Welcome to this guided project on creating and training an Object Localization model with TensorFlow. In this project, we will use TensorFlow's Keras API to create a convolutional neural network that can classify and localize emojis in images. The network will predict both the class of the emoji and its position within the image.

This project is designed for individuals who have prior programming experience in Python and familiarity with TensorFlow. It provides a hands-on learning experience for those who want to apply their theoretical understanding of Neural Networks, Convolutional Neural Networks (CNNs), and optimization algorithms like Gradient Descent to solve computer vision tasks like Object Localization.

The project is divided into the following tasks:

Introduction: Setting the context and explaining project goals.

Download and Visualize Data: Obtaining and visualizing the dataset.

Create Examples: Preparing the dataset by generating examples.

Plot Bounding Boxes: Visualizing bounding boxes around emojis.

Data Generator: Creating a data generator for training.

Model: Defining the architecture of the CNN model.

Custom Metric: IoU: Implementing a custom metric for evaluation.

Compile The Model: Compiling the model with loss, optimizer, and metrics.

Custom Callback: Creating a custom callback for monitoring training.

Model Training: Training the multi-output neural network.

To follow along and complete this project, you will need prior programming experience in Python and familiarity with TensorFlow. The project assumes that you already understand Neural Networks, Convolutional Neural Networks, and optimization algorithms.

Python (>=3.x)

TensorFlow (>=2.x)

Jupyter Notebook

Clone this repository to your local machine.

Open the Jupyter Notebook Object Localisation With Tensorflow.ipynb.

Follow the instructions in the notebook to complete each task.

Refer to the provided code and explanations for guidance.

By completing this guided project, you will have gained practical experience in creating and training a multi-output CNN model for object localization using TensorFlow. You'll understand how to generate synthetic data, define custom metrics and callbacks, and effectively train a model for computer vision tasks.

Feel free to explore and modify the code to further enhance your understanding and experiment with different approaches.

Happy learning!