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

# Training Overview

> Overview of Donkeycar autopilot types and training approaches

Donkeycar supports three distinct autopilot approaches, each suited for different racing scenarios and skill levels.

## Autopilot Types

### 1. Deep Learning Autopilot

The most popular approach, using neural networks trained on driving data to predict steering and throttle.

**How it works:**

* Collect training data by manually driving your car
* Train a neural network model using TensorFlow or PyTorch
* Deploy the trained model for autonomous driving

**Best for:**

* Complex tracks with varying conditions
* Racing scenarios requiring smooth control
* Users comfortable with machine learning workflows

**See:** [Deep Learning Training](/training/deep-learning)

### 2. Computer Vision Autopilot

Uses OpenCV and traditional computer vision techniques to follow lines or detect features.

**How it works:**

* Configure color thresholds and image processing parameters
* Use PID controllers to follow detected lines
* No training data required

**Best for:**

* Simple line-following tasks
* Testing and prototyping
* Environments where collecting training data is difficult
* Users who want immediate results without training

**See:** [Computer Vision Autopilot](/training/computer-vision)

### 3. GPS Path Following Autopilot

Records and follows GPS waypoint paths using position sensors like GPS modules or Intel RealSense T265.

**How it works:**

* Drive manually to record a path of waypoints
* Save the path with position and optionally throttle data
* Follow the recorded path using PID control and cross-track error

**Best for:**

* Outdoor racing with GPS coverage
* Precise path following
* Repeatable autonomous missions
* Indoor odometry with wheel encoders or T265

**See:** [GPS Path Following](/training/gps-autopilot)

## Choosing an Autopilot Type

| Criteria           | Deep Learning         | Computer Vision | GPS Path Following |
| ------------------ | --------------------- | --------------- | ------------------ |
| Training Required  | Yes (20+ min driving) | No              | Yes (1 lap)        |
| Setup Complexity   | Medium                | Low             | Medium-High        |
| Track Adaptability | High                  | Low             | Medium             |
| Hardware Required  | Camera                | Camera          | GPS/T265/Encoders  |
| Performance        | Excellent             | Good            | Excellent          |
| Tuning Required    | Minimal               | Significant     | Moderate           |

## Data Collection

For both deep learning and GPS path following, you'll need to collect training data:

**Deep Learning:**

* Drive smoothly for 10-20 minutes
* Collect diverse examples (centerline, recovery, turns)
* Aim for 10,000-20,000 frames

**GPS Path Following:**

* Drive one complete lap of your track
* Maintain consistent speed
* Start and end at approximately the same position

**See:** [Data Collection Guide](/training/data-collection)

## Training Workflow

Typical deep learning training workflow:

1. **Collect Data** - Drive manually to record training data
2. **Review Data** - Clean and filter bad frames using `donkey tubclean`
3. **Train Model** - Run `donkey train` to create a model
4. **Test Model** - Deploy and test in autopilot mode
5. **Iterate** - Collect more data or adjust hyperparameters

## Model Architectures

Donkeycar includes several pre-built model architectures:

* **Linear** - Simple CNN outputting continuous steering/throttle
* **Categorical** - CNN with categorical outputs (discrete bins)
* **LSTM** - Recurrent network using temporal sequences
* **3D CNN** - 3D convolutions over image sequences
* **IMU** - Multi-input model combining camera and IMU data
* **Behavioral** - Conditional model for multiple driving behaviors

## Next Steps

<CardGroup cols={3}>
  <Card title="Data Collection" icon="database" href="/training/data-collection">
    Learn how to collect high-quality training data
  </Card>

  <Card title="Deep Learning" icon="brain" href="/training/deep-learning">
    Train neural network models with TensorFlow/PyTorch
  </Card>

  <Card title="Computer Vision" icon="eye" href="/training/computer-vision">
    Configure line-following with OpenCV
  </Card>

  <Card title="GPS Autopilot" icon="location-dot" href="/training/gps-autopilot">
    Record and follow GPS waypoint paths
  </Card>
</CardGroup>