Welcome back to the next chapter in our license plate detection series! In previous videos, we put in the hard work of training custom license plate detection models, and now, we're ready to unleash their power in this exciting showdown on a Raspberry Pi. 🚗🔍
🔶 TensorFlow Lite:
We'll kick things off by deploying our custom pre-trained TensorFlow Lite model on the Raspberry Pi. Witness its real-time performance as it identifies license plates with remarkable precision and speed.
🔶 YOLOv8:
Our custom-trained YOLOv8 model takes center stage next. As we run it on the Raspberry Pi, you'll see firsthand how it handles license plate detection in diverse scenarios. The results are bound to impress!
📊 Comparison:
This video is all about the ultimate face-off between these custom pre-trained models. We'll compare TensorFlow Lite and YOLOv8 in terms of real-world performance on the Raspberry Pi. Which one will prove to be the superior choice for license plate detection? The answer awaits you in this showdown!
🛠️ Code & Resources:
If you're eager to replicate our results, fear not! All the custom pre-trained models, code, and resources are readily available in the video description. Dive in, follow along, and experiment with these models on your own Raspberry Pi.
👍 If you've been following our license plate detection journey and can't wait to see the results, please hit the like button, share this video with your Raspberry Pi community, and subscribe to our channel for more thrilling AI and IoT content.
Github Repo for TensorFlow Lite: [ Ссылка ]
Github Repo for YOLO V8: [ Ссылка ]
Train TensorFlow Lite Model for Custom Object (License Plate) Detection with Custom Dataset: [ Ссылка ]
Train YOLO V8 on Custom Dataset for Object Detection : [ Ссылка ]
Custom Object (Licence Plate) Detection in Raspberry Pi with YOLO V8 and Python: [ Ссылка ]
Licence Plate Recognition with YOLO V8 and Easy OCR using Custom Dataset: [ Ссылка ]
For future updates follow us on facebook: [ Ссылка ]
You can also follow me on instagram for regular updates : [ Ссылка ]
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