模型库统计¶
在本页面中，我们列举了我们支持的所有算法。你可以点击链接跳转至对应的模型详情页面。
另外，我们还列出了我们提供的所有模型权重文件。你可以使用排序和搜索功能找到需要的模型权重，并使用链接跳转至模型详情页面。
所有已支持的算法¶
论文数量：84
Algorithm: 84
模型权重文件数量：520
[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 (2 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)
预训练模型¶
模型 |
参数量 (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 |
图像分类¶
ImageNet-1k¶
模型 |
参数量 (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¶
模型 |
参数量 (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¶
模型 |
参数量 (M) |
Flops (G) |
Top-1 (%) |
Top-5 (%) |
Readme |
|---|---|---|---|---|---|
resnet50_8xb16_cifar100 |
23.71 |
1.31 |
79.90 |
95.19 |
CUB-200-2011¶
模型 |
参数量 (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¶
模型 |
参数量 (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 |
图像多标签分类¶
PASCAL VOC 2007¶
模型 |
参数量 (M) |
Flops (G) |
CF1 |
OF1 |
mAP |
Readme |
|---|---|---|---|---|---|---|
resnet101-csra_1xb16_voc07-448px |
23.55 |
4.12 |
89.16 |
90.80 |
94.98 |
图像检索¶
InShop¶
模型 |
参数量 (M) |
Flops (G) |
Recall@1 |
mAP@10 |
Readme |
|---|---|---|---|---|---|
resnet50-arcface_inshop |
31.69 |
16.57 |
90.18 |
69.30 |