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Create synthetic data for model training
Create and train a multi output neural network to perform object localization
Create custom metrics and calbacks in Keras
在面试中展现此实践经验
Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object. Please note that you will need prior programming experience in Python. You will also need familiarity with TensorFlow. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like Gradient Descent but want to understand how to use use TensorFlow to solve computer vision tasks like Object Localization.
Prior programming experience in Python. Conceptual understanding of Neural Networks. Prior experience with TensorFlow and Keras.
Deep Learning
Machine Learning
Tensorflow
Computer Vision
keras
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
Introduction
Download and Visualize Data
Create Examples
Plot Bouding Boxes
Data Generator
Model
Custom Metric: IoU
Compile the Model
Custom Callback
Model Training
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在分屏视频中，您的授课教师会为您提供分步指导
由 MI 提供
Mar 31, 2021I really liked the course and how it was the next step from a classification problem.
由 AO 提供
Mar 29, 2021A very good and helpful project for object detection. It would be absolute 5-stars guided-project if there was also an example for multiple object detection.
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