hacksession image recognition - c/o data science...1. form groups (2-3 persons each, 1 google...
TRANSCRIPT
Hacksession Image Recognition
Dr. Thorben Jensen
Outline
07.11.2019
1 Intro Image Recognition
2 Object Detection and YOLO
3 Session Targets
4 Setup & Code Intro
2
Intro Image Recognition
Image classification in a nutshell
4Image source: https://www.mathworks.com
Images are matrices
5
Filters can match patterns
6
Applying multiple filters
7
To detect more complex features: apply filters after filters
8Image source: https://www.slideshare.net
Low-level Mid-level High-level Result
T. Cruise: 99 %
Input
T. Jensen: 1 %
Object detection & YOLO
Types of Image Recognition Tasks
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YOLO – „you only look once“
Conventional Object Detection
separately proposes and classifies ‘boxes’
YOLO (“you only look once”)
parallelizes proposing and classifying ‘boxes’
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https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e
Network pre-trained on COCO-Dataset
• 80 object classes
• 330.000 images
• Networks pre-trained on COCO dataset freely available
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Session Targets
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https://56f2a99952126.streamlock.net/833/default.stream/playlist.m3u8
1. Form groups (2-3 persons each, 1 Google account per group)
2. Intro into sample code
3. Diving into sample code with Google Colab
4. Choose a new use case, and code it with your group
– How many bicycles?
– Delivery trucks? (trucks only allowed at limited hours)
– Remove certain objects from an image?
– < your idea here >
5. 16:30-16:45: present your results to us
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Agenda
Setup & Code Intro
Contact
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Dr. Thorben Jensen Data Scientist
+49 160 69 666 42
www.informationsfabrik.de