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<h1 id="results">Results</h1>
<p>CSV files containing an ImageNet-1K validation and out-of-distribution (OOD) test set validation results for all included models with pretrained weights and default configurations is located <a href="https://github.com/rwightman/pytorch-image-models/tree/master/results">here</a>.</p>
<h2 id="self-trained-weights">Self-trained Weights</h2>
<p>I've leveraged the training scripts in this repository to train a few of the models with to good levels of performance.</p>
<table>
<thead>
<tr>
<th>Model</th>
<th>Acc@1 (Err)</th>
<th>Acc@5 (Err)</th>
<th>Param # (M)</th>
<th>Interpolation</th>
<th>Image Size</th>
</tr>
</thead>
<tbody>
<tr>
<td>efficientnet_b3a</td>
<td>82.242 (17.758)</td>
<td>96.114 (3.886)</td>
<td>12.23</td>
<td>bicubic</td>
<td>320 (1.0 crop)</td>
</tr>
<tr>
<td>efficientnet_b3</td>
<td>82.076 (17.924)</td>
<td>96.020 (3.980)</td>
<td>12.23</td>
<td>bicubic</td>
<td>300</td>
</tr>
<tr>
<td>regnet_32</td>
<td>82.002 (17.998)</td>
<td>95.906 (4.094)</td>
<td>19.44</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>skresnext50d_32x4d</td>
<td>81.278 (18.722)</td>
<td>95.366 (4.634)</td>
<td>27.5</td>
<td>bicubic</td>
<td>288 (1.0 crop)</td>
</tr>
<tr>
<td>seresnext50d_32x4d</td>
<td>81.266 (18.734)</td>
<td>95.620 (4.380)</td>
<td>27.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>efficientnet_b2a</td>
<td>80.608 (19.392)</td>
<td>95.310 (4.690)</td>
<td>9.11</td>
<td>bicubic</td>
<td>288 (1.0 crop)</td>
</tr>
<tr>
<td>resnet50d</td>
<td>80.530 (19.470)</td>
<td>95.160 (4.840)</td>
<td>25.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mixnet_xl</td>
<td>80.478 (19.522)</td>
<td>94.932 (5.068)</td>
<td>11.90</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>efficientnet_b2</td>
<td>80.402 (19.598)</td>
<td>95.076 (4.924)</td>
<td>9.11</td>
<td>bicubic</td>
<td>260</td>
</tr>
<tr>
<td>seresnet50</td>
<td>80.274 (19.726)</td>
<td>95.070 (4.930)</td>
<td>28.1</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>skresnext50d_32x4d</td>
<td>80.156 (19.844)</td>
<td>94.642 (5.358)</td>
<td>27.5</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>cspdarknet53</td>
<td>80.058 (19.942)</td>
<td>95.084 (4.916)</td>
<td>27.6</td>
<td>bicubic</td>
<td>256</td>
</tr>
<tr>
<td>cspresnext50</td>
<td>80.040 (19.960)</td>
<td>94.944 (5.056)</td>
<td>20.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnext50_32x4d</td>
<td>79.762 (20.238)</td>
<td>94.600 (5.400)</td>
<td>25</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnext50d_32x4d</td>
<td>79.674 (20.326)</td>
<td>94.868 (5.132)</td>
<td>25.1</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>cspresnet50</td>
<td>79.574 (20.426)</td>
<td>94.712 (5.288)</td>
<td>21.6</td>
<td>bicubic</td>
<td>256</td>
</tr>
<tr>
<td>ese_vovnet39b</td>
<td>79.320 (20.680)</td>
<td>94.710 (5.290)</td>
<td>24.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnetblur50</td>
<td>79.290 (20.710)</td>
<td>94.632 (5.368)</td>
<td>25.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>dpn68b</td>
<td>79.216 (20.784)</td>
<td>94.414 (5.586)</td>
<td>12.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnet50</td>
<td>79.038 (20.962)</td>
<td>94.390 (5.610)</td>
<td>25.6</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mixnet_l</td>
<td>78.976 (21.024</td>
<td>94.184 (5.816)</td>
<td>7.33</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>efficientnet_b1</td>
<td>78.692 (21.308)</td>
<td>94.086 (5.914)</td>
<td>7.79</td>
<td>bicubic</td>
<td>240</td>
</tr>
<tr>
<td>efficientnet_es</td>
<td>78.066 (21.934)</td>
<td>93.926 (6.074)</td>
<td>5.44</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>seresnext26t_32x4d</td>
<td>77.998 (22.002)</td>
<td>93.708 (6.292)</td>
<td>16.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>seresnext26tn_32x4d</td>
<td>77.986 (22.014)</td>
<td>93.746 (6.254)</td>
<td>16.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>efficientnet_b0</td>
<td>77.698 (22.302)</td>
<td>93.532 (6.468)</td>
<td>5.29</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>seresnext26d_32x4d</td>
<td>77.602 (22.398)</td>
<td>93.608 (6.392)</td>
<td>16.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mobilenetv2_120d</td>
<td>77.294 (22.706</td>
<td>93.502 (6.498)</td>
<td>5.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mixnet_m</td>
<td>77.256 (22.744)</td>
<td>93.418 (6.582)</td>
<td>5.01</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnet34d</td>
<td>77.116 (22.884)</td>
<td>93.382 (6.618)</td>
<td>21.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>seresnext26_32x4d</td>
<td>77.104 (22.896)</td>
<td>93.316 (6.684)</td>
<td>16.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>skresnet34</td>
<td>76.912 (23.088)</td>
<td>93.322 (6.678)</td>
<td>22.2</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>ese_vovnet19b_dw</td>
<td>76.798 (23.202)</td>
<td>93.268 (6.732)</td>
<td>6.5</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnet26d</td>
<td>76.68 (23.32)</td>
<td>93.166 (6.834)</td>
<td>16</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>densenetblur121d</td>
<td>76.576 (23.424)</td>
<td>93.190 (6.810)</td>
<td>8.0</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mobilenetv2_140</td>
<td>76.524 (23.476)</td>
<td>92.990 (7.010)</td>
<td>6.1</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mixnet_s</td>
<td>75.988 (24.012)</td>
<td>92.794 (7.206)</td>
<td>4.13</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mobilenetv3_large_100</td>
<td>75.766 (24.234)</td>
<td>92.542 (7.458)</td>
<td>5.5</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mobilenetv3_rw</td>
<td>75.634 (24.366)</td>
<td>92.708 (7.292)</td>
<td>5.5</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mnasnet_a1</td>
<td>75.448 (24.552)</td>
<td>92.604 (7.396)</td>
<td>3.89</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnet26</td>
<td>75.292 (24.708)</td>
<td>92.57 (7.43)</td>
<td>16</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>fbnetc_100</td>
<td>75.124 (24.876)</td>
<td>92.386 (7.614)</td>
<td>5.6</td>
<td>bilinear</td>
<td>224</td>
</tr>
<tr>
<td>resnet34</td>
<td>75.110 (24.890)</td>
<td>92.284 (7.716)</td>
<td>22</td>
<td>bilinear</td>
<td>224</td>
</tr>
<tr>
<td>mobilenetv2_110d</td>
<td>75.052 (24.948)</td>
<td>92.180 (7.820)</td>
<td>4.5</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>seresnet34</td>
<td>74.808 (25.192)</td>
<td>92.124 (7.876)</td>
<td>22</td>
<td>bilinear</td>
<td>224</td>
</tr>
<tr>
<td>mnasnet_b1</td>
<td>74.658 (25.342)</td>
<td>92.114 (7.886)</td>
<td>4.38</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>spnasnet_100</td>
<td>74.084 (25.916)</td>
<td>91.818 (8.182)</td>
<td>4.42</td>
<td>bilinear</td>
<td>224</td>
</tr>
<tr>
<td>skresnet18</td>
<td>73.038 (26.962)</td>
<td>91.168 (8.832)</td>
<td>11.9</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>mobilenetv2_100</td>
<td>72.978 (27.022)</td>
<td>91.016 (8.984)</td>
<td>3.5</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>resnet18d</td>
<td>72.260 (27.740)</td>
<td>90.696 (9.304)</td>
<td>11.7</td>
<td>bicubic</td>
<td>224</td>
</tr>
<tr>
<td>seresnet18</td>
<td>71.742 (28.258)</td>
<td>90.334 (9.666)</td>
<td>11.8</td>
<td>bicubic</td>
<td>224</td>
</tr>
</tbody>
</table>
<h2 id="ported-and-other-weights">Ported and Other Weights</h2>
<p>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 <a href="https://github.com/rwightman/pytorch-image-models/tree/master/results">results tables</a>.</p>
<p>Model code .py files contain links to original sources of models and weights.</p>
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