Real Time Face Mask Detection with Tensorflow and Python | Custom Object Detection w/ MobileNet SSD
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: https://github.com/nicknochnack/RealTimeObjectDetection
Other Links Mentioned in the Video
Kaggle Repo: https://www.kaggle.com/wobotintelligence/face-mask-detection-dataset
Installing the Tensorflow Object Detection API: https://tensorflow-object-detection-api-tutorial.readthedocs.io/en/latest/install.html
Tensorflow Models: https://github.com/tensorflow/models
Tensorflow 2 Detection Model Zoo (for alternate pre-trained models): https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
P.s. Let me know how you go and drop a comment if you need a hand!