Results
Results
CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository results folder.
Self-trained Weights
The table below includes ImageNet-1k validation results of model weights that I’ve trained myself. It is not updated as frequently as the csv results outputs linked above.
efficientnet_b3a
82.242 (17.758)
96.114 (3.886)
12.23
bicubic
320 (1.0 crop)
efficientnet_b3
82.076 (17.924)
96.020 (3.980)
12.23
bicubic
300
regnet_32
82.002 (17.998)
95.906 (4.094)
19.44
bicubic
224
skresnext50d_32x4d
81.278 (18.722)
95.366 (4.634)
27.5
bicubic
288 (1.0 crop)
seresnext50d_32x4d
81.266 (18.734)
95.620 (4.380)
27.6
bicubic
224
efficientnet_b2a
80.608 (19.392)
95.310 (4.690)
9.11
bicubic
288 (1.0 crop)
resnet50d
80.530 (19.470)
95.160 (4.840)
25.6
bicubic
224
mixnet_xl
80.478 (19.522)
94.932 (5.068)
11.90
bicubic
224
efficientnet_b2
80.402 (19.598)
95.076 (4.924)
9.11
bicubic
260
seresnet50
80.274 (19.726)
95.070 (4.930)
28.1
bicubic
224
skresnext50d_32x4d
80.156 (19.844)
94.642 (5.358)
27.5
bicubic
224
cspdarknet53
80.058 (19.942)
95.084 (4.916)
27.6
bicubic
256
cspresnext50
80.040 (19.960)
94.944 (5.056)
20.6
bicubic
224
resnext50_32x4d
79.762 (20.238)
94.600 (5.400)
25
bicubic
224
resnext50d_32x4d
79.674 (20.326)
94.868 (5.132)
25.1
bicubic
224
cspresnet50
79.574 (20.426)
94.712 (5.288)
21.6
bicubic
256
ese_vovnet39b
79.320 (20.680)
94.710 (5.290)
24.6
bicubic
224
resnetblur50
79.290 (20.710)
94.632 (5.368)
25.6
bicubic
224
dpn68b
79.216 (20.784)
94.414 (5.586)
12.6
bicubic
224
resnet50
79.038 (20.962)
94.390 (5.610)
25.6
bicubic
224
mixnet_l
78.976 (21.024
94.184 (5.816)
7.33
bicubic
224
efficientnet_b1
78.692 (21.308)
94.086 (5.914)
7.79
bicubic
240
efficientnet_es
78.066 (21.934)
93.926 (6.074)
5.44
bicubic
224
seresnext26t_32x4d
77.998 (22.002)
93.708 (6.292)
16.8
bicubic
224
seresnext26tn_32x4d
77.986 (22.014)
93.746 (6.254)
16.8
bicubic
224
efficientnet_b0
77.698 (22.302)
93.532 (6.468)
5.29
bicubic
224
seresnext26d_32x4d
77.602 (22.398)
93.608 (6.392)
16.8
bicubic
224
mobilenetv2_120d
77.294 (22.706
93.502 (6.498)
5.8
bicubic
224
mixnet_m
77.256 (22.744)
93.418 (6.582)
5.01
bicubic
224
resnet34d
77.116 (22.884)
93.382 (6.618)
21.8
bicubic
224
seresnext26_32x4d
77.104 (22.896)
93.316 (6.684)
16.8
bicubic
224
skresnet34
76.912 (23.088)
93.322 (6.678)
22.2
bicubic
224
ese_vovnet19b_dw
76.798 (23.202)
93.268 (6.732)
6.5
bicubic
224
resnet26d
76.68 (23.32)
93.166 (6.834)
16
bicubic
224
densenetblur121d
76.576 (23.424)
93.190 (6.810)
8.0
bicubic
224
mobilenetv2_140
76.524 (23.476)
92.990 (7.010)
6.1
bicubic
224
mixnet_s
75.988 (24.012)
92.794 (7.206)
4.13
bicubic
224
mobilenetv3_large_100
75.766 (24.234)
92.542 (7.458)
5.5
bicubic
224
mobilenetv3_rw
75.634 (24.366)
92.708 (7.292)
5.5
bicubic
224
mnasnet_a1
75.448 (24.552)
92.604 (7.396)
3.89
bicubic
224
resnet26
75.292 (24.708)
92.57 (7.43)
16
bicubic
224
fbnetc_100
75.124 (24.876)
92.386 (7.614)
5.6
bilinear
224
resnet34
75.110 (24.890)
92.284 (7.716)
22
bilinear
224
mobilenetv2_110d
75.052 (24.948)
92.180 (7.820)
4.5
bicubic
224
seresnet34
74.808 (25.192)
92.124 (7.876)
22
bilinear
224
mnasnet_b1
74.658 (25.342)
92.114 (7.886)
4.38
bicubic
224
spnasnet_100
74.084 (25.916)
91.818 (8.182)
4.42
bilinear
224
skresnet18
73.038 (26.962)
91.168 (8.832)
11.9
bicubic
224
mobilenetv2_100
72.978 (27.022)
91.016 (8.984)
3.5
bicubic
224
resnet18d
72.260 (27.740)
90.696 (9.304)
11.7
bicubic
224
seresnet18
71.742 (28.258)
90.334 (9.666)
11.8
bicubic
224
Ported and Other Weights
For weights ported from other deep learning frameworks (Tensorflow, MXNet GluonCV) or copied from other PyTorch sources, please see the full results tables for ImageNet and various OOD test sets at in the results tables.
Model code .py files contain links to original sources of models and weights.
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