Train a custom object detection model using your data

Learn how to train a custom object detection model for Raspberry Pi to detect less common objects like versions of a logo using your own collection of data.

00:00 Introduction
00:49 The 3 steps of training a custom model
01:24 Step 1: Create a training dataset
04:01 Step 2: Train a custom model with TensorFlow Lite Model Maker
09:03 Step 3: Deploy the custom model to Raspberry Pi
11:08 What’s next

Colab notebook to train a custom object detection model → https://goo.gle/3ocbqmI
Sample app to run the object detection model on Raspberry Pi → https://goo.gle/3GaABw3
Android figurine dataset → https://goo.gle/31DtXPL
Explaining “average precision” → https://goo.gle/3lBR5p6
Transfer learning → https://goo.gle/3pBDiAh
Responsible AI → https://goo.gle/2QEEuVV

Choose an object detection model architecture for Raspberry Pi → https://goo.gle/3lDe9DO
Make object detection run faster by using Coral → https://goo.gle/3EuH3xn

Watch all Machine Learning for Raspberry Pi videos → https://goo.gle/ML-raspberrypi
Subscribe to TensorFlow → https://goo.gle/TensorFlow

#TensorFlow #MachineLearning #ML #RaspberryPi #EdgeAI

product: TensorFlow – TensorFlow Lite, TensorFlow – General; fullname: Khanh LeViet;

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