> ## Documentation Index
> Fetch the complete documentation index at: https://docs.useocular.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Quick-start

> Train your first custom YOLOv11 model in minutes.

# 🚀 Quick-start Guide

New to Ocular Foundry's model training capabilities? Follow this five-minute path to train and evaluate your
first custom model. Once you are comfortable, circle back to the advanced
pages for deep dives.

## 1. Requirements

| Item                        | Why you need it                                                 |
| --------------------------- | --------------------------------------------------------------- |
| At least one **Version**    | Training uses the frames and annotations in a specific version. |
| **Member** role permissions | You must have member rights to launch training jobs.            |

## 2. Open the **Training** tab

1. Navigate to the **Versions** tab in a project.
2. Select the version you wish to use to train the model
3. Click on the **Train** in the right-hand corner of that version's details page. You will see the following popup to configure and start a training run:

<Frame>
  <img src="https://mintcdn.com/ocularai/FeaFuA3bMytYuXie/images/model-training/train-model-panel-light.png?fit=max&auto=format&n=FeaFuA3bMytYuXie&q=85&s=94a2dfd2c3e148e4f8f3d19c7a5233fb" alt="Training Config UI" width="724" height="1590" data-path="images/model-training/train-model-panel-light.png" />
</Frame>

## 3. Adjust Configuration Settings

Choose the model type, model variant, number of epochs, and image size.

| Setting       | Default | Rationale                                    |
| ------------- | ------- | -------------------------------------------- |
| Model Variant | `m`     | Best blend of speed and accuracy.            |
| Epochs        | `10`    | Enough to verify the workflow.               |
| Image Size    | `640`   | Detects small objects yet fits in 8 GB VRAM. |

<Tip>
  YOLOv11 supports detection, segmentation, pose, classification, and oriented
  bounding boxes (OBB). Ocular currently supports **detection** and **segmentation** models for object detection or segmentation project types, respectively; other types are
  on the roadmap.
</Tip>

## 4. Click **Create Model**

You will be redirected to the "Models" tab, where the run appears instantly in **Model Training History** with a processing 🔵 status. When the training run has completed, it will have a competed 🟢 status.

## 5. Observe Performance Metrics

Performance Metrics and loss curves will be available on the model details page after the training run has completed 🟢 .
You will see the precision, recall, and mAP metrics over epochs, the confusion matrix, and previews of validation set data, and more.

<Check>That's it — you now own a custom detector fine-tuned on your data.</Check>

Next up:

* Fine-tune settings → [Model Training](/build-ai/model-training)
* Interpret metrics → [Model Evaluation](/build-ai/model-evaluation)
* Programmatic workflows → [Notebooks](/build-ai/notebooks)