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
7e161d9774
Single class train update ( #1719 )
2020-12-17 12:02:03 -08:00
6bd5e8bca7
nn.SiLU() export support ( #1713 )
2020-12-16 17:55:57 -08:00
c923fbff90
W&B artifacts feature addition ( #1712 )
...
* Log artifacts
* cleanup
2020-12-16 17:52:12 -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
799724108f
Update C3 module ( #1705 )
2020-12-15 22:13:08 -08:00
7947c86b57
Update COCO train postprocessing ( #1702 )
2020-12-15 21:50:28 -08:00
035ac82ed0
Fix torch multi-GPU --device error ( #1701 )
...
* 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
e92245a38c
Simplified PyTorch hub for custom models ( #1677 )
2020-12-12 11:42:27 -08:00
f7a923b446
Simplified PyTorch hub for custom models ( #1677 )
2020-12-12 11:37:53 -08:00
87ca35b922
Simplified PyTorch hub for custom models ( #1677 )
2020-12-12 11:16:57 -08:00
54043a9fa4
Streaming --save-txt bug fix ( #1672 )
...
* Streaming --save-txt bug fix
* cleanup
2020-12-11 15:45:32 -08:00
bc52ea2d5f
Profile() feature addition
2020-12-11 09:34:27 -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
b2bef8f6d8
vast.ai compatability updates ( #1657 )
2020-12-10 06:00:47 -08:00
2e8e02745b
vast.ai compatability updates ( #1657 )
2020-12-09 19:01:08 -08:00
fa8f1fb0e9
Simplify autoshape() post-process ( #1653 )
...
* 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
86f4247515
Hybrid auto-labelling support ( #1646 )
2020-12-08 18:15:39 -08:00
0bb43953eb
Reinstate PR curve sentinel values ( #1645 )
2020-12-08 17:40:49 -08:00
b53c8ac179
Create codeql-analysis.yml ( #1644 )
...
* Create codeql-analysis.yml
* Update ci-testing.yml
2020-12-08 17:03:16 -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 )
...
* Pycocotools best.pt after COCO train
* cleanup
2020-12-06 14:58:33 +01:00
d929bb656c
Implement default class names ( #1609 )
2020-12-06 12:41:37 +01:00
efa7a915d8
Update download_weights.sh with usage example ( #1613 )
2020-12-06 10:09:09 +01:00
8918e63476
Increase FLOPS robustness ( #1608 )
2020-12-05 11:41:34 +01:00
ba48f867ea
Add bias to Classify() ( #1601 )
2020-12-04 15:06:33 +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
2c99560a98
Update .gitignore datasets dir ( #1577 )
2020-12-02 13:01:22 +01:00
577f298d9b
plot_images() scale bug fix ( #1566 )
...
fix bugs in plot_images
2020-12-01 11:29:59 +01:00
b6ed1104a6
Daemon thread plotting ( #1561 )
...
* Daemon thread plotting
* remove process_batch
* plot after print
2020-11-30 16:44:14 +01:00
68211f72c9
FROM nvcr.io/nvidia/pytorch:20.10-py3 ( #1553 )
...
* FROM pytorch/pytorch:latest
* FROM nvcr.io/nvidia/pytorch:20.10-py3
2020-11-29 17:47:51 +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
97a5227a1a
Ignore W&B logging dir wandb/ ( #1534 )
2020-11-27 01:38:09 +01:00
5dbf0c96f4
FROM nvcr.io/nvidia/pytorch:20.11-py3
2020-11-27 01:28:33 +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 )
...
* 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
7d629fde05
Update README.md
2020-11-25 11:00:29 +01:00
Glenn Jocher
Polydefkis Gkagkos
NanoCode012
SergioSanchezMontesUAM
Hu Ye
yxNONG