Comments (5)
Hi Yihui,
There may be some fluctuation in training performance from time to time. That's the number we get in our experiment, it may take you some trial to reach the same ones.
PS: to get the same evaluation results, it's recommended to use the evaluation script which evaluates test shapes in all rotations instead of only a single default rotation for each shape. The test set only has 2468 shapes thus evaluating without rotations will be very unstable.
from pointnet.
closing due to no continuing conversation.
from pointnet.
Hi @charlesq34, Thanks for the code! Great work!
A couple of questions:
- Are you saying that the variance of the performance is high and that you report the highest achieved accuracy in the paper (the 89.2%)?
- How many rotations did you use to evaluate the method in the paper - to get the 89.2%?
- Best performance is with Adam or SGD?
- Is the best performing model trained with exponential decay every 20 epochs for both learning rate AND batch normalization momentum? It's confusing because in the paper it says that the LR is reduced every 20 epochs but in the code the default setting is 20,000 iterations for both BN momentum and LR. Which one achieves the 89.2%?
from pointnet.
HI @Tgaaly
It has been a while since I checked the repo's issues. sorry for the delay. Firstly thanks for your interest!
There is some variance of the accuracies so it's more stable if we evaluate on several rotated version of the point clouds. The accuracy on the testing set during training process can fluctuate from around 88.6 to 89.1 as I remember. I think I used evaluate.py with num_votes=12 to get the final accuracy number.
The best model is trained with Adam. Both BN and LR has decays. I used 20 epochs for the step size for both of the decays.
Hope it helps.
from pointnet.
@charlesq34 For the train.py for running point_cls model, I found the decay_step is out of the range, but you mentioned to @Tgaaly
I used 20 epochs for the step size for both of the decays.
parser.add_argument('--decay_step', type=int, default=200000, help='Decay step for lr decay [default: 200000]')
which one is correct? Thanks.
from pointnet.
Related Issues (20)
- TypeError: float() argument must be a string or a number, not 'mpc'
- from tensorflow_graphics.util import export_api ImportError: No module named tensorflow_graphics.util Can someone help me to revolve this issue ?
- Add neuron to the ouput layer HOT 1
- s3dis dataset :ValueError: need at least one array to concatenate
- Unable to open H5py file HOT 3
- prepare dataset from h5 files with classification label per point
- meshlab
- PointNet for angle regression
- Area_5/hallway_6 HOT 1
- Issued certificate has expired HOT 1
- Annotation HOT 1
- A lightweight Cylinder3D model with much higher performance is now available!!!
- Isn't this section incorrect? HOT 1
- __init__() missing 1 required positional argument: 'dtype'
- How much memory needed for sem_seg training HOT 2
- How to visualize the semantic segmentation results through ROS
- how to use PointNet model in live inference HOT 1
- cant download dataset with HDF5 data. Help! HOT 1
- Segment point clouds with different point numbers
- Cannot get modelnet40 from server 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 pointnet.