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：49
Algorithm: 49
模型权重文件数量：335
[Algorithm] MobileNetV2: Inverted Residuals and Linear Bottlenecks (1 ckpts)
[Algorithm] Searching for MobileNetV3 (6 ckpts)
[Algorithm] Deep Residual Learning for Image Recognition (25 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 (14 ckpts)
[Algorithm] Very Deep Convolutional Networks for Large-Scale Image (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 (8 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 (16 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 (32 ckpts)
[Algorithm] A ConvNet for the 2020s (23 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 (9 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 (1 ckpts)
[Algorithm] BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers (1 ckpts)
[Algorithm] EVA: Exploring the Limits of Masked Visual Representation Learning at Scale (10 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 (1 ckpts)
[Algorithm] EfficientNetV2: Smaller Models and Faster Training (15 ckpts)
所有模型权重文件¶
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 |
69.90 |
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 |
94.70 |
|
resnet50_8xb32-fp16_in1k |
25.56 |
4.12 |
76.30 |
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 |
78.30 |
93.80 |
|
resnetv1c50_8xb32_in1k |
25.58 |
4.36 |
77.01 |
93.58 |
|
resnetv1c101_8xb32_in1k |
44.57 |
8.09 |
78.30 |
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 |
79.20 |
94.36 |
|
res2net101-w26-s4_3rdparty_8xb32_in1k |
45.21 |
8.12 |
79.19 |
94.44 |
|
resnext50-32x4d_8xb32_in1k |
25.03 |
4.27 |
77.90 |
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 |
97.50 |
|
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 |
90.80 |
|
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 |
91.80 |
|
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-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384 |
86.86 |
33.03 |
85.43 |
97.77 |
|
vit-base-p32_in21k-pre-3rdparty_ft-64xb64_in1k-384 |
88.30 |
8.56 |
84.01 |
97.08 |
|
vit-large-p16_in21k-pre-3rdparty_ft-64xb64_in1k-384 |
304.72 |
116.68 |
85.63 |
97.63 |
|
vit-base-p16_pt-32xb128-mae_in1k |
86.86 |
33.03 |
82.37 |
96.15 |
|
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_8xb128_in1k |
23.52 |
4.90 |
81.31 |
95.60 |
|
conformer-small-p16_3rdparty_8xb128_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_8xb128_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 |
78.60 |
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_pt-4xb256_in1k |
5.72 |
1.08 |
74.50 |
92.24 |
|
deit-tiny-distilled_3rdparty_pt-4xb256_in1k |
5.72 |
1.08 |
74.51 |
91.90 |
|
deit-small_pt-4xb256_in1k |
22.05 |
4.24 |
80.69 |
95.06 |
|
deit-small-distilled_3rdparty_pt-4xb256_in1k |
22.05 |
4.24 |
81.17 |
95.40 |
|
deit-base_pt-16xb64_in1k |
86.57 |
16.86 |
81.76 |
95.81 |
|
deit-base_3rdparty_pt-16xb64_in1k |
86.57 |
16.86 |
81.79 |
95.59 |
|
deit-base-distilled_3rdparty_pt-16xb64_in1k |
86.57 |
16.86 |
83.33 |
96.49 |
|
deit-base_3rdparty_ft-16xb32_in1k-384px |
86.86 |
49.37 |
83.04 |
96.31 |
|
deit-base-distilled_3rdparty_ft-16xb32_in1k-384px |
86.86 |
49.37 |
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 |
83.60 |
96.50 |
|
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 |
94.00 |
|
efficientnet-b1_3rdparty_8xb32_in1k |
7.79 |
0.74 |
78.68 |
94.28 |
|
efficientnet-b1_3rdparty_8xb32-aa_in1k |
7.79 |
0.74 |
79.20 |
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 |
94.80 |
|
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 |
82.90 |
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 |
96.60 |
|
convnext-base_3rdparty_in1k-384px |
88.59 |
45.21 |
85.10 |
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 |
84.30 |
96.89 |
|
convnext-large_3rdparty_in1k-384px |
197.77 |
101.10 |
85.50 |
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 |
98.20 |
|
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 |
95.70 |
|
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.py |
96.45 |
9.69 |
81.11 |
95.50 |
|
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_8xb128_in1k |
4.11 |
0.88 |
75.41 |
93.02 |
|
van-small_8xb128_in1k |
13.86 |
2.52 |
81.01 |
95.63 |
|
van-base_8xb128_in1k |
26.58 |
5.03 |
82.80 |
96.21 |
|
van-large_8xb128_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_10xb64_in1k |
21.11 |
19.62 |
80.16 |
95.08 |
|
convmixer-1024-20_10xb64_in1k |
24.38 |
5.55 |
76.94 |
93.36 |
|
convmixer-1536-20_10xb64_in1k |
51.63 |
48.71 |
81.37 |
95.61 |
|
densenet121_4xb256_in1k |
7.98 |
2.88 |
74.96 |
92.21 |
|
densenet169_4xb256_in1k |
14.15 |
3.42 |
76.08 |
93.11 |
|
densenet201_4xb256_in1k |
20.01 |
4.37 |
77.32 |
93.64 |
|
densenet161_4xb256_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 |
71.20 |
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 |
96.20 |
|
edgenext-base_3rdparty-usi_in1k |
18.51 |
3.81 |
83.67 |
96.70 |
|
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 |
83.40 |
96.60 |
|
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 |
96.60 |
|
swinv2-small-w16_3rdparty_in1k-256px |
49.73 |
8.57 |
84.13 |
96.83 |
|
swinv2-base-w8_3rdparty_in1k-256px |
87.92 |
14.99 |
84.20 |
96.86 |
|
swinv2-base-w16_3rdparty_in1k-256px |
87.92 |
15.14 |
84.60 |
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 |
83.80 |
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 |
85.70 |
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 |
97.60 |
|
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 |
85.20 |
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 |
98.00 |
|
replknet-XL_meg73m-pre_3rdparty_in1k-320px |
335.44 |
129.57 |
87.57 |
98.39 |
|
beit-base_3rdparty_in1k |
86.53 |
17.58 |
85.28 |
97.59 |
|
beitv2-base_3rdparty_in1k |
86.53 |
17.58 |
86.47 |
97.99 |
|
eva-g-p14_30m-in21k-pre_3rdparty_in1k-336px |
1013.01 |
620.64 |
89.61 |
98.93 |
|
eva-g-p14_30m-in21k-pre_3rdparty_in1k-560px |
1014.45 |
1906.76 |
89.71 |
98.96 |
|
eva-l-p14_mim-pre_3rdparty_in1k-336px |
304.53 |
191.10 |
88.66 |
98.75 |
|
eva-l-p14_mim-in21k-pre_3rdparty_in1k-336px |
304.53 |
191.10 |
89.17 |
98.86 |
|
eva-l-p14_mim-pre_3rdparty_in1k-196px |
304.14 |
61.57 |
87.94 |
98.50 |
|
eva-l-p14_mim-in21k-pre_3rdparty_in1k-196px |
304.14 |
61.57 |
88.58 |
98.65 |
|
revvit-small_3rdparty_in1k |
22.44 |
4.58 |
79.87 |
94.90 |
|
revvit-base_3rdparty_in1k |
87.34 |
17.49 |
81.81 |
95.56 |
|
clip-vit-base-p32_laion2b-in12k-pre_3rdparty_in1k |
88.22 |
4.36 |
83.06 |
96.49 |
|
clip-vit-base-p32_laion2b-pre_3rdparty_in1k |
88.22 |
4.36 |
82.46 |
96.12 |
|
clip-vit-base-p32_openai-pre_3rdparty_in1k |
88.22 |
4.36 |
81.77 |
95.89 |
|
clip-vit-base-p32_laion2b-in12k-pre_3rdparty_in1k-384px |
88.22 |
12.66 |
85.39 |
97.67 |
|
clip-vit-base-p32_openai-in12k-pre_3rdparty_in1k-384px |
88.22 |
12.66 |
85.13 |
97.42 |
|
clip-vit-base-p16_laion2b-in12k-pre_3rdparty_in1k |
86.57 |
16.86 |
86.02 |
97.76 |
|
clip-vit-base-p16_laion2b-pre_3rdparty_in1k |
86.57 |
16.86 |
85.49 |
97.59 |
|
clip-vit-base-p16_openai-in12k-pre_3rdparty_in1k |
86.57 |
16.86 |
85.99 |
97.72 |
|
clip-vit-base-p16_openai-pre_3rdparty_in1k |
86.57 |
16.86 |
85.30 |
97.50 |
|
clip-vit-base-p32_laion2b-in12k-pre_3rdparty_in1k-448px |
88.22 |
17.20 |
85.76 |
97.63 |
|
clip-vit-base-p16_laion2b-in12k-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
87.17 |
98.02 |
|
clip-vit-base-p16_laion2b-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
86.52 |
97.97 |
|
clip-vit-base-p16_openai-in12k-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
86.87 |
98.05 |
|
clip-vit-base-p16_openai-pre_3rdparty_in1k-384px |
86.57 |
49.37 |
86.25 |
97.90 |
|
mixmim-base_3rdparty_in1k |
88.34 |
16.35 |
84.60 |
97.00 |
|
efficientnetv2-b0_3rdparty_in1k |
7.14 |
0.92 |
78.52 |
94.44 |
|
efficientnetv2-b1_3rdparty_in1k |
8.14 |
1.44 |
79.80 |
94.89 |
|
efficientnetv2-b2_3rdparty_in1k |
10.10 |
1.99 |
80.63 |
95.30 |
|
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 |
CIFAR-10¶
模型 |
参数量 (M) |
Flops (G) |
Top-1 (%) |
Top-5 (%) |
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 (%) |
Top-5 (%) |
Readme |
|---|---|---|---|---|---|
resnet50_8xb8_cub |
23.92 |
16.48 |
88.45 |
||
swin-large_8xb8_cub_384px |
195.51 |
100.04 |
91.87 |
PASCAL VOC 2007¶
模型 |
参数量 (M) |
Flops (G) |
Top-1 (%) |
Top-5 (%) |
Readme |
|---|---|---|---|---|---|
resnet101-csra_1xb16_voc07-448px |
23.55 |
4.12 |