> For the complete documentation index, see [llms.txt](https://docs.eyepop.ai/developer-documentation/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/training-in-progress.md).

# Training in Progress

As the model begins training, EyePop.ai’s training system first augments the dataset. This automatically applies transformations such as noise, rotation, blur, color, and position transformation to simulate a larger dataset. After augmentation, the model begins training in earnest, providing real-time progress feedback by graphing a **Curve of Confidence**.

## **Model Metrics**

The Curve of Confidence is created by graphing **Precision** on the y-axis versus **Recall** on the x-axis.

**Precision** answers the question: did the model find only what I wanted it to find and nothing else? We want to know the model isn’t predicting false negatives.

**Recall** answers the question: did the model find the object in each scene regardless of whether extra objects were identified? We want to know the model is picking up all instances of the object, even if there are extra boxes.

You can expect a training duration of approximately 10-20 minutes for 500 images, depending on the complexity. Exit the training any time by clicking **back to my models** in the upper right corner, and come back once it has finished running.