mnist in yolov3

Mnist is a dataset of handwritten digits that is commonly used for image classification tasks. YOLOv3, on the other hand, is a state-of-the-art object detection algorithm that can detect and classify objects in real-time.

It is not directly possible to apply YOLOv3 to mnist dataset, as YOLOv3 is designed for object detection tasks and mnist dataset is a classification task. However, one can create a custom object detection dataset by converting the mnist images to object detection format, where each digit in the image is treated as a separate object.

This can be done by first converting the mnist images to the YOLOv3 format, which involves resizing the images to a fixed size and generating annotations for each object in the image. The annotations would contain the class label (which digit is present in the image) and the bounding box coordinates of the digit.

Once the custom dataset is created, it can be used to train YOLOv3 to detect the digits in the mnist images. However, it is important to note that YOLOv3 is a complex algorithm that requires a large amount of training data and computational resources. Therefore, it may not be practical to apply YOLOv3 to mnist dataset for most use cases.