--- 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/v0.60.0/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 | |---|---|---|---|---| | ONNX | float | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/rf_detr/releases/v0.60.0/rf_detr-onnx-float.zip) | 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/v0.60.0/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/v0.60.0/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/v0.60.0/src/qai_hub_models/models/rf_detr) for usage instructions. ## Model Details **Model Type:** Model_use_case.object_detection **Model Stats:** - Input resolution: 512x512 - Model checkpoint: RF-DETR-small - Model size (float): 109 MB - Number of parameters: 28.5M - Supported variants: nano (384x384), small (512x512), medium (576x576), base (560x560) ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | RF-DETR | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 30.758 ms | 0 - 408 MB | NPU | RF-DETR | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 99.23 ms | 13 - 431 MB | NPU | RF-DETR | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 51.536 ms | 9 - 16 MB | NPU | RF-DETR | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 41.839 ms | 6 - 10 MB | NPU | RF-DETR | ONNX | float | Qualcomm® QCS8450 | 99.23 ms | 13 - 431 MB | NPU | RF-DETR | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 50.38 ms | 11 - 17 MB | NPU | RF-DETR | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 24.011 ms | 10 - 332 MB | NPU | RF-DETR | ONNX | float | Snapdragon® 8 Elite Mobile | 24.011 ms | 10 - 332 MB | NPU | RF-DETR | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 20.115 ms | 11 - 364 MB | NPU | RF-DETR | QNN_DLC | float | Snapdragon® X2 Elite | 25.273 ms | 3 - 3 MB | NPU | RF-DETR | QNN_DLC | float | Snapdragon® X Elite | 50.732 ms | 3 - 3 MB | NPU | RF-DETR | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 36.375 ms | 0 - 472 MB | NPU | RF-DETR | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 99.537 ms | 3 - 477 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 56.325 ms | 3 - 8 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 135.464 ms | 1 - 358 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 49.887 ms | 3 - 6 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® SA8775P | 56.777 ms | 1 - 385 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® SA8650P | 56.777 ms | 1 - 385 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® SA8255P | 56.777 ms | 1 - 385 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® QCS8450 | 99.537 ms | 3 - 477 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 57.242 ms | 5 - 10 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 50.732 ms | 3 - 3 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 28.162 ms | 0 - 407 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® SA7255P | 135.464 ms | 1 - 358 MB | NPU | RF-DETR | QNN_DLC | float | Qualcomm® SA8295P | 81.197 ms | 0 - 365 MB | NPU | RF-DETR | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 28.162 ms | 0 - 407 MB | NPU | RF-DETR | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 23.364 ms | 3 - 437 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).