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Update version, results csv files, and move remaining dropbox weights to github

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pull/23/head
Ross Wightman 6 years ago
parent dfa9298b4e
commit d4debe6597
6 changed files with 15 additions and 9 deletions
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7
results/results-all.csv
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@ -13,20 +13,22 @@ 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
tf_mixnet_s,75.648,24.352,92.636,7.364,4.13,224,0.875,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
tf_mixnet_m,76.95,23.05,93.156,6.844,5.01,224,0.875,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
densenet161,77.348,22.652,93.648,6.352,28.68,224,0.875,bicubic
resnet101,77.374,22.626,93.546,6.454,44.55,224,0.875,bilinear
inception_v3,77.434,22.566,93.478,6.522,27.16,299,0.875,bicubic
inception_v3,77.432,22.568,93.478,6.522,27.16,299,0.875,bicubic
dpn68b,77.514,22.486,93.822,6.178,12.61,224,0.875,bicubic
adv_inception_v3,77.576,22.424,93.724,6.276,23.83,299,0.875,bicubic
gluon_resnet50_v1b,77.578,22.422,93.718,6.282,25.56,224,0.875,bicubic
adv_inception_v3,77.58,22.42,93.724,6.276,23.83,299,0.875,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
@ -40,6 +42,7 @@ tf_efficientnet_b1,78.554,21.446,94.098,5.902,7.79,240,0.882,bicubic
seresnet152,78.658,21.342,94.374,5.626,66.82,224,0.875,bilinear
efficientnet_b1,78.692,21.308,94.086,5.914,7.79,240,0.882,bicubic
gluon_resnet50_v1s,78.712,21.288,94.242,5.758,25.68,224,0.875,bicubic
tf_mixnet_l,78.77,21.23,94.004,5.996,7.33,224,0.875,bicubic
gluon_inception_v3,78.804,21.196,94.38,5.62,23.83,299,0.875,bicubic
wide_resnet101_2,78.846,21.154,94.284,5.716,126.89,224,0.875,bilinear
xception,79.048,20.952,94.392,5.608,22.86,299,0.8975,bicubic

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
13 resnet26 75.292 24.708 92.57 7.43 16 224 0.875 bicubic
14 semnasnet_100 75.456 24.544 92.592 7.408 3.89 224 0.875 bicubic
15 mobilenetv3_100 75.628 24.372 92.708 7.292 5.48 224 0.875 bicubic
16 tf_mixnet_s 75.648 24.352 92.636 7.364 4.13 224 0.875 bicubic
17 densenet169 75.912 24.088 93.024 6.976 14.15 224 0.875 bicubic
18 tv_resnet50 76.13 23.87 92.862 7.138 25.56 224 0.875 bilinear
19 dpn68 76.306 23.694 92.97 7.03 12.61 224 0.875 bicubic
20 tf_efficientnet_b0 76.528 23.472 93.01 6.99 5.29 224 0.875 bicubic
21 resnet26d 76.68 23.32 93.166 6.834 16.01 224 0.875 bicubic
22 efficientnet_b0 76.914 23.086 93.206 6.794 5.29 224 0.875 bicubic
23 tf_mixnet_m 76.95 23.05 93.156 6.844 5.01 224 0.875 bicubic
24 seresnext26_32x4d 77.1 22.9 93.31 6.69 16.79 224 0.875 bicubic
25 densenet201 77.29 22.71 93.478 6.522 20.01 224 0.875 bicubic
26 densenet161 77.348 22.652 93.648 6.352 28.68 224 0.875 bicubic
27 resnet101 77.374 22.626 93.546 6.454 44.55 224 0.875 bilinear
28 inception_v3 77.434 77.432 22.566 22.568 93.478 6.522 27.16 299 0.875 bicubic
29 dpn68b 77.514 22.486 93.822 6.178 12.61 224 0.875 bicubic
adv_inception_v3 77.576 22.424 93.724 6.276 23.83 299 0.875 bicubic
30 gluon_resnet50_v1b 77.578 22.422 93.718 6.282 25.56 224 0.875 bicubic
31 adv_inception_v3 77.58 22.42 93.724 6.276 23.83 299 0.875 bicubic
32 tv_resnext50_32x4d 77.618 22.382 93.698 6.302 25.03 224 0.875 bilinear
33 seresnet50 77.636 22.364 93.752 6.248 28.09 224 0.875 bilinear
34 tf_inception_v3 77.856 22.144 93.644 6.356 23.83 299 0.875 bicubic
42 seresnet152 78.658 21.342 94.374 5.626 66.82 224 0.875 bilinear
43 efficientnet_b1 78.692 21.308 94.086 5.914 7.79 240 0.882 bicubic
44 gluon_resnet50_v1s 78.712 21.288 94.242 5.758 25.68 224 0.875 bicubic
45 tf_mixnet_l 78.77 21.23 94.004 5.996 7.33 224 0.875 bicubic
46 gluon_inception_v3 78.804 21.196 94.38 5.62 23.83 299 0.875 bicubic
47 wide_resnet101_2 78.846 21.154 94.284 5.716 126.89 224 0.875 bilinear
48 xception 79.048 20.952 94.392 5.608 22.86 299 0.8975 bicubic

3
results/results-inv2-matched-frequency.csv
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@ -15,7 +15,9 @@ 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
tf_mixnet_s,63.59,36.41,84.27,15.73,4.13,224,0.875,bicubic
dpn68,64.22,35.78,85.18,14.82,12.61,224,0.875,bicubic
tf_mixnet_m,64.27,35.73,85.09,14.91,5.01,224,0.875,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
@ -34,6 +36,7 @@ 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
tf_mixnet_l,66.78,33.22,86.46,13.54,7.33,224,0.875,bicubic
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

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
15 tv_resnet50 63.33 36.67 84.65 15.35 25.56 224 0.875 bilinear
16 resnet26 63.45 36.55 84.27 15.73 16 224 0.875 bicubic
17 tf_efficientnet_b0 63.53 36.47 84.88 15.12 5.29 224 0.875 bicubic
18 tf_mixnet_s 63.59 36.41 84.27 15.73 4.13 224 0.875 bicubic
19 dpn68 64.22 35.78 85.18 14.82 12.61 224 0.875 bicubic
20 tf_mixnet_m 64.27 35.73 85.09 14.91 5.01 224 0.875 bicubic
21 efficientnet_b0 64.58 35.42 85.89 14.11 5.29 224 0.875 bicubic
22 resnet26d 64.63 35.37 85.12 14.88 16.01 224 0.875 bicubic
23 densenet169 64.78 35.22 85.25 14.75 14.15 224 0.875 bicubic
36 adv_inception_v3 66.6 33.4 86.56 13.44 23.83 299 0.875 bicubic
37 wide_resnet50_2 66.65 33.35 86.81 13.19 68.88 224 0.875 bilinear
38 wide_resnet101_2 66.68 33.32 87.04 12.96 126.89 224 0.875 bilinear
39 tf_mixnet_l 66.78 33.22 86.46 13.54 7.33 224 0.875 bicubic
40 resnet50 66.81 33.19 87 13 25.56 224 0.875 bicubic
41 resnext50_32x4d 66.88 33.12 86.36 13.64 25.03 224 0.875 bicubic
42 resnet152 67.02 32.98 87.57 12.43 60.19 224 0.875 bilinear

2
timm/models/gen_efficientnet.py
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@ -68,7 +68,7 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth',
interpolation='bilinear'),
'spnasnet_100': _cfg(
url='https://www.dropbox.com/s/iieopt18rytkgaa/spnasnet_100-048bc3f4.pth?dl=1',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth',
interpolation='bilinear'),
'efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0-d6904d92.pth'),

6
timm/models/inception_v3.py
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@ -20,7 +20,7 @@ default_cfgs = {
},
# my port of Tensorflow SLIM weights (http://download.tensorflow.org/models/inception_v3_2016_08_28.tar.gz)
'tf_inception_v3': {
'url': 'https://www.dropbox.com/s/xdh32bpdgqzpx8t/tf_inception_v3-e0069de4.pth?dl=1',
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_inception_v3-e0069de4.pth',
'input_size': (3, 299, 299),
'crop_pct': 0.875,
'interpolation': 'bicubic',
@ -33,7 +33,7 @@ default_cfgs = {
# my port of Tensorflow adversarially trained Inception V3 from
# http://download.tensorflow.org/models/adv_inception_v3_2017_08_18.tar.gz
'adv_inception_v3': {
'url': 'https://www.dropbox.com/s/b5pudqh84gtl7i8/adv_inception_v3-9e27bd63.pth?dl=1',
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/adv_inception_v3-9e27bd63.pth',
'input_size': (3, 299, 299),
'crop_pct': 0.875,
'interpolation': 'bicubic',
@ -46,7 +46,7 @@ default_cfgs = {
# from gluon pretrained models, best performing in terms of accuracy/loss metrics
# https://gluon-cv.mxnet.io/model_zoo/classification.html
'gluon_inception_v3': {
'url': 'https://www.dropbox.com/s/8uv6wrl6it6394u/gluon_inception_v3-9f746940.pth?dl=1',
'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gluon_inception_v3-9f746940.pth',
'input_size': (3, 299, 299),
'crop_pct': 0.875,
'interpolation': 'bicubic',

4
timm/models/senet.py
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@ -36,10 +36,10 @@ default_cfgs = {
'senet154':
_cfg(url='http://data.lip6.fr/cadene/pretrainedmodels/senet154-c7b49a05.pth'),
'seresnet18': _cfg(
url='https://www.dropbox.com/s/3o3nd8mfhxod7rq/seresnet18-4bb0ce65.pth?dl=1',
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet18-4bb0ce65.pth',
interpolation='bicubic'),
'seresnet34': _cfg(
url='https://www.dropbox.com/s/q31ccy22aq0fju7/seresnet34-a4004e63.pth?dl=1'),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet34-a4004e63.pth'),
'seresnet50': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-cadene/se_resnet50-ce0d4300.pth'),
'seresnet101': _cfg(

2
timm/version.py
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@ -1 +1 @@
__version__ = '0.1.8'
__version__ = '0.1.10'

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