Unstable training about monodepth-pytorch

[NikolasEnt]

Hi, @alwynmathew, we retrain our model from scratch with the following parameters:

'model':'resnet18_md',
'learning_rate':1e-2,
'batch_size':8,
'adjust_lr':True,
'do_augmentation':True,
'augment_parameters':[0.8, 1.2, 0.5, 2.0, 0.8, 1.2],

Here it is the result. Obviously, it should be trained further, however, it is stable with lr=1e-2 and batch size 8.

Full Kitti dataset from the original repo was utilized for training.

[NikolasEnt]

Hello, @alwynmathew!
What data and hyperparameters do you use for training?
I'd like to reproduce the issue.

[alwynmathew]

I used exact same data and hyperparameters you have used in the demo notebook.

[NikolasEnt]

Ok, I'll examine it. There were some issues with the parameters.

Meanwhile you can play with our pretrained model - it was trained for 75 epochs with lr=1e-2; batch=20 (You may try to learn with the parameters). Or a better one - it was trained for extra 35 epochs with lr=1e-4 (with the first model as pretrain).

[alwynmathew]

I have also ported original monodepth to pytorch here. I faced the exact same issue that the disparities get degraded after few epochs. What are the hyperparameters you used to get better results?

[NikolasEnt]

lr=1e-2; batch=20 is good enough (see above) for our implementation

[alwynmathew]

@NikolasEnt batch_size= 20 is too big.
Epoch 10 with batch_size = 8 and lr=1e-2

Epoch 16

Its still unstable.

[voeykovroman]

Hello, @alwynmathew!

Once again. Are you sure you downloaded correct dataset as described here?
Because there are 38237 images and you attached your results for the first 10 epochs after approximately an hour after @NikolasEnt answered you about lr. And you used smaller batch size what means that you will need more time for training than we do while we needed something about 45 minutes for 1 epoch using single GTX1080 Ti. It looks like something wrong with your data or equipment.
During this week we will try to reproduce our training using exactly this repo without any changes downloaded on new machine and publish disparities we get after 10 epochs in this thread.

[alwynmathew]

Hi @Sparkling-Brick, according to the notebook provided in the repo, the data loader is just loading from one of the kitti dataset subfolder 'data_dir':'../../2011_09_26/' .

[voeykovroman]

Yes. Thanks for noting it, the path was changed to check whether notebook working or not before publishing. However if you noticed that path in the notebook is just for one subfolder you should be able to change it to load the whole dataset.
Moreover if you read our README you would notice the structure of data and path variable are described here.

[alwynmathew]

But @Sparkling-Brick do you think just added more data will solve the problem? The original implementation used batch size as small as 8, why do you recommend higher batch size?

[alwynmathew]

Thank you @NikolasEnt for the effort of training the model from scratch.

I do get perfect disparity map for selected images but it doesn't seem to applies to all of the kitti test images even after training for 17 epoch with same parameters on full kitti dataset.

'model':'resnet18_md',
'learning_rate':1e-2,
'batch_size':8,
'adjust_lr':True,
'do_augmentation':True,
'augment_parameters':[0.8, 1.2, 0.5, 2.0, 0.8, 1.2]

Is it just me or do you face the same problem?

Reconstructed images and corresponding disparities during my training:

[NikolasEnt]

Hi, @alwynmathew. It looks like the second original raw image has some issues. They may be due to video->image transformation process or image encoding in the dataset. Personally, I didn't observe such examples, however, I didn't exam the whole dataset.
Ideally, such images should be excluded from train/val subsets.