Learn how to implement a YOLOv4 Object Detector with TensorFlow 2.0, TensorFlow Lite, and TensorFlow TensorRT Models. Perform object detections on images, video and webcam with high accuracy and speed.

This video will walk-through the steps of setting up the code, installing dependencies, converting YOLO Darknet style weights into saved TensorFlow models, and running the models. Take advantage of YOLOv4 as a TensorFlow Lite model, it’s small lightweight size makes it perfect for mobile and edge devices such as a raspberry pi. Looking to harness the full powers of a GPU? Then run YOLOv4 with TensorFlow TensorRT to increase performance by up to 8x times.

In this video I cover:

1. Cloning or Downloading the Code
2. Installing Required Dependencies for CPU or GPU
3. Downloading and Converting YOLOv4 Weights into a saved TensorFlow
4. Performing YOLOv4 Object Detections with TensorFlow on images, video and webcam
5. Converting TensorFlow model into a TensorFlow Lite .tflite model
6. Converting TensorFlow model into TensorFlow TensorRT model
7. Running YOLOv4 Object Detections with TensorFlow Lite

GET THE CODE HERE: https://github.com/theAIGuysCode/tensorflow-yolov4-tflite

#yolov4 #python #tensorflow