In [1]:
%load_ext autoreload
%autoreload 2
import os
import aphrodite.results
import pandas as pd
import vislab
import vislab.datasets
import vislab.results
/Users/sergeyk/anaconda/lib/python2.7/site-packages/configobj.py:145: DeprecationWarning: The compiler package is deprecated and removed in Python 3.x.
import compiler
In [2]:
c = vislab.util.get_mongodb_client()['predict']['ava_style_oct22']
if c.find({'features': 'noise'}).count() > 0:
c.remove({'features': 'noise'})
pd.DataFrame([x for x in c.find()])
Out[2]:
_id
data
features
num_test
num_train
num_val
pred_df
quadratic
results_name
score_test
score_val
task
0
52677171efd80f8750020095
ava_style_style_ALL
[lab_hist]
2573
8501
2769
�cpandas.core.frame\nDataFrame\nq)�qcpandas...
NaN
0.284286
0.247508
clf
1
52687f4043d2db703b9f97e6
ava_style_style_ALL
[gist_256]
2573
8501
2769
�cpandas.core.frame\nDataFrame\nq)�qcpandas...
False
NaN
0.187143
0.184385
clf
2
5268e26a43d2db703b9f97e7
ava_style_style_ALL
[mc_bit]
2573
8501
2769
�cpandas.core.frame\nDataFrame\nq)�qcpandas...
False
NaN
0.434286
0.417647
clf
3
5269dc0c43d2db703b9f9825
ava_style_style_ALL
[decaf_fc6]
2573
8501
2769
�cpandas.core.frame\nDataFrame\nq)�qcpandas...
False
NaN
0.488571
0.529900
clf
4
5269f802265138430c54b4d6
ava_style_style_ALL
[decaf_fc6_flatten]
2573
8501
2769
�cpandas.core.frame\nDataFrame\nq)�qcpandas...
False
NaN
0.435714
0.823920
clf
5
526edf859622958641aaee55
ava_style_style_ALL
[gbvs_saliency]
2573
8501
2769
NaN
False
data_ava_style_style_ALL_features_['gbvs_salie...
0.084666
0.181594
clf
6
52719c9e9622958641af6842
ava_style_style_ALL
[decaf_tuned_fc6]
2573
8501
2769
NaN
False
data_ava_style_style_ALL_features_['decaf_tune...
0.417053
0.696795
clf
7
5271e1399622958641af6845
ava_style_style_ALL
[decaf_tuned_fc6_flatten]
2573
8501
2769
NaN
False
data_ava_style_style_ALL_features_['decaf_tune...
0.363299
0.820871
clf
8
52724df68dc9cfbe92ca2344
ava_style_style_ALL
[fusion_ava_style_oct22]
2573
8501
2769
NaN
None
data_ava_style_style_ALL_features_['fusion_ava...
0.449765
0.756779
clf
9
527293fd9622958641af684f
ava_style_style_ALL
[decaf_fc6, pascal_mc_for_decaf_fc6]
2573
8501
2769
NaN
pd
data_ava_style_style_ALL_features_['decaf_fc6'...
0.384902
0.766639
clf
10
52730a969622958641b1a34b
ava_style_style_ALL
[decaf_tuned_fc6_ud]
2573
8501
2769
NaN
None
data_ava_style_style_ALL_features_['decaf_tune...
0.389562
0.684470
clf
11
52731a968dc9cfbe92ca2348
ava_style_style_ALL
[fusion_ava_style_oct22, pascal_mc_for_fusion_...
2573
8501
2769
NaN
fp
data_ava_style_style_ALL_features_['fusion_ava...
0.437559
0.766639
clf
12
527c68079622958641b3d7b7
ava_style_style_ALL
[decaf_imagenet]
2573
8501
2769
NaN
None
data_ava_style_style_ALL_features_['decaf_imag...
0.281454
0.462613
clf
In [3]:
label_df = vislab.datasets.ava.get_style_df()
In [23]:
results_df, preds_panel = aphrodite.results.load_pred_results(
'ava_style_oct22', os.path.expanduser('~/work/aphrodite/data/results2'),
multiclass=True, force=True)
pred_prefix = 'pred'
print preds_panel.minor_axis
Results in collection ava_style_oct22: 13
Index([u'decaf_fc6 False vw', u'decaf_fc6,pascal_mc_for_decaf_fc6 pd vw', u'decaf_fc6_flatten False vw', u'decaf_imagenet None vw', u'decaf_tuned_fc6 False vw', u'decaf_tuned_fc6_flatten False vw', u'decaf_tuned_fc6_ud None vw', u'fusion_ava_style_oct22 None vw', u'fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw', u'gbvs_saliency False vw', u'gist_256 False vw', u'lab_hist vw', u'mc_bit False vw'], dtype='object')
In [24]:
feat_to_evaluate = [
u'decaf_fc6 False vw',
#u'decaf_fc6,pascal_mc_for_decaf_fc6 pd vw',
u'decaf_fc6_flatten False vw',
u'decaf_tuned_fc6 False vw',
#u'decaf_tuned_fc6_flatten False vw',
#u'decaf_tuned_fc6_ud None vw',
#u'fusion_ava_style_oct22 None vw',
u'fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw',
u'gbvs_saliency False vw',
u'gist_256 False vw',
u'lab_hist vw',
u'mc_bit False vw',
u'decaf_imagenet None vw'
]
preds_panel = preds_panel.select(lambda x: x in feat_to_evaluate, 'minor')
In [25]:
nice_feat_names = {
'decaf_fc6 False vw': 'DeCAF_6',
'decaf_fc6_flatten False vw': 'DeCAF_5',
'decaf_tuned_fc6 False vw': 'Fine-tuned DeCAF_6',
'fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw': 'Late-fusion x Content',
'mc_bit False vw': 'MC-bit',
'gbvs_saliency False vw': 'Graph-based Saliency',
'gist_256 False vw': 'GIST',
'lab_hist vw': 'L*a*b* Histogram',
'decaf_imagenet None vw': 'ImageNet scores'
}
mc_metrics = vislab.results.multiclass_metrics_feat_comparison(
preds_panel, label_df, pred_prefix, features=preds_panel.minor_axis.tolist() + ['random'],
balanced=True, with_plot=False, with_print=True, nice_feat_names=nice_feat_names)
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************decaf_fc6 False vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.634921 0.338983 0.441989 118
style_Duotones 0.823529 0.234310 0.364821 239
style_HDR 0.486842 0.521127 0.503401 71
style_Image_Grain 0.211765 0.428571 0.283465 42
style_Light_On_White 0.598361 0.924051 0.726368 79
style_Long_Exposure 0.436364 0.289157 0.347826 83
style_Macro 0.403509 0.252747 0.310811 91
style_Motion_Blur 0.304348 0.446809 0.362069 47
style_Negative_Image 0.487500 0.661017 0.561151 59
style_Rule_of_Thirds 0.372093 0.205128 0.264463 78
style_Shallow_DOF 0.121951 0.277778 0.169492 36
style_Silhouettes 0.440000 0.758621 0.556962 58
style_Soft_Focus 0.173333 0.419355 0.245283 31
style_Vanishing_Point 0.316327 0.756098 0.446043 41
accuracy: 0.414725069897
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************decaf_fc6_flatten False vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.470588 0.403361 0.434389 119
style_Duotones 0.696629 0.281818 0.401294 220
style_HDR 0.750000 0.211268 0.329670 71
style_Image_Grain 0.218750 0.212121 0.215385 33
style_Light_On_White 0.471831 0.893333 0.617512 75
style_Long_Exposure 0.294118 0.240964 0.264901 83
style_Macro 0.655172 0.213483 0.322034 89
style_Motion_Blur 0.396825 0.454545 0.423729 55
style_Negative_Image 0.393258 0.573770 0.466667 61
style_Rule_of_Thirds 0.226415 0.151899 0.181818 79
style_Shallow_DOF 0.205479 0.326087 0.252101 46
style_Silhouettes 0.409091 0.818182 0.545455 55
style_Soft_Focus 0.158730 0.344828 0.217391 29
style_Vanishing_Point 0.285714 0.818182 0.423529 44
accuracy: 0.392823418319
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************decaf_imagenet None vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.493976 0.356522 0.414141 115
style_Duotones 0.500000 0.069767 0.122449 215
style_HDR 0.367089 0.408451 0.386667 71
style_Image_Grain 0.106667 0.173913 0.132231 46
style_Light_On_White 0.375000 0.633803 0.471204 71
style_Long_Exposure 0.194444 0.170732 0.181818 82
style_Macro 0.384615 0.376344 0.380435 93
style_Motion_Blur 0.216667 0.260000 0.236364 50
style_Negative_Image 0.244444 0.400000 0.303448 55
style_Rule_of_Thirds 0.178571 0.063291 0.093458 79
style_Shallow_DOF 0.112903 0.152174 0.129630 46
style_Silhouettes 0.333333 0.573770 0.421687 61
style_Soft_Focus 0.102041 0.172414 0.128205 29
style_Vanishing_Point 0.224299 0.631579 0.331034 38
accuracy: 0.283539486204
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************decaf_tuned_fc6 False vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.608696 0.344262 0.439791 122
style_Duotones 0.728395 0.255411 0.378205 231
style_HDR 0.523077 0.478873 0.500000 71
style_Image_Grain 0.229730 0.425000 0.298246 40
style_Light_On_White 0.532110 0.816901 0.644444 71
style_Long_Exposure 0.426471 0.322222 0.367089 90
style_Macro 0.461538 0.242424 0.317881 99
style_Motion_Blur 0.208955 0.304348 0.247788 46
style_Negative_Image 0.410526 0.650000 0.503226 60
style_Rule_of_Thirds 0.266667 0.225352 0.244275 71
style_Shallow_DOF 0.164179 0.268293 0.203704 41
style_Silhouettes 0.510417 0.816667 0.628205 60
style_Soft_Focus 0.148649 0.407407 0.217822 27
style_Vanishing_Point 0.376344 0.853659 0.522388 41
accuracy: 0.409345794393
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.538462 0.307018 0.391061 114
style_Duotones 0.727273 0.252252 0.374582 222
style_HDR 0.685185 0.521127 0.592000 71
style_Image_Grain 0.305556 0.523810 0.385965 42
style_Light_On_White 0.735632 0.853333 0.790123 75
style_Long_Exposure 0.464286 0.305882 0.368794 85
style_Macro 0.391304 0.382979 0.387097 94
style_Motion_Blur 0.297619 0.480769 0.367647 52
style_Negative_Image 0.445652 0.672131 0.535948 61
style_Rule_of_Thirds 0.276923 0.240000 0.257143 75
style_Shallow_DOF 0.217822 0.468085 0.297297 47
style_Silhouettes 0.478261 0.589286 0.528000 56
style_Soft_Focus 0.301887 0.551724 0.390244 29
style_Vanishing_Point 0.364583 0.875000 0.514706 40
accuracy: 0.438381937912
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************gbvs_saliency False vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.000000 0.000000 0.000000 112
style_Duotones 0.152542 0.039823 0.063158 226
style_HDR 0.052083 0.070423 0.059880 71
style_Image_Grain 0.025316 0.042553 0.031746 47
style_Light_On_White 0.153846 0.263158 0.194175 76
style_Long_Exposure 0.196078 0.113636 0.143885 88
style_Macro 0.114286 0.043011 0.062500 93
style_Motion_Blur 0.071429 0.120000 0.089552 50
style_Negative_Image 0.064516 0.096774 0.077419 62
style_Rule_of_Thirds 0.131783 0.229730 0.167488 74
style_Shallow_DOF 0.105263 0.122449 0.113208 49
style_Silhouettes 0.085106 0.133333 0.103896 60
style_Soft_Focus 0.028571 0.064516 0.039604 31
style_Vanishing_Point 0.055556 0.097561 0.070796 41
accuracy: 0.0916666666667
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************gist_256 False vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.259740 0.186916 0.217391 107
style_Duotones 0.341463 0.059322 0.101083 236
style_HDR 0.210526 0.281690 0.240964 71
style_Image_Grain 0.011905 0.024390 0.016000 41
style_Light_On_White 0.442029 0.835616 0.578199 73
style_Long_Exposure 0.150000 0.102273 0.121622 88
style_Macro 0.215909 0.191919 0.203209 99
style_Motion_Blur 0.105263 0.186047 0.134454 43
style_Negative_Image 0.112245 0.177419 0.137500 62
style_Rule_of_Thirds 0.047619 0.012987 0.020408 77
style_Shallow_DOF 0.060606 0.046512 0.052632 43
style_Silhouettes 0.161017 0.380000 0.226190 50
style_Soft_Focus 0.000000 0.000000 0.000000 33
style_Vanishing_Point 0.069767 0.146341 0.094488 41
accuracy: 0.179511278195
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************lab_hist vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.430233 0.316239 0.364532 117
style_Duotones 0.668605 0.493562 0.567901 233
style_HDR 0.239130 0.309859 0.269939 71
style_Image_Grain 0.082192 0.146341 0.105263 41
style_Light_On_White 0.484472 0.987342 0.650000 79
style_Long_Exposure 0.255102 0.284091 0.268817 88
style_Macro 0.191489 0.102273 0.133333 88
style_Motion_Blur 0.115385 0.061224 0.080000 49
style_Negative_Image 0.306452 0.311475 0.308943 61
style_Rule_of_Thirds 0.156250 0.064103 0.090909 78
style_Shallow_DOF 0.096774 0.061224 0.075000 49
style_Silhouettes 0.204082 0.350877 0.258065 57
style_Soft_Focus 0.074074 0.129032 0.094118 31
style_Vanishing_Point 0.156863 0.195122 0.173913 41
accuracy: 0.326869806094
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************mc_bit False vw********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.466667 0.128440 0.201439 109
style_Duotones 0.812500 0.105691 0.187050 246
style_HDR 0.680000 0.485714 0.566667 70
style_Image_Grain 0.276316 0.488372 0.352941 43
style_Light_On_White 0.645161 0.779221 0.705882 77
style_Long_Exposure 0.473684 0.216867 0.297521 83
style_Macro 0.295775 0.471910 0.363636 89
style_Motion_Blur 0.238095 0.520833 0.326797 48
style_Negative_Image 0.325843 0.527273 0.402778 55
style_Rule_of_Thirds 0.255319 0.155844 0.193548 77
style_Shallow_DOF 0.178082 0.309524 0.226087 42
style_Silhouettes 0.525424 0.632653 0.574074 49
style_Soft_Focus 0.214286 0.206897 0.210526 29
style_Vanishing_Point 0.191919 0.883721 0.315353 43
accuracy: 0.348113207547
Looks like the preds frame already has gt info.
Only taking 'test' split predictions.
********************random********************
------------------------------------------------------------
Classification metrics on {} balanced
precision recall f1-score support
style_Complementary_Colors 0.160920 0.115702 0.134615 121
style_Duotones 0.275362 0.078838 0.122581 241
style_HDR 0.064103 0.070423 0.067114 71
style_Image_Grain 0.012987 0.024390 0.016949 41
style_Light_On_White 0.093333 0.100000 0.096552 70
style_Long_Exposure 0.090909 0.095238 0.093023 84
style_Macro 0.058140 0.054348 0.056180 92
style_Motion_Blur 0.051948 0.081633 0.063492 49
style_Negative_Image 0.029851 0.033898 0.031746 59
style_Rule_of_Thirds 0.102564 0.123077 0.111888 65
style_Shallow_DOF 0.092308 0.120000 0.104348 50
style_Silhouettes 0.089744 0.120690 0.102941 58
style_Soft_Focus 0.014706 0.038462 0.021277 26
style_Vanishing_Point 0.054054 0.100000 0.070175 40
accuracy: 0.0852858481724
In [26]:
ap_df = mc_metrics['ap_df'].copy()
ap_df = ap_df[sorted(ap_df.columns)]
ap_df['Murray-CVPR-2012'] = 0
ap_df['Murray-CVPR-2012'].iloc[:-1] = [
.44, .51, .64, .74, .73, .43, .50, .40, .69, .30, .48, .72, .39, .57]
ap_df['Murray-CVPR-2012'].iloc[-1] = ap_df['Murray-CVPR-2012'].iloc[:-1].mean()
ap_df
Out[26]:
DeCAF_5
DeCAF_6
Fine-tuned DeCAF_6
GIST
Graph-based Saliency
ImageNet scores
L*a*b* Histogram
Late-fusion x Content
MC-bit
random
Murray-CVPR-2012
style_Complementary_Colors
0.3683569
0.5483643
0.5140856
0.2225432
0.1113646
0.3890846
0.293766
0.4685007
0.3293508
0.1299992
0.440000
style_Duotones
0.3628165
0.7372331
0.6648225
0.2546116
0.2329081
0.3829415
0.581615
0.6764965
0.6117758
0.2586782
0.510000
style_HDR
0.4940769
0.5942884
0.5161139
0.123698
0.100939
0.3350302
0.1940499
0.6691423
0.6238012
0.0909909
0.640000
style_Image_Grain
0.5346317
0.5454793
0.563017
0.1035828
0.1035253
0.2188047
0.2125874
0.6472646
0.7435566
0.0992926
0.740000
style_Light_On_White
0.8049557
0.9149291
0.8604619
0.7041506
0.1720978
0.5084818
0.86734
0.9078907
0.8018531
0.1129146
0.730000
style_Long_Exposure
0.207902
0.4308969
0.4435733
0.1588824
0.1465081
0.2421516
0.2316887
0.4526636
0.419627
0.1274396
0.430000
style_Macro
0.3757973
0.4266545
0.4876671
0.2692966
0.1605435
0.4375507
0.2298055
0.4779298
0.4132621
0.1541289
0.500000
style_Motion_Blur
0.3265708
0.4670489
0.3804504
0.1143102
0.1223063
0.186066
0.1166354
0.4777465
0.4584309
0.0931889
0.400000
style_Negative_Image
0.4270504
0.6190784
0.5607143
0.1886564
0.122612
0.3229297
0.2677185
0.5948597
0.4990895
0.103901
0.690000
style_Rule_of_Thirds
0.2686728
0.3526135
0.2903744
0.1665279
0.2279246
0.2437902
0.188014
0.3515689
0.2355964
0.1705617
0.300000
style_Shallow_DOF
0.5221978
0.6593021
0.6267157
0.2758458
0.2228438
0.5170692
0.331842
0.6235331
0.6369155
0.208029
0.480000
style_Silhouettes
0.6087679
0.8010796
0.8348417
0.2634183
0.1295271
0.40075
0.2605854
0.7910539
0.8011245
0.1169305
0.720000
style_Soft_Focus
0.2247693
0.3536739
0.304915
0.1258997
0.1142462
0.1699457
0.1269511
0.3119699
0.2901328
0.1022715
0.390000
style_Vanishing_Point
0.5269731
0.658135
0.6456404
0.1069214
0.1613084
0.541642
0.122763
0.6837973
0.6852887
0.09170469
0.570000
_mean
0.4323956
0.5791984
0.5495281
0.2198818
0.1520468
0.3497313
0.2875259
0.5810298
0.5392718
0.1328594
0.538571
In [20]:
conf_df = mc_metrics['feat_metrics']['fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw']['conf_df'].astype(float)
conf_df.index = [x.replace('style_', '') for x in conf_df.index]
conf_df.columns = [x.replace('style_', '') for x in conf_df.columns]
fig = vislab.dataset_viz.plot_conditional_occurrence(conf_df, sort_by_prior=False)
fig.savefig('/Users/sergeyk/work/aphrodite-writeup/figures/evaluation/ava_style_conf.pdf', bbox_inches='tight')
In [27]:
acc_df = mc_metrics['acc_df']
fig = vislab.results_viz.plot_top_k_accuracies(acc_df, font_size=16)
fig.savefig('/Users/sergeyk/work/aphrodite-writeup/figures/evaluation/ava_style_top_k.pdf', bbox_inches='tight')
In [28]:
column_order = ap_df.columns[(-ap_df.ix['_mean']).argsort().values]
ap_df.index = [x.replace('style_', '') for x in ap_df.index]
ap_df = ap_df.reindex_axis(column_order, axis=1)
ap_df.to_csv('/Users/sergeyk/work/aphrodite-writeup/results/ava_style_ap_df.csv')
fig = vislab.results_viz.plot_df_bar(ap_df, fontsize=14)
fig.savefig('/Users/sergeyk/work/aphrodite-writeup/figures/ava_style_ap_barplot.pdf', bbox_inches='tight')
In [32]:
del ap_df['random']
print ap_df.to_latex(float_format=lambda x: '%.3f'%x if not np.isnan(x) else '-')
\begin{tabular}{lllllrlllll}
\toprule
{} & Late-fusion x Content & DeCAF\_6 & Fine-tuned DeCAF\_6 & MC-bit & Murray-CVPR-2012 & DeCAF\_5 & ImageNet scores & L*a*b* Histogram & GIST & Graph-based Saliency \\
\midrule
Complementary\_Colors & 0.469 & 0.548 & 0.514 & 0.329 & 0.440 & 0.368 & 0.389 & 0.294 & 0.223 & 0.111 \\
Duotones & 0.676 & 0.737 & 0.665 & 0.612 & 0.510 & 0.363 & 0.383 & 0.582 & 0.255 & 0.233 \\
HDR & 0.669 & 0.594 & 0.516 & 0.624 & 0.640 & 0.494 & 0.335 & 0.194 & 0.124 & 0.101 \\
Image\_Grain & 0.647 & 0.545 & 0.563 & 0.744 & 0.740 & 0.535 & 0.219 & 0.213 & 0.104 & 0.104 \\
Light\_On\_White & 0.908 & 0.915 & 0.860 & 0.802 & 0.730 & 0.805 & 0.508 & 0.867 & 0.704 & 0.172 \\
Long\_Exposure & 0.453 & 0.431 & 0.444 & 0.420 & 0.430 & 0.208 & 0.242 & 0.232 & 0.159 & 0.147 \\
Macro & 0.478 & 0.427 & 0.488 & 0.413 & 0.500 & 0.376 & 0.438 & 0.230 & 0.269 & 0.161 \\
Motion\_Blur & 0.478 & 0.467 & 0.380 & 0.458 & 0.400 & 0.327 & 0.186 & 0.117 & 0.114 & 0.122 \\
Negative\_Image & 0.595 & 0.619 & 0.561 & 0.499 & 0.690 & 0.427 & 0.323 & 0.268 & 0.189 & 0.123 \\
Rule\_of\_Thirds & 0.352 & 0.353 & 0.290 & 0.236 & 0.300 & 0.269 & 0.244 & 0.188 & 0.167 & 0.228 \\
Shallow\_DOF & 0.624 & 0.659 & 0.627 & 0.637 & 0.480 & 0.522 & 0.517 & 0.332 & 0.276 & 0.223 \\
Silhouettes & 0.791 & 0.801 & 0.835 & 0.801 & 0.720 & 0.609 & 0.401 & 0.261 & 0.263 & 0.130 \\
Soft\_Focus & 0.312 & 0.354 & 0.305 & 0.290 & 0.390 & 0.225 & 0.170 & 0.127 & 0.126 & 0.114 \\
Vanishing\_Point & 0.684 & 0.658 & 0.646 & 0.685 & 0.570 & 0.527 & 0.542 & 0.123 & 0.107 & 0.161 \\
\_mean & 0.581 & 0.579 & 0.550 & 0.539 & 0.539 & 0.432 & 0.350 & 0.288 & 0.220 & 0.152 \\
\bottomrule
\end{tabular}
Content source: Jai-Chaudhary/vislab
Similar notebooks: