# Human Review

Now you’ve reached human review (that’s you!). On the right side of the screen, EyePop.ai displays the images that are prioritized for human review based on the resulting health of your model. Image data shown to the left includes the number of images in the dataset, the percentage that are labeled, the Image ID for each image, and the image priority (or how critical it is to train the model on this particular image).

Your task is to verify that all target objects are properly labeled. **Approve** accurate labels, **ignore** irrelevant or low-quality images, and save negative images that correctly did not identify the object. You can also adjust the bounding boxes to fine-tune the labeled areas. **Keyboard shortcuts** are your friends here. You can click the keyboard icon above the image data to see a full list of keyboard shortcuts. Some of the most commonly used include:

* S (Save): this means everything looks good, continue on.
* R (Ignore): this rejects the image from the dataset.
* Arrow keys can be used to navigate left and right through the images.
* Pinch zoom or scroll in with the mouse to enlarge images.

Once you have an acceptable amount of data, the app will give you a **go to training** button (see it pop up in the bottom right corner). Click on this to move into the pretraining check.

### Common Labeling Mistakes

1. **Over-sized Box**: Likely caused by manual copying. This can reduce prediction accuracy as:
   * It’s hard to verify correctness due to massive overlaps.
   * It could cover other objects if they are close.

<figure><img src="/files/FWKcCNx0AS3KYwFZLeBR" alt="" width="375"><figcaption></figcaption></figure>

2. **Double Boxes**: Likely caused by the auto-labeler. This is very harmful to model learning.

<figure><img src="/files/1K8jhw5BGsWaG0SnvXFH" alt=""><figcaption></figcaption></figure>

3. **Missed Box**: Likely caused by manual review errors. When objects are grouped together, it’s easy to miss one box between multiple boxes due to overlapping annotations.

<figure><img src="/files/4LOYcjELsW4ePauIUGD3" alt=""><figcaption></figcaption></figure>


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# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.eyepop.ai/developer-documentation/self-service-training/how-to-train-a-model/human-review.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.