Using keras 2.9.0, import keras_applications as ka gives the following error:- ModuleNotFoundError: No module named 'keras_applications'

Instead using from keras import applications as ka works!

Hello @ZFTurbo,

if you have time, please push the current updated status (with ConvNeXt) of this repo to PyPI. :)

Thanks again for the great work and your time!

Cheers, Dominik

Hello ,

base_model, preprocess_input = Classifiers.get('seresnext50') model = base_model(input_shape=(512, 512, 20, 1 ), weights=None , include_top = False ) x = Flatten()(model.output) x = Dense(1024, activation= 'sigmoid')(x) x = Dense(2, activation= 'sigmoid')(x)

Trying to train a model , the accuracy is everything resides upto expectation, but the gradcam are quite off from the region of the focus - how the accuracy is good but the grad cam is off the focus of targeted area .

Using the layer - 'activation-161' as output ref - https://github.com/fitushar/3D-Grad-CAM/blob/master/3DGrad-CAM.ipynb for the gradcam generation code , the results are always at the border of the image.

Thank you for the great work.

I am experiencing the following error over and over, even though I created a brand new tensorflow environment and installed all the necessary libraries in it. Could you please have a look on it and guide me how do I solve this problem? Thank you.

ImportError: Unable to import 'VersionAwareLayers' from 'keras.layers' (/home/ubuntu/anaconda3/envs/cm_3d/lib/python3.7/site-packages/keras/layers/init.py)

Hello and sorry to bother you beforehand,

I am currently conducting my master thesis project and I am trying to implement a 3D DenseNet-121 with knee MRIs as input data. While I was searching on how to implement a 3D version of the DenseNet I came across your repository and tried to change it for my application.

I have some issues regarding my try and I didn't know where else to ask about it and again I am sorry if I am completely of topic asking them here.

Firstly, my input shapes are (250,320,18,1) and when I give them as input to the 3D DenseNet I developed with stride_size=1 for my Conv_block and pooling_size=(2,2,2) and strides=(2,2,1) for my AveragePooling3D layer in the transition block, the model is constructed properly with the specific input_size, while when I am trying to load a DenseNet121 from classification_models_3d.tfkeras classifiers I am unable to construct it with input_shape(250,320,18,1), stride_size=1 and kernel_size=2. It gives as an error "Negative dimension size... for node pool4_pool/AvgPool3D". Is there a way to specifically define the strides for AvgPool3D layer in the transition block?

And secondly, I was thinking to load the 3D weights to my 3D DenseNet 121, is there a folder in your repository where I can find your pre-trained weights on imagenet??

Again thank you for having this repository publicly available and sorry if I am completely of topic asking such things here.

I look forward for you answer, Kind regards, Anastasis

I seem to always get this error when I try to create InceptionV3 model no matter what input_shape. What are the limitations on input shape there?

InvalidArgumentError: Negative dimension size caused by subtracting 3 from 2 for '{{node conv3d_314/Conv3D}} = 
Conv3D[T=DT_FLOAT, data_format="NDHWC", dilations=[1, 1, 1, 1, 1], padding="VALID", strides=[1, 2, 2, 2, 1]](Placeholder, 
conv3d_314/Conv3D/ReadVariableOp)' with input shapes: [?,2,17,17,192], [3,3,3,192,320].