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ERM_results.out
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ERM_results.out
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./data/wildcam_denoised/train_43
== Found 858 items
== Found 2 classes
train environment: 43
['/coyote', '/raccoon']
class_indices: [(0, '/coyote'), (1, '/raccoon')]
./data/wildcam_denoised/train_46
== Found 753 items
== Found 2 classes
train environment: 46
['/coyote', '/raccoon']
class_indices: [(0, '/coyote'), (1, '/raccoon')]
./data/wildcam_denoised/test
== Found 522 items
== Found 2 classes
['/coyote', '/raccoon']
working with 2 training environments:
env['images']: 858
env['labels']: 858
class distribution: 582 coyotes and 276 raccoons. baseline accuracy 0.68 (always coyote).
env['images']: 753
env['labels']: 753
class distribution: 512 coyotes and 241 raccoons. baseline accuracy 0.68 (always coyote).
x_test: 522
y_test: 522
test class distribution: 144 coyotes and 378 raccoons. baseline accuracy 0.28 (always coyote).
Using GPU - True
========================================ERM========================================
{'n_restarts': 5, 'steps': 121, 'n_classes': 2, 'fc_only': True, 'model_path': './models/', 'transform': {'train': Compose(
CenterCrop(size=(224, 224))
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
), 'test': Compose(
CenterCrop(size=(224, 224))
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)}, 'loader_tr_args': {'batch_size': 100, 'num_workers': 1}, 'loader_te_args': {'batch_size': 100, 'num_workers': 1}, 'loader_sample_args': {'batch_size': 100, 'num_workers': 1}, 'optimizer_args': {'lr': 0.001, 'l2_regularizer_weight': 0.001, 'penalty_anneal_iters': 0, 'penalty_weight': 0.0}}
Restart 0
step train nll train acc train penalty test nll test acc test prec test rec
0 0.84201 0.29020 0.03132 0.82703 0.26864 0.56522 0.03439
10 0.54008 0.67832 0.00219 1.00853 0.28136 0.00000 0.00000
20 0.40773 0.83993 0.02088 0.75663 0.35136 0.75610 0.16402
30 0.34317 0.84261 0.01184 0.77303 0.31985 0.67391 0.08201
40 0.31180 0.86315 0.01024 0.72175 0.36803 0.73636 0.21429
50 0.29814 0.86478 0.00695 0.78943 0.31879 0.71667 0.11376
60 0.27918 0.87737 0.00725 0.78338 0.34485 0.75410 0.12169
70 0.27351 0.88458 0.00583 0.84746 0.28182 0.75000 0.07143
80 0.25651 0.89147 0.00650 0.83273 0.30970 0.74286 0.06878
90 0.25156 0.89522 0.00625 0.84272 0.29530 0.72093 0.08201
100 0.24202 0.90476 0.00665 0.86236 0.30212 0.75000 0.06349
110 0.24710 0.89888 0.00609 0.86557 0.30288 0.75000 0.07937
120 0.22917 0.91167 0.00744 0.87135 0.33076 0.78788 0.06878
Restart 1
step train nll train acc train penalty test nll test acc test prec test rec
0 0.77001 0.40002 0.01066 0.71149 0.51606 0.73387 0.48148
10 0.50196 0.67938 0.00283 0.94291 0.26364 0.50000 0.00794
20 0.39647 0.84119 0.02098 0.65313 0.56833 0.79924 0.55820
30 0.33906 0.83226 0.01034 0.75466 0.31606 0.66667 0.18519
40 0.31393 0.85812 0.00825 0.65434 0.52682 0.78378 0.46032
50 0.29799 0.86268 0.00671 0.72644 0.39576 0.70714 0.26190
60 0.28549 0.86683 0.00532 0.72801 0.39727 0.72297 0.28307
70 0.27636 0.87617 0.00601 0.75414 0.35727 0.69444 0.19841
80 0.25888 0.89195 0.00590 0.73216 0.39424 0.72727 0.21164
90 0.25526 0.89078 0.00547 0.75774 0.37970 0.76577 0.22487
100 0.24717 0.89252 0.00569 0.78334 0.37500 0.76250 0.16138
110 0.25092 0.89100 0.00564 0.79035 0.36485 0.76543 0.16402
120 0.24115 0.90723 0.00626 0.82449 0.35152 0.78689 0.12698
Restart 2
step train nll train acc train penalty test nll test acc test prec test rec
0 0.68354 0.56027 0.00036 0.76839 0.29106 0.55618 0.26190
10 0.45373 0.71801 0.00950 0.75696 0.31530 0.69620 0.14550
20 0.36018 0.84049 0.01707 0.65655 0.48000 0.73973 0.42857
30 0.32130 0.84765 0.00958 0.65636 0.47591 0.73913 0.40476
40 0.29940 0.85685 0.00674 0.65686 0.49515 0.75845 0.41534
50 0.28127 0.87028 0.00650 0.70628 0.37803 0.72308 0.24868
60 0.26966 0.87284 0.00552 0.69500 0.41485 0.74497 0.29365
70 0.25726 0.88936 0.00583 0.75471 0.36470 0.74038 0.20370
80 0.25324 0.89273 0.00557 0.76688 0.34864 0.73832 0.20899
90 0.24090 0.89817 0.00583 0.75507 0.36212 0.74528 0.20899
100 0.24005 0.90434 0.00624 0.78643 0.35136 0.75000 0.16667
110 0.23069 0.90674 0.00582 0.77407 0.35485 0.74026 0.15079
120 0.22308 0.91311 0.00649 0.78849 0.35045 0.76471 0.17196
Restart 3
step train nll train acc train penalty test nll test acc test prec test rec
0 0.77014 0.41664 0.01233 0.73993 0.42076 0.71345 0.32275
10 0.49794 0.68371 0.00240 1.01037 0.26364 0.00000 0.00000
20 0.39065 0.84313 0.02242 0.68701 0.46818 0.89474 0.31481
30 0.33549 0.85169 0.01145 0.80377 0.29697 0.95455 0.05556
40 0.30671 0.85821 0.00955 0.69118 0.44136 0.88710 0.29101
50 0.28770 0.86591 0.00783 0.76090 0.35545 0.94444 0.13492
60 0.27782 0.87569 0.00637 0.75884 0.36470 0.90323 0.14815
70 0.27115 0.88190 0.00607 0.77752 0.34379 0.90196 0.12169
80 0.26310 0.88708 0.00633 0.82955 0.31955 0.96875 0.08201
90 0.25133 0.89701 0.00645 0.79680 0.35227 0.93333 0.11111
100 0.23872 0.89944 0.00691 0.86119 0.30773 0.96875 0.08201
110 0.23626 0.90370 0.00651 0.83909 0.34061 0.97059 0.08730
120 0.23852 0.89917 0.00602 0.83859 0.34485 0.96970 0.08466
Restart 4
step train nll train acc train penalty test nll test acc test prec test rec
0 0.85345 0.33348 0.04186 0.62254 0.69455 0.72979 0.90741
10 0.55167 0.68045 0.00620 0.89009 0.26106 1.00000 0.00529
20 0.42990 0.80280 0.01807 0.55897 0.79303 0.83668 0.88095
30 0.36153 0.82375 0.01034 0.66969 0.47167 0.81646 0.34127
40 0.32700 0.85365 0.00956 0.59150 0.62606 0.82971 0.60582
50 0.30631 0.85654 0.00751 0.61786 0.55924 0.81702 0.50794
60 0.29543 0.86586 0.00647 0.65653 0.49000 0.81006 0.38360
70 0.28143 0.87242 0.00657 0.65951 0.44742 0.79085 0.32011
80 0.27752 0.87455 0.00536 0.71383 0.41061 0.78906 0.26720
90 0.26699 0.87822 0.00555 0.74381 0.36545 0.76471 0.20635
100 0.24937 0.89435 0.00698 0.73236 0.37652 0.77778 0.18519
110 0.24709 0.90284 0.00638 0.72893 0.38485 0.77778 0.20370
120 0.24378 0.90428 0.00732 0.74523 0.37061 0.76596 0.19048
Final train acc (mean/std across restarts so far):
0.907 0.005
Final test acc (mean/std across restarts so far):
0.35 0.013
Final test precision:
0.766
Final test recall:
0.1905
confusion matrix:
[[122 22]
[306 72]]
tn = 122, fp = 22, fn = 306, tp = 72
./data/wildcam_denoised/train_43
== Found 858 items
== Found 2 classes
train environment: 43
['/coyote', '/raccoon']
class_indices: [(0, '/coyote'), (1, '/raccoon')]
./data/wildcam_denoised/train_46
== Found 753 items
== Found 2 classes
train environment: 46
['/coyote', '/raccoon']
class_indices: [(0, '/coyote'), (1, '/raccoon')]
./data/wildcam_denoised/test
== Found 522 items
== Found 2 classes
['/coyote', '/raccoon']
working with 2 training environments:
env['images']: 858
env['labels']: 858
class distribution: 582 coyotes and 276 raccoons. baseline accuracy 0.68 (always coyote).
env['images']: 753
env['labels']: 753
class distribution: 512 coyotes and 241 raccoons. baseline accuracy 0.68 (always coyote).
x_test: 522
y_test: 522
test class distribution: 144 coyotes and 378 raccoons. baseline accuracy 0.28 (always coyote).
Using GPU - True
========================================ERM========================================
{'n_restarts': 1, 'steps': 121, 'n_classes': 2, 'fc_only': True, 'model_path': './models/', 'transform': {'train': Compose(
CenterCrop(size=(224, 224))
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
), 'test': Compose(
CenterCrop(size=(224, 224))
ToTensor()
Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
)}, 'loader_tr_args': {'batch_size': 100, 'num_workers': 1}, 'loader_te_args': {'batch_size': 100, 'num_workers': 1}, 'loader_sample_args': {'batch_size': 100, 'num_workers': 1}, 'optimizer_args': {'lr': 0.001, 'l2_regularizer_weight': 0.001, 'penalty_anneal_iters': 0, 'penalty_weight': 0.0}}
Restart 0
step train nll train acc train penalty test nll test acc test prec test rec
0 0.84201 0.29020 0.03132 0.81487 0.28045 0.56522 0.03439
10 0.54008 0.67832 0.00219 1.03878 0.26364 0.00000 0.00000
20 0.40773 0.83993 0.02088 0.74452 0.35136 0.75610 0.16402
30 0.34317 0.84261 0.01184 0.79673 0.29621 0.67391 0.08201
40 0.31180 0.86315 0.01024 0.73027 0.35621 0.73636 0.21429
50 0.29814 0.86478 0.00695 0.78550 0.33061 0.71667 0.11376
60 0.27918 0.87737 0.00725 0.80783 0.30939 0.75410 0.12169
70 0.27351 0.88458 0.00583 0.80265 0.32909 0.75000 0.07143
80 0.25651 0.89147 0.00650 0.84403 0.31561 0.74286 0.06878
90 0.25156 0.89522 0.00625 0.82941 0.31894 0.72093 0.08201
100 0.24202 0.90476 0.00665 0.88375 0.30803 0.75000 0.06349
110 0.24710 0.89888 0.00609 0.86043 0.31470 0.75000 0.07937
120 0.22917 0.91167 0.00744 0.88292 0.31303 0.78788 0.06878
Final train acc (mean/std across restarts so far):
0.912 0.0
Final test acc (mean/std across restarts so far):
0.313 0.0
Final test precision:
0.7879
Final test recall:
0.0688
confusion matrix:
[[137 7]
[352 26]]
tn = 137, fp = 7, fn = 352, tp = 26