.00 |
|
vit-huge-p16_sam-pre_3rdparty_sa1b-1024px |
637.00 |
2982.00 |
|
swin-t_glip-pre_3rdparty |
29.06 |
4.51 |
|
swin-l_glip-pre_3rdparty_384px |
196.74 |
104.08 |
|
vit-tiny-p14_eva02-pre_in21k |
5.50 |
1.70 |
|
vit-small-p14_eva02-pre_in21k |
21.62 |
6.14 |
|
vit-base-p14_eva02-pre_in21k |
85.77 |
23.22 |
|
vit-large-p14_eva02-pre_in21k |
303.29 |
81.15 |
|
vit-large-p14_eva02-pre_m38m |
303.29 |
81.15 |
|
vit-small-p14_dinov2-pre_3rdparty |
22.06 |
46.76 |
|
vit-base-p14_dinov2-pre_3rdparty |
86.58 |
152.00 |
|
vit-large-p14_dinov2-pre_3rdparty |
304.00 |
507.00 |
|
vit-giant-p14_dinov2-pre_3rdparty |
1136.00 |
1784.00 |
|
itpn-clip-b_hivit-base-p16_8xb256-amp-coslr-800e_in1k |
233.00 |
18.47 |
|
itpn-pixel_hivit-base-p16_8xb512-amp-coslr-800e_in1k |
103.00 |
18.47 |
|
itpn-pixel_hivit-large-p16_8xb512-amp-coslr-800e_in1k |
314.00 |
63.98 |
|
spark_sparse-resnet50_800e_in1k |
37.97 |
4.10 |
|
spark_sparse-convnextv2-tiny_800e_in1k |
39.73 |
4.47 |
|
mff_vit-base-p16_8xb512-amp-coslr-300e_in1k |
85.88 |
17.58 |
|
mff_vit-base-p16_8xb512-amp-coslr-800e_in1k |
85.88 |
17.58 |
Image Classification¶
ImageNet-1k¶
Model |
Params (M) |
Flops (G) |
Top-1 (%) |
Top-5 (%) |
Readme |
|---|---|---|---|---|---|
mobilenet-v2_8xb32_in1k |
3.50 |
0.32 |
71.86 |
90.42 |
|
mobilenet-v3-small-050_3rdparty_in1k |
1.59 |
0.02 |
57.91 |
80.19 |
|
mobilenet-v3-small-075_3rdparty_in1k |
2.04 |
0.04 |
65.23 |
85.44 |
|
mobilenet-v3-small_8xb128_in1k |
2.54 |
0.06 |
66.68 |
86.74 |
|
mobilenet-v3-small_3rdparty_in1k |
2.54 |
0.06 |
67.66 |
87.41 |
|
mobilenet-v3-large_8xb128_in1k |
5.48 |
0.23 |
73.49 |
91.31 |
|
mobilenet-v3-large_3rdparty_in1k |
5.48 |
0.23 |
74.04 |
91.34 |
|
resnet18_8xb32_in1k |
11.69 |
1.82 |
69.9 |
89.43 |
|
resnet34_8xb32_in1k |
2.18 |
3.68 |
73.62 |
91.59 |
|
resnet50_8xb32_in1k |
25.56 |
4.12 |
76.55 |
93.06 |
|
resnet101_8xb32_in1k |
44.55 |
7.85 |
77.97 |
94.06 |
|
resnet152_8xb32_in1k |
60.19 |
11.58 |
78.48 |
94.13 |
|
resnetv1d50_8xb32_in1k |
25.58 |
4.36 |
77.54 |
93.57 |
|
resnetv1d101_8xb32_in1k |
44.57 |
8.09 |
78.93 |
94.48 |
|
resnetv1d152_8xb32_in1k |
60.21 |
11.82 |
79.41 |
94.7 |
|
resnet50_8xb32-fp16_in1k |
25.56 |
4.12 |
76.3 |
93.07 |
|
resnet50_8xb256-rsb-a1-600e_in1k |
25.56 |
4.12 |
80.12 |
94.78 |
|
resnet50_8xb256-rsb-a2-300e_in1k |
25.56 |
4.12 |
79.55 |
94.37 |
|
resnet50_8xb256-rsb-a3-100e_in1k |
25.56 |
4.12 |
78.3 |
93.8 |
|
resnetv1c50_8xb32_in1k |
25.58 |
4.36 |
77.01 |
93.58 |
|
resnetv1c101_8xb32_in1k |
44.57 |
8.09 |
78.3 |
94.27 |
|
resnetv1c152_8xb32_in1k |
60.21 |
11.82 |
78.76 |
94.41 |
|
res2net50-w14-s8_3rdparty_8xb32_in1k |
25.06 |
4.22 |
78.14 |
93.85 |
|
res2net50-w26-s8_3rdparty_8xb32_in1k |
48.40 |
8.39 |
79.2 |
94.36 |
|
res2net101-w26-s4_3rdparty_8xb32_in1k |
45.21 |
8.12 |
79.19 |
94.44 |
|
resnext50-32x4d_8xb32_in1k |
25.03 |
4.27 |
77.9 |
93.66 |
|
resnext101-32x4d_8xb32_in1k |
44.18 |
8.03 |
78.61 |
94.17 |
|
resnext101-32x8d_8xb32_in1k |
88.79 |
16.50 |
79.27 |
94.58 |
|
resnext152-32x4d_8xb32_in1k |
59.95 |
11.80 |
78.88 |
94.33 |
|
seresnet50_8xb32_in1k |
28.09 |
4.13 |
77.74 |
93.84 |
|
seresnet101_8xb32_in1k |
49.33 |
7.86 |
78.26 |
94.07 |
|
shufflenet-v1-1x_16xb64_in1k |
1.87 |
0.15 |
68.13 |
87.81 |
|
shufflenet-v2-1x_16xb64_in1k |
2.28 |
0.15 |
69.55 |
88.92 |
|
swin-tiny_16xb64_in1k |
28.29 |
4.36 |
81.18 |
95.61 |
|
swin-small_16xb64_in1k |
49.61 |
8.52 |
83.02 |
96.29 |
|
swin-base_16xb64_in1k |
87.77 |
15.14 |
83.36 |
96.44 |
|
swin-tiny_3rdparty_in1k |
28.29 |
4.36 |
81.18 |
95.52 |
|
swin-small_3rdparty_in1k |
49.61 |
8.52 |
83.21 |
96.25 |
|
swin-base_3rdparty_in1k |
87.77 |
15.14 |
83.42 |
96.44 |
|
swin-base_3rdparty_in1k-384 |
87.90 |
44.49 |
84.49 |
96.95 |
|
swin-base_in21k-pre-3rdparty_in1k |
87.77 |
15.14 |
85.16 |
97.5 |
|
swin-base_in21k-pre-3rdparty_in1k-384 |
87.90 |
44.49 |
86.44 |
98.05 |
|
swin-large_in21k-pre-3rdparty_in1k |
196.53 |
34.04 |
86.24 |
97.88 |
|
swin-large_in21k-pre-3rdparty_in1k-384 |
196.74 |
100.04 |
87.25 |
98.25 |
|
vgg11_8xb32_in1k |
132.86 |
7.63 |
68.75 |
88.87 |
|
vgg13_8xb32_in1k |
133.05 |
11.34 |
70.02 |
89.46 |
|
vgg16_8xb32_in1k |
138.36 |
15.50 |
71.62 |
90.49 |
|
vgg19_8xb32_in1k |
143.67 |
19.67 |
72.41 |
90.8 |
|
vgg11bn_8xb32_in1k |
132.87 |
7.64 |
70.67 |
90.16 |
|
vgg13bn_8xb32_in1k |
133.05 |
11.36 |
72.12 |
90.66 |
|
vgg16bn_8xb32_in1k |
138.37 |
15.53 |
73.74 |
91.66 |
|
vgg19bn_8xb32_in1k |
143.68 |
19.70 |
74.68 |
92.27 |
|
repvgg-A0_8xb32_in1k |
8.31 |
1.36 |
72.37 |
90.56 |
|
repvgg-A1_8xb32_in1k |
12.79 |
2.36 |
74.23 |
91.8 |
|
repvgg-A2_8xb32_in1k |
25.50 |
5.12 |
76.49 |
93.09 |
|
repvgg-B0_8xb32_in1k |
3.42 |
15.82 |
75.27 |
92.21 |
|
repvgg-B1_8xb32_in1k |
51.83 |
11.81 |
78.19 |
94.04 |
|
repvgg-B1g2_8xb32_in1k |
41.36 |
8.81 |
77.87 |
93.99 |
|
repvgg-B1g4_8xb32_in1k |
36.13 |
7.30 |
77.81 |
93.77 |
|
repvgg-B2_8xb32_in1k |
80.32 |
18.37 |
78.58 |
94.23 |
|
repvgg-B2g4_8xb32_in1k |
55.78 |
11.33 |
79.44 |
94.72 |
|
repvgg-B3_8xb32_in1k |
110.96 |
26.21 |
80.58 |
95.33 |
|
repvgg-B3g4_8xb32_in1k |
75.63 |
16.06 |
80.26 |
95.15 |
|
repvgg-D2se_3rdparty_in1k |
120.39 |
32.84 |
81.81 |
95.94 |
|
tnt-small-p16_3rdparty_in1k |
23.76 |
3.36 |
81.52 |
95.73 |
|
vit-base-p32_in21k-pre_3rdparty_in1k-384px |
88.30 |
13.06 |
84.01 |
97.08 |
|
vit-base-p16_32xb128-mae_in1k |
86.57 |
17.58 |
82.37 |
96.15 |
|
vit-base-p16_in21k-pre_3rdparty_in1k-384px |
86.86 |
55.54 |
85.43 |
97.77 |
|
vit-large-p16_in21k-pre_3rdparty_in1k-384px |
304.72 |
191.21 |
85.63 |
97.63 |
|
t2t-vit-t-14_8xb64_in1k |
21.47 |
4.34 |
81.83 |
95.84 |
|
t2t-vit-t-19_8xb64_in1k |
39.08 |
7.80 |
82.63 |
96.18 |
|
t2t-vit-t-24_8xb64_in1k |
64.00 |
12.69 |
82.71 |
96.09 |
|
tinyvit-5m_3rdparty_in1k |
5.39 |
1.29 |
79.02 |
94.74 |
|
tinyvit-5m_in21k-distill-pre_3rdparty_in1k |
5.39 |
1.29 |
80.71 |
95.57 |
|
tinyvit-11m_3rdparty_in1k |
11.00 |
2.05 |
81.44 |
95.79 |
|
tinyvit-11m_in21k-distill-pre_3rdparty_in1k |
11.00 |
2.05 |
83.19 |
96.53 |
|
tinyvit-21m_3rdparty_in1k |
21.20 |
4.30 |
83.08 |
96.58 |
|
tinyvit-21m_in21k-distill-pre_3rdparty_in1k |
21.20 |
4.30 |
84.85 |
97.27 |
|
tinyvit-21m_in21k-distill-pre_3rdparty_in1k-384px |
21.23 |
13.85 |
86.21 |
97.77 |
|
tinyvit-21m_in21k-distill-pre_3rdparty_in1k-512px |
21.27 |
27.15 |
86.44 |
97.89 |
|
mlp-mixer-base-p16_3rdparty_64xb64_in1k |
59.88 |
12.61 |
76.68 |
92.25 |
|
mlp-mixer-large-p16_3rdparty_64xb64_in1k |
208.20 |
44.57 |
72.34 |
88.02 |
|
conformer-tiny-p16_3rdparty_in1k |
23.52 |
4.90 |
81.31 |
95.6 |
|
conformer-small-p16_3rdparty_in1k |
37.67 |
10.31 |
83.32 |
96.46 |
|
conformer-small-p32_8xb128_in1k |
38.85 |
7.09 |
81.96 |
96.02 |
|
conformer-base-p16_3rdparty_in1k |
83.29 |
22.89 |
83.82 |
96.59 |
|
regnetx-400mf_8xb128_in1k |
5.16 |
0.41 |
72.56 |
90.78 |
|
regnetx-800mf_8xb128_in1k |
7.26 |
0.81 |
74.76 |
92.32 |
|
regnetx-1.6gf_8xb128_in1k |
9.19 |
1.63 |
76.84 |
93.31 |
|
regnetx-3.2gf_8xb64_in1k |
3.21 |
1.53 |
78.09 |
94.08 |
|
regnetx-4.0gf_8xb64_in1k |
22.12 |
4.00 |
78.6 |
94.17 |
|
regnetx-6.4gf_8xb64_in1k |
26.21 |
6.51 |
79.38 |
94.65 |
|
regnetx-8.0gf_8xb64_in1k |
39.57 |
8.03 |
79.12 |
94.51 |
|
regnetx-12gf_8xb64_in1k |
46.11 |
12.15 |
79.67 |
95.03 |
|
deit-tiny_4xb256_in1k |
5.72 |
1.26 |
74.5 |
92.24 |
|
deit-tiny-distilled_3rdparty_in1k |
5.91 |
1.27 |
74.51 |
91.9 |
|
deit-small_4xb256_in1k |
22.05 |
4.61 |
80.69 |
95.06 |
|
deit-small-distilled_3rdparty_in1k |
22.44 |
4.63 |
81.17 |
95.4 |
|
deit-base_16xb64_in1k |
86.57 |
17.58 |
81.76 |
95.81 |
|
deit-base_3rdparty_in1k |
86.57 |
17.58 |
81.79 |
95.59 |
|
deit-base-distilled_3rdparty_in1k |
87.34 |
17.67 |
83.33 |
96.49 |
|
deit-base_224px-pre_3rdparty_in1k-384px |
86.86 |
55.54 |
83.04 |
96.31 |
|
deit-base-distilled_224px-pre_3rdparty_in1k-384px |
87.63 |
55.65 |
85.55 |
97.35 |
|
twins-pcpvt-small_3rdparty_8xb128_in1k |
24.11 |
3.67 |
81.14 |
95.69 |
|
twins-pcpvt-base_3rdparty_8xb128_in1k |
43.83 |
6.45 |
82.66 |
96.26 |
|
twins-pcpvt-large_3rdparty_16xb64_in1k |
60.99 |
9.51 |
83.09 |
96.59 |
|
twins-svt-small_3rdparty_8xb128_in1k |
24.06 |
2.82 |
81.77 |
95.57 |
|
twins-svt-base_8xb128_3rdparty_in1k |
56.07 |
8.35 |
83.13 |
96.29 |
|
twins-svt-large_3rdparty_16xb64_in1k |
99.27 |
14.82 |
83.6 |
96.5 |
|
efficientnet-b0_3rdparty_8xb32_in1k |
5.29 |
0.42 |
76.74 |
93.17 |
|
efficientnet-b0_3rdparty_8xb32-aa_in1k |
5.29 |
0.42 |
77.26 |
93.41 |
|
efficientnet-b0_3rdparty_8xb32-aa-advprop_in1k |
5.29 |
0.42 |
77.53 |
93.61 |
|
efficientnet-b0_3rdparty-ra-noisystudent_in1k |
5.29 |
0.42 |
77.63 |
94.0 |
|
efficientnet-b1_3rdparty_8xb32_in1k |
7.79 |
0.74 |
78.68 |
94.28 |
|
efficientnet-b1_3rdparty_8xb32-aa_in1k |
7.79 |
0.74 |
79.2 |
94.42 |
|
efficientnet-b1_3rdparty_8xb32-aa-advprop_in1k |
7.79 |
0.74 |
79.52 |
94.43 |
|
efficientnet-b1_3rdparty-ra-noisystudent_in1k |
7.79 |
0.74 |
81.44 |
95.83 |
|
efficientnet-b2_3rdparty_8xb32_in1k |
9.11 |
1.07 |
79.64 |
94.8 |
|
efficientnet-b2_3rdparty_8xb32-aa_in1k |
9.11 |
1.07 |
80.21 |
94.96 |
|
efficientnet-b2_3rdparty_8xb32-aa-advprop_in1k |
9.11 |
1.07 |
80.45 |
95.07 |
|
efficientnet-b2_3rdparty-ra-noisystudent_in1k |
9.11 |
1.07 |
82.47 |
96.23 |
|
efficientnet-b3_3rdparty_8xb32_in1k |
12.23 |
1.95 |
81.01 |
95.34 |
|
efficientnet-b3_3rdparty_8xb32-aa_in1k |
12.23 |
1.95 |
81.58 |
95.67 |
|
efficientnet-b3_3rdparty_8xb32-aa-advprop_in1k |
12.23 |
1.95 |
81.81 |
95.69 |
|
efficientnet-b3_3rdparty-ra-noisystudent_in1k |
12.23 |
1.95 |
84.02 |
96.89 |
|
efficientnet-b4_3rdparty_8xb32_in1k |
19.34 |
4.66 |
82.57 |
96.09 |
|
efficientnet-b4_3rdparty_8xb32-aa_in1k |
19.34 |
4.66 |
82.95 |
96.26 |
|
efficientnet-b4_3rdparty_8xb32-aa-advprop_in1k |
19.34 |
4.66 |
83.25 |
96.44 |
|
efficientnet-b4_3rdparty-ra-noisystudent_in1k |
19.34 |
4.66 |
85.25 |
97.52 |
|
efficientnet-b5_3rdparty_8xb32_in1k |
30.39 |
10.80 |
83.18 |
96.47 |
|
efficientnet-b5_3rdparty_8xb32-aa_in1k |
30.39 |
10.80 |
83.82 |
96.76 |
|
efficientnet-b5_3rdparty_8xb32-aa-advprop_in1k |
30.39 |
10.80 |
84.21 |
96.98 |
|
efficientnet-b5_3rdparty-ra-noisystudent_in1k |
30.39 |
10.80 |
86.08 |
97.75 |
|
efficientnet-b6_3rdparty_8xb32-aa_in1k |
43.04 |
19.97 |
84.05 |
96.82 |
|
efficientnet-b6_3rdparty_8xb32-aa-advprop_in1k |
43.04 |
19.97 |
84.74 |
97.14 |
|
efficientnet-b6_3rdparty-ra-noisystudent_in1k |
43.04 |
19.97 |
86.47 |
97.87 |
|
efficientnet-b7_3rdparty_8xb32-aa_in1k |
66.35 |
39.32 |
84.38 |
96.88 |
|
efficientnet-b7_3rdparty_8xb32-aa-advprop_in1k |
66.35 |
39.32 |
85.14 |
97.23 |
|
efficientnet-b7_3rdparty-ra-noisystudent_in1k |
66.35 |
39.32 |
86.83 |
98.08 |
|
efficientnet-b8_3rdparty_8xb32-aa-advprop_in1k |
87.41 |
65.00 |
85.38 |
97.28 |
|
efficientnet-l2_3rdparty-ra-noisystudent_in1k-800px |
480.31 |
174.20 |
88.33 |
98.65 |
|
efficientnet-l2_3rdparty-ra-noisystudent_in1k-475px |
480.31 |
484.98 |
88.18 |
98.55 |
|
convnext-tiny_32xb128_in1k |
28.59 |
4.46 |
82.14 |
96.06 |
|
convnext-tiny_32xb128-noema_in1k |
28.59 |
4.46 |
81.95 |
95.89 |
|
convnext-tiny_in21k-pre_3rdparty_in1k |
28.59 |
4.46 |
82.9 |
96.62 |
|
convnext-tiny_in21k-pre_3rdparty_in1k-384px |
28.59 |
13.14 |
84.11 |
97.14 |
|
convnext-small_32xb128_in1k |
50.22 |
8.69 |
83.16 |
96.56 |
|
convnext-small_32xb128-noema_in1k |
50.22 |
8.69 |
83.21 |
96.48 |
|
convnext-small_in21k-pre_3rdparty_in1k |
50.22 |
8.69 |
84.59 |
97.41 |
|
convnext-small_in21k-pre_3rdparty_in1k-384px |
50.22 |
25.58 |
85.75 |
97.88 |
|
convnext-base_32xb128_in1k |
88.59 |
15.36 |
83.66 |
96.74 |
|
convnext-base_32xb128-noema_in1k |
88.59 |
15.36 |
83.64 |
96.61 |
|
convnext-base_3rdparty_in1k |
88.59 |
15.36 |
83.85 |
96.74 |
|
convnext-base_3rdparty-noema_in1k |
88.59 |
15.36 |
83.71 |
96.6 |
|
convnext-base_3rdparty_in1k-384px |
88.59 |
45.21 |
85.1 |
97.34 |
|
convnext-base_in21k-pre_3rdparty_in1k |
88.59 |
15.36 |
85.81 |
97.86 |
|
convnext-base_in21k-pre-3rdparty_in1k-384px |
88.59 |
45.21 |
86.82 |
98.25 |
|
convnext-large_3rdparty_in1k |
197.77 |
34.37 |
84.3 |
96.89 |
|
convnext-large_3rdparty_in1k-384px |
197.77 |
101.10 |
85.5 |
97.59 |
|
convnext-large_in21k-pre_3rdparty_in1k |
197.77 |
34.37 |
86.61 |
98.04 |
|
convnext-large_in21k-pre-3rdparty_in1k-384px |
197.77 |
101.10 |
87.46 |
98.37 |
|
convnext-xlarge_in21k-pre_3rdparty_in1k |
350.20 |
60.93 |
86.97 |
98.2 |
|
convnext-xlarge_in21k-pre-3rdparty_in1k-384px |
350.20 |
179.20 |
87.76 |
98.55 |
|
hrnet-w18_3rdparty_8xb32_in1k |
21.30 |
4.33 |
76.75 |
93.44 |
|
hrnet-w30_3rdparty_8xb32_in1k |
37.71 |
8.17 |
78.19 |
94.22 |
|
hrnet-w32_3rdparty_8xb32_in1k |
41.23 |
8.99 |
78.44 |
94.19 |
|
hrnet-w40_3rdparty_8xb32_in1k |
57.55 |
12.77 |
78.94 |
94.47 |
|
hrnet-w44_3rdparty_8xb32_in1k |
67.06 |
14.96 |
78.88 |
94.37 |
|
hrnet-w48_3rdparty_8xb32_in1k |
77.47 |
17.36 |
79.32 |
94.52 |
|
hrnet-w64_3rdparty_8xb32_in1k |
128.06 |
29.00 |
79.46 |
94.65 |
|
hrnet-w18_3rdparty_8xb32-ssld_in1k |
21.30 |
4.33 |
81.06 |
95.7 |
|
hrnet-w48_3rdparty_8xb32-ssld_in1k |
77.47 |
17.36 |
83.63 |
96.79 |
|
repmlp-base_3rdparty_8xb64_in1k |
68.24 |
6.71 |
80.41 |
95.14 |
|
repmlp-base_3rdparty_8xb64_in1k-256px |
96.45 |
9.69 |
81.11 |
95.5 |
|
wide-resnet50_3rdparty_8xb32_in1k |
68.88 |
11.44 |
78.48 |
94.08 |
|
wide-resnet101_3rdparty_8xb32_in1k |
126.89 |
22.81 |
78.84 |
94.28 |
|
wide-resnet50_3rdparty-timm_8xb32_in1k |
68.88 |
11.44 |
81.45 |
95.53 |
|
van-tiny_3rdparty_in1k |
4.11 |
0.88 |
75.41 |
93.02 |
|
van-small_3rdparty_in1k |
13.86 |
2.52 |
81.01 |
95.63 |
|
van-base_3rdparty_in1k |
26.58 |
5.03 |
82.8 |
96.21 |
|
van-large_3rdparty_in1k |
44.77 |
8.99 |
83.86 |
96.73 |
|
cspdarknet50_3rdparty_8xb32_in1k |
27.64 |
5.04 |
80.05 |
95.07 |
|
cspresnet50_3rdparty_8xb32_in1k |
21.62 |
3.48 |
79.55 |
94.68 |
|
cspresnext50_3rdparty_8xb32_in1k |
20.57 |
3.11 |
79.96 |
94.96 |
|
convmixer-768-32_3rdparty_in1k |
21.11 |
19.62 |
80.16 |
95.08 |
|
convmixer-1024-20_3rdparty_in1k |
24.38 |
5.55 |
76.94 |
93.36 |
|
convmixer-1536-20_3rdparty_in1k |
51.63 |
48.71 |
81.37 |
95.61 |
|
densenet121_3rdparty_in1k |
7.98 |
2.88 |
74.96 |
92.21 |
|
densenet169_3rdparty_in1k |
14.15 |
3.42 |
76.08 |
93.11 |
|
densenet201_3rdparty_in1k |
20.01 |
4.37 |
77.32 |
93.64 |
|
densenet161_3rdparty_in1k |
28.68 |
7.82 |
77.61 |
93.83 |
|
poolformer-s12_3rdparty_32xb128_in1k |
11.92 |
1.87 |
77.24 |
93.51 |
|
poolformer-s24_3rdparty_32xb128_in1k |
21.39 |
3.51 |
80.33 |
95.05 |
|
poolformer-s36_3rdparty_32xb128_in1k |
30.86 |
5.15 |
81.43 |
95.45 |
|
poolformer-m36_3rdparty_32xb128_in1k |
56.17 |
8.96 |
82.14 |
95.71 |
|
poolformer-m48_3rdparty_32xb128_in1k |
73.47 |
11.80 |
82.51 |
95.95 |
|
inception-v3_3rdparty_8xb32_in1k |
23.83 |
5.75 |
77.57 |
93.58 |
|
mvitv2-tiny_3rdparty_in1k |
24.17 |
4.70 |
82.33 |
96.15 |
|
mvitv2-small_3rdparty_in1k |
34.87 |
7.00 |
83.63 |
96.51 |
|
mvitv2-base_3rdparty_in1k |
51.47 |
10.16 |
84.34 |
96.86 |
|
mvitv2-large_3rdparty_in1k |
217.99 |
43.87 |
85.25 |
97.14 |
|
edgenext-xxsmall_3rdparty_in1k |
1.33 |
0.26 |
71.2 |
89.91 |
|
edgenext-xsmall_3rdparty_in1k |
2.34 |
0.53 |
74.86 |
92.31 |
|
edgenext-small_3rdparty_in1k |
5.59 |
1.25 |
79.41 |
94.53 |
|
edgenext-small-usi_3rdparty_in1k |
5.59 |
1.25 |
81.06 |
95.34 |
|
edgenext-base_3rdparty_in1k |
18.51 |
3.81 |
82.48 |
96.2 |
|
edgenext-base_3rdparty-usi_in1k |
18.51 |
3.81 |
83.67 |
96.7 |
|
mobileone-s0_8xb32_in1k |
2.08 |
0.27 |
71.34 |
89.87 |
|
mobileone-s1_8xb32_in1k |
4.76 |
0.82 |
75.72 |
92.54 |
|
mobileone-s2_8xb32_in1k |
7.81 |
1.30 |
77.37 |
93.34 |
|
mobileone-s3_8xb32_in1k |
10.08 |
1.89 |
78.06 |
93.83 |
|
mobileone-s4_8xb32_in1k |
14.84 |
2.98 |
79.69 |
94.46 |
|
efficientformer-l1_3rdparty_8xb128_in1k |
12.28 |
1.30 |
80.46 |
94.99 |
|
efficientformer-l3_3rdparty_8xb128_in1k |
31.41 |
3.74 |
82.45 |
96.18 |
|
efficientformer-l7_3rdparty_8xb128_in1k |
82.23 |
10.16 |
83.4 |
96.6 |
|
swinv2-tiny-w8_3rdparty_in1k-256px |
28.35 |
4.35 |
81.76 |
95.87 |
|
swinv2-tiny-w16_3rdparty_in1k-256px |
28.35 |
4.40 |
82.81 |
96.23 |
|
swinv2-small-w8_3rdparty_in1k-256px |
49.73 |
8.45 |
83.74 |
96.6 |
|
swinv2-small-w16_3rdparty_in1k-256px |
49.73 |
8.57 |
84.13 |
96.83 |
|
swinv2-base-w8_3rdparty_in1k-256px |
87.92 |
14.99 |
84.2 |
96.86 |
|
swinv2-base-w16_3rdparty_in1k-256px |
87.92 |
15.14 |
84.6 |
97.05 |
|
swinv2-base-w16_in21k-pre_3rdparty_in1k-256px |
87.92 |
15.14 |
86.17 |
97.88 |
|
swinv2-base-w24_in21k-pre_3rdparty_in1k-384px |
87.92 |
34.07 |
87.14 |
98.23 |
|
swinv2-large-w16_in21k-pre_3rdparty_in1k-256px |
196.75 |
33.86 |
86.93 |
98.06 |
|
swinv2-large-w24_in21k-pre_3rdparty_in1k-384px |
196.75 |
76.20 |
87.59 |
98.27 |
|
deit3-small-p16_3rdparty_in1k |
22.06 |
4.61 |
81.35 |
95.31 |
|
deit3-small-p16_3rdparty_in1k-384px |
22.21 |
15.52 |
83.43 |
96.68 |
|
deit3-small-p16_in21k-pre_3rdparty_in1k |
22.06 |
4.61 |
83.06 |
96.77 |
|
deit3-small-p16_in21k-pre_3rdparty_in1k-384px |
22.21 |
15.52 |
84.84 |
97.48 |
|
deit3-medium-p16_3rdparty_in1k |
38.85 |
8.00 |
82.99 |
96.22 |
|
deit3-medium-p16_in21k-pre_3rdparty_in1k |
38.85 |
8.00 |
84.56 |
97.19 |
|
deit3-base-p16_3rdparty_in1k |
86.59 |
17.58 |
83.8 |
96.55 |
|
deit3-base-p16_3rdparty_in1k-384px |
86.88 |
55.54 |
85.08 |
97.25 |
|
deit3-base-p16_in21k-pre_3rdparty_in1k |
86.59 |
17.58 |
85.7 |
97.75 |
|
deit3-base-p16_in21k-pre_3rdparty_in1k-384px |
86.88 |
55.54 |
86.73 |
98.11 |
|
deit3-large-p16_3rdparty_in1k |
304.37 |
61.60 |
84.87 |
97.01 |
|
deit3-large-p16_3rdparty_in1k-384px |
304.76 |
191.21 |
85.82 |
97.6 |
|
deit3-large-p16_in21k-pre_3rdparty_in1k |
304.37 |
61.60 |
86.97 |
98.24 |
|
deit3-large-p16_in21k-pre_3rdparty_in1k-384px |
304.76 |
191.21 |
87.73 |
98.51 |
|
deit3-huge-p14_3rdparty_in1k |
632.13 |
167.40 |
85.21 |
97.36 |
|
deit3-huge-p14_in21k-pre_3rdparty_in1k |
632.13 |
167.40 |
87.19 |
98.26 |
|
hornet-tiny_3rdparty_in1k |
22.41 |
3.98 |
82.84 |
96.24 |
|
hornet-tiny-gf_3rdparty_in1k |
22.99 |
3.90 |
82.98 |
96.38 |
|
hornet-small_3rdparty_in1k |
49.53 |
8.83 |
83.79 |
96.75 |
|
hornet-small-gf_3rdparty_in1k |
50.40 |
8.71 |
83.98 |
96.77 |
|
hornet-base_3rdparty_in1k |
87.26 |
15.58 |
84.24 |
96.94 |
|
hornet-base-gf_3rdparty_in1k |
88.42 |
15.42 |
84.32 |
96.95 |
|
mobilevit-small_3rdparty_in1k |
5.58 |
2.03 |
78.25 |
94.09 |
|
mobilevit-xsmall_3rdparty_in1k |
2.32 |
1.05 |
74.75 |
92.32 |
|
mobilevit-xxsmall_3rdparty_in1k |
1.27 |
0.42 |
69.02 |
88.91 |
|
davit-tiny_3rdparty_in1k |
28.36 |
4.54 |
82.24 |
96.13 |
|
davit-small_3rdparty_in1k |
49.75 |
8.80 |
83.61 |
96.75 |
|
davit-base_3rdparty_in1k |
87.95 |
15.51 |
84.09 |
96.82 |
|
replknet-31B_3rdparty_in1k |
79.86 |
15.64 |
83.48 |
96.57 |
|
replknet-31B_3rdparty_in1k-384px |
79.86 |
45.95 |
84.84 |
97.34 |
|
replknet-31B_in21k-pre_3rdparty_in1k |
79.86 |
15.64 |
85.2 |
97.56 |
|
replknet-31B_in21k-pre_3rdparty_in1k-384px |
79.86 |
45.95 |
85.99 |
97.75 |
|
replknet-31L_in21k-pre_3rdparty_in1k-384px |
172.67 |
97.24 |
86.63 |
98.0 |
|
replknet-XL_meg73m-pre_3rdparty_in1k-320px |
335.44 |
129.57 |
87.57 |
98.39 |
|
beit-base-p16_beit-pre_8xb128-coslr-100e_in1k |
86.53 |
17.58 |
83.1 |
||
beit-base-p16_beit-in21k-pre_3rdparty_in1k |
86.53 |
17.58 |
85.28 |
97.59 |
|
beit-base-p16_beitv2-pre_8xb128-coslr-100e_in1k |
86.53 |
17.58 |
85.0 |
||
beit-base-p16_beitv2-in21k-pre_3rdparty_in1k |
86.53 |
17.58 |
86.47 |
97.99 |
|
vit-base-p16_eva-mae-style-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.7 |
||
vit-base-p16_eva-mae-style-pre_8xb2048-linear-coslr-100e_in1k |
86.57 |
17.58 |
69.0 |
||
beit-l-p14_eva-pre_3rdparty_in1k-196px |
304.14 |
61.57 |
87.94 |
98.5 |
|
beit-l-p14_eva-in21k-pre_3rdparty_in1k-196px |
304.14 |
61.57 |
88.58 |
98.65 |
|
beit-l-p14_eva-pre_3rdparty_in1k-336px |
304.53 |
191.10 |
88.66 |
98.75 |
|
beit-l-p14_eva-in21k-pre_3rdparty_in1k-336px |
304.53 |
191.10 |
89.17 |
98.86 |
|
beit-g-p14_eva-30m-in21k-pre_3rdparty_in1k-336px |
1013.01 |
620.64 |
89.61 |
98.93 |
|
beit-g-p14_eva-30m-in21k-pre_3rdparty_in1k-560px |
1014.45 |
1906.76 |
89.71 |
98.96 |
|
revvit-small_3rdparty_in1k |
22.44 |
4.58 |
79.87 |
94.9 |
|
revvit-base_3rdparty_in1k |
87.34 |
17.49 |
81.81 |
95.56 |
|
vit-base-p32_clip-openai-pre_3rdparty_in1k |
88.22 |
4.36 |
81.77 |
95.89 |
|
vit-base-p32_clip-laion2b-pre_3rdparty_in1k |
88.22 |
4.36 |
82.46 |
96.12 |
|
vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k |
88.22 |
4.36 |
83.06 |
96.49 |
|
vit-base-p32_clip-openai-in12k-pre_3rdparty_in1k-384px |
88.22 |
12.66 |
85.13 |
97.42 |
|
vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k-384px |
88.22 |
12.66 |
85.39 |
97.67 |
|
vit-base-p16_clip-openai-pre_3rdparty_in1k |
86.57 |
16.86 |
85.3 |
97.5 |
|
vit-base-p16_clip-laion2b-pre_3rdparty_in1k |
86.57 |
16.86 |
85.49 |
97.59 |
|
vit-base-p16_clip-openai-in12k-pre_3rdparty_in1k |
86.57 |
16.86 |
85.99 |
97.72 |
|
vit-base-p16_clip-laion2b-in12k-pre_3rdparty_in1k |
86.57 |
16.86 |
86.02 |
97.76 |
|
vit-base-p32_clip-laion2b-in12k-pre_3rdparty_in1k-448px |
88.22 |
17.20 |
85.76 |
97.63 |
|
vit-base-p16_clip-openai-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
86.25 |
97.9 |
|
vit-base-p16_clip-laion2b-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
86.52 |
97.97 |
|
vit-base-p16_clip-openai-in12k-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
86.87 |
98.05 |
|
vit-base-p16_clip-laion2b-in12k-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
87.17 |
98.02 |
|
mixmim-base_mixmim-pre_8xb128-coslr-100e_in1k |
88.34 |
16.35 |
84.63 |
||
efficientnetv2-b0_3rdparty_in1k |
7.14 |
0.92 |
78.52 |
94.44 |
|
efficientnetv2-b1_3rdparty_in1k |
8.14 |
1.44 |
79.8 |
94.89 |
|
efficientnetv2-b2_3rdparty_in1k |
10.10 |
1.99 |
80.63 |
95.3 |
|
efficientnetv2-b3_3rdparty_in1k |
14.36 |
3.50 |
82.03 |
95.88 |
|
efficientnetv2-s_3rdparty_in1k |
21.46 |
9.72 |
83.82 |
96.67 |
|
efficientnetv2-m_3rdparty_in1k |
54.14 |
26.88 |
85.01 |
97.26 |
|
efficientnetv2-l_3rdparty_in1k |
118.52 |
60.14 |
85.43 |
97.31 |
|
efficientnetv2-s_in21k-pre_3rdparty_in1k |
21.46 |
9.72 |
84.29 |
97.26 |
|
efficientnetv2-m_in21k-pre_3rdparty_in1k |
54.14 |
26.88 |
85.47 |
97.76 |
|
efficientnetv2-l_in21k-pre_3rdparty_in1k |
118.52 |
60.14 |
86.31 |
97.99 |
|
efficientnetv2-xl_in21k-pre_3rdparty_in1k |
208.12 |
98.34 |
86.39 |
97.83 |
|
convnext-v2-atto_fcmae-pre_3rdparty_in1k |
3.71 |
0.55 |
76.64 |
93.04 |
|
convnext-v2-femto_fcmae-pre_3rdparty_in1k |
5.23 |
0.78 |
78.48 |
93.98 |
|
convnext-v2-pico_fcmae-pre_3rdparty_in1k |
9.07 |
1.37 |
80.31 |
95.08 |
|
convnext-v2-nano_fcmae-pre_3rdparty_in1k |
15.62 |
2.45 |
81.86 |
95.75 |
|
convnext-v2-nano_fcmae-in21k-pre_3rdparty_in1k |
15.62 |
2.45 |
82.04 |
96.16 |
|
convnext-v2-tiny_fcmae-pre_3rdparty_in1k |
28.64 |
4.47 |
82.94 |
96.29 |
|
convnext-v2-tiny_fcmae-in21k-pre_3rdparty_in1k |
28.64 |
4.47 |
83.89 |
96.96 |
|
convnext-v2-nano_fcmae-in21k-pre_3rdparty_in1k-384px |
15.62 |
7.21 |
83.36 |
96.75 |
|
convnext-v2-tiny_fcmae-in21k-pre_3rdparty_in1k-384px |
28.64 |
13.14 |
85.09 |
97.63 |
|
convnext-v2-base_fcmae-pre_3rdparty_in1k |
88.72 |
15.38 |
84.87 |
97.08 |
|
convnext-v2-base_fcmae-in21k-pre_3rdparty_in1k |
88.72 |
15.38 |
86.74 |
98.02 |
|
convnext-v2-large_fcmae-pre_3rdparty_in1k |
197.96 |
34.40 |
85.76 |
97.59 |
|
convnext-v2-large_fcmae-in21k-pre_3rdparty_in1k |
197.96 |
34.40 |
87.26 |
98.24 |
|
convnext-v2-base_fcmae-in21k-pre_3rdparty_in1k-384px |
88.72 |
45.21 |
87.63 |
98.42 |
|
convnext-v2-large_fcmae-in21k-pre_3rdparty_in1k-384px |
197.96 |
101.10 |
88.18 |
98.52 |
|
convnext-v2-huge_fcmae-pre_3rdparty_in1k |
660.29 |
115.00 |
86.25 |
97.75 |
|
convnext-v2-huge_fcmae-in21k-pre_3rdparty_in1k-384px |
660.29 |
337.96 |
88.68 |
98.73 |
|
convnext-v2-huge_fcmae-in21k-pre_3rdparty_in1k-512px |
660.29 |
600.81 |
88.86 |
98.74 |
|
levit-128s_3rdparty_in1k |
7.39 |
0.31 |
76.51 |
92.9 |
|
levit-128_3rdparty_in1k |
8.83 |
0.41 |
78.58 |
93.95 |
|
levit-192_3rdparty_in1k |
10.56 |
0.67 |
79.86 |
94.75 |
|
levit-256_3rdparty_in1k |
18.38 |
1.14 |
81.59 |
95.46 |
|
levit-384_3rdparty_in1k |
38.36 |
2.37 |
82.59 |
95.95 |
|
vig-tiny_3rdparty_in1k |
7.18 |
1.31 |
74.4 |
92.34 |
|
vig-small_3rdparty_in1k |
22.75 |
4.54 |
80.61 |
95.28 |
|
vig-base_3rdparty_in1k |
20.68 |
17.68 |
82.62 |
96.04 |
|
pvig-tiny_3rdparty_in1k |
9.46 |
1.71 |
78.38 |
94.38 |
|
pvig-small_3rdparty_in1k |
29.02 |
4.57 |
82.0 |
95.97 |
|
pvig-medium_3rdparty_in1k |
51.68 |
8.89 |
83.12 |
96.35 |
|
pvig-base_3rdparty_in1k |
95.21 |
16.86 |
83.59 |
96.52 |
|
xcit-nano-12-p16_3rdparty_in1k |
3.05 |
0.56 |
70.35 |
89.98 |
|
xcit-nano-12-p16_3rdparty-dist_in1k |
3.05 |
0.56 |
72.36 |
91.02 |
|
xcit-tiny-12-p16_3rdparty_in1k |
6.72 |
1.24 |
77.21 |
93.62 |
|
xcit-tiny-12-p16_3rdparty-dist_in1k |
6.72 |
1.24 |
78.7 |
94.12 |
|
xcit-nano-12-p16_3rdparty-dist_in1k-384px |
3.05 |
1.64 |
74.93 |
92.42 |
|
xcit-nano-12-p8_3rdparty_in1k |
3.05 |
2.16 |
73.8 |
92.08 |
|
xcit-nano-12-p8_3rdparty-dist_in1k |
3.05 |
2.16 |
76.17 |
93.08 |
|
xcit-tiny-24-p16_3rdparty_in1k |
12.12 |
2.34 |
79.47 |
94.85 |
|
xcit-tiny-24-p16_3rdparty-dist_in1k |
12.12 |
2.34 |
80.51 |
95.17 |
|
xcit-tiny-12-p16_3rdparty-dist_in1k-384px |
6.72 |
3.64 |
80.58 |
95.38 |
|
xcit-tiny-12-p8_3rdparty_in1k |
6.71 |
4.81 |
79.75 |
94.88 |
|
xcit-tiny-12-p8_3rdparty-dist_in1k |
6.71 |
4.81 |
81.26 |
95.46 |
|
xcit-small-12-p16_3rdparty_in1k |
26.25 |
4.81 |
81.87 |
95.77 |
|
xcit-small-12-p16_3rdparty-dist_in1k |
26.25 |
4.81 |
83.12 |
96.41 |
|
xcit-nano-12-p8_3rdparty-dist_in1k-384px |
3.05 |
6.34 |
77.69 |
94.09 |
|
xcit-tiny-24-p16_3rdparty-dist_in1k-384px |
12.12 |
6.87 |
82.43 |
96.2 |
|
xcit-small-24-p16_3rdparty_in1k |
47.67 |
9.10 |
82.38 |
95.93 |
|
xcit-small-24-p16_3rdparty-dist_in1k |
47.67 |
9.10 |
83.7 |
96.61 |
|
xcit-tiny-24-p8_3rdparty_in1k |
12.11 |
9.21 |
81.7 |
95.9 |
|
xcit-tiny-24-p8_3rdparty-dist_in1k |
12.11 |
9.21 |
82.62 |
96.16 |
|
xcit-tiny-12-p8_3rdparty-dist_in1k-384px |
6.71 |
14.13 |
82.46 |
96.22 |
|
xcit-small-12-p16_3rdparty-dist_in1k-384px |
26.25 |
14.14 |
84.74 |
97.19 |
|
xcit-medium-24-p16_3rdparty_in1k |
84.40 |
16.13 |
82.56 |
95.82 |
|
xcit-medium-24-p16_3rdparty-dist_in1k |
84.40 |
16.13 |
84.15 |
96.82 |
|
xcit-small-12-p8_3rdparty_in1k |
26.21 |
18.69 |
83.21 |
96.41 |
|
xcit-small-12-p8_3rdparty-dist_in1k |
26.21 |
18.69 |
83.97 |
96.81 |
|
xcit-small-24-p16_3rdparty-dist_in1k-384px |
47.67 |
26.72 |
85.1 |
97.32 |
|
xcit-tiny-24-p8_3rdparty-dist_in1k-384px |
12.11 |
27.05 |
83.77 |
96.72 |
|
xcit-small-24-p8_3rdparty_in1k |
47.63 |
35.81 |
83.62 |
96.51 |
|
xcit-small-24-p8_3rdparty-dist_in1k |
47.63 |
35.81 |
84.68 |
97.07 |
|
xcit-large-24-p16_3rdparty_in1k |
189.10 |
35.86 |
82.97 |
95.86 |
|
xcit-large-24-p16_3rdparty-dist_in1k |
189.10 |
35.86 |
84.61 |
97.07 |
|
xcit-medium-24-p16_3rdparty-dist_in1k-384px |
84.40 |
47.39 |
85.47 |
97.49 |
|
xcit-small-12-p8_3rdparty-dist_in1k-384px |
26.21 |
54.92 |
85.12 |
97.31 |
|
xcit-medium-24-p8_3rdparty_in1k |
84.32 |
63.52 |
83.61 |
96.23 |
|
xcit-medium-24-p8_3rdparty-dist_in1k |
84.32 |
63.52 |
85.0 |
97.16 |
|
xcit-small-24-p8_3rdparty-dist_in1k-384px |
47.63 |
105.24 |
85.57 |
97.6 |
|
xcit-large-24-p16_3rdparty-dist_in1k-384px |
189.10 |
105.35 |
85.78 |
97.6 |
|
xcit-large-24-p8_3rdparty_in1k |
188.93 |
141.23 |
84.23 |
96.58 |
|
xcit-large-24-p8_3rdparty-dist_in1k |
188.93 |
141.23 |
85.14 |
97.32 |
|
xcit-medium-24-p8_3rdparty-dist_in1k-384px |
84.32 |
186.67 |
85.87 |
97.61 |
|
xcit-large-24-p8_3rdparty-dist_in1k-384px |
188.93 |
415.00 |
86.13 |
97.75 |
|
resnet50_byol-pre_8xb512-linear-coslr-90e_in1k |
25.56 |
4.11 |
71.8 |
||
resnet50_densecl-pre_8xb32-linear-steplr-100e_in1k |
25.56 |
4.11 |
63.5 |
||
resnet50_mocov2-pre_8xb32-linear-steplr-100e_in1k |
25.56 |
4.11 |
67.5 |
||
resnet50_mocov3-100e-pre_8xb128-linear-coslr-90e_in1k |
25.56 |
4.11 |
69.6 |
||
resnet50_mocov3-300e-pre_8xb128-linear-coslr-90e_in1k |
25.56 |
4.11 |
72.8 |
||
resnet50_mocov3-800e-pre_8xb128-linear-coslr-90e_in1k |
25.56 |
4.11 |
74.4 |
||
vit-small-p16_mocov3-pre_8xb128-linear-coslr-90e_in1k |
22.05 |
4.61 |
73.6 |
||
vit-base-p16_mocov3-pre_8xb64-coslr-150e_in1k |
86.57 |
17.58 |
83.0 |
||
vit-base-p16_mocov3-pre_8xb128-linear-coslr-90e_in1k |
86.57 |
17.58 |
76.9 |
||
vit-large-p16_mocov3-pre_8xb64-coslr-100e_in1k |
304.33 |
61.60 |
83.7 |
||
resnet50_simclr-200e-pre_8xb512-linear-coslr-90e_in1k |
25.56 |
4.11 |
66.9 |
||
resnet50_simclr-800e-pre_8xb512-linear-coslr-90e_in1k |
25.56 |
4.11 |
69.2 |
||
resnet50_simsiam-100e-pre_8xb512-linear-coslr-90e_in1k |
25.56 |
4.11 |
68.3 |
||
resnet50_simsiam-200e-pre_8xb512-linear-coslr-90e_in1k |
25.56 |
4.11 |
69.8 |
||
resnet50_swav-pre_8xb32-linear-coslr-100e_in1k |
25.56 |
4.11 |
70.5 |
||
vit-base-p16_mae-300e-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.1 |
||
vit-base-p16_mae-400e-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.3 |
||
vit-base-p16_mae-800e-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.3 |
||
vit-base-p16_mae-1600e-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.5 |
||
vit-base-p16_mae-300e-pre_8xb2048-linear-coslr-90e_in1k |
86.57 |
17.58 |
60.8 |
||
vit-base-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k |
86.57 |
17.58 |
62.5 |
||
vit-base-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k |
86.57 |
17.58 |
65.1 |
||
vit-base-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k |
86.57 |
17.58 |
67.1 |
||
vit-large-p16_mae-400e-pre_8xb128-coslr-50e_in1k |
304.32 |
61.60 |
85.2 |
||
vit-large-p16_mae-800e-pre_8xb128-coslr-50e_in1k |
304.32 |
61.60 |
85.4 |
||
vit-large-p16_mae-1600e-pre_8xb128-coslr-50e_in1k |
304.32 |
61.60 |
85.7 |
||
vit-large-p16_mae-400e-pre_8xb2048-linear-coslr-90e_in1k |
304.33 |
61.60 |
70.7 |
||
vit-large-p16_mae-800e-pre_8xb2048-linear-coslr-90e_in1k |
304.33 |
61.60 |
73.7 |
||
vit-large-p16_mae-1600e-pre_8xb2048-linear-coslr-90e_in1k |
304.33 |
61.60 |
75.5 |
||
vit-huge-p14_mae-1600e-pre_8xb128-coslr-50e_in1k |
632.04 |
167.40 |
86.9 |
||
vit-huge-p14_mae-1600e-pre_32xb8-coslr-50e_in1k-448px |
633.03 |
732.13 |
87.3 |
||
swin-base-w6_simmim-100e-pre_8xb256-coslr-100e_in1k-192px |
87.75 |
11.30 |
82.7 |
||
swin-base-w7_simmim-100e-pre_8xb256-coslr-100e_in1k |
87.77 |
15.47 |
83.5 |
||
swin-base-w6_simmim-800e-pre_8xb256-coslr-100e_in1k-192px |
87.77 |
15.47 |
83.8 |
||
swin-large-w14_simmim-800e-pre_8xb256-coslr-100e_in1k |
196.85 |
38.85 |
84.8 |
||
resnet50_barlowtwins-pre_8xb32-linear-coslr-100e_in1k |
25.56 |
4.11 |
71.8 |
||
beit-base-p16_cae-pre_8xb128-coslr-100e_in1k |
86.68 |
17.58 |
83.2 |
||
vit-base-p16_maskfeat-pre_8xb256-coslr-100e_in1k |
86.57 |
17.58 |
83.4 |
||
vit-base-p16_milan-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
85.3 |
||
vit-base-p16_milan-pre_8xb2048-linear-coslr-100e_in1k |
86.57 |
17.58 |
78.9 |
||
riformer-s12_in1k |
11.91 |
1.82 |
76.9 |
93.06 |
|
riformer-s24_in1k |
21.39 |
3.41 |
80.28 |
94.8 |
|
riformer-s36_in1k |
30.86 |
5.00 |
81.29 |
95.41 |
|
riformer-m36_in1k |
56.17 |
8.80 |
82.57 |
95.99 |
|
riformer-m48_in1k |
73.47 |
11.59 |
82.75 |
96.11 |
|
riformer-s12_in1k-384 |
11.91 |
5.36 |
78.29 |
93.93 |
|
riformer-s24_in1k-384 |
21.39 |
10.03 |
81.36 |
95.4 |
|
riformer-s36_in1k-384 |
30.86 |
14.70 |
82.22 |
95.95 |
|
riformer-m36_in1k-384 |
56.17 |
25.86 |
83.39 |
96.4 |
|
riformer-m48_in1k-384 |
73.47 |
34.06 |
83.7 |
96.6 |
|
vit-tiny-p14_eva02-in21k-pre_3rdparty_in1k-336px |
5.76 |
4.68 |
80.69 |
95.54 |
|
vit-small-p14_eva02-in21k-pre_3rdparty_in1k-336px |
22.13 |
15.48 |
85.78 |
97.6 |
|
vit-base-p14_eva02-in21k-pre_3rdparty_in1k-448px |
87.13 |
107.11 |
88.29 |
98.53 |
|
vit-base-p14_eva02-in21k-pre_in21k-medft_3rdparty_in1k-448px |
87.13 |
107.11 |
88.47 |
98.62 |
|
vit-large-p14_eva02-in21k-pre_in21k-medft_3rdparty_in1k-448px |
305.10 |
362.33 |
89.65 |
98.95 |
|
vit-large-p14_eva02_m38m-pre_in21k-medft_3rdparty_in1k-448px |
305.10 |
362.33 |
89.83 |
99.0 |
|
hivit-tiny-p16_16xb64_in1k |
19.18 |
4.60 |
82.1 |
||
hivit-small-p16_16xb64_in1k |
37.53 |
9.07 |
None |
||
hivit-base-p16_16xb64_in1k |
79.05 |
18.47 |
None |
||
resnet50_spark-pre_300e_in1k |
23.52 |
1.31 |
80.1 |
94.9 |
|
convnextv2-tiny_spark-pre_300e_in1k |
28.64 |
4.47 |
82.8 |
96.3 |
|
vit-base-p16_mff-300e-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.0 |
||
vit-base-p16_mff-800e-pre_8xb128-coslr-100e_in1k |
86.57 |
17.58 |
83.7 |
||
vit-base-p16_mff-300e-pre_8xb2048-linear-coslr-90e_in1k |
86.57 |
17.58 |
64.2 |
||
vit-base-p16_mff-800e-pre_8xb2048-linear-coslr-90e_in1k |
86.57 |
17.58 |
68.3 |
CIFAR-10¶
Model |
Params (M) |
Flops (G) |
Top-1 (%) |
Readme |
|---|---|---|---|---|
resnet18_8xb16_cifar10 |
11.17 |
0.56 |
94.82 |
|
resnet34_8xb16_cifar10 |
21.28 |
1.16 |
95.34 |
|
resnet50_8xb16_cifar10 |
23.52 |
1.31 |
95.55 |
|
resnet101_8xb16_cifar10 |
42.51 |
2.52 |
95.58 |
|
resnet152_8xb16_cifar10 |
58.16 |
3.74 |
95.76 |
CIFAR-100¶
Model |
Params (M) |
Flops (G) |
Top-1 (%) |
Top-5 (%) |
Readme |
|---|---|---|---|---|---|
resnet50_8xb16_cifar100 |
23.71 |
1.31 |
79.90 |
95.19 |
CUB-200-2011¶
Model |
Params (M) |
Flops (G) |
Top-1 (%) |
Readme |
|---|---|---|---|---|
resnet50_8xb8_cub |
23.92 |
16.48 |
88.45 |
|
swin-large_8xb8_cub-384px |
195.51 |
100.04 |
91.87 |
CIFAR100¶
Model |
Params (M) |
Flops (G) |
Top-1 (%) |
Readme |
|---|---|---|---|---|
cn-clip_resnet50_zeroshot-cls_cifar100 |
77.00 |
40.70 |
||
cn-clip_vit-base-p16_zeroshot-cls_cifar100 |
188.00 |
64.50 |
||
cn-clip_vit-large-p14_zeroshot-cls_cifar100 |
406.00 |
74.80 |
||
cn-clip_vit-huge-p14_zeroshot-cls_cifar100 |
958.00 |
79.10 |
Multi-Label Classification¶
PASCAL VOC 2007¶
Model |
Params (M) |
Flops (G) |
CF1 |
OF1 |
mAP |
Readme |
|---|---|---|---|---|---|---|
resnet101-csra_1xb16_voc07-448px |
23.55 |
4.12 |
89.16 |
90.80 |
94.98 |
Image Retrieval¶
InShop¶
Model |
Params (M) |
Flops (G) |
Recall@1 |
mAP@10 |
Readme |
|---|---|---|---|---|---|
resnet50-arcface_inshop |
31.69 |
16.57 |
90.18 |
69.30 |