I am using pertained models (vgg16, vgg19, resent ,MobileNet)
I have 2 different dataset with below details ,
1. Dataset1: the data is divided in the folders, each contains the label. for example, Folder1 named cat: contains all cats images… Folder2 name Dog: contains all dog images.
2. Dataset2: all the data are located in one folder, the is .csv file which contain image_name and their lables.
I want the following to be done:
1. read all images
2. predict them using those pertained models (vgg16, vgg19, resent ,MobileNet)
3. save the result of the prediction , for each image , for each model
4. in new variable calculate the commutative prediction value for all (vgg16, vgg19, resent ,MobileNet)
5. The program should support any kind of images (including .pgm and .jpg formate )
6. The program should support gray and RGB images (write a function/class to check the input images and convert them to the format supported by those models if needed)
7. the model should divided into functions for easiest monitoring and modifications if needed
8. write the comment is each line showing the purpose of their use.
9. each dataset will have its own .py file
10. using keras
in the end, I need a session to describe the code for me.
The price is FIXED,, Thank you for cooperation.
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Hi, i’m an expert in deep learning and machine learning techniques. I’ve worked on same kind of jobs before. Please check my profile for more information and feel free to contact me. Regards Žiga