Comments (16)
@ufohuang98 I could not fix this problem. It seems the developers need to pay attention to this problem, and explain why this is happening and how to solve it. @dshahrokhian we can hope to get clear explanations from you about this problem? Or do you prefer to leave us alone with this problem? :)
from m2det.
@dshahrokhian @Maxfashko I also meet the the problem.....and I dont know how to solve.
I retrain VOC0712, and get good results, but when train my dataset for one class, get random result.
from m2det.
@dshahrokhian @Maxfashko @deqiangwang @Xiehuaiqi @ufohuang98 @rw1995 maybe you should adjust the weights between "loss_l" and "loss_c", you could find "loss = loss_l + loss_c" in "train.py" of this project, and you can introduce a parameter lambda to change the loss, like "loss = loss_l + 0.1*loss_c", finally, you may get the correct results~
from m2det.
pos:(4.4,-49.1,156.7,199.6), ids:person, score:1.000
pos:(119.9,-47.5,279.2,201.6), ids:person, score:1.000
pos:(1748.8,-38.1,1903.9,212.6), ids:person, score:1.000
pos:(18.3,485.6,142.4,695.9), ids:person, score:1.000
pos:(70.4,538.3,208.6,767.5), ids:person, score:1.000
pos:(185.5,525.4,332.5,771.1), ids:person, score:1.000
pos:(302.0,515.1,455.5,770.1), ids:person, score:1.000
pos:(122.0,592.4,278.3,839.3), ids:person, score:1.000
pos:(19.2,632.0,167.2,871.7), ids:person, score:1.000
pos:(235.8,613.2,399.0,877.5), ids:person, score:1.000
pos:(355.3,608.2,513.1,884.7), ids:person, score:1.000
pos:(480.6,646.6,637.0,931.0), ids:person, score:1.000
pos:(1191.1,629.0,1369.8,917.5), ids:person, score:1.000
pos:(602.5,688.8,752.3,965.3), ids:person, score:1.000
pos:(727.8,688.2,874.3,966.8), ids:person, score:1.000
pos:(850.8,681.3,1000.4,966.4), ids:person, score:1.000
pos:(958.8,673.6,1123.7,962.8), ids:person, score:1.000
pos:(1072.2,670.2,1242.4,957.1), ids:person, score:1.000
pos:(1310.2,669.1,1483.1,953.0), ids:person, score:1.000
pos:(1438.5,673.7,1605.4,966.8), ids:person, score:1.000
pos:(62.7,733.4,233.6,950.0), ids:person, score:1.000
pos:(299.5,729.1,474.0,963.3), ids:person, score:1.000
pos:(412.1,729.8,578.8,992.9), ids:person, score:1.000
pos:(1574.7,737.8,1724.7,990.1), ids:person, score:1.000
pos:(1748.5,732.4,1903.9,1004.1), ids:person, score:1.000
pos:(203.6,766.6,362.1,964.2), ids:person, score:1.000
pos:(785.6,755.0,941.1,1028.1), ids:person, score:1.000
pos:(530.8,772.5,697.8,1041.7), ids:person, score:1.000
pos:(654.1,772.0,817.2,1048.8), ids:person, score:1.000
pos:(901.4,767.8,1058.5,1053.1), ids:person, score:1.000
pos:(1020.6,767.3,1181.0,1053.5), ids:person, score:1.000
pos:(1142.7,776.6,1299.8,1046.9), ids:person, score:1.000
pos:(1269.4,783.5,1420.9,1040.9), ids:person, score:1.000
pos:(1403.6,792.3,1547.0,1031.9), ids:person, score:1.000
pos:(1522.8,705.5,1652.6,1089.5), ids:person, score:1.000
pos:(1649.7,791.9,1779.5,1030.4), ids:person, score:1.000
pos:(-25.3,852.3,136.6,1073.3), ids:person, score:1.000
pos:(112.4,779.8,283.6,1116.6), ids:person, score:1.000
pos:(369.2,794.7,514.0,1157.2), ids:person, score:1.000
pos:(1224.7,771.4,1345.4,1168.1), ids:person, score:1.000
pos:(241.4,818.6,402.8,1182.6), ids:person, score:1.000Confidence 1.0 on all objects - this is at least strange :)
can u help me? when i run the demo.py following the progress ,but have a wrong .
I do sh make.sh and python demo.py -c=configs/m2det512_vgg.py -m=weights/m2det512_vgg.pth --show
but the result isThe Anchor info:
{'variance': [0.1, 0.2], 'min_sizes': [30.72, 76.8, 168.96, 261.12, 353.28, 445.44], 'aspect_ratios': [[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]], 'clip': True, 'feature_maps': [64, 32, 16, 8, 4, 2], 'min_dim': 512, 'max_sizes': [76.8, 168.96, 261.12, 353.28, 445.44, 537.6], 'steps': [8, 16, 32, 64, 128, 256]}
===> Constructing M2Det model
Loading resume network...
===> Finished constructing and loading model
<class 'm2det.M2Det'>
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=663 error=11 : invalid argument
Traceback (most recent call last):
File "demo.py", line 113, in
out = net(img)
File "/home/csy/anaconda3/envs/pytorch0.4.1/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/home/csy/M2Det/m2det.py", line 106, in forward
x = self.basek
File "/home/csy/anaconda3/envs/pytorch0.4.1/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/home/csy/anaconda3/envs/pytorch0.4.1/lib/python3.5/site-packages/torch/nn/modules/conv.py", line 301, in forward
self.padding, self.dilation, self.groups)
RuntimeError: cuda runtime error (11) : invalid argument at /pytorch/aten/src/THC/THCGeneral.cpp:663how can you run the demo.py? do you modified some locations?I will be grateful to you
@Maxfashko
ok,i solve it .just my environment about pytorch is wrong .
from m2det.
@Maxfashko
I have the same issue ,would you please tell me how did you fixed it?
I use pytorch 0.4.1, coco dota format and trained 26 epoch.
from m2det.
Just a random thought, did not try it myself: num_classes=2
, since __background__
is already a class.
from m2det.
@dshahrokhian thank, it helped.
I have one more question. My model trained 10 epoch (600 image). I run demo.py
script and see than bbox aligned randomly over image. What shall i do? Is this a normal situation or i need to train more time?
from m2det.
pos:(4.4,-49.1,156.7,199.6), ids:person, score:1.000
pos:(119.9,-47.5,279.2,201.6), ids:person, score:1.000
pos:(1748.8,-38.1,1903.9,212.6), ids:person, score:1.000
pos:(18.3,485.6,142.4,695.9), ids:person, score:1.000
pos:(70.4,538.3,208.6,767.5), ids:person, score:1.000
pos:(185.5,525.4,332.5,771.1), ids:person, score:1.000
pos:(302.0,515.1,455.5,770.1), ids:person, score:1.000
pos:(122.0,592.4,278.3,839.3), ids:person, score:1.000
pos:(19.2,632.0,167.2,871.7), ids:person, score:1.000
pos:(235.8,613.2,399.0,877.5), ids:person, score:1.000
pos:(355.3,608.2,513.1,884.7), ids:person, score:1.000
pos:(480.6,646.6,637.0,931.0), ids:person, score:1.000
pos:(1191.1,629.0,1369.8,917.5), ids:person, score:1.000
pos:(602.5,688.8,752.3,965.3), ids:person, score:1.000
pos:(727.8,688.2,874.3,966.8), ids:person, score:1.000
pos:(850.8,681.3,1000.4,966.4), ids:person, score:1.000
pos:(958.8,673.6,1123.7,962.8), ids:person, score:1.000
pos:(1072.2,670.2,1242.4,957.1), ids:person, score:1.000
pos:(1310.2,669.1,1483.1,953.0), ids:person, score:1.000
pos:(1438.5,673.7,1605.4,966.8), ids:person, score:1.000
pos:(62.7,733.4,233.6,950.0), ids:person, score:1.000
pos:(299.5,729.1,474.0,963.3), ids:person, score:1.000
pos:(412.1,729.8,578.8,992.9), ids:person, score:1.000
pos:(1574.7,737.8,1724.7,990.1), ids:person, score:1.000
pos:(1748.5,732.4,1903.9,1004.1), ids:person, score:1.000
pos:(203.6,766.6,362.1,964.2), ids:person, score:1.000
pos:(785.6,755.0,941.1,1028.1), ids:person, score:1.000
pos:(530.8,772.5,697.8,1041.7), ids:person, score:1.000
pos:(654.1,772.0,817.2,1048.8), ids:person, score:1.000
pos:(901.4,767.8,1058.5,1053.1), ids:person, score:1.000
pos:(1020.6,767.3,1181.0,1053.5), ids:person, score:1.000
pos:(1142.7,776.6,1299.8,1046.9), ids:person, score:1.000
pos:(1269.4,783.5,1420.9,1040.9), ids:person, score:1.000
pos:(1403.6,792.3,1547.0,1031.9), ids:person, score:1.000
pos:(1522.8,705.5,1652.6,1089.5), ids:person, score:1.000
pos:(1649.7,791.9,1779.5,1030.4), ids:person, score:1.000
pos:(-25.3,852.3,136.6,1073.3), ids:person, score:1.000
pos:(112.4,779.8,283.6,1116.6), ids:person, score:1.000
pos:(369.2,794.7,514.0,1157.2), ids:person, score:1.000
pos:(1224.7,771.4,1345.4,1168.1), ids:person, score:1.000
pos:(241.4,818.6,402.8,1182.6), ids:person, score:1.000
Confidence 1.0 on all objects - this is at least strange :)
from m2det.
pos:(4.4,-49.1,156.7,199.6), ids:person, score:1.000
pos:(119.9,-47.5,279.2,201.6), ids:person, score:1.000
pos:(1748.8,-38.1,1903.9,212.6), ids:person, score:1.000
pos:(18.3,485.6,142.4,695.9), ids:person, score:1.000
pos:(70.4,538.3,208.6,767.5), ids:person, score:1.000
pos:(185.5,525.4,332.5,771.1), ids:person, score:1.000
pos:(302.0,515.1,455.5,770.1), ids:person, score:1.000
pos:(122.0,592.4,278.3,839.3), ids:person, score:1.000
pos:(19.2,632.0,167.2,871.7), ids:person, score:1.000
pos:(235.8,613.2,399.0,877.5), ids:person, score:1.000
pos:(355.3,608.2,513.1,884.7), ids:person, score:1.000
pos:(480.6,646.6,637.0,931.0), ids:person, score:1.000
pos:(1191.1,629.0,1369.8,917.5), ids:person, score:1.000
pos:(602.5,688.8,752.3,965.3), ids:person, score:1.000
pos:(727.8,688.2,874.3,966.8), ids:person, score:1.000
pos:(850.8,681.3,1000.4,966.4), ids:person, score:1.000
pos:(958.8,673.6,1123.7,962.8), ids:person, score:1.000
pos:(1072.2,670.2,1242.4,957.1), ids:person, score:1.000
pos:(1310.2,669.1,1483.1,953.0), ids:person, score:1.000
pos:(1438.5,673.7,1605.4,966.8), ids:person, score:1.000
pos:(62.7,733.4,233.6,950.0), ids:person, score:1.000
pos:(299.5,729.1,474.0,963.3), ids:person, score:1.000
pos:(412.1,729.8,578.8,992.9), ids:person, score:1.000
pos:(1574.7,737.8,1724.7,990.1), ids:person, score:1.000
pos:(1748.5,732.4,1903.9,1004.1), ids:person, score:1.000
pos:(203.6,766.6,362.1,964.2), ids:person, score:1.000
pos:(785.6,755.0,941.1,1028.1), ids:person, score:1.000
pos:(530.8,772.5,697.8,1041.7), ids:person, score:1.000
pos:(654.1,772.0,817.2,1048.8), ids:person, score:1.000
pos:(901.4,767.8,1058.5,1053.1), ids:person, score:1.000
pos:(1020.6,767.3,1181.0,1053.5), ids:person, score:1.000
pos:(1142.7,776.6,1299.8,1046.9), ids:person, score:1.000
pos:(1269.4,783.5,1420.9,1040.9), ids:person, score:1.000
pos:(1403.6,792.3,1547.0,1031.9), ids:person, score:1.000
pos:(1522.8,705.5,1652.6,1089.5), ids:person, score:1.000
pos:(1649.7,791.9,1779.5,1030.4), ids:person, score:1.000
pos:(-25.3,852.3,136.6,1073.3), ids:person, score:1.000
pos:(112.4,779.8,283.6,1116.6), ids:person, score:1.000
pos:(369.2,794.7,514.0,1157.2), ids:person, score:1.000
pos:(1224.7,771.4,1345.4,1168.1), ids:person, score:1.000
pos:(241.4,818.6,402.8,1182.6), ids:person, score:1.000Confidence 1.0 on all objects - this is at least strange :)
can u help me? when i run the demo.py following the progress ,but have a wrong .
I do sh make.sh and python demo.py -c=configs/m2det512_vgg.py -m=weights/m2det512_vgg.pth --show
but the result is
The Anchor info:
{'variance': [0.1, 0.2], 'min_sizes': [30.72, 76.8, 168.96, 261.12, 353.28, 445.44], 'aspect_ratios': [[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]], 'clip': True, 'feature_maps': [64, 32, 16, 8, 4, 2], 'min_dim': 512, 'max_sizes': [76.8, 168.96, 261.12, 353.28, 445.44, 537.6], 'steps': [8, 16, 32, 64, 128, 256]}
===> Constructing M2Det model
Loading resume network...
===> Finished constructing and loading model
<class 'm2det.M2Det'>
THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=663 error=11 : invalid argument
Traceback (most recent call last):
File "demo.py", line 113, in
out = net(img)
File "/home/csy/anaconda3/envs/pytorch0.4.1/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/home/csy/M2Det/m2det.py", line 106, in forward
x = self.basek
File "/home/csy/anaconda3/envs/pytorch0.4.1/lib/python3.5/site-packages/torch/nn/modules/module.py", line 477, in call
result = self.forward(*input, **kwargs)
File "/home/csy/anaconda3/envs/pytorch0.4.1/lib/python3.5/site-packages/torch/nn/modules/conv.py", line 301, in forward
self.padding, self.dilation, self.groups)
RuntimeError: cuda runtime error (11) : invalid argument at /pytorch/aten/src/THC/THCGeneral.cpp:663
how can you run the demo.py? do you modified some locations?I will be grateful to you
@Maxfashko
from m2det.
Did it work with pytorch 0.4?
from m2det.
@dshahrokhian no, i use pytorch 1.0
from m2det.
I meant @rw1995, as it seems to work for him now, and the repo README says to use 0.4.1
from m2det.
I meet the same question to train one class.if you solve ,please help me
from m2det.
@Xiehuaiqi I stopped trying to make the code work that is obviously not working :)
The author chose to ignore our questions. Either @dshahrokhian is not competent, or my assumption is true - the code is not working :)
from m2det.
@lonl Hi, you mentioned weighting the 2 loss values as a possible resolution. Did this work out for you?
from m2det.
@taehyunzzz, I have conducted this way to try to solve the problem, however, some pics still have many error bounding boxes, and maybe this way could not work ~
from m2det.
Related Issues (20)
- I wrote how to run on mac os.
- The test for coco2017 and get mAP 0.0? model is m2det512_vgg.pth
- Someone have test result on VOC2007 ? HOT 5
- train error HOT 5
- 论文中有一处错误
- Are you using batch size 8 for bn update?
- Why adjust_learning_rate() only refers to step_lr of COCO? HOT 1
- can anyone explain what is the loss_L and loss_c in the training results ? HOT 1
- AttributeError: 'CustomDataset' object has no attribute 'evaluate_detections'AttributeError: 'CustomDataset' object has no attribute 'evaluate_detections'
- Two question about upsample process
- error when run demo.py
- RuntimeError: all tensors must be on devices[0] HOT 1
- mAP精度问题
- On which dataset was m2det512_vgg.pth trained?
- Hard Negative Mining problem
- demo | qt.qpa.xcb: could not connect to display HOT 1
- Does anyone have the weight of resnet101 HOT 1
- File "home/CLNet/main.py", line 87, in <module> main () File "home/CLNet/main.py", line 25, in main model = init_model(args) File "/content/drive/My Drive/home/CLNet/utils/init.py", line 51, in init_model model.load_state_dict(state_dict) File "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py", line 1407, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for CLNet: HOT 1
- GFLOPs!!!
- ImportError: /home/wdc/M2Det/utils/nms/gpu_nms.cpython-37m-x86_64-linux-gnu.so: undefined symbol: cudaSetupArgument
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 m2det.