How many images do you need to train a model?
Blog post from Roboflow
In the realm of computer vision, determining the number of images required to train a model varies widely depending on the specific application and desired accuracy levels. Roboflow suggests constructing a machine learning pipeline to facilitate continuous improvement through active learning, allowing for the integration of new data and model updates. The process can start with a limited number of images, which can be increased through data augmentation techniques. Feedback from models with low confidence scores can be used to refine them further. This dynamic approach contrasts with static models, which may struggle with changes such as new product packaging in retail scenarios. A data-centric approach to deploying computer vision is advocated, emphasizing the adaptability and ease of updating data rather than modifying the models themselves. Roboflow offers tools like Annotate, CVAT, and others, which make deploying effective computer vision models accessible to a broader audience without requiring advanced degrees.
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