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Implementation of Zero-Shot Learning algorithm using Word2Vecs as class embeddings

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zero-shot-learning

Implementation of Zero-Shot Learning algorithm

Zero-Shot learning method aims to solve a task without receiving any example of that task at training phase.
It simply allows us to recognize objects we have not seen before.

Check the Medium story that I wrote for details: https://medium.com/@cetinsamet/zero-shot-learning-53080995d45f

Classes

Train Classes:
arm, boy, bread, chicken, child, computer, ear, house, leg, sandwich, television, truck, vehicle, watch, woman
Zero-Shot Classes:
car, food, hand, man, neck

Usage

$python3 detect_object.py input-image-path

Example

$cd src
$python3 detect_object.py ../test.jpg
-> --- Top-5 Prediction ---
-> 1- vehicle
-> 2- truck
-> 3- car
-> 4- house
-> 5- chicken

Example Image
Test image is a beautiful green Jaguar E-Type.
All related prediction results are ranked in first three.

P.S. Remember, the prediction results are only allowed to be among above classes (train and zero-shot classes).
Algorithm will fail (although it will do its best to predict most related class) in case you try to detect an object from different other classes.

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