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YOLO or
You Only Look Once, is a popular real-time object detection algorithm. -
YOLO combines what was once a multi-step process, using a single neural network to perform both
classificationandpredictionof bounding boxes for detected objects. -
network divides the image into regions and predicts bounding boxes and probabilities for each region.
- In this repo implemented one of the simple and fast version of YOLO from scratch.
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Pascal-VOC dataset is the one of the collections for object detection
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Pretrained weights in this implemetation are based on training yolo team on VOC dataset
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You can check the yolo website for defferent variation pretrained weights for different sizes or datasets.
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Download used weights in this project from here
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Loading this weights is not like conventional methods (like loading .pth, .pt, ... formats) so they should put on the model's body
- Predictions and bounding boxes not so much accurate but it's fast
Note: predicted objects is around defined class names so it can not predict out of this such as below image
