CODEWITHSUNDEEP
kerascomputer-visionobject-detectionyolo
Nitish Gupta
Nitish Gupta

Reputation: 1

Can I train YOLO on small already segmented out images and test it on a large image for detection?

I have been thinking about building a YOLO model for detecting parking lot occupancy, I have all the small segmented out images for every parking space. Can I train YOLO on these small images already divided into separate empty and occupied classes and test it on a test image like the ariel view of a parking lot with say 28 parking spots and the model should detect the occupied and empty spaces. If yes then can someone guide me how to approach the problem? I will be using YOLO implemented on Keras.

Upvotes: -1

Views: 624

Answers (1)

Chandan M S
Chandan M S

Reputation: 411

YOLO is a n object detection model. During training, it takes coordinates of bounding boxes in an image as input and learns to identify the images inside such bounding boxes. As per your problem statement, if you have a aerial view of parking lot then draw the bounding boxes, generate xml files (as per your training requirement) and start training. This ideally should give you the desired model to predict.

Free tool to label images - https://github.com/tzutalin/labelImg

Github project to get an idea of how to train Yolo in Keras on custom dataset - https://github.com/experiencor/keras-yolo2

By any means, this is not a perfect tailor made solution for your problem given you haven't provided any code or images. But this is a good place to start.

Upvotes: 1

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