---
library_name: pytorch
license: other
tags:
- android
pipeline_tag: object-detection

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rf_detr/web-assets/model_demo.png)

# RF-DETR: Optimized for Qualcomm Devices

DETR is a machine learning model that can detect objects (trained on COCO dataset).

This is based on the implementation of RF-DETR found [here](https://github.com/roboflow/rf-detr).
This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/rf_detr) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).

Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.

## Getting Started
There are two ways to deploy this model on your device:

### Option 1: Download Pre-Exported Models

Below are pre-exported model assets ready for deployment.

| Runtime | Precision | Chipset | SDK Versions | Download |
|---|---|---|---|---|
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rf_detr/releases/v0.51.0/rf_detr-qnn_dlc-float.zip)

For more device-specific assets and performance metrics, visit **[RF-DETR on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/rf_detr)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/rf_detr) Python library to compile and export the model with your own:
- Custom weights (e.g., fine-tuned checkpoints)
- Custom input shapes
- Target device and runtime configurations

This option is ideal if you need to customize the model beyond the default configuration provided here.

See our repository for [RF-DETR on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/rf_detr) for usage instructions.

## Model Details

**Model Type:** Model_use_case.object_detection

**Model Stats:**
- Model checkpoint: RF-DETR-base
- Input resolution: 560x560
- Number of parameters: 29.0M
- Model size: 116MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| RF-DETR | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 33.702 ms | 2 - 799 MB | NPU
| RF-DETR | QNN_DLC | float | Snapdragon® X2 Elite | 33.407 ms | 4 - 4 MB | NPU
| RF-DETR | QNN_DLC | float | Snapdragon® X Elite | 73.048 ms | 4 - 4 MB | NPU
| RF-DETR | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 54.481 ms | 3 - 980 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 163.164 ms | 2 - 890 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 74.367 ms | 4 - 6 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® SA8775P | 76.852 ms | 1 - 881 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® QCS9075 | 102.453 ms | 4 - 9 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 109.951 ms | 3 - 1056 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® SA7255P | 163.164 ms | 2 - 890 MB | NPU
| RF-DETR | QNN_DLC | float | Qualcomm® SA8295P | 102.604 ms | 0 - 896 MB | NPU
| RF-DETR | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 38.219 ms | 0 - 847 MB | NPU

## License
* The license for the original implementation of RF-DETR can be found
  [here](https://github.com/roboflow/rf-detr/blob/develop/LICENSE).

## References
* [RF-DETR A SOTA Real-Time Object Detection Model](https://blog.roboflow.com/rf-detr/)
* [Source Model Implementation](https://github.com/roboflow/rf-detr)

## Community
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).