imyhxy
4e841b9b16
Reuse `de_parallel()` rather than `is_parallel()` ( #6354 )
2022-01-20 10:50:17 -10:00
Glenn Jocher
5866646cc8
Fix float zeros format ( #5491 )
...
* Fix float zeros format
* 255 to integer
2021-11-03 23:36:53 +01:00
Jirka Borovec
ed887b5976
Add pre-commit CI actions ( #4982 )
...
* define pre-commit
* add CI code
* configure
* apply pre-commit
* fstring
* apply MD
* pre-commit
* Update torch_utils.py
* Update print strings
* notes
* Cleanup code-format.yml
* Update setup.cfg
* Update .pre-commit-config.yaml
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2021-10-28 18:35:01 +02:00
Zhiqiang Wang
8e5f9ddbdb
Remove redundant `ComputeLoss` code ( #4701 )
2021-09-08 12:48:33 +02:00
Glenn Jocher
24bea5e4b7
Standardize headers and docstrings ( #4417 )
...
* Implement new headers
* Reformat 1
* Reformat 2
* Reformat 3 - math
* Reformat 4 - yaml
2021-08-14 21:17:51 +02:00
Glenn Jocher
96e36a7c91
New CSV Logger ( #4148 )
...
* New CSV Logger
* cleanup
* move batch plots into Logger
* rename comment
* Remove total loss from progress bar
* mloss :-1 bug fix
* Update plot_results()
* Update plot_results()
* plot_results bug fix
2021-07-25 19:06:37 +02:00
Glenn Jocher
63dd65e7ed
Update train.py ( #4136 )
...
* Refactor train.py
* Update imports
* Update imports
* Update optimizer
* cleanup
2021-07-24 16:11:39 +02:00
Glenn Jocher
5ea771d93d
Move IoU functions to metrics.py ( #3820 )
2021-06-29 13:18:13 +02:00
Glenn Jocher
8035b61682
Update objectness IoU sort ( #3786 )
2021-06-26 14:52:18 +02:00
Glenn Jocher
157aa2f886
Objectness IoU Sort ( #3610 )
...
Co-authored-by: U-LAPTOP-5N89P8V7\banhu <ban.huang@foxmail.com>
2021-06-26 14:45:53 +02:00
Phat Tran
9c803f2f7e
Add --label-smoothing eps argument to train.py (default 0.0) ( #2344 )
...
* Add label smoothing option
* Correct data type
* add_log
* Remove log
* Add log
* Update loss.py
remove comment (too versbose)
Co-authored-by: phattran <phat.tranhoang@cyberlogitec.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2021-03-29 18:45:46 +02:00
Glenn Jocher
6f5d6fcdaa
Robust objectness loss balancing ( #2256 )
2021-02-20 11:19:01 -08:00
Glenn Jocher
bdd88e1ed7
YOLOv5 Segmentation Dataloader Updates ( #2188 )
...
* Update C3 module
* Update C3 module
* Update C3 module
* Update C3 module
* update
* update
* update
* update
* update
* update
* update
* update
* update
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* update
* update
* update
* update
* updates
* updates
* updates
* updates
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update datasets
* update
* update
* update
* update attempt_downlaod()
* merge
* merge
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* parameterize eps
* comments
* gs-multiple
* update
* max_nms implemented
* Create one_cycle() function
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* GitHub API rate limit fix
* update
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* ComputeLoss
* astuple
* epochs
* update
* update
* ComputeLoss()
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* merge
* merge
* merge
* merge
* update
* update
* update
* update
* commit=tag == tags[-1]
* Update cudnn.benchmark
* update
* update
* update
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* update
* update
* update
* update
* update
* mosaic9
* update
* update
* update
* update
* update
* update
* institute cache versioning
* only display on existing cache
* reverse cache exists booleans
2021-02-11 21:22:45 -08:00
Glenn Jocher
86897e3663
Update train.py test batch_size ( #2148 )
...
* Update train.py
* Update loss.py
2021-02-06 10:29:32 -08:00
Glenn Jocher
ca9babb8e6
Add ComputeLoss() class ( #1950 )
2021-01-15 13:50:24 -08:00
Glenn Jocher
6ab589583c
Add colorstr() ( #1887 )
...
* Add colorful()
* update
* newline fix
* add git description
* --always
* update loss scaling
* update loss scaling 2
* rename to colorstr()
2021-01-09 15:24:18 -08:00
Glenn Jocher
69be8e738f
YOLOv5 v4.0 Release ( #1837 )
...
* Update C3 module
* Update C3 module
* Update C3 module
* Update C3 module
* update
* update
* update
* update
* update
* update
* update
* update
* update
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* updates
* update
* update
* update
* update
* updates
* updates
* updates
* updates
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update datasets
* update
* update
* update
* update attempt_downlaod()
* merge
* merge
* update
* update
* update
* update
* update
* update
* update
* update
* update
* update
* parameterize eps
* comments
* gs-multiple
* update
* max_nms implemented
* Create one_cycle() function
* update
* update
* update
* update
* update
* update
* update
* update study.png
* update study.png
* Update datasets.py
2021-01-04 19:54:09 -08:00
Glenn Jocher
8bc0027afc
Update loss criteria constructor ( #1711 )
2020-12-16 08:39:35 -08:00
yxNONG
b3ceffb513
Add QFocalLoss() ( #1482 )
...
* Update loss.py
implement the quality focal loss which is a more general case of focal loss
more detail in https://arxiv.org/abs/2006.04388
In the obj loss (or the case cls loss with label smooth), the targets is no long barely be 0 or 1 (can be 0.7), in this case, the normal focal loss is not work accurately
quality focal loss in behave the same as focal loss when the target is equal to 0 or 1, and work accurately when targets in (0, 1)
example:
targets:
tensor([[0.6225, 0.0000, 0.0000],
[0.9000, 0.0000, 0.0000],
[1.0000, 0.0000, 0.0000]])
___________________________
pred_prob:
tensor([[0.6225, 0.2689, 0.1192],
[0.7773, 0.5000, 0.2227],
[0.8176, 0.8808, 0.1978]])
____________________________
focal_loss
tensor([[0.0937, 0.0328, 0.0039],
[0.0166, 0.1838, 0.0199],
[0.0039, 1.3186, 0.0145]])
______________
qfocal_loss
tensor([[7.5373e-08, 3.2768e-02, 3.9179e-03],
[4.8601e-03, 1.8380e-01, 1.9857e-02],
[3.9233e-03, 1.3186e+00, 1.4545e-02]])
we can see that targets[0][0] = 0.6255 is almost the same as pred_prob[0][0] = 0.6225,
the targets[1][0] = 0.9 is greater then pred_prob[1][0] = 0.7773 by 0.1227
however, the focal loss[0][0] = 0.0937 larger then focal loss[1][0] = 0.0166 (which against the purpose of focal loss)
for the quality focal loss , it implement the case of targets not equal to 0 or 1
* Update loss.py
2020-11-25 19:32:27 +01:00
Glenn Jocher
fe341fa44d
Utils reorganization ( #1392 )
...
* Utils reorganization
* Add new utils files
* cleanup
* simplify
* reduce datasets.py
* remove evolve.sh
* loadWebcam cleanup
2020-11-14 11:50:32 +01:00