trainingimage

A training image is an image used during the training phase of a computer vision model. It is a component of the training dataset and, in supervised learning, is typically paired with a ground-truth label such as a class designation, a bounding box, or a segmentation mask. Training images are used to adjust model parameters through optimization algorithms in order to minimize a loss function on the training data.

Preparation of training images includes collection and curation, labeling (when required), and preprocessing. Common preprocessing steps are resizing to a common resolution, normalization, and color space conversion. Data augmentation techniques such as rotations, flips, crops, and color changes are often applied to increase variation, improve robustness, and reduce overfitting. Datasets are usually split into training, validation, and test sets to assess generalization.

The quality and diversity of training images strongly influence model performance. Biases, imbalanced classes, or limited representation of real-world variation can degrade results. Licensing and provenance are important considerations, and copyright restrictions may govern how images are used. Synthetic data can supplement real images in some applications.

Training images underpin a wide range of tasks, including image classification, object detection, semantic segmentation, and image generation. In transfer learning, a model may be pre-trained on a large training image dataset and then fine-tuned on task-specific data. Practical trade-offs involve dataset size, labeling effort, and computational resources needed for training.