--- library_name: pytorch license: apache-2.0 tags: - android pipeline_tag: object-detection --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/dfine/web-assets/model_demo.png) # D-FINE: Optimized for Qualcomm Devices D-FINE is a state-of-the-art real-time object detection model that redefines the regression task in DETRs as fine-grained distribution refinement. The default variant is trained on the COCO dataset. This is based on the implementation of D-FINE found [here](https://github.com/Peterande/D-FINE). 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/v0.66.0/src/qai_hub_models/models/dfine) 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 2.50, ONNX Runtime 1.30.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/dfine/releases/v0.66.0/dfine-onnx-float.zip) | QNN_DLC | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/dfine/releases/v0.66.0/dfine-qnn_dlc-float.zip) | TFLITE | float | Universal | QAIRT 2.50 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/dfine/releases/v0.66.0/dfine-tflite-float.zip) For more device-specific assets and performance metrics, visit **[D-FINE on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/dfine)**. ### Option 2: Export with Custom Configurations Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.66.0/src/qai_hub_models/models/dfine) 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 [D-FINE on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.66.0/src/qai_hub_models/models/dfine) for usage instructions. ## Model Details **Model Type:** Model_use_case.object_detection **Model Stats:** - Input resolution: 640x640 - Model checkpoint: dfine-nano-coco - Model size (float): 14.6 MB - Number of parameters: 3.79M ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | D-FINE | ONNX | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 6.613 ms | 0 - 296 MB | NPU | D-FINE | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.999 ms | 0 - 285 MB | NPU | D-FINE | ONNX | float | Snapdragon® X2 Elite | 6.843 ms | 5 - 5 MB | NPU | D-FINE | ONNX | float | Snapdragon® X Elite | 16.057 ms | 6 - 6 MB | NPU | D-FINE | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 11.078 ms | 5 - 338 MB | NPU | D-FINE | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 18.557 ms | 5 - 340 MB | NPU | D-FINE | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 15.691 ms | 5 - 9 MB | NPU | D-FINE | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 15.263 ms | 0 - 10 MB | NPU | D-FINE | ONNX | float | Qualcomm® QCS8450 | 18.557 ms | 5 - 340 MB | NPU | D-FINE | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 16.579 ms | 5 - 8 MB | NPU | D-FINE | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 16.057 ms | 6 - 6 MB | NPU | D-FINE | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 7.999 ms | 0 - 285 MB | NPU | D-FINE | ONNX | float | Snapdragon® 8 Elite Extreme Gen 6 Mobile | 5.534 ms | 5 - 183 MB | NPU | D-FINE | ONNX | float | Snapdragon® 8 Elite Gen 6 Mobile | 8.391 ms | 5 - 304 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 For Galaxy Mobile | 5.784 ms | 5 - 167 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.365 ms | 0 - 162 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® X2 Elite | 6.519 ms | 5 - 5 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® X Elite | 14.976 ms | 5 - 5 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 9.816 ms | 5 - 199 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 16.287 ms | 5 - 197 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 14.293 ms | 5 - 12 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 14.112 ms | 5 - 7 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® SA8775P | 15.206 ms | 1 - 149 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® SA8650P | 15.206 ms | 1 - 149 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® SA8255P | 15.206 ms | 1 - 149 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® QCS8450 | 16.287 ms | 5 - 197 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 15.179 ms | 5 - 12 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 14.976 ms | 5 - 5 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 7.365 ms | 0 - 162 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® SA7255P | 29.337 ms | 0 - 146 MB | NPU | D-FINE | QNN_DLC | float | Qualcomm® SA8295P | 17.411 ms | 0 - 154 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® 8 Elite Extreme Gen 6 Mobile | 5.125 ms | 5 - 170 MB | NPU | D-FINE | QNN_DLC | float | Snapdragon® 8 Elite Gen 6 Mobile | 7.711 ms | 4 - 170 MB | NPU | D-FINE | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 21.947 ms | 0 - 491 MB | NPU | D-FINE | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 29.465 ms | 0 - 653 MB | NPU | D-FINE | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 46.231 ms | 0 - 553 MB | NPU | D-FINE | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 42.608 ms | 0 - 7 MB | NPU | D-FINE | TFLITE | float | Qualcomm® QCS8450 | 46.231 ms | 0 - 553 MB | NPU | D-FINE | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 43.635 ms | 0 - 22 MB | NPU | D-FINE | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 21.947 ms | 0 - 491 MB | NPU | D-FINE | TFLITE | float | Qualcomm® SA8295P | 46.048 ms | 0 - 459 MB | NPU | D-FINE | TFLITE | float | Snapdragon® 8 Elite Gen 6 Mobile | 24.201 ms | 0 - 767 MB | NPU ## License * The license for the original implementation of D-FINE can be found [here](https://github.com/Peterande/D-FINE/blob/master/LICENSE). ## References * [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) * [Source Model Implementation](https://github.com/Peterande/D-FINE) ## 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).