Comments (15)
目前还不支持,后续会加上的
可以大家一起实现
from pytorch-yolov4.
我也在研究这个,我的微信17868380981,可以一起搞嘛
from pytorch-yolov4.
@Tianxiaomo when can we expect the support code to train ??
from pytorch-yolov4.
我最近需要用在自己的data上,希望能參與開發。qq: 2379467558
from pytorch-yolov4.
@chunleiml 应该可以训练
from pytorch-yolov4.
@Tianxiaomo
Dear Author, thank you very much for the great work. I suppose you are working on training and reproducing the performance of original repo. When you are done, may you release the training flow? btw, Pytorch is great~ Thanks again
from pytorch-yolov4.
from pytorch-yolov4.
@chunleiml 应该可以训练
可以写一下怎么训练吗? 有哪些需要改动的?
from pytorch-yolov4.
我魔改了一下可以支持自己的数据了,训练了几十个epoch后输出的框不太正确,作者大大能帮忙看下么 @Tianxiaomo
from pytorch-yolov4.
什么问题,都不正确,还是有不正确的 @okideal
from pytorch-yolov4.
什么问题,都不正确,还是有不正确的 @okideal
都不正确,而且相邻epoch保存的checkpoint输出的结果都相差很大,框都是散布在图片上的,没有规律。
from pytorch-yolov4.
2020-05-19 09:41:24,363 train.py[line:317] DEBUG: Train step_252232: loss : 1.4532806873321533,loss xy : 12.427532196044922,loss wh : 1.475596308708191,loss obj : 6.721678256988525,loss cls : 2.627683162689209,loss l2 : 5.853010177612305
2020-05-19 09:41:28,438 train.py[line:317] DEBUG: Train step_252252: loss : 0.5830423831939697,loss xy : 4.975176811218262,loss wh : 0.122866690158844,loss obj : 4.1869707107543945,loss cls : 0.043664395809173584,loss l2 : 1.4445288181304932
2020-05-19 09:41:32,533 train.py[line:317] DEBUG: Train step_252272: loss : 1.1059558391571045,loss xy : 9.491128921508789,loss wh : 0.018527325242757797,loss obj : 1.4653393030166626,loss cls : 6.720297813415527,loss l2 : 2.798774480819702
2020-05-19 09:41:36,753 train.py[line:317] DEBUG: Train step_252292: loss : 0.7335749268531799,loss xy : 6.689797878265381,loss wh : 0.04984629526734352,loss obj : 4.740152359008789,loss cls : 0.2574022710323334,loss l2 : 1.7148456573486328
2020-05-19 09:41:40,661 train.py[line:317] DEBUG: Train step_252312: loss : 1.0617437362670898,loss xy : 14.04965877532959,loss wh : 0.08109669387340546,loss obj : 2.62939453125,loss cls : 0.22774934768676758,loss l2 : 0.8850184082984924
2020-05-19 09:41:44,627 train.py[line:317] DEBUG: Train step_252332: loss : 1.2092291116714478,loss xy : 8.662763595581055,loss wh : 0.019287779927253723,loss obj : 2.9575741291046143,loss cls : 7.708040237426758,loss l2 : 2.6127541065216064
2020-05-19 09:41:48,755 train.py[line:317] DEBUG: Train step_252352: loss : 0.8405617475509644,loss xy : 8.541584014892578,loss wh : 0.032777439802885056,loss obj : 4.75701904296875,loss cls : 0.11760733276605606,loss l2 : 1.3595856428146362
2020-05-19 09:41:52,804 train.py[line:317] DEBUG: Train step_252372: loss : 0.3742732107639313,loss xy : 4.4370269775390625,loss wh : 0.017715638503432274,loss obj : 1.478102445602417,loss cls : 0.055526264011859894,loss l2 : 0.43905097246170044
2020-05-19 09:41:56,729 train.py[line:317] DEBUG: Train step_252392: loss : 1.0849196910858154,loss xy : 11.690910339355469,loss wh : 0.05718810483813286,loss obj : 3.792856216430664,loss cls : 1.8177597522735596,loss l2 : 1.5996325016021729
训练的时候loss震荡也很大,这算正常的么?
from pytorch-yolov4.
2020-05-19 09:41:24,363 train.py[line:317] DEBUG: Train step_252232: loss : 1.4532806873321533,loss xy : 12.427532196044922,loss wh : 1.475596308708191,loss obj : 6.721678256988525,loss cls : 2.627683162689209,loss l2 : 5.853010177612305
2020-05-19 09:41:28,438 train.py[line:317] DEBUG: Train step_252252: loss : 0.5830423831939697,loss xy : 4.975176811218262,loss wh : 0.122866690158844,loss obj : 4.1869707107543945,loss cls : 0.043664395809173584,loss l2 : 1.4445288181304932
2020-05-19 09:41:32,533 train.py[line:317] DEBUG: Train step_252272: loss : 1.1059558391571045,loss xy : 9.491128921508789,loss wh : 0.018527325242757797,loss obj : 1.4653393030166626,loss cls : 6.720297813415527,loss l2 : 2.798774480819702
2020-05-19 09:41:36,753 train.py[line:317] DEBUG: Train step_252292: loss : 0.7335749268531799,loss xy : 6.689797878265381,loss wh : 0.04984629526734352,loss obj : 4.740152359008789,loss cls : 0.2574022710323334,loss l2 : 1.7148456573486328
2020-05-19 09:41:40,661 train.py[line:317] DEBUG: Train step_252312: loss : 1.0617437362670898,loss xy : 14.04965877532959,loss wh : 0.08109669387340546,loss obj : 2.62939453125,loss cls : 0.22774934768676758,loss l2 : 0.8850184082984924
2020-05-19 09:41:44,627 train.py[line:317] DEBUG: Train step_252332: loss : 1.2092291116714478,loss xy : 8.662763595581055,loss wh : 0.019287779927253723,loss obj : 2.9575741291046143,loss cls : 7.708040237426758,loss l2 : 2.6127541065216064
2020-05-19 09:41:48,755 train.py[line:317] DEBUG: Train step_252352: loss : 0.8405617475509644,loss xy : 8.541584014892578,loss wh : 0.032777439802885056,loss obj : 4.75701904296875,loss cls : 0.11760733276605606,loss l2 : 1.3595856428146362
2020-05-19 09:41:52,804 train.py[line:317] DEBUG: Train step_252372: loss : 0.3742732107639313,loss xy : 4.4370269775390625,loss wh : 0.017715638503432274,loss obj : 1.478102445602417,loss cls : 0.055526264011859894,loss l2 : 0.43905097246170044
2020-05-19 09:41:56,729 train.py[line:317] DEBUG: Train step_252392: loss : 1.0849196910858154,loss xy : 11.690910339355469,loss wh : 0.05718810483813286,loss obj : 3.792856216430664,loss cls : 1.8177597522735596,loss l2 : 1.5996325016021729
训练的时候loss震荡也很大,这算正常的么?
数据增强原来有bug,另外学习率在训练时有没有调整
from pytorch-yolov4.
数据增强之前我把mosaic关掉了,只用了普通的增强。学习率的话,adam自己也会调整把,只是不会指数衰减
from pytorch-yolov4.
我魔改了一下可以支持自己的数据了,训练了几十个epoch后输出的框不太正确,作者大大能帮忙看下么 @Tianxiaomo
大佬可以分享一下魔改方法嘛,最近也想在bdd100k上训练一版pytorch-v4
from pytorch-yolov4.
Related Issues (20)
- "python models.py 80 "./checkpoints/yolov4.pth" "./data/dog.jpg" 576 768" Error HOT 1
- 请问train.txt和val.txt是怎么生成的?
- Loss decreases when noise is introduced to the inputs. Loss calculation might be wrong.
- RuntimeError: view size is not compatible with input tensor's size and stride HOT 1
- Yolov4 c++ inferencing using onnx runtime
- COCO eval results boost by changing NMS function HOT 1
- Use YoloX HOT 1
- Move to YOLOV7 or YOLOX project unmaintained HOT 4
- Inference Issue HOT 1
- RuntimeError: The size of tensor a (52) must match the size of tensor b (19) at non-singleton dimension 3 HOT 1
- RECOMMENDATION FOR NEW USERS
- Negative values of x1 and y1???
- 预训练模型是空的?
- "illegal instruction" (core dumped) When Running demo_darknet2onnx.py HOT 1
- 在Yolov5-in-Deepstream-5.0-master/Deepstream 5.0/nvdsinfer_custom_impl_Yolo中执行make编译报错
- ValueError: could not convert string to float: '' HOT 1
- TypeError: Caught TypeError in DataLoader worker process 0. HOT 1
- Is there any way to avoid using the reshape operation after inference?
- Output ONNX file
- how to train the img with size of 1920*1080 HOT 3
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from pytorch-yolov4.