3ba7fc69b8
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94234c80b2
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...
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-04 14:58:18 +02:00
6cd98c46d8
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-04 09:20:03 +02:00
447292eb36
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-03 16:20:55 +02:00
2877ac9286
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-02 14:28:13 +02:00
ea61b46b31
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-02 14:02:08 +02:00
8f913ba82a
updates
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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2019-09-02 11:59:13 +02:00
360a32811c
weight_decay fix
2019-08-31 17:55:19 +02:00
8eb381dc88
updates
2019-08-29 15:49:51 +02:00
408baf66e2
weight_decay fix
2019-08-29 15:44:15 +02:00
7d9ffe6d4e
weight_decay fix
2019-08-29 14:29:07 +02:00
31d807e589
weight_decay fix
2019-08-29 14:20:54 +02:00
85a24dbc7e
weight_decay fix
2019-08-28 16:50:34 +02:00
c906047db3
updates
2019-08-27 12:57:19 +02:00
798a7396f1
weight_decay fix
2019-08-26 16:24:19 +02:00
ff82e4d488
weight_decay fix
2019-08-26 14:47:36 +02:00
bf8f0f3987
updates
2019-08-25 20:20:15 +02:00
6260ac266f
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991362df57
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2019-08-24 23:58:08 +02:00
70be0d5d14
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2019-08-24 21:39:25 +02:00
1064c37600
removed xy/wh loss reporting
2019-08-24 21:35:56 +02:00
ca38c9050f
updates
2019-08-24 21:20:25 +02:00
790e25592f
removed xy/wh loss reporting
2019-08-24 20:55:01 +02:00
25a579e417
removed xy/wh loss reporting
2019-08-24 17:16:20 +02:00
195adaea7d
removed xy/wh loss reporting
2019-08-24 16:45:49 +02:00
39dcf0d561
removed xy/wh loss reporting
2019-08-24 16:43:43 +02:00
bbe22dd7b4
updates
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5ff6e6b3a5
tensorboard updates
2019-08-09 19:35:02 +02:00
f9755f8dac
tensorboard updates
2019-08-09 19:29:36 +02:00
298463e530
updates
2019-08-09 18:22:27 +02:00
933f85f632
tensorboard updates
2019-08-09 16:37:19 +02:00
fdd5afa229
updates
2019-08-08 22:37:54 +02:00
22f75469ac
Tensorboard support ( #435 )
2019-08-08 22:30:34 +02:00
a21b9891b9
updates
2019-08-08 21:16:09 +02:00
25bc5e5392
updates
2019-08-08 20:16:32 +02:00
c49fe688b7
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2019-08-08 19:49:15 +02:00
37c3e762e1
@ktian08 50.5 mAP evolved hyperparameters
2019-08-08 19:41:26 +02:00
f43170817c
updates
2019-08-07 16:45:13 +02:00
b53d6d6ecf
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2019-08-01 18:29:57 +02:00
f8de211f77
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2019-08-01 03:31:12 +02:00
7b3d9f02ec
prevent failure when no training_results available ( #409 )
...
Use `chkpt.get('training_results')` instead of `chkpt.get('training_results')` so if the dict doesn't contain this key it won't throw a `KeyError
2019-07-31 14:12:27 +02:00
97edba0b7a
Remove Unused Argument of parser : --var ( #408 )
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Remove Unused Argument of parser : --var
2019-07-31 14:05:43 +02:00
07ffc4e7f9
updates
2019-07-30 18:27:37 +02:00
9ef8d42f0e
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0aa9759a90
updates
2019-07-12 15:44:39 +02:00
bb38391342
updates
2019-07-12 14:28:46 +02:00
bd9789aa00
equal layer weights
2019-07-12 12:23:17 +02:00
5886200401
updates
2019-07-12 01:19:32 +02:00
a2909c59f8
updates
2019-07-11 11:57:10 +02:00
b005a17eff
updates
2019-07-11 11:56:46 +02:00
3373006d0e
updates
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4f6ef59d92
updates
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a9e42a16f1
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bb1e551150
updates
2019-07-08 19:26:46 +02:00
0bd763f528
updates
2019-07-08 18:32:31 +02:00
feeaf734f2
updates
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da9ec7d12f
updates
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60bc2c1fbd
updates
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94669fb704
updates
2019-07-08 15:24:20 +02:00
291c3ec9c7
updates
2019-07-08 15:02:20 +02:00
70f6379601
GIoU to default
2019-07-07 23:24:34 +02:00
32a52dfb02
GIoU to default
2019-07-05 12:33:37 +02:00
429bd3b8a9
GIoU to default
2019-07-05 11:41:43 +02:00
b649a95c9a
GIoU to default
2019-07-05 00:36:37 +02:00
abf59f1565
updates
2019-07-04 22:10:46 +02:00
d0eace6cec
updates
2019-07-04 21:34:33 +02:00
109991198c
updates
2019-07-03 16:18:08 +02:00
1e62ee2152
updates
2019-07-03 16:17:46 +02:00
ab141fcc1f
updates
2019-07-03 15:37:04 +02:00
a8cf64af31
updates
2019-07-02 18:21:28 +02:00
ccf757b3ea
changed the criteria for the best weight file ( #356 )
...
* changed the criteria for the best weight file
changed the criteria for the best weight file from loss to mAP
I trained the model on my custom dataset. But I failed to get a good results when I load the weight file that has the lowest loss on test dataset.
I thought that the loss used in YOLO is not proper criteria for detection performance. So I changed the criteria from loss to mAP.
what do you think of this?
* Update train.py
2019-07-02 12:24:18 +02:00
1fd871abd8
updates
2019-07-01 17:44:42 +02:00
f43ee6ef94
updates
2019-07-01 17:17:29 +02:00
cf51cf9c99
updates
2019-07-01 17:14:42 +02:00
05358accbb
updates
2019-07-01 15:23:30 +02:00
c4409aa2ed
updates
2019-07-01 15:22:22 +02:00
b0d62e5204
updates
2019-07-01 15:21:06 +02:00
5e2b802f68
updates
2019-07-01 14:48:44 +02:00
63036deeb7
updates
2019-07-01 00:41:13 +02:00
32f5ea955b
updates
2019-06-30 17:47:10 +02:00
db2674aa31
updates
2019-06-30 17:34:29 +02:00
388b66dcd0
updates
2019-06-30 15:24:34 +02:00
45540c787f
updates
2019-06-25 19:36:11 +02:00
1d76751e1f
updates
2019-06-25 12:37:24 +02:00
e4cc830690
updates
2019-06-25 12:21:27 +02:00
9a56d97059
updates
2019-06-25 11:54:19 +02:00
6b222df35d
updates
2019-06-24 15:56:20 +02:00
d208f006a1
updates
2019-06-24 14:46:00 +02:00
0005823d1f
updates
2019-06-24 13:43:17 +02:00
57b616b8b1
updates
2019-06-23 22:01:11 +02:00
ef3e1343e2
updates
2019-06-22 15:52:27 +02:00
f501a0fc9d
updates
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4f7fee45ff
updates
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5f6c2b3d12
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3223c0171a
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2019-06-21 10:58:12 +02:00
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updates
2019-06-21 10:24:06 +02:00
Glenn Jocher
Marc
idow09
Yonghye Kwon