> 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/train-your-own-model/iterative-training.md).

# Iterative Training

Find weaknesses, add data, and retrain until accuracy holds

Model training is an iterative process. As you begin testing out real world examples on your model, you can identify weaknesses and use test results to pinpoint areas for improvement.

One lever is the **confidence threshold** you set when you run the model — `confidenceThreshold` on the inference component, not a setting in the console. If your model didn’t find an object you wanted it to, see if it picks it up with a lower confidence threshold. If your model is predicting too many of your object and you’re getting a lot of false positives, see if you can increase the confidence threshold to get better results.

If you’re not able to balance the confidence for these two scenarios, begin to analyze the image you’re working with. Add that image to your dataset so that you can train your model on it the next time around. Then, go out and find similar images to add into your dataset. Think about lighting conditions, camera angle, object size, background noise, etc. Think through what might be confusing for the model and bring in more similar examples.

Repeat this process to enhance accuracy and reliability until you are satisfied with your model results. Congrats! You’ve done it.