PyTorch Classification :: YOLOv5 TXT
YOLOv5 for Classification
Classification with YOLOv5
Classification assigns a given image to an array of possible classes and can be binary or multi-class. Using classification to identify one particular class could mean you could train a model to identify a specific fruit and then pass images of plants through the model to identify what fruit is in the image. With multi-class classification, you may want to know each class represented in the image.
Classification do not localize in the image where the objects of interest are, how many there are, or their size.
Find a free classification dataset to try YOLOv5 for classification.
If you have your own data, label your images for free using Roboflow Annotate. Example:
YOLOv5 is regarded as smaller and generally easier to use in production thanks to being implemented in Pytorch. This means you can use YOLOv5 classification on the edge on devices like iPhones or cameras. Read more about YOLOv5 performance.