After trying [3D denoising] CARE model I decided to try 2D Probabilistic denoising to see if this denoising can give better results than 3D denoising for organoid segmentation. However, after generating my training data and perform my training (successfully, I think), when I try to apply the model I keep getting the same error “ValueError: axes (SZYXC) must be of length 3.” when I apply model.predict() function.

I already tried to change the values of the axes theRawData.from_folder() function inputs parameters and use SZYXC as axes values, but the same error are still appearing. I do not know if I am trying to do something that requires more than just changing this parameter or if I am just trying to do something that is not as easy as I thing. Reading the documentation help me to understand best how to use CARE but I have not found anything related to this issue. Thank you for your time

the output of the function `load_training_data gives()`

me

```
number of training images: 2765
number of validation images: 307
image size (3D): (16, 64, 64)
axes: SZYXC
channels in / out: 1 / 1
```

the output of the model configuration is

```
{'n_dim': 3,
'axes': 'ZYXC',
'n_channel_in': 1,
'n_channel_out': 1,
'train_checkpoint': 'weights_best.h5',
'train_checkpoint_last': 'weights_last.h5',
'train_checkpoint_epoch': 'weights_now.h5',
'probabilistic': True,
'unet_residual': True,
'unet_n_depth': 2,
'unet_kern_size': 3,
'unet_n_first': 32,
'unet_last_activation': 'linear',
'unet_input_shape': (None, None, None, 1),
'train_loss': 'laplace',
'train_epochs': 100,
'train_steps_per_epoch': 50,
'train_learning_rate': 0.0004,
'train_batch_size': 16,
'train_tensorboard': True,
'train_reduce_lr': {'factor': 0.5, 'patience': 10, 'min_delta': 0}}
```