7b833e37bf
LoadStreams() frame loss bug fix ( #2222 )
2021-02-15 11:02:20 -08:00
bdd88e1ed7
YOLOv5 Segmentation Dataloader Updates ( #2188 )
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* 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
a5d5f9262d
Changed socket port and added timeout ( #2176 )
2021-02-10 13:01:48 -08:00
c9bda112ae
Update utils/datasets.py to support .webp files ( #2174 )
...
Simply added 'webp' as an image format to the img_formats array so that webp image files can be used as training data.
2021-02-10 00:16:38 -08:00
86897e3663
Update train.py test batch_size ( #2148 )
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* Update train.py
* Update loss.py
2021-02-06 10:29:32 -08:00
e9b3de463a
Unique *.cache filenames fix ( #2134 )
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* fix #2121
* Update test.py
* Update train.py
* Update autoanchor.py
* Update datasets.py
* Update log_dataset.py
* Update datasets.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2021-02-05 11:12:43 -08:00
4bdc5a397e
LoadImages() pathlib update ( #2140 )
2021-02-05 09:06:23 -08:00
73a0669930
Start setup for improved W&B integration ( #1948 )
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* Add helper functions for wandb and artifacts
* cleanup
* Reorganize files
* Update wandb_utils.py
* Update log_dataset.py
We can remove this code, as the giou hyp has been deprecated for a while now.
* Reorganize and update dataloader call
* yaml.SafeLoader
* PEP8 reformat
* remove redundant checks
* Add helper functions for wandb and artifacts
* cleanup
* Reorganize files
* Update wandb_utils.py
* Update log_dataset.py
We can remove this code, as the giou hyp has been deprecated for a while now.
* Reorganize and update dataloader call
* yaml.SafeLoader
* PEP8 reformat
* remove redundant checks
* Update util files
* Update wandb_utils.py
* Remove word size
* Change path of labels.zip
* remove unused imports
* remove --rect
* log_dataset.py cleanup
* log_dataset.py cleanup2
* wandb_utils.py cleanup
* remove redundant id_count
* wandb_utils.py cleanup2
* rename cls
* use pathlib for zip
* rename dataloader to dataset
* Change import order
* Remove redundant code
* remove unused import
* remove unused imports
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2021-02-01 21:38:41 -08:00
9646ca438a
Update plot_study() ( #2112 )
2021-02-01 13:51:11 -08:00
be9edffded
Update test.py --task speed and study ( #2099 )
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* Add --speed benchmark
* test range 256 - 1536
* update
* update
* update
* update
2021-01-31 13:58:49 -08:00
a18efc3a73
Add variable-stride inference support ( #2091 )
2021-01-30 13:47:23 -08:00
630ec06af6
Update to colors.TABLEAU_COLORS ( #2069 )
2021-01-28 12:37:21 -08:00
f639e14e4d
Metric-Confidence plots feature addition ( #2057 )
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* Metric-Confidence plots feature addition
* cleanup
* Metric-Confidence plots feature addition
* cleanup
* Update run-once lines
* cleanup
* save all 4 curves to wandb
2021-01-27 17:10:53 -08:00
f59f80114c
Add 'exclude' tuple to check_requirements() ( #2041 )
2021-01-26 21:17:36 -08:00
59c21c7bcb
W&B log epoch ( #1946 )
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* W&B log epoch
* capitalize
* W&B log epoch
* capitalize
* Update train.py
New try using https://docs.wandb.ai/library/log#incremental-logging
* Update train.py
* Update test.py
* Update train.py
* Update plots.py
* Update train.py
* Update train.py
* label plot step -1
* update
* update
* update
* update
* update
* update
* Update train.py
* Update train.py
2021-01-26 21:16:01 -08:00
08d3119e09
Add histogram equalization fcn ( #2049 )
2021-01-26 17:30:42 -08:00
8fcbe43423
Check im.format during dataset caching ( #2042 )
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* Check im.format during dataset caching
* Update datasets.py
2021-01-25 20:55:35 -08:00
3b7feeafdf
Update general.py check_git_status() fix ( #2020 )
2021-01-22 15:59:01 -08:00
aac33f87ad
Update inference multiple-counting ( #2019 )
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* Update inference multiple-counting
* update github check
2021-01-22 15:39:08 -08:00
77fd83225f
Update google_utils.py ( #2017 )
2021-01-22 15:08:42 -08:00
1445ab2b27
check_git_status() Windows fix ( #2015 )
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* check_git_status() Windows fix
* Update general.py
* Update general.py
* Update general.py
* Update general.py
* Update general.py
* Update general.py
2021-01-22 12:37:12 -08:00
d9212140b3
Add xywhn2xyxy() ( #1983 )
2021-01-19 13:33:52 -08:00
b1cf25dd9a
check_git_status() asserts ( #1977 )
2021-01-18 10:49:28 -08:00
17751b9891
Update autoanchor.py with yaml.SafeLoader ( #1971 )
2021-01-18 10:47:42 -08:00
35400dc7b7
Update plots.py with yaml.SafeLoader ( #1968 )
2021-01-18 10:46:20 -08:00
b26a2f6242
check_git_status() when not exist /workspace ( #1966 )
2021-01-17 11:55:25 -08:00
e8a41e8916
prevent check_git_status() in docker images ( #1951 )
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* prevent check_git_status() running docker images
* Update general.py
2021-01-16 01:15:38 -08:00
ca9babb8e6
Add ComputeLoss() class ( #1950 )
2021-01-15 13:50:24 -08:00
b75c432ea0
GitHub API rate limit fallback ( #1930 )
2021-01-13 21:09:50 -08:00
051e9e80de
check_git_status() bug fix ( #1925 )
2021-01-13 10:25:53 -08:00
509dd51aca
check_git_status() improvements ( #1916 )
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* check_online()
* Update general.py
* update check_git_status()
* reverse rev-parse order
* fetch
* improved responsiveness
* comment
* comment
* remove hyp['giou'] compat warning
2021-01-12 21:51:49 -08:00
dd03b20ba5
colorstr() updates ( #1909 )
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* W&B ImportError message fix
* colorstr() updates
* colorstr() updates
* colorstr() default to 'blue', 'bold'
* train: magenta
* train: blue
2021-01-12 10:33:15 -08:00
1d1c0567a4
PyTorch Hub results.render() ( #1897 )
2021-01-10 20:06:11 -08:00
ffef77124e
Update torch_utils.py ( #1895 )
2021-01-10 12:26:29 -08:00
0f11aaf551
GitHub API rate limit newline ( #1894 )
2021-01-10 11:47:00 -08:00
d06ad3bc3d
GitHub API rate limit fix ( #1894 )
2021-01-10 11:42:45 -08:00
6ab589583c
Add colorstr() ( #1887 )
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* 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
fea9c9b80c
nn.SiLU() citation correction ( #1713 )
2021-01-06 20:58:41 -08:00
e77c77f580
Add check_requirements() ( #1853 )
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* Add check_requirements()
* add import
* parameterize filename
* add to detect, test
2021-01-06 16:35:40 -08:00
135ec5c5ce
W&B ID reset on training completion ( #1852 )
...
* Update train.py
Fix the bug of always the same W&B ID and continue overwrite with the old logging.
BUG report
https://github.com/ultralytics/yolov5/issues/1851
* Fix the bug of duplicate W&B ID
fix the bug of https://github.com/ultralytics/yolov5/issues/1851
If we had trained on yolov5s.pt, the program will generate a new unique W&B ID.
If we hadn't, the program will keep the old code, we can still use --resume aug.
* Update general.py
* revert train.py changes
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2021-01-06 16:27:22 -08:00
69be8e738f
YOLOv5 v4.0 Release ( #1837 )
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* 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
0e341c5660
Create one_cycle() function ( #1836 )
2021-01-04 15:49:08 -08:00
9f5a18bb80
Torch CUDA synchronize update ( #1826 )
...
* torch.cuda.synchronize() update
* torch.cuda.synchronize() update
* torch.cuda.synchronize() update
* newline
2021-01-03 11:23:12 -08:00
c0ffcdf998
Display correct CUDA devices ( #1776 )
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* Display correct CUDA devices
* cleanup
2020-12-24 13:01:35 -08:00
3004fb5bc1
Automatic m.half() profile on x.half()
2020-12-21 15:20:33 -08:00
0bd9c48609
Update torch_utils.py
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FLOPS to GFLOPS
2020-12-21 13:29:52 -08:00
394d1c89f3
Input channel yaml['ch'] addition ( #1741 )
2020-12-19 10:54:01 -08:00
685d601308
Increase plot_labels() speed ( #1736 )
2020-12-18 18:05:38 -08:00
49abc722fc
Update profile_idetection() ( #1727 )
2020-12-18 01:19:17 -08:00
d5289b54c4
clean_str() function addition ( #1674 )
...
* clean_str() function addition
* cleanup
* add euro symbol €
* add closing exclamation (spanish)
* cleanup
2020-12-17 17:20:20 -08:00
6bd5e8bca7
nn.SiLU() export support ( #1713 )
2020-12-16 17:55:57 -08:00
1fc9d42a64
NMS --classes 0 bug fix ( #1710 )
2020-12-16 08:58:51 -08:00
8bc0027afc
Update loss criteria constructor ( #1711 )
2020-12-16 08:39:35 -08:00
035ac82ed0
Fix torch multi-GPU --device error ( #1701 )
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* Fix torch GPU error
* Update torch_utils.py
single-line device =
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2020-12-15 20:42:14 -08:00
69ea70cd3b
Add idetection_profile() function to plots.py ( #1700 )
2020-12-15 18:35:47 -08:00
54043a9fa4
Streaming --save-txt bug fix ( #1672 )
...
* Streaming --save-txt bug fix
* cleanup
2020-12-11 15:45:32 -08:00
ada90e3901
Profile() feature addition ( #1673 )
...
* Profile() feature addition
* cleanup
2020-12-11 09:30:39 -08:00
94a7f55c4e
FReLU bias=False bug fix ( #1666 )
2020-12-10 13:06:15 -08:00
2e8e02745b
vast.ai compatability updates ( #1657 )
2020-12-09 19:01:08 -08:00
fa8f1fb0e9
Simplify autoshape() post-process ( #1653 )
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* Simplify autoshape() post-process
* cleanup
* cleanup
2020-12-09 07:44:06 -08:00
84f9bb5d92
Normalized mosaic plotting bug fix ( #1647 )
2020-12-08 18:44:13 -08:00
0bb43953eb
Reinstate PR curve sentinel values ( #1645 )
2020-12-08 17:40:49 -08:00
68e6ab668b
Hub device mismatch bug fix ( #1619 )
2020-12-06 17:53:38 +01:00
791dadb51c
Pycocotools best.pt after COCO train ( #1616 )
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* Pycocotools best.pt after COCO train
* cleanup
2020-12-06 14:58:33 +01:00
8918e63476
Increase FLOPS robustness ( #1608 )
2020-12-05 11:41:34 +01:00
f010147578
Update matplotlib.use('Agg') tight ( #1583 )
...
* Update matplotlib tight_layout=True
* udpate
* udpate
* update
* png to ps
* update
* update
2020-12-02 15:53:16 +01:00
784feae30a
Update matplotlib svg backend ( #1580 )
2020-12-02 14:05:12 +01:00
577f298d9b
plot_images() scale bug fix ( #1566 )
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fix bugs in plot_images
2020-12-01 11:29:59 +01:00
b6ed1104a6
Daemon thread plotting ( #1561 )
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* Daemon thread plotting
* remove process_batch
* plot after print
2020-11-30 16:44:14 +01:00
cff9263490
f.read().strip() ( #1551 )
2020-11-29 11:59:52 +01:00
9fa7f9f598
f.read().strip()
2020-11-29 11:58:14 +01:00
96a84468b9
Update labels_to_image_weights() ( #1545 )
2020-11-28 12:25:45 +01:00
c9798ae0e1
Update plot_study_txt() ( #1533 )
2020-11-26 22:18:17 +01:00
0f2057ed33
Targets scaling bug fix ( #1529 )
2020-11-26 18:33:28 +01:00
2c3efa430b
Mosaic plots bug fix ( #1526 )
2020-11-26 14:02:22 +01:00
12499f1c01
--image_weights bug fix ( #1524 )
2020-11-26 13:25:51 +01:00
9728e2b8ae
--image_weights bug fix ( #1524 )
2020-11-26 11:49:01 +01:00
e9a0ae6f19
Cache bug fix ( #1513 )
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* Caching bug fix #1508
* np.zeros((0,5)) x2
2020-11-25 20:33:14 +01:00
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
2026d4c5eb
Update caching ( #1496 )
2020-11-24 16:25:21 +01:00
bde5d9aaaa
Update caching ( #1496 )
2020-11-24 16:23:00 +01:00
0822cda781
Update caching ( #1496 )
2020-11-24 16:22:02 +01:00
89c7a5b8dc
Update caching ( #1496 )
2020-11-24 16:13:04 +01:00
44f42b1589
changed prints to logging in utils/datasets ( #1315 )
...
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2020-11-24 16:03:19 +01:00
7aeef2dca5
Prevent PR plotting ( #1489 )
2020-11-24 00:46:01 +01:00
354109c54c
Autosplit ( #1488 )
2020-11-23 18:35:25 +01:00
4798e66fdf
Autosplit ( #1488 )
2020-11-23 17:18:21 +01:00
0a3ff71ae0
Confusion matrix ( #1474 )
...
* initial commit
* add plotting
* matrix to cpu
* bug fix
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* seaborn pandas to requirements.txt
* seaborn pandas to requirements.txt
* update wandb plotting
* remove pandas
* if plots
* if plots
* if plots
* if plots
* if plots
* initial commit
* add plotting
* matrix to cpu
* bug fix
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* update plot
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* cleanup
* seaborn pandas to requirements.txt
* seaborn pandas to requirements.txt
* update wandb plotting
* remove pandas
* if plots
* if plots
* if plots
* if plots
* if plots
* Cat apriori to autolabels
* cleanup
2020-11-23 15:27:14 +01:00
95fa65339f
Cat apriori to autolabels ( #1484 )
2020-11-23 13:38:47 +01:00
201bafc7cf
Sync train and test iou_thresh ( #1465 )
...
* Sync train and test iou_thresh
* Sync train and test iou_thresh
* weights names .lower()
* Notebook update
2020-11-21 12:38:35 +01:00
a70e554fc3
Remove redundant downloads mirror ( #1461 )
2020-11-20 13:10:56 +01:00
394131c2aa
Use torchvision.ops.nms ( #1460 )
2020-11-20 11:23:36 +01:00
05a955a3f6
FLOPS computation device bug fix ( #1447 )
...
* Update torch_utils.py
fix issue#113 , inputs device should be same with model parameters' device
* Update torch_utils.py
* Update torch_utils.py
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
2020-11-19 12:56:20 +01:00
64bce3c822
Update labels.png with rectangles fix ( #1432 )
2020-11-18 12:27:30 +01:00
1542ccadb2
Update labels.png with rectangles ( #1432 )
2020-11-18 00:17:33 +01:00
4250f84dfb
Update PR curve ( #1428 )
...
* Update PR curve
* legend outside
* list(Path().glob())
2020-11-17 17:29:55 +01:00
8d2d6d2349
Update img2label_paths() ( #1414 )
2020-11-17 13:26:06 +01:00
f5429260ca
PyTorch Hub and autoShape update ( #1415 )
...
* PyTorch Hub and autoShape update
* comment x for imgs
* reduce comment
2020-11-16 23:09:55 +01:00
92c9b72832
Matplotlib AGG backend ( #1413 )
2020-11-16 16:34:07 +01:00
548a98a391
Recursive directories dataset capability ( #1408 )
...
* Recursive directories dataset capability
* x.split('.')[-1]
* f += glob.glob(f"{p}{os.sep}**.*", recursive=True)
* f += glob.glob(str(p / '**.*'), recursive=True)
* remove . from image and vid formats
* .txt to txt
* str(p / '**' / '*.*')
2020-11-16 16:24:57 +01:00
Glenn Jocher
NanoCode012
Transigent
train255
Ayush Chaurasia
Abhiram V
Tommy in Tongji
Polydefkis Gkagkos
Hu Ye
yxNONG
igornishka
yujun
Jacobsolawetz