D-FINE / README.md

v0.66.0

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---
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).

	---
	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).