5886200401
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2019-07-12 01:19:32 +02:00
a2909c59f8
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2019-07-11 11:57:10 +02:00
b005a17eff
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2019-07-11 11:56:46 +02:00
3373006d0e
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2019-07-10 22:11:48 +02:00
4f6ef59d92
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2019-07-10 20:47:05 +02:00
a9e42a16f1
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2019-07-10 19:48:29 +02:00
bb1e551150
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2019-07-08 19:26:46 +02:00
0bd763f528
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2019-07-08 18:32:31 +02:00
feeaf734f2
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2019-07-08 18:04:44 +02:00
da9ec7d12f
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2019-07-08 18:00:19 +02:00
60bc2c1fbd
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2019-07-08 15:43:46 +02:00
94669fb704
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2019-07-08 15:24:20 +02:00
291c3ec9c7
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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
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2019-07-04 22:10:46 +02:00
d0eace6cec
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2019-07-04 21:34:33 +02:00
109991198c
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2019-07-03 16:18:08 +02:00
1e62ee2152
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2019-07-03 16:17:46 +02:00
ab141fcc1f
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2019-07-03 15:37:04 +02:00
a8cf64af31
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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
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2019-07-01 17:44:42 +02:00
f43ee6ef94
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2019-07-01 17:17:29 +02:00
cf51cf9c99
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2019-07-01 17:14:42 +02:00
05358accbb
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2019-07-01 15:23:30 +02:00
c4409aa2ed
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2019-07-01 15:22:22 +02:00
b0d62e5204
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2019-07-01 15:21:06 +02:00
5e2b802f68
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2019-07-01 14:48:44 +02:00
63036deeb7
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2019-07-01 00:41:13 +02:00
32f5ea955b
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2019-06-30 17:47:10 +02:00
db2674aa31
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2019-06-30 17:34:29 +02:00
388b66dcd0
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2019-06-30 15:24:34 +02:00
45540c787f
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2019-06-25 19:36:11 +02:00
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2019-06-25 12:37:24 +02:00
e4cc830690
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2019-06-25 12:21:27 +02:00
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2019-06-25 11:54:19 +02:00
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2019-06-24 15:56:20 +02:00
d208f006a1
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2019-06-24 14:46:00 +02:00
0005823d1f
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2019-06-24 13:43:17 +02:00
57b616b8b1
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2019-06-23 22:01:11 +02:00
ef3e1343e2
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2019-06-22 15:52:27 +02:00
f501a0fc9d
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2019-06-22 15:50:04 +02:00
4f7fee45ff
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2019-06-21 21:27:50 +02:00
5f6c2b3d12
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2019-06-21 13:19:23 +02:00
1a9aa30efc
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2019-06-21 11:57:26 +02:00
3223c0171a
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2019-06-21 10:58:12 +02:00
a7e21b4315
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2019-06-21 10:24:06 +02:00
Glenn Jocher
Yonghye Kwon