💪We build a Python machine learning application for deadlifts in 15 minutes.
🔎We use pose estimation to track and analyze deadlifts in real-time.
📷We capture webcam input and convert it into RGB format for processing.
🤖We load a pre-trained machine learning model for deadlift classification.
🔄We reset the counter and update the UI with the current stage and rep count.
How long does it take to build the application?
—We build the machine learning application for deadlifts in just 15 minutes.
What does the application use to track and analyze deadlifts?
—The application uses pose estimation and machine learning models to track and analyze deadlifts in real-time.
What input does the application use?
—The application captures webcam input and converts it into RGB format for processing.
Does the application use pre-trained models?
—Yes, the application loads a pre-trained machine learning model for deadlift classification.
Can the counter be reset?
—Yes, the counter can be reset and the UI will be updated with the current stage and rep count.
00:00Introduction to building a deadlift machine learning application in Python.
00:10Explanation of the application and its capabilities.
01:32Code implementation and import of necessary libraries.
03:25Creation of labels and buttons for user interface.
04:59Development of the main detect function to capture frames and process them.
08:43Loading of pre-trained machine learning model for deadlift classification.
09:23Integration of webcam capture and conversion of frames.
11:16Demonstration of the application and its functionality.
