The YOLO (You Only Look Once) family of models
[https://blog.roboflow.com/guide-to-yolo-models/] continues to grow and right
after YOLOv6 was released, YOLOv7 was delivered quickly after
CLIP is a gigantic leap forward, bringing many of the recent developments from
the realm of natural language processing into the mainstream of computer vision:
unsupervised learning, transformers, and multimodality
Computer Vision (and Machine Learning in general) is one of those fields that can seem hard to approach because there are so many industry-specific words (or common words used in novel ways) that it can feel a bit like you're trying to learn a new language when you're trying to get started.
At Roboflow, we often get asked, what is the train, validation, test split and why do I need it? The motivation is quite simple: you should separate you data into train, validation, and test splits to prevent your model from overfitting and to accurately evaluate your model.
And that's a problem that is extremely dangerous.
Machine learning, the process of teaching computer algorithms to perform new
tasks by example, is poised to transform industries from agriculture