ship class Object detection with custom model weights I have made a model to classify different categories of ships(yacht,catamaran,rubber boat) in Python and i hit a 70%  accuracy in training so now i have my weights.hdf5 file.Now i need to object detection the images i used with boundaries boxes and rectangles to find the different ships in each image, like a Yolo pretrained model but with my own weights that i trained for classes,so that the detector is going to find the ship in the image and display is's label with is's probability,
etc yacht(70.0% )rubber boat(65.0%)...
Can anyone with some more experience drive me to achieving this task?
You should be able to use keras.models.load_model to load the hdf5 model file. See also the tensorflow documentation.
# saving the model
model = ...  # Get model (Sequential, Functional Model, or Model subclass)
model.save('weights.hdf5')

# loading the model
from tensorflow import keras
model = keras.models.load_model('weights.hdf5')