I'm training a yolov5 model and I have a dataset but I couldn't understand the differences between validation and test data. Should I split my dataset to train/test/val or just train/test?
The validation dataset provides an unbiased assessment of a fitted model on the training dataset while fitting the model's hyper-parameters (e.g. the number of hidden units - layers and layer widths - in a neural network). Validation datasets can be used for early stop regularization (stopping training when the error in the validation dataset increases, as this is a sign of overfitting to the training dataset)