MobileNet | MobileNet build with Tensorflow | Computer Vision library

Make sure you have the same image size (224, 224) in flow_from_directory and in the hub.KerasLayer. Here is a working example:

When you call the base model as follows, it will initiate with the default argument. Among them, include_top is set as True.

As already stated here:

[...] mobilenet.preprocess_input will scale input pixels between -1 and 1.

As already mentioned, you could also check out the source code itself. With opencv, you would just use cv2.resize(*) and cv2.normalize(*).

there is some tricks to help with orver fitting problem:

1. Adding data augmentation, this method will slightly transform each time the input with rotation, random croping, etc. and the model will see more example of the same image it will help the model to better generalize.
2. Adding dropout layer, this layer will randomly sets input units to 0 with in the training process, so in that the model will make more epoch before over fitting.
3. L1 and L2 regularization , this method will penalize the absolute value of the weights by adding them to the total loss.(enter link description here
4. It's better to change your callback withcallback = tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=3), I think your model stopped when there is still room for emprovement.

The reason that is happening is:

• _EnumDict tracks all used names
• _EnumDict thinks my_exception_instance should be a member
• Python clears the as variable when leaving the except clause
  • by assigning None to my_exception_instance (and then deleting the variable)
  • causing _EnumDict to think a key is being reused

One workaround (as of Python 3.7) is to add my_exception_instance to an _ignore_¹ attribute:

For V3 Large, you should do

Your classification is correct if the purpose is - why they were invented initially. However rather than the task based taxonomy, CNNs are better studied on the basis of what different they are doing. Initially CNNs were designed for image classification, but the same network works for Object detection with slight modifications in last layer. For example, Faster RCNN (designed for Object detection) can use any of the architecture designed for classification such as VGG, ResNet etc (link). Similarly Faster-RCNN can be modified to do segmentation task in Mask-RCNN architecture (link).

Here is a chart showing evolutionary history of deep CNNs showing architectural innovations (source)

Here is another taxonomy showing different categories based on architecture style.

Seems like an oversight by TFJS team so Image type is not recognized. do this instead:

Sequential models only support models where the layers are arranged like a linked list - each layer takes the output of only one layer, and each layer's output is only fed to a single layer. Your two base models have residual blocks, which breaks the above assumption, and turns the model architecture into directed acyclic graph (DAG).

To do what you want to do, you'll need to use the Functional API. With the Functional API, you explicitly control the intermediate activations aka KerasTensors.

For the first model, you can skip that extra work and just create a new model from a subset of the existing graph like this

sub_mobile = keras.models.Model(mobile_model.inputs, mobile_model.layers[36].output)

Wiring some of the layers of the second model is much more difficult. It's easy to slice off the end of a keras model - it's much more difficult to slice of the beginning because of the need for a tf.keras.Input placeholder. To do this successfully, you'll need to write a model walking algorithm that go through the layers, tracks the output KerasTensors, then calls each layer with the new inputs to create a new output KerasTensor.

You could avoid all that work by simply finding some source code for an InceptionResNet and adding layers via Python rather than introspecting an existing model. Here's one which may fit the bill.

https://github.com/yuyang-huang/keras-inception-resnet-v2/blob/master/inception_resnet_v2.py