tao-train-nvdinov2

Self-supervised learning trains vision transformers via self-distillation without labels. You can train an NVDINOv2 backbone by executing `tao-train-nvdinov2 --dataset=/path/to/dataset --num_epochs=10` to produce general-purpose visual features.

Self-supervised visual representation learning extracts general-purpose features from images without labeled data. It uses a teacher-student self-distillation mechanism to train the NVDINOv2 architecture, creating a backbone suitable for various downstream image classification tasks.

Yes, NVDINOv2 trains a vision transformer backbone using self-supervised learning techniques. This produces general-purpose visual features applicable to image classification tasks, requiring only a pre-defined dataset and configurable training parameters.

No, you do not need a labeled dataset to train an NVDINOv2 model. The training process uses self-supervised learning through self-distillation, requiring only a pre-defined dataset to generate visual representations.

Self-supervised learning for vision transformers requires a pre-defined dataset and configurable parameters like `num_epochs`. While it produces general-purpose features without labels, downstream tasks may still require additional fine-tuning for specific image classification objectives.