+++
disableToc = false
title = "Object Detection"
weight = 13
url = "/features/object-detection/"
+++

LocalAI supports object detection and image segmentation through various backends. This feature allows you to identify and locate objects within images with high accuracy and real-time performance. Available backends include [RF-DETR](https://github.com/roboflow/rf-detr) (Python) and [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp) (native C++/ggml) for object detection and segmentation, and [sam3.cpp](https://github.com/PABannier/sam3.cpp) for image segmentation (SAM 3/2/EdgeTAM).

For detecting **faces** specifically, see the dedicated
[Face Recognition](/features/face-recognition/) feature — its
`/v1/detection` support is tuned for face bounding boxes and ships
with commercially-safe model options.

## Overview

Object detection in LocalAI is implemented through dedicated backends that can identify and locate objects within images. Each backend provides different capabilities and model architectures.

**Key Features:**
- Real-time object detection
- High accuracy detection with bounding boxes
- Image segmentation with binary masks (SAM backends)
- Text-prompted, point-prompted, and box-prompted segmentation
- Support for multiple hardware accelerators (CPU, NVIDIA GPU, Intel GPU, AMD GPU)
- Structured detection results with confidence scores
- Easy integration through the `/v1/detection` endpoint

## Usage

### Detection Endpoint

LocalAI provides a dedicated `/v1/detection` endpoint for object detection tasks. This endpoint is specifically designed for object detection and returns structured detection results with bounding boxes and confidence scores.

### API Reference

To perform object detection, send a POST request to the `/v1/detection` endpoint:

```bash
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "rfdetr-base",
    "image": "https://media.roboflow.com/dog.jpeg"
  }'
```

### Request Format

The request body should contain:

- `model`: The name of the object detection model (e.g., "rfdetr-base")
- `image`: The image to analyze, which can be:
  - A URL to an image
  - A base64-encoded image
- `prompt` (optional): Text prompt for text-prompted segmentation (SAM 3 only)
- `points` (optional): Point coordinates as `[x, y, label, ...]` triples (label: 1=positive, 0=negative)
- `boxes` (optional): Box coordinates as `[x1, y1, x2, y2, ...]` quads
- `threshold` (optional): Detection confidence threshold (default: 0.5)

### Response Format

The API returns a JSON response with detected objects:

```json
{
  "detections": [
    {
      "x": 100.5,
      "y": 150.2,
      "width": 200.0,
      "height": 300.0,
      "confidence": 0.95,
      "class_name": "dog"
    },
    {
      "x": 400.0,
      "y": 200.0,
      "width": 150.0,
      "height": 250.0,
      "confidence": 0.87,
      "class_name": "person"
    }
  ]
}
```

Each detection includes:
- `x`, `y`: Coordinates of the bounding box top-left corner
- `width`, `height`: Dimensions of the bounding box
- `confidence`: Detection confidence score (0.0 to 1.0)
- `class_name`: The detected object class
- `mask` (optional): Base64-encoded PNG binary segmentation mask (SAM backends only)

## Backends

### RF-DETR Backend

The RF-DETR backend is implemented as a Python-based gRPC service that integrates seamlessly with LocalAI. It provides object detection capabilities using the RF-DETR model architecture and supports multiple hardware configurations:

- **CPU**: Optimized for CPU inference
- **NVIDIA GPU**: CUDA acceleration for NVIDIA GPUs
- **Intel GPU**: Intel oneAPI optimization
- **AMD GPU**: ROCm acceleration for AMD GPUs
- **NVIDIA Jetson**: Optimized for ARM64 NVIDIA Jetson devices

#### Setup

1. **Using the Model Gallery (Recommended)**

   The easiest way to get started is using the model gallery. The `rfdetr-base` model is available in the official LocalAI gallery:

   ```bash
   # Install and run the rfdetr-base model
   local-ai run rfdetr-base
   ```

   You can also install it through the web interface by navigating to the Models section and searching for "rfdetr-base".

2. **Manual Configuration**

   Create a model configuration file in your `models` directory:

   ```yaml
   name: rfdetr
   backend: rfdetr
   parameters:
     model: rfdetr-base
   ```

#### Available Models

Currently, the following model is available in the [Model Gallery]({{%relref "features/model-gallery" %}}):

- **rfdetr-base**: Base model with balanced performance and accuracy

You can browse and install this model through the LocalAI web interface or using the command line.

### RF-DETR Native Backend (rfdetr-cpp)

The `rfdetr-cpp` backend is a native C++/ggml implementation of RF-DETR
inference based on [rf-detr.cpp](https://github.com/mudler/rf-detr.cpp). It
runs as a Go gRPC service that dlopens a per-CPU-variant shared library, so
there is no Python runtime on the inference path — startup is fast and the
binary is self-contained.

Compared to the Python `rfdetr` backend, the native backend:

- Has no Python or PyTorch dependency at inference time
- Loads quantized GGUF models (F32, F16, Q8_0, Q4_K) for smaller footprint
- Supports both detection and segmentation variants of RF-DETR
- Returns segmentation masks as PNG bytes in `Detection.mask`

#### Setup

1. **Install the backend**

   ```bash
   local-ai backends install rfdetr-cpp
   ```

2. **Using the Model Gallery (Recommended)**

   The gallery ships ready-to-run entries for every published variant:

   ```bash
   # Detection variants
   local-ai run rfdetr-cpp-nano
   local-ai run rfdetr-cpp-small
   local-ai run rfdetr-cpp-base
   local-ai run rfdetr-cpp-medium
   local-ai run rfdetr-cpp-large

   # Segmentation variants (return per-instance PNG masks)
   local-ai run rfdetr-cpp-seg-nano
   local-ai run rfdetr-cpp-seg-small
   local-ai run rfdetr-cpp-seg-medium
   local-ai run rfdetr-cpp-seg-large
   local-ai run rfdetr-cpp-seg-xlarge
   local-ai run rfdetr-cpp-seg-2xlarge
   ```

3. **Manual Configuration**

   ```yaml
   name: rfdetr-cpp-seg-nano
   backend: rfdetr-cpp
   parameters:
     model: rfdetr-seg-nano-f16.gguf
     threads: 4
   known_usecases:
     - detection
   ```

   Pre-quantized GGUFs are published under
   [`mudler/rfdetr-cpp-*`](https://huggingface.co/mudler?search_models=rfdetr-cpp)
   on Hugging Face. Each repo carries the F32/F16/Q8_0/Q4_K quants — F16 is
   the recommended default (matches F32 accuracy, ~1.86x smaller).

#### Segmentation Output

When running a segmentation model (any `rfdetr-cpp-seg-*` variant), each
`Detection` in the response carries a `mask` field with a base64-encoded
PNG of the per-instance binary mask. The mask is sized to the original
image resolution and aligns with the corresponding bounding box.

### SAM3 Backend (sam3-cpp)

The sam3-cpp backend provides image segmentation using [sam3.cpp](https://github.com/PABannier/sam3.cpp), a portable C++ implementation of Meta's Segment Anything Model. It supports multiple model architectures:

- **SAM 3**: Full model with text encoder for text-prompted detection and segmentation
- **SAM 2 / SAM 2.1**: Hiera backbone models in multiple sizes
- **SAM 3 Visual-Only**: Point/box segmentation without text encoder
- **EdgeTAM**: Ultra-efficient mobile variant (~15MB quantized)

#### Setup

1. **Manual Configuration**

   Create a model configuration file in your `models` directory:

   ```yaml
   name: sam3
   backend: sam3-cpp
   parameters:
     model: edgetam_q4_0.ggml
     threads: 4
   known_usecases:
     - detection
   ```

   Download the model from [Hugging Face](https://huggingface.co/PABannier/sam3.cpp).

#### Segmentation Modes

**Point-prompted segmentation** (all models):

```bash
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sam3",
    "image": "data:image/jpeg;base64,...",
    "points": [256.0, 256.0, 1.0],
    "threshold": 0.5
  }'
```

**Box-prompted segmentation** (all models):

```bash
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sam3",
    "image": "data:image/jpeg;base64,...",
    "boxes": [100.0, 100.0, 400.0, 400.0],
    "threshold": 0.5
  }'
```

**Text-prompted segmentation** (SAM 3 full model only):

```bash
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "sam3",
    "image": "data:image/jpeg;base64,...",
    "prompt": "cat",
    "threshold": 0.5
  }'
```

The response includes segmentation masks as base64-encoded PNGs in the `mask` field of each detection.

## Examples

### Basic Object Detection

```bash
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d '{
    "model": "rfdetr-base",
    "image": "https://example.com/image.jpg"
  }'
```

### Base64 Image Detection

```bash
base64_image=$(base64 -w 0 image.jpg)
curl -X POST http://localhost:8080/v1/detection \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"rfdetr-base\",
    \"image\": \"data:image/jpeg;base64,$base64_image\"
  }"
```

## Troubleshooting

### Common Issues

1. **Model Loading Errors**
   - Ensure the model file is properly downloaded
   - Check available disk space
   - Verify model compatibility with your backend version

2. **Low Detection Accuracy**
   - Ensure good image quality and lighting
   - Check if objects are clearly visible
   - Consider using a larger model for better accuracy

3. **Slow Performance**
   - Enable GPU acceleration if available
   - Use a smaller model for faster inference
   - Optimize image resolution

### Debug Mode

Enable debug logging for troubleshooting:

```bash
local-ai run --debug rfdetr-base
```

## Object Detection Category

LocalAI includes a dedicated **object-detection** category for models and backends that specialize in identifying and locating objects within images. This category currently includes:

- **RF-DETR**: Real-time transformer-based object detection (Python backend)
- **rfdetr-cpp**: Native C++/ggml RF-DETR for detection + segmentation
- **sam3-cpp**: SAM 3/2/EdgeTAM image segmentation

Additional object detection models and backends will be added to this category in the future. You can filter models by the `object-detection` tag in the model gallery to find all available object detection models.

## Related Features

- [🎨 Image generation]({{%relref "features/image-generation" %}}): Generate images with AI
- [📖 Text generation]({{%relref "features/text-generation" %}}): Generate text with language models
- [🔍 GPT Vision]({{%relref "features/gpt-vision" %}}): Analyze images with language models
- [🚀 GPU acceleration]({{%relref "features/GPU-acceleration" %}}): Optimize performance with GPU acceleration