Object detection is a computer vision task that involves both localizing one or more objects within an image and classifying each object in the image.
It is a challenging computer vision task that requires both successful object localization in order to locate and draw a bounding box around each object in an image, and object classification to predict the correct class of object that was localized. Yolo is a faster object detection algorithm in computer vision and first described by Joseph Redmon, Santosh Divvala, Ross Girshick and Ali Farhadi in 'You Only Look Once: Unified, Real-Time Object Detection'
This notebook implements an object detection based on a pre-trained model - YOLOv3 Pre-trained Weights (yolov3.weights) (237 MB). The model architecture is called a “DarkNet” and was originally loosely based on the VGG-16 model.
python detection.py
>>> Where is your image path?
>>> images/traffic.jpg
For details of this project please check notebook
- Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
- Darknet, https://github.com/pjreddie/darknet
- YOLO3 (Detection, Training, and Evaluation), https://github.com/experiencor/keras-yolo3