Is it possible to extend python imageai pretrained model for more classes?

Question

I am working on a project which uses imageai with YOLOv3 which works fast and accurately for my purpose. However this model is able to detect only 80 classes out of which I want some of them but want to add some more classes as well. I referred to https://imageai.readthedocs.io/en/latest/customdetection/index.html to train my own custom model with 3 more classes. However, I am unable to detect the 80 classes that were provided by YOLOv3. Is there a way to generate a model that extends the existing YOLOv3 and can detect all 80 classes + extra classes that I want?

P.S. I am new to tensorflow and imageai so I don't know too much. Please bear with me.

Answer 1

I have not yet found a way to extend an existing model, but i can assure you that training your own model is far more efficient than using all the classes noones wants. If it still interests you, this person had a similar question: Loading a trained Keras model and continue training

This is his finished code example:

"""
Model by: http://machinelearningmastery.com/
"""
import numpy
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import load_model


numpy.random.seed(7)

def baseline_model():
    model = Sequential()
    model.add(Dense(num_pixels, input_dim=num_pixels, activation='relu'))
    model.add(Dense(num_classes, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

if __name__ == '__main__':
    # load data
    (X_train, y_train), (X_test, y_test) = mnist.load_data()

    # flatten 28*28 images to a 784 vector for each image
    num_pixels = X_train.shape[1] * X_train.shape[2]
    X_train = X_train.reshape(X_train.shape[0], num_pixels).astype('float32')
    X_test = X_test.reshape(X_test.shape[0], num_pixels).astype('float32')
    # normalize inputs from 0-255 to 0-1
    X_train = X_train / 255
    X_test = X_test / 255
    # one hot encode outputs
    y_train = np_utils.to_categorical(y_train)
    y_test = np_utils.to_categorical(y_test)
    num_classes = y_test.shape[1]

    # build the model
    model = baseline_model()

    #Partly train model
    dataset1_x = X_train[:3000]
    dataset1_y = y_train[:3000]
    model.fit(dataset1_x, dataset1_y, nb_epoch=10, batch_size=200, verbose=2)

    # Final evaluation of the model
    scores = model.evaluate(X_test, y_test, verbose=0)
    print("Baseline Error: %.2f%%" % (100-scores[1]*100))

    #Save partly trained model
    model.save('partly_trained.h5')
    del model

    #Reload model
    model = load_model('partly_trained.h5')

    #Continue training
    dataset2_x = X_train[3000:]
    dataset2_y = y_train[3000:]
    model.fit(dataset2_x, dataset2_y, nb_epoch=10, batch_size=200, verbose=2)
    scores = model.evaluate(X_test, y_test, verbose=0)
    print("Baseline Error: %.2f%%" % (100-scores[1]*100))