Pretrained Models and Transfer Learning in YOLO

Training from scratch

Training from scratch refers to the process of training a neural network model without using any prior knowledge or weights from a previously trained model. Instead, the model’s weights are initialized randomly or using specific initialization techniques, and the model learns entirely from the provided dataset. Because the model starts with no prior knowledge, it learns all the features and patterns from the ground up. This means the model tries to capture general and task-specific features from the dataset. However, training from scratch can be time-consuming, especially for complex models and large datasets. The model has to undergo numerous iterations to learn the intricacies of the data.