Add new results csv and update README with 3 new ResNet weight results

README.md

@@ -18,8 +18,9 @@ The work of many others is present here. I've tried to make sure all source mate
 
 I've included a few of my favourite models, but this is not an exhaustive collection. You can't do better than Cadene's collection in that regard. Most models do have pretrained weights from their respective sources or original authors. 
 
-* ResNet/ResNeXt (from [torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models) with ResNeXt mods by myself)
+* ResNet/ResNeXt (from [torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models) with mods by myself)
     * ResNet-18, ResNet-34, ResNet-50, ResNet-101, ResNet-152, ResNeXt50 (32x4d), ResNeXt101 (32x4d and 64x4d)
+    * 'Bag of Tricks' / Gluon C, D, E, S variations (https://arxiv.org/abs/1812.01187)
     * Instagram trained / ImageNet tuned ResNeXt101-32x8d to 32x48d from from [facebookresearch](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/)
 * DenseNet (from [torchvision](https://github.com/pytorch/vision/tree/master/torchvision/models))
     * DenseNet-121, DenseNet-169, DenseNet-201, DenseNet-161
@@ -70,12 +71,15 @@ I've leveraged the training scripts in this repository to train a few of the mod
 #### @ 224x224
 |Model | Prec@1 (Err) | Prec@5 (Err) | Param # | Image Scaling  |
 |---|---|---|---|---|
+| resnext50d_32x4d | 79.674 (20.326) | 94.868 (5.132) | 25.1M | bicubic |
 | resnext50_32x4d | 78.512 (21.488) | 94.042 (5.958) | 25M | bicubic |
 | resnet50 | 78.470 (21.530) | 94.266 (5.734) | 25.6M | bicubic |
 | seresnext26_32x4d | 77.104 (22.896) | 93.316 (6.684) | 16.8M | bicubic |
 | efficientnet_b0 | 76.912 (23.088) | 93.210 (6.790) | 5.29M | bicubic |
+| resnet26d | 76.68 (23.32) | 93.166 (6.834) | 16M | bicubic |
 | mobilenetv3_100 | 75.634 (24.366) | 92.708 (7.292) | 5.5M | bicubic |
 | mnasnet_a1 | 75.448 (24.552) | 92.604 (7.396) | 3.89M | bicubic |
+| resnet26 | 75.292 (24.708) | 92.57 (7.43) | 16M | bicubic |
 | fbnetc_100 | 75.124 (24.876) | 92.386 (7.614) | 5.6M | bilinear |
 | resnet34 | 75.110 (24.890) | 92.284 (7.716) | 22M | bilinear |
 | seresnet34 | 74.808 (25.192) | 92.124 (7.876) | 22M | bilinear |
@@ -120,8 +124,6 @@ I've leveraged the training scripts in this repository to train a few of the mod
 | tf_efficientnet_b0 *tfp  | 76.828 (23.172) | 93.226 (6.774) | 5.29  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
 | tf_efficientnet_b0       | 76.528 (23.472) | 93.010 (6.990) | 5.29  | bicubic | [Google](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) |
 | gluon_resnet34_v1b       | 74.580 (25.420) | 91.988 (8.012) | 21.80 | bicubic | |
-| tflite_semnasnet_100     | 73.086 (26.914) | 91.336 (8.664) | 3.87  | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) |
-| tflite_mnasnet_100       | 72.398 (27.602) | 90.930 (9.070) |  4.36 | bicubic | [Google TFLite](https://github.com/tensorflow/tpu/tree/master/models/official/mnasnet) 
 | gluon_resnet18_v1b       | 70.830 (29.170) | 89.756 (10.244) | 11.69 | bicubic | |
 
 #### @ 240x240

results/results-all.csv

@@ -2,8 +2,6 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation
 resnet18,69.758,30.242,89.078,10.922,11.69,224,0.875,bilinear
 gluon_resnet18_v1b,70.83,29.17,89.756,10.244,11.69,224,0.875,bicubic
 seresnet18,71.758,28.242,90.334,9.666,11.78,224,0.875,bicubic
-tflite_mnasnet_100,72.4,27.6,90.936,9.064,4.36,224,0.875,bicubic
-tflite_semnasnet_100,73.078,26.922,91.334,8.666,3.87,224,0.875,bicubic
 tv_resnet34,73.314,26.686,91.42,8.58,21.8,224,0.875,bilinear
 spnasnet_100,74.08,25.92,91.832,8.168,4.42,224,0.875,bilinear
 gluon_resnet34_v1b,74.58,25.42,91.988,8.012,21.8,224,0.875,bicubic
@@ -12,12 +10,14 @@ densenet121,74.752,25.248,92.152,7.848,7.98,224,0.875,bicubic
 seresnet34,74.808,25.192,92.126,7.874,21.96,224,0.875,bilinear
 resnet34,75.112,24.888,92.288,7.712,21.8,224,0.875,bilinear
 fbnetc_100,75.12,24.88,92.386,7.614,5.57,224,0.875,bilinear
+resnet26,75.292,24.708,92.57,7.43,16,224,0.875,bicubic
 semnasnet_100,75.456,24.544,92.592,7.408,3.89,224,0.875,bicubic
 mobilenetv3_100,75.628,24.372,92.708,7.292,5.48,224,0.875,bicubic
 densenet169,75.912,24.088,93.024,6.976,14.15,224,0.875,bicubic
 tv_resnet50,76.13,23.87,92.862,7.138,25.56,224,0.875,bilinear
 dpn68,76.306,23.694,92.97,7.03,12.61,224,0.875,bicubic
 tf_efficientnet_b0,76.528,23.472,93.01,6.99,5.29,224,0.875,bicubic
+resnet26d,76.68,23.32,93.166,6.834,16.01,224,0.875,bicubic
 efficientnet_b0,76.914,23.086,93.206,6.794,5.29,224,0.875,bicubic
 seresnext26_32x4d,77.1,22.9,93.31,6.69,16.79,224,0.875,bicubic
 densenet201,77.29,22.71,93.478,6.522,20.01,224,0.875,bicubic
@@ -30,7 +30,7 @@ gluon_resnet50_v1b,77.578,22.422,93.718,6.282,25.56,224,0.875,bicubic
 tv_resnext50_32x4d,77.618,22.382,93.698,6.302,25.03,224,0.875,bilinear
 seresnet50,77.636,22.364,93.752,6.248,28.09,224,0.875,bilinear
 tf_inception_v3,77.856,22.144,93.644,6.356,23.83,299,0.875,bicubic
-gluon_resnet50_v1c,78.01,21.99,93.988,6.012,25.58,224,0.875,bicubic
+gluon_resnet50_v1c,78.012,21.988,93.988,6.012,25.58,224,0.875,bicubic
 resnet152,78.312,21.688,94.046,5.954,60.19,224,0.875,bilinear
 seresnet101,78.396,21.604,94.258,5.742,49.33,224,0.875,bilinear
 wide_resnet50_2,78.468,21.532,94.086,5.914,68.88,224,0.875,bilinear
@@ -51,6 +51,7 @@ gluon_resnext50_32x4d,79.356,20.644,94.424,5.576,25.03,224,0.875,bicubic
 gluon_resnet101_v1c,79.544,20.456,94.586,5.414,44.57,224,0.875,bicubic
 tf_efficientnet_b2,79.606,20.394,94.712,5.288,9.11,260,0.89,bicubic
 dpn98,79.636,20.364,94.594,5.406,61.57,224,0.875,bicubic
+resnext50d_32x4d,79.674,20.326,94.868,5.132,25.05,224,0.875,bicubic
 gluon_resnet152_v1b,79.692,20.308,94.738,5.262,60.19,224,0.875,bicubic
 efficientnet_b2,79.752,20.248,94.71,5.29,9.11,260,0.89,bicubic
 dpn131,79.828,20.172,94.704,5.296,79.25,224,0.875,bicubic

results/results-inv2-matched-frequency.csv

+model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation
+resnet18,57.18,42.82,80.19,19.81,11.69,224,0.875,bilinear
+gluon_resnet18_v1b,58.32,41.68,80.96,19.04,11.69,224,0.875,bicubic
+seresnet18,59.81,40.19,81.68,18.32,11.78,224,0.875,bicubic
+tv_resnet34,61.2,38.8,82.72,17.28,21.8,224,0.875,bilinear
+spnasnet_100,61.21,38.79,82.77,17.23,4.42,224,0.875,bilinear
+mnasnet_100,61.91,38.09,83.71,16.29,4.38,224,0.875,bicubic
+fbnetc_100,62.43,37.57,83.39,16.61,5.57,224,0.875,bilinear
+gluon_resnet34_v1b,62.56,37.44,84,16,21.8,224,0.875,bicubic
+resnet34,62.82,37.18,84.12,15.88,21.8,224,0.875,bilinear
+seresnet34,62.89,37.11,84.22,15.78,21.96,224,0.875,bilinear
+densenet121,62.94,37.06,84.26,15.74,7.98,224,0.875,bicubic
+semnasnet_100,63.12,36.88,84.53,15.47,3.89,224,0.875,bicubic
+mobilenetv3_100,63.23,36.77,84.52,15.48,5.48,224,0.875,bicubic
+tv_resnet50,63.33,36.67,84.65,15.35,25.56,224,0.875,bilinear
+resnet26,63.45,36.55,84.27,15.73,16,224,0.875,bicubic
+tf_efficientnet_b0,63.53,36.47,84.88,15.12,5.29,224,0.875,bicubic
+dpn68,64.22,35.78,85.18,14.82,12.61,224,0.875,bicubic
+efficientnet_b0,64.58,35.42,85.89,14.11,5.29,224,0.875,bicubic
+resnet26d,64.63,35.37,85.12,14.88,16.01,224,0.875,bicubic
+densenet169,64.78,35.22,85.25,14.75,14.15,224,0.875,bicubic
+seresnext26_32x4d,65.04,34.96,85.65,14.35,16.79,224,0.875,bicubic
+densenet201,65.28,34.72,85.67,14.33,20.01,224,0.875,bicubic
+dpn68b,65.6,34.4,85.94,14.06,12.61,224,0.875,bicubic
+resnet101,65.68,34.32,85.98,14.02,44.55,224,0.875,bilinear
+densenet161,65.85,34.15,86.46,13.54,28.68,224,0.875,bicubic
+gluon_resnet50_v1b,66.04,33.96,86.27,13.73,25.56,224,0.875,bicubic
+inception_v3,66.12,33.88,86.34,13.66,27.16,299,0.875,bicubic
+tv_resnext50_32x4d,66.18,33.82,86.04,13.96,25.03,224,0.875,bilinear
+seresnet50,66.24,33.76,86.33,13.67,28.09,224,0.875,bilinear
+tf_inception_v3,66.41,33.59,86.68,13.32,23.83,299,0.875,bicubic
+tf_efficientnet_b1,66.52,33.48,86.68,13.32,7.79,240,0.882,bicubic
+gluon_resnet50_v1c,66.54,33.46,86.16,13.84,25.58,224,0.875,bicubic
+adv_inception_v3,66.6,33.4,86.56,13.44,23.83,299,0.875,bicubic
+wide_resnet50_2,66.65,33.35,86.81,13.19,68.88,224,0.875,bilinear
+wide_resnet101_2,66.68,33.32,87.04,12.96,126.89,224,0.875,bilinear
+resnet50,66.81,33.19,87,13,25.56,224,0.875,bicubic
+resnext50_32x4d,66.88,33.12,86.36,13.64,25.03,224,0.875,bicubic
+resnet152,67.02,32.98,87.57,12.43,60.19,224,0.875,bilinear
+gluon_resnet50_v1s,67.1,32.9,86.86,13.14,25.68,224,0.875,bicubic
+seresnet101,67.15,32.85,87.05,12.95,49.33,224,0.875,bilinear
+tf_efficientnet_b2,67.4,32.6,87.58,12.42,9.11,260,0.89,bicubic
+gluon_resnet101_v1b,67.45,32.55,87.23,12.77,44.55,224,0.875,bicubic
+efficientnet_b1,67.55,32.45,87.29,12.71,7.79,240,0.882,bicubic
+seresnet152,67.55,32.45,87.39,12.61,66.82,224,0.875,bilinear
+gluon_resnet101_v1c,67.56,32.44,87.16,12.84,44.57,224,0.875,bicubic
+gluon_inception_v3,67.59,32.41,87.46,12.54,23.83,299,0.875,bicubic
+xception,67.67,32.33,87.57,12.43,22.86,299,0.8975,bicubic
+efficientnet_b2,67.8,32.2,88.2,11.8,9.11,260,0.89,bicubic
+resnext101_32x8d,67.85,32.15,87.48,12.52,88.79,224,0.875,bilinear
+seresnext50_32x4d,67.87,32.13,87.62,12.38,27.56,224,0.875,bilinear
+gluon_resnet50_v1d,67.91,32.09,87.12,12.88,25.58,224,0.875,bicubic
+dpn92,68.01,31.99,87.59,12.41,37.67,224,0.875,bicubic
+gluon_resnext50_32x4d,68.28,31.72,87.32,12.68,25.03,224,0.875,bicubic
+tf_efficientnet_b3,68.52,31.48,88.7,11.3,12.23,300,0.904,bicubic
+dpn98,68.58,31.42,87.66,12.34,61.57,224,0.875,bicubic
+gluon_seresnext50_32x4d,68.67,31.33,88.32,11.68,27.56,224,0.875,bicubic
+dpn107,68.71,31.29,88.13,11.87,86.92,224,0.875,bicubic
+gluon_resnet101_v1s,68.72,31.28,87.9,12.1,44.67,224,0.875,bicubic
+resnext50d_32x4d,68.75,31.25,88.31,11.69,25.05,224,0.875,bicubic
+dpn131,68.76,31.24,87.48,12.52,79.25,224,0.875,bicubic
+gluon_resnet152_v1b,68.81,31.19,87.71,12.29,60.19,224,0.875,bicubic
+gluon_resnext101_32x4d,68.96,31.04,88.34,11.66,44.18,224,0.875,bicubic
+gluon_resnet101_v1d,68.99,31.01,88.08,11.92,44.57,224,0.875,bicubic
+gluon_resnet152_v1c,69.13,30.87,87.89,12.11,60.21,224,0.875,bicubic
+seresnext101_32x4d,69.34,30.66,88.05,11.95,48.96,224,0.875,bilinear
+inception_v4,69.35,30.65,88.78,11.22,42.68,299,0.875,bicubic
+ens_adv_inception_resnet_v2,69.52,30.48,88.5,11.5,55.84,299,0.8975,bicubic
+gluon_resnext101_64x4d,69.69,30.31,88.26,11.74,83.46,224,0.875,bicubic
+gluon_resnet152_v1d,69.95,30.05,88.47,11.53,60.21,224,0.875,bicubic
+gluon_seresnext101_32x4d,70.01,29.99,88.91,11.09,48.96,224,0.875,bicubic
+inception_resnet_v2,70.12,29.88,88.68,11.32,55.84,299,0.8975,bicubic
+gluon_resnet152_v1s,70.32,29.68,88.87,11.13,60.32,224,0.875,bicubic
+gluon_seresnext101_64x4d,70.44,29.56,89.35,10.65,88.23,224,0.875,bicubic
+senet154,70.48,29.52,88.99,11.01,115.09,224,0.875,bilinear
+gluon_senet154,70.6,29.4,88.92,11.08,115.09,224,0.875,bicubic
+tf_efficientnet_b4,71.34,28.66,90.11,9.89,19.34,380,0.922,bicubic
+nasnetalarge,72.31,27.69,90.51,9.49,88.75,331,0.875,bicubic
+pnasnet5large,72.37,27.63,90.26,9.74,86.06,331,0.875,bicubic
+tf_efficientnet_b5,72.56,27.44,91.1,8.9,30.39,456,0.934,bicubic
+ig_resnext101_32x8d,73.66,26.34,92.15,7.85,88.79,224,0.875,bilinear
+ig_resnext101_32x16d,75.71,24.29,92.9,7.1,194.03,224,0.875,bilinear
+ig_resnext101_32x32d,76.84,23.16,93.19,6.81,468.53,224,0.875,bilinear
+ig_resnext101_32x48d,76.87,23.13,93.32,6.68,828.41,224,0.875,bilinear