How data is shuffled with "model.train"

[aus10powell]

I'm training an object detection model to use counting objects in video. I'm wondering about the details how data is shuffled for the object detection model?

Below are some details

results = model.train(
          data="./data_loader.yaml",
          epochs=config["epochs"],

have pre-defined folders in "data_loader.yaml"

train: train/ 
val: valid/ #
test: test/

I'm wondering how data is randomized since from my understanding it is good to not have the data sequentially trained for the object detection even if it's for a video.

Question #2: What is the learning rate decay from the parameters lr0 = 0.5 to (for example) lrf=0.05

[glenn-jocher]

@aus10powell hello! When you initiate training with model.train, the data is shuffled at the beginning of each epoch to ensure that the model does not learn any inherent sequence from the training set, which is crucial for generalization. The shuffling process is handled internally, so you don't need to worry about manually randomizing the data in your data_loader.yaml file.

Regarding your second question, the learning rate decay is a process where the learning rate decreases from an initial value (lr0) to a final value (lrf) over the course of the training epochs. This is typically done to allow the model to make larger updates to the weights at the beginning of the training (when the learning rate is higher) and smaller, more precise updates towards the end (when the learning rate is lower). The exact schedule and method of decay can vary, but it's often implemented as a function that reduces the learning rate gradually each epoch.

For more detailed information on training parameters and configurations, please refer to the Ultralytics Docs. 😊