> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/deploy/active-learning.md).

# Active Learning

Active Learning collects production data from your deployed models and surfaces it for review and retraining. When your model runs inference in production, Active Learning automatically samples images based on rules you configure (such as low-confidence predictions or specific classes) and queues them for human review.

{% hint style="info" %}
Active Learning requires a cloud deployment. It is available on plans with the Annotation Review feature.
{% endhint %}

## Enable Active Learning

1. Open your Project and select "Active Learning" in the left sidebar.
2. Toggle Active Learning on.

Your Project must have a trained model and an active cloud deployment for Active Learning to collect data.

## Configure Collection Rules

Active Learning has two categories of configuration: collection limits and conditions.

### Collection Limits

Control how much data Active Learning collects:

* Sampling rate: the percentage of inferences to sample (ex: 0.5%)
* Per-minute, hourly, and daily image caps
* Compression level and maximum image dimensions
* Batch recreation frequency (daily, weekly, monthly, or never)
* Whether to persist model predictions with collected images

To edit these settings, click "Edit" in the Collection Limits section and update the values in the configuration modal.

### Conditions

Conditions filter which images get collected. You can define rules based on your model type:

* Confidence thresholds (ex: collect images where confidence falls below 60%)
* Specific classes of interest
* Detection count requirements

To set up conditions, click "Edit" in the Conditions section.

## Review Collected Images

As Active Learning collects images, they appear in the Images section of the Active Learning page. The page shows counts of images ready for review and images currently being reviewed.

To start reviewing:

1. Click "Review Images" to open the review queue.
2. Browse batches by status (Not Started, In Progress) and sort by creation date, image count, or progress.
3. Select a batch to begin reviewing.

## Assign Reviewers

You can distribute review work across your team:

1. Open the review queue and select a batch.
2. Click to assign reviewers.
3. Choose team members, set the number of images to assign, and optionally add review instructions.
4. Toggle "Shuffle Images" to randomize which images each reviewer receives.

Reviewers are notified when images are assigned to them. You can reassign images to a different reviewer at any time.

## How Active Learning Fits Your Training Loop

The typical workflow:

1. Train and deploy a model.
2. Enable Active Learning with conditions tuned to your use case.
3. Production data that matches your conditions is collected and queued.
4. Reviewers label the collected images.
5. Add the reviewed images to your dataset and retrain to improve your model.

This creates a feedback loop where your model improves on the cases where it struggles most.


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