From d97510fd4a315952418e018633736eb05865c535 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Sat, 20 Jul 2019 08:45:05 -0700 Subject: [PATCH] Add new results csv and update README with 3 new ResNet weight results --- README.md | 8 ++- results/results-all.csv | 7 +- results/results-inv2-matched-frequency.csv | 84 ++++++++++++++++++++++ 3 files changed, 93 insertions(+), 6 deletions(-) create mode 100644 results/results-inv2-matched-frequency.csv diff --git a/README.md b/README.md index 3160bba3..ff534700 100644 --- a/README.md +++ b/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 diff --git a/results/results-all.csv b/results/results-all.csv index a3cc0347..619444c2 100644 --- a/results/results-all.csv +++ b/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 diff --git a/results/results-inv2-matched-frequency.csv b/results/results-inv2-matched-frequency.csv new file mode 100644 index 00000000..14e85131 --- /dev/null +++ b/results/results-inv2-matched-frequency.csv @@ -0,0 +1,84 @@ +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