
Learn to create and customize object detection with Teachable Machine, TensorFlow, and Python. Build datasets, train and validate models, download and extract models, install PyCharm, and run the project.
Explore Teachable Machine's features—image project, audio project, and post project—and learn how to use its code for custom object detection with TensorFlow.
Create a dataset for object detection using Teachable Machine image project with three classes: Identicard, calculator, and mobile phone. Capture 200 images from different angles with webcam or upload.
Train the dataset into a TensorFlow model with Teachable Machine by adjusting epoch, batch size, and learning rate, converting data into a model in seconds.
Validate trained Teachable Machine model by testing with webcam or upload, importing the model from Google, using at least 500 images with adjusted batch size and learning rate for accuracy.
Discover how to download a Teachable Machine model via export options TensorFlow.js, TensorFlow, and TensorFlow Lite, and select Python with Keras for this project.
Extract the model and its files from the extracted folder, including the Keras dot h file and the labels dot txt, for three classes: identity card, calculator, and mobile phone.
Install the PyCharm community edition, set up a Python project, and run a Teachable Machine object recognition workflow using TensorFlow, Keras, OpenCV, and numpy with a webcam.
Execute the custom object detection project using TensorFlow in PyCharm IDE by installing numpy, OpenCV, and Keras on Python 3.9, then run and validate with webcam tests.
Train custom models with Teachable Machine to recognize images, songs, and poses, converting datasets into TensorFlow and Keras models in seconds for facial recognition projects.
Create a facial recognition dataset in Teachable Machine by setting up five classes, organizing folders, and importing images via webcam, upload, or Google Drive, with 500 images per class.
Learn to train datasets in Teachable Machine by uploading or using webcam data, then adjust epoch, batch size, and learning rate to build a TensorFlow and Keras model quickly.
Test and verify facial recognition models in Teachable Machine using webcam and image upload, assess accuracy and precision, and prepare to download the trained model.
Export and download your Teachable Machine model across TensorFlow.js, TensorFlow, and TensorFlow Lite. Use JavaScript or p5.js for web, and Keras or OpenCV Keras for Python and Android.
Learn to extract the model from Teachable Machine and locate the two extracted files: the keras model .h5 and the labels.txt, for the five classes.
Run and install packages in PyCharm for facial recognition using Teachable Machine, including Python 3.9, OpenCV, Keras, and NumPy, and paste code from Teachable Machine into PyCharm with model files.
Execute a facial recognition project in PyCharm using Teachable Machine, covering dataset creation, training with epochs, batch size, learning rate, validation, and model download for Python.
Course Title: Object Detection and Recognition Using TensorFlow and Python
Course Description:
Welcome to the Object Detection and Recognition Using TensorFlow and Python course, a hands-on exploration of the dynamic field of computer vision. This comprehensive course is designed for learners seeking to gain practical expertise in building robust object detection and recognition systems using the powerful combination of TensorFlow and Python.
What You Will Learn:
Introduction to Object Detection and Recognition:
Gain a foundational understanding of the principles and applications of object detection and recognition in various domains.
Setting Up Your Python Development Environment:
Configure and set up a Python development environment, ensuring a smooth workflow for your computer vision projects.
Python Basics and Key Libraries:
Review essential Python programming concepts and explore key libraries, including TensorFlow, NumPy, and OpenCV, essential for computer vision projects.
Data Collection and Preprocessing:
Learn effective techniques for collecting and preprocessing data, laying the groundwork for creating a high-quality dataset for model training.
Building an Object Detection Model:
Dive into the architecture and design principles of building an object detection model using TensorFlow, covering key concepts and implementation strategies.
Training the Model:
Understand the process of training your object detection model, optimizing it for accuracy and efficiency through hands-on exercises.
Integration with OpenCV:
Integrate your trained model with OpenCV to create real-time object detection and recognition applications, adding practicality to your skills.
Handling Real-World Challenges:
Address common challenges encountered in object detection, including different object sizes, variations in lighting, and complex backgrounds.
Customizing and Fine-Tuning Models:
Explore advanced techniques for customizing and fine-tuning pre-trained models, tailoring them to specific object detection and recognition tasks.
Ethical Considerations and Best Practices:
Engage in discussions on ethical considerations in computer vision projects and adhere to best practices for responsible development.
Why Enroll:
Hands-On Projects: Engage in practical projects to reinforce your learning through direct application.
Real-World Applications: Acquire skills applicable to real-world scenarios, enhancing your ability to create effective object detection and recognition systems.
Community Support: Join a community of learners, share experiences, and seek assistance from instructors and peers throughout your learning journey.
Embark on this exciting learning journey and become proficient in Object Detection and Recognition Using TensorFlow and Python. Enroll now and elevate your skills in the dynamic world of computer vision!