colab notebook 2D

[pr4deepr]

Adding google colab notebook for 2D segmentation

[its-jd]

Hi, I have trained the model using a custom dataset and now I want to use this new weights for inference. I am loading weights while creating an instance of Cellpose class as shown below. Could you please let me know if this is the correct way to load custom weights?

Further, when I load weights in this way and proceed to prediction, I get the following error.

Could you please let me know if the error is from the way I am loading the weights?

Thanks in advance!!

[pr4deepr]

Hi, is this in google colab?? Assuming you are doing it in your local environment from what I can tell.

Regardless, if you are using a pretrained model, check this documentation:
https://cellpose.readthedocs.io/en/latest/api.html#cellpose.models.CellposeModel

class cellpose.models.CellposeModel(gpu=False, pretrained_model=False, torch=True, diam_mean=30.0, net_avg=True, device=None, residual_on=True, style_on=True, concatenation=False)

Parameters

        gpu (bool (optional, default False)) – whether or not to save model to GPU, will check if GPU available

        pretrained_model (str or list of strings (optional, default False)) – path to pretrained cellpose model(s), if False, no model loaded; if None, built-in ‘cyto’ model loaded

        net_avg (bool (optional, default True)) – loads the 4 built-in networks and averages them if True, loads one network if False

        diam_mean (float (optional, default 27.)) – mean ‘diameter’, 27. is built in value for ‘cyto’ model

        device (mxnet device (optional, default None)) – where model is saved (mx.gpu() or mx.cpu()), overrides gpu input, recommended if you want to use a specific GPU (e.g. mx.gpu(4))

I haven't used this option in a while, but in your case it should be:
models.CellposeModel(gpu=True, pretrained_model=model_type)

Check the eval option here as well: https://cellpose.readthedocs.io/en/latest/api.html#cellpose.models.CellposeModel.eval

Your post has made me realise that I should add an option of using a custom trained model in the google colab notebook... 😃

[its-jd]

Thanks for the reply. You guessed it right, I am doing everything locally.

I am creating an object of class Cellpose() which combines Cellposemodel and size model and while creating an object, I pass the path of the weight as model_type argument. So, in the end, the model_type argument is passed to the object of the CellposeModel class.

In the constructor of the CellPose(), I added a condition to check if the custom_weights are loaded or the default 'cyto' or 'nuclei' model.