# After Annotation

You've finished annotating your dataset! 🎉

<figure><img src="/files/ktbnETPTd8ad4lPImBgX" alt="Annotation Finished" width="400"><figcaption><p>YAY!</p></figcaption></figure>

A high-quality dataset is consistent. You may easily follow this document to perform a quick audit of your annotations before moving to training.

***

## Quick-Check via Dataset Filters

In the Image Annotation Window, use the filter dropdown to isolate specific labeling states.

| Filter        | Logic               | What to look for                                   |
| ------------- | ------------------- | -------------------------------------------------- |
| **All**       | Total dataset       | General overview of project volume.                |
| **Annotated** | ≥1 Bounding Box     | Ensure boxes are tight and classes are correct.    |
| **Empty**     | Background/Negative | **Critical:** Confirm these truly have no objects. |
| **Excluded**  | No annotation file  | Ensure no usable data was accidentally hidden.     |

{% hint style="info" %}
**Common Pitfall:** Having "Empty" images that actually contain objects will confuse the model. If an object is there, it must be labeled or the image must be "Excluded."
{% endhint %}

***

## Quick Review: Keyboard Shortcuts

These shortcuts allow for rapid auditing without leaving the canvas.

### Navigation & Class Selection

* `D` / `A`: Next / Previous image.
* `Shift + D` / `Shift + A`: Jump forward/back by 10 images.
* `S` / `W`: Next / Previous class.
* `Shift + S` / `Shift + W`: Jump classes by 3.
* `H` (Hold): Temporarily hide annotations to see the raw image.

### Labeling & File Management

* `O`: **Mark as Background** (Creates/clears an empty annotation file).
* `P`: **Exclude Image** (Removes the annotation file).
* `X`: Remove last bounding box.
* `Shift + C`: Clear all boxes on the current image.
* `M`: Move image + annotation to a `/moved` subfolder (Folder Mode).
* `Shift + Delete`: **Permanently delete** image + annotation.

***

## Advanced Analysis Tools

### A) Class Frequency Analysis

Open **Tools → Class Frequency Analysis** to visualize your data distribution.

* **Rare Classes:** If a class is significantly lower than others, the model may ignore it.
* **Dominant Classes:** If one class makes up the bulk of the data, the model may over-predict it.

If you find imbalances, consider collecting more data for rare classes or removing some examples of dominant classes with redundant images.

Another option is to use data augmentation techniques to artificially increase the variety of underrepresented classes.

### B) Pattern Recognition

Watch for these "Annotation Quality" issues during your review:

* **Loose Boxes:** Too much background noise inside the box.
* **Inconsistent Style:** Mixing tight and loose boxes across the same class.
* **Missing Negatives:** Not enough "Empty" images to teach the model what *isn't* an object.

***

## Audit Routine

1. **Filter to Annotated:** Review \~20–50 images across the entire set (not just the first page).
2. **Filter to Empty:** Review \~10–20 images to ensure they are truly empty.
3. **Spot-check Excluded:** Ensure no high-quality data is sitting idle.
4. **Edge-Case Pass:** Search for the smallest objects, worst glare, and heaviest motion blur.

***

## Validation Set

Pick 30–100 images or video clips that represent "Real World" challenges (bad lighting, clutter, etc.).

Keep these labeled perfectly. Use this set as your final "reality check" before deploying any model to production.

{% hint style="danger" %}
**Backup Reminder:** Always duplicate your dataset folder before running batch operations or mass-deletions.
{% endhint %}


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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.augelab.com/key-features/annotate-data-for-object-detection/after-annotation.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.