DeepSeek employs several data augmentation techniques to enhance the diversity and quantity of training data, ultimately improving the performance of its models. One of the primary techniques is image flipping, where images can be flipped horizontally or vertically. This method helps the model learn to recognize objects regardless of their orientation. For instance, if a model trained on images of vehicles only sees them in a specific orientation, it may struggle to identify them when presented differently. By flipping images, DeepSeek ensures models gain more robust features and better generalization.
Another technique used by DeepSeek is rotation. This involves rotating images by various angles—such as 90, 180, and 270 degrees—to expose the model to different perspectives of the same object. For example, when training a model to identify landmarks, rotating images helps the model learn that a building looks different from various viewpoints. Rotation ensures that the model does not become biased toward a specific orientation, enhancing its ability to recognize landmarks in real-world scenarios.
Additionally, DeepSeek implements random cropping and scaling. Cropping involves taking sub-regions of an image, which helps the model learn to focus on different parts of an object rather than the whole image. This is particularly useful in scenarios where the subject might not always occupy the center of the image. Scaling adjusts the size of images, allowing the model to learn from objects of varying sizes. Together, these techniques create a richer dataset for training, improving the robustness and accuracy of models, especially in tasks like image classification and object detection.