---
title: "Finding and Sorting Images"
description: "Find images in LumiScan Annotation Web by file name, show only those without boxes, with a label or with notes, and let the project's network say which images to annotate first."
product: "LumiScan Annotation Web"
component: "lumiscan-annotation-web"
version: "2.2.0"
display_version: "2.2.0.0"
latest_version: false
language: "en"
url: "https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/finding-and-sorting-images.html"
---

# Finding and Sorting Images – LumiScan Annotation Web

Above the image list are the controls that decide which images the list shows and in which order.

![Image list with the search field, Show, Label and Sort, the button Rate with network and images with their uncertainty in percent](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/_images/screens/image-filters.png)

## Search

Type part of a file name, for example `14-13` or `part_03`. The list shows only the matching images. `Enter` opens the first match, `Esc` clears the search.

## Show

|   |   |
| --- | --- |
| All images | Every image of the folder. |
| Without boxes | Images that still need annotating. With [several annotators per image](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/several-annotators-per-image.html): **Not annotated by me** (annotating) or **Need a review** (reviewing). |
| With boxes | Images with an annotation. With several annotators: **Annotated by me** or **Finished**. |
| With notes for me | Images with an open [revision note](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/revision-notes.html) addressed to you. |
| Network unsure | Images the project’s network is unsure about (50 % or more), after [rating](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/finding-and-sorting-images.html#rate-with-network). |

The image you have open always stays in the list. **Previous** and **Next** (`A` / `D`) skip the images the list does not show.

## Label

Only images with at least one box of that label, or **No boxes**. Useful to check all boxes of a rare class, or to find images where a class is missing. The labels come from the saved annotations, so unsaved changes do not count yet.

> [!NOTE]
> With several annotators per image, annotators work without seeing the reference annotation, so the label filter is only offered to reviewers there.

## Sort

|   |   |
| --- | --- |
| File name | The order of the folder. |
| Largest differences first | Reviewing with several annotators: images to check first, the most differences on top. |
| Network least sure first | After rating: the images the network is least sure about on top, with the percentage next to each name. |

## Rate with network (active learning) {#rate-with-network}

Not every image is equally useful for training. Images the network already handles well add little; the ones it is unsure about teach it the most. **Rate with network** lets the project’s trained network look at every image of the folder and say how sure it is.

[Video](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/_images/videos/finding-and-rating-images.mp4)

1. Choose the folder.
2. Click **Rate with network**. By default only images without boxes are rated; tick **Also images with boxes** to rate all, and **Rate again** after a new network was trained.
3. The rating runs on the server; you can keep working. The progress is shown next to the button.
4. The list switches to **Network least sure first**. Annotate from the top.

The button is shown when the server has [box suggestions](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/box-suggestions.html). The network is the one chosen in the [project settings](https://docs.hdvisionsystems.com/en/lumiscan-annotation-web/2.2.0/project-settings.html#box-suggestions).

How the percentage is computed: the network looks for objects down to a very low confidence. A detection exactly at the project’s minimum confidence (default 30 %) counts as 100 % unsure: the network cannot decide whether something is there. A detection near 0 % or 100 % counts as sure. The image gets the value of its most uncertain detection. An image without any detection gets 0 %.

> [!TIP]
> Rate, annotate the 50 to 100 least sure images, train, rate again with the new network. This finds the difficult cases much faster than annotating in file order.