Comments (3)
wvangansbeke
commented on June 15, 2024
Hi @leilegelei1,
I use a learning rate of 1e-3 with a batch size of 7. I also use the plateau scheduler of Pytorch during training. I trained the model for 60 epochs. The default values of the hyperparameters are normally correct and the loss function too. I can train it another time if I have time to double check. However, if I use de weights of my trained model (which you can download), I get a RMSE of 802mm. So the model seems fine. Maybe try to load the pretrained model and see if you get the same results or check the dataloading process.
Kind regards,
Wouter
from sparse-depth-completion.
leilegelei1
commented on June 15, 2024
@wvangansbeke
Thanks!
from sparse-depth-completion.
wvangansbeke
commented on June 15, 2024
Hi @leilegelei1,
I've heard from two persons that they obtained my reported numbers exactly with this code. So it should be fine.
Best,
Wouter
from sparse-depth-completion.
Related Issues (20)
- Testing with other KITTI sequences or other data HOT 8
- How did you render the results shown in video? HOT 2
- Size of image from an imported folder HOT 7
- Can't load the model HOT 4
- Incorrect depth estimation when there is low LiDAR density HOT 6
- Training conditions HOT 2
- Trying to create tensor with negative dimension -2: [-2, 34, 3, 3] HOT 1
- try to run /Download/download_raw_files.sh HOT 3
- Evaluating your pretrained model HOT 17
- Can't run the code HOT 3
- Confusions about dataset HOT 4
- num_samples should be a positive integer value, but got num_samples=0 HOT 4
- Dataset Prepare HOT 2
- The problem about RGB dataset HOT 1
- why the localNet not use global depth prediction as input HOT 4
- Script for only prediction
- Code stuck at testing
- About pretrained model on only trained with sparse data
- About data preprocessing
- About the loss function
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