vgg | ''' pre_vgg19_model | Computer Vision library

İ am working on transfer learning for multiclass classification of image datasets that consists of 12 classes. As a result, İ am using VGG19. However, the accuracy of the model is as much lower than the expectation. İn addition train and valid accuracy do not increase. Besides that İ ma trying to decrease the batch size which is still 383

My code:

İ am working on transfer learning for multiclass classification of image datasets that consists of 12 classes. As a result, İ am using VGG19. However, I am facing an error i.e. Facing ValueError: Shapes (None, None) and (None, 256, 256, 12) are incompatible. Moreover, İ have flaten layers too

My code:

I have modified VGG16 in pytorch to insert things like BN and dropout within the feature extractor. By chance I now noticed something strange when I changed the definition of the forward method from:

For image clustering I was using a piece of code which worked perfectly.

I'm implementing SRGAN (and am not very experienced in this field), which uses a pre-trained VGG19 model to extract features. The following code was working fine on Keras 2.1.2 and tf 1.15.0 till yesterday. then it started throwing an "AttributeError: module 'keras.utils.generic_utils' has no attribute 'populate_dict_with_module_objects'" So i updated the keras version to 2.4.3 and tf to 2.5.0. but then its showing a "Input 0 of layer fc1 is incompatible with the layer: expected axis -1 of input shape to have value 25088 but received input with shape (None, 32768)" on the following line

I am trying to Convert Annotated image to Binary mask Image using cordinates present in json file.
Image is Annotated using VGG annotation.

Below are the Actual image, Json data, and the result i want.

Here are the Cordinates of the above image

being new to Deep Learning i am struggling to understand the difference between different state of the art algos and their uses. like how is resnet or vgg diff from yolo or rcnn family. are they subcomponents of these detection models? also are SSDs another family like yolo or rcnn?

I have a Convolutional neural network (VGG16) that performs well on a classifying task on 26 image classes. Now I want to visualize the data distribution with t-SNE on tensorboard. I removed the last layer of the CNN, therefore the output is the 4096 features. Because the classification works fine (~90% val_accuracy) I expect to see something like a pattern in t-SNE. But no matter what I do, the distribution stays random (-> data is aligned in a circle/sphere and classes are cluttered). Did I do something wrong? Do I misunderstand t-SNE or tensorboard? It´s my first time working with that.

Here´s my code for getting the features:

Currently I am using VGG-16 to extract features of an image dataset. I trained it on a certain type of data set and what I would like to do is the following to get the last convolution block weights as a flatten format in order to create a 25088-D feature vector. My model summary is the following :