bd2d3cc5d1
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e1c407dab1
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2195bb0e89
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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 )
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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
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2019-07-30 18:27:37 +02:00
9ef8d42f0e
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bb38391342
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2019-07-12 14:28:46 +02:00
bd9789aa00
equal layer weights
2019-07-12 12:23:17 +02:00
5886200401
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2019-07-12 01:19:32 +02:00
a2909c59f8
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94669fb704
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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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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 )
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* 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
Glenn Jocher
idow09
Yonghye Kwon