Closed zhangmaoxiansheng closed 4 years ago
Hi @zhangmaoxiansheng , for now, we are not planning to release the training code. You can easily reimplement it on your own by extending monodepth2.
To train self-teaching, you need to load a pre-trained network to compute the distilled labels. Otherwise, you can pre-compute offline and load them as pseudo ground truth. In my code, I was able to compute them on-the-fly on a single GPU without memory issues.
About drop+self, it is missing in the paper because dropout itself, taken alone, performs poorly. Anyway, you can find below the performance of drop+self with M supervision:
abs_rel | | rmse | | a1 | |
AUSE | AURG | AUSE | AURG | AUSE | AURG |
& 0.048 & 0.016 & 3.265 & 0.473 & 0.069 & 0.030 \\
Thx
Thx for your nice work! I wonder how to train the self-teaching network. It seems you didn't give the code for training. According to (14), a group of results is needed, will it takes a lot of GPU memory during training? Besides, I didn't find the drop+self-teaching result in the paper, I wonder the performance of this way.