Questions about folds and resuming training
tfwittwer opened this issue · comments
Hi,
I'm new to the world of ANNs, so please forgive me if these are stupid questions.
- For some datasets, you use multiple folds, while for others you don't. What's the reasoning behind this? If I understand correctly, multiple folds mean that you train multiple models on subsets of the training data, correct? How would you combine the results of multiple folds?
- How would one resume training an existing model with new data? Increase the number of maximum iterations, then resume training with the new data replacing the initial data?
1- we perform 6-fold cross validation on s3dis because there is a natural fold partition. For each fold, we train a model and evaluate the 5 other folds and evaluate it on the fold in question. The prediction are aggregated over the 6 fold, covering the entire dataset.
Cross validation is a good practice for evaluating an algorithm. If your goal is to have the best possible model on indoor data, use all 6bfolds to train.
2- see our guides for 3dis and sema3d. You set --n_epoch to -1 and --resume to 1
Hi!
We are releasing a new version of SuperPoint Graph called SuperPoint Transformer (SPT).
It is better in any way:
| ✨ SPT in numbers ✨ |
|---|
| 📊 SOTA results: 76.0 mIoU S3DIS 6-Fold, 63.5 mIoU on KITTI-360 Val, 79.6 mIoU on DALES |
| 🦋 212k parameters only! |
| ⚡ Trains on S3DIS in 3h on 1 GPU |
| ⚡ Preprocessing is x7 faster than SPG! |
| 🚀 Easy install (no more boost!) |
If you are interested in lightweight, high-performance 3D deep learning, you should check it out. In the meantime, we will finally retire SPG and stop maintaining this repo.