Ever wanted to build your very own custom object detector?

Got lost with all the tutorials and installation?

Well…I hear you…I went through the

EXACT. SAME. THING.

So, let’s flip it up. In this video we’re going to go through how to build a custom object detection model that can be used to make detections in real time. Now whilst we’re using it for detecting face masks this can be easily repurposed to perform real-time detection for a whole range of use cases simply by updating the annotations and the label map.

In this video you’ll learn how to:
1. Install labelImg and label images from scratch for object detection
2. Train and run a custom object detector for face mask detection
3. Use transfer learning to train on top of existing SOTA models
4. Setup a proper workflow and directory structure for training
5. Make detections in real time using the trained model

Get the training template here: [ Ссылка ]

Other Links Mentioned in the Video
LabelImg: [ Ссылка ]
Kaggle Repo: [ Ссылка ]
Installing the Tensorflow Object Detection API: [ Ссылка ]
Tensorflow Models: [ Ссылка ]
Tensorflow 2 Detection Model Zoo (for alternate pre-trained models): [ Ссылка ]

Oh, and don't forget to connect with me!
LinkedIn: [ Ссылка ]
Facebook: [ Ссылка ]
GitHub: [ Ссылка ]

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

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