Comments (6)
@cxy-ccc, I guess there are two potential issues. (1). The class label in the first colum should be 'Car', not 'car', you could change
CLOCs/second/pytorch/models/voxelnet.py
Line 393 in cad14fd
, replace 'Car' with 'car'. (2). I suggest visualize some of your yolo predictions on the image plane to see if these yolo boxes are correct. The box you showed for 000001.txt seems not right. Don't worry about the number of predictions in Cascade-RCNN, most of them have very low confidence score, it won't affect the results.
As for the nms setting for YOLO, usually there are 2 nms thresholds, one is nms IoU thresh, the other is nms score thresh. I guess you are talking about IoU threshold. The meaning for IoU thresh is that, any box overlap larger than the thresh will be discarded, so I think it should not be set to 0.0, that would result in output boxes with NO overlaps at all (any boxes with overlaps will be removed). If you want more predictions, the nms IoU thresh should be set to a large value, but based on my experience, I recommend 0.5 or 0.7, too large will also be harmful.
Thank you so much for your advice!!!The most critical problem should be the error of my labels. I checked the labels and AP can be displayed normally. For car,
bev AP:89.39, 87.09, 84.96
3d AP:89.36, 85.07, 84.76
And, actually the results I restored of your paper is very high and close to 99
bev AP:99.32, 96.56, 93.74
3d AP:99.51, 96.47, 93.62
from clocs.
Hello @cxy-ccc, thank you for your interests in CLOCs. May I know which class are you trying to fuse, and what is the score scale of your 2D detections, is it sigmoid score (between 0.0-1.0) or log score (between -inf to +inf)? Also, may I know if you can get the results that I provided in the repo?
from clocs.
Hello @cxy-ccc, thank you for your interests in CLOCs. May I know which class are you trying to fuse, and what is the score scale of your 2D detections, is it sigmoid score (between 0.0-1.0) or log score (between -inf to +inf)? Also, may I know if you can get the results that I provided in the repo?
I use the class "car" to fuse. And yolov5 is sigmoid score. I can restore your results with the methods mentioned in your paper (C-RCNN+SECOND in class car).
from clocs.
@cxy-ccc , everything looks right. If '2d detection path' is the only thing that you changed, could you manually check the 2d detection txt files? maybe compare it with the default ones (cascade-rcnn) and see if the number of colums, box encodings are the same? You could also share some samples here so I can also have a look.
from clocs.
@cxy-ccc , everything looks right. If '2d detection path' is the only thing that you changed, could you manually check the 2d detection txt files? maybe compare it with the default ones (cascade-rcnn) and see if the number of colums, box encodings are the same? You could also share some samples here so I can also have a look.
Thank you for your tips. The results are not familiar. I set the nms to 0. Maybe confidence should be adjusted?
car -1 -1 -10 27.1 171.61 226.1 297.61 -1 -1 -1 -1000 -1000 -1000 -10 0.9692
This is the content of 000001.txt. It only export one result. However, the results of c-rcnn is
Car -1 -1 -10 387.97 181.29 425.20 202.74 -1 -1 -1 -1000 -1000 -1000 -10 0.8444
Car -1 -1 -10 469.85 59.68 519.34 88.63 -1 -1 -1 -1000 -1000 -1000 -10 0.0275
Car -1 -1 -10 599.46 163.49 629.96 190.04 -1 -1 -1 -1000 -1000 -1000 -10 0.0235
Car -1 -1 -10 756.64 183.04 810.01 203.49 -1 -1 -1 -1000 -1000 -1000 -10 0.0064
Car -1 -1 -10 41.09 129.28 118.02 180.63 -1 -1 -1 -1000 -1000 -1000 -10 0.0052
Car -1 -1 -10 831.21 120.47 877.35 145.45 -1 -1 -1 -1000 -1000 -1000 -10 0.0018
Car -1 -1 -10 1044.50 200.98 1110.56 234.57 -1 -1 -1 -1000 -1000 -1000 -10 0.0008
Car -1 -1 -10 831.93 152.59 895.27 181.13 -1 -1 -1 -1000 -1000 -1000 -10 0.0006
Car -1 -1 -10 378.20 59.09 449.14 89.29 -1 -1 -1 -1000 -1000 -1000 -10 0.0006
Car -1 -1 -10 964.38 189.73 1041.31 223.06 -1 -1 -1 -1000 -1000 -1000 -10 0.0001
Car -1 -1 -10 1062.60 198.48 1135.43 235.10 -1 -1 -1 -1000 -1000 -1000 -10 0.0001
Car -1 -1 -10 810.65 148.52 904.82 189.11 -1 -1 -1 -1000 -1000 -1000 -10 0.0000
from clocs.
@cxy-ccc, I guess there are two potential issues. (1). The class label in the first colum should be 'Car', not 'car', you could change
CLOCs/second/pytorch/models/voxelnet.py
Line 393 in cad14fd
As for the nms setting for YOLO, usually there are 2 nms thresholds, one is nms IoU thresh, the other is nms score thresh. I guess you are talking about IoU threshold. The meaning for IoU thresh is that, any box overlap larger than the thresh will be discarded, so I think it should not be set to 0.0, that would result in output boxes with NO overlaps at all (any boxes with overlaps will be removed). If you want more predictions, the nms IoU thresh should be set to a large value, but based on my experience, I recommend 0.5 or 0.7, too large will also be harmful.
from clocs.
Related Issues (20)
- About ablation study HOT 1
- Add a column of data HOT 11
- training time HOT 1
- About evaluation
- no model named second HOT 2
- when i running train.py
- Depth information from 2D boxes(Cascade-Rcnn) HOT 4
- Questions about draw figures of qualitative results. HOT 1
- Fast CLOCs HOT 1
- RuntimeError
- The result of your paper is different from the result of the code. HOT 5
- How to get the result of old 11 recall points?
- Empty cyclist tensor HOT 1
- which file has the IoU calculation code to select non-empty elements ( candidates)? HOT 1
- second.data
- efficiency
- ModuleNotFoundError: No module named 'second' HOT 1
- How to extend to multiple classes
- about the "fusion.py" ---self.corner_points_feature
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 clocs.