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.

#yolov4 #tensorflow #objectdetection

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.

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

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

———————–Resources————————
Train Your Own YOLOv4 Custom Object Detector in the Cloud: https://youtu.be/mmj3nxGT2YQ
The Official YOLOv4 paper: https://arxiv.org/abs/2004.10934

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– The AI Guy

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