Mis on DINO ja DINO-v2?

DINO (Distillation without labels) and DINO-v2 are self-supervised learning methods in the field of computer vision, based on transformers and convolutional neural networks.

DINO is an approach where the model learns to extract useful representations from images without using labeled data. The main idea is to use two neural networks (student and teacher), where the teacher is updated using an exponential moving average of the student's parameters. The model learns to match different augmentations of the same image, allowing it to form stable and informative features.

DINO-v2 is an improved version of DINO, which extends the capabilities and quality of feature extraction. It uses more powerful architectures and optimizations, achieving better results in classification, segmentation, and similar image search tasks.

Both methods are widely used for pretraining models, which can then be fine-tuned for specific tasks with less labeled data.