Comments (9)
I do not get the high mAP.
you should check the train data(the data and label are wrong)
from caffe-yolo9000.
- It is the difference between caffe and darknet. Gradient is added in caffe, but it is subtracted in darknet.
- I didn't add the reorg layer temporarily. I guess that maybe it is your problem.
from caffe-yolo9000.
- I use the model that you release:gnet_yolo_region_darknet_v3_pretrain_rectify_iter_200000.caffemodel, and test the mAP in voc2007 test dataset , and the mAP is much lower than the darknet yolo2(55% vs 72%). Do you test the mAP on voc2007.
- I retrain the model caffeyolo9000, and the result is lower. (train data :voc07+12,test data:voc07). I do not use the pretraining model(gnet_yolo_region_darknet_v3_pretrain_iter_600000.caffemodel), because i do not have the model. Could you support the model? Do the pretraining model lead to the low mAP?
Thank you!
from caffe-yolo9000.
- I have been finding where is the problem which results in low mAP.
- Pre-trained model is important, it refers to #4.
from caffe-yolo9000.
Thanks you for reply!
1.Could you show the details of the problem which results in low mAP, if you convenience, or share the code in github?
- Do you get convergence when add the reorg layer ?
Thank you!
from caffe-yolo9000.
Details are all in the original repository. But I have to delete it for some reasons. I haven't add the reorg layer. I would tell you if I get a high mAP with reorg layer next week.
from caffe-yolo9000.
Do you add reorg layer and get a high mAP?
from caffe-yolo9000.
@chengshuai @choasup
can you get a high mAp now?
i train my own data,but i can run successfully. some values bacame Nan or something like 0.0000000
from caffe-yolo9000.
@choasup
void axpy_cpu(int N, float ALPHA, float *X, int INCX, float *Y, int INCY) { int i; for(i = 0; i < N; ++i) Y[i*INCY] += ALPHA*X[i*INCX]; }
Sorry, in this function, is the gradient is added in darknet? Or if defferent with caffe, where is it in darknet?Thx!
from caffe-yolo9000.
Related Issues (20)
- YOLO9000-CAFFE HOT 4
- YOLO-9000 HOT 1
- YOLO 9000 Code HOT 2
- YOLO9000 caffe HOT 1
- want to test YOLO9000 in caffe HOT 8
- caffe version
- Why the codes are gone? HOT 2
- Why the codes is gone? HOT 1
- region_loss_layer error
- Readme request HOT 2
- Build error HOT 2
- about this cpp “eval_detection_layer.cpp” HOT 6
- yolo-9000 tree example
- some trouble when preparing data.
- how to joint train
- loss diffenece between darknet caffe-yolo9000 HOT 5
- 无法正常收敛
- Problems detection for my own data HOT 2
- caffe yolo-9000 help!!!! HOT 1
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 caffe-yolo9000.