---
title: DETR-ResNet50
canonical_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/DETR-ResNet50"
md_url: "https://www.modelscope.cn/models/Qualcomm-AI-Hub/DETR-ResNet50.md"
repository: Qualcomm-AI-Hub/DETR-ResNet50
last_updated: 2026-10-16
license: apache-2.0
downloads: 0
stars: 0
tags:
  - bu_auto
  - android
---

# DETR-ResNet50

> DETR-ResNet50 - Qualcomm-AI-Hub 在 ModelScope 开源的模型。DETR-ResNet50: Optimized for Qualcomm Devices

Qualcomm-AI-Hub/DETR-ResNet50 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: Qualcomm-AI-Hub/DETR-ResNet50
- **License**: apache-2.0
- **Tags**: bu_auto, android
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-10-16

Source: https://www.modelscope.cn/models/Qualcomm-AI-Hub/DETR-ResNet50

---

![](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/detr_resnet50/web-assets/model_demo.png)

# DETR-ResNet50: 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 DETR-ResNet50 found [here](https://github.com/facebookresearch/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-cn/blob/main/src/qai_hub_models/models/detr_resnet50) 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.cn) 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://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/detr_resnet50/releases/v0.62.1/detr_resnet50-onnx-float.zip)
| ONNX | w8a16_mixed_fp16 | Universal | QAIRT 2.45, ONNX Runtime 1.27.1 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/detr_resnet50/releases/v0.62.1/detr_resnet50-onnx-w8a16_mixed_fp16.zip)
| QNN_DLC | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/detr_resnet50/releases/v0.62.1/detr_resnet50-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.45 | [Download](https://china-qaihub-public-assets.s3.cn-northwest-1.amazonaws.com.cn/qai-hub-models/models/detr_resnet50/releases/v0.62.1/detr_resnet50-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[DETR-ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.cn/models/detr_resnet50)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/detr_resnet50) 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 [DETR-ResNet50 on GitHub](https://github.com/qualcomm/ai-hub-models-cn/blob/main/src/qai_hub_models/models/detr_resnet50) for usage instructions.

## Model Details

**Model Type:** Model_use_case.object_detection

**Model Stats:**
- Input resolution: 480x480
- Model checkpoint: ResNet50
- Model size (float): 158 MB
- Number of parameters: 41.4M

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| DETR-ResNet50 | ONNX | float | Snapdragon® X2 Elite | 8.242 ms | 5 - 5 MB | NPU
| DETR-ResNet50 | ONNX | float | Snapdragon® X Elite | 17.971 ms | 78 - 78 MB | NPU
| DETR-ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 13.419 ms | 5 - 374 MB | NPU
| DETR-ResNet50 | ONNX | float | Snapdragon® 8 Gen 1 Mobile | 36.007 ms | 1 - 318 MB | NPU
| DETR-ResNet50 | ONNX | float | Qualcomm® Dragonwing™ IQ-8275 | 29.042 ms | 5 - 13 MB | NPU
| DETR-ResNet50 | ONNX | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 17.775 ms | 0 - 94 MB | NPU
| DETR-ResNet50 | ONNX | float | Qualcomm® QCS8450 | 36.007 ms | 1 - 318 MB | NPU
| DETR-ResNet50 | ONNX | float | Qualcomm® Dragonwing™ IQ-9075 | 27.57 ms | 4 - 12 MB | NPU
| DETR-ResNet50 | ONNX | float | Qualcomm® Dragonwing™ IQ-X7181 | 17.971 ms | 78 - 78 MB | NPU
| DETR-ResNet50 | ONNX | float | Qualcomm® Dragonwing™ Q-8750 | 9.975 ms | 3 - 271 MB | NPU
| DETR-ResNet50 | ONNX | float | Snapdragon® 8 Elite Mobile | 9.975 ms | 3 - 271 MB | NPU
| DETR-ResNet50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.436 ms | 2 - 317 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Gen 3 Mobile | 39.415 ms | 6 - 351 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Qualcomm® Dragonwing™ IQ-8275 | 68.232 ms | 16 - 19 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 50.417 ms | 16 - 21 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Qualcomm® QCS8450 | 97.068 ms | 7 - 466 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Qualcomm® Dragonwing™ IQ-9075 | 51.175 ms | 16 - 19 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Qualcomm® Dragonwing™ Q-8750 | 33.662 ms | 15 - 275 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite Mobile | 33.662 ms | 15 - 275 MB | NPU
| DETR-ResNet50 | ONNX | w8a16_mixed_fp16 | Snapdragon® 8 Elite Gen 5 Mobile | 28.229 ms | 15 - 283 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Snapdragon® X2 Elite | 8.917 ms | 5 - 5 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 19.561 ms | 5 - 5 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 13.7 ms | 2 - 357 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 1 Mobile | 41.417 ms | 5 - 303 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-8275 | 27.712 ms | 5 - 12 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 19.181 ms | 5 - 7 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 26.853 ms | 1 - 300 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8650P | 26.853 ms | 1 - 300 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8255P | 26.853 ms | 1 - 300 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 | 41.417 ms | 5 - 303 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-9075 | 27.178 ms | 7 - 13 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® Dragonwing™ IQ-X7181 | 19.561 ms | 5 - 5 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® Dragonwing™ Q-8750 | 10.448 ms | 0 - 273 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 86.011 ms | 1 - 268 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 31.185 ms | 0 - 224 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Mobile | 10.448 ms | 0 - 273 MB | NPU
| DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 7.898 ms | 5 - 290 MB | NPU
| DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 13.771 ms | 0 - 390 MB | NPU
| DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 1 Mobile | 41.717 ms | 0 - 327 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® Dragonwing™ IQ-8275 | 27.739 ms | 0 - 89 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® Dragonwing™ QCS8550 (Proxy) | 19.235 ms | 0 - 2 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 26.917 ms | 0 - 323 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® SA8650P | 26.917 ms | 0 - 323 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® SA8255P | 26.917 ms | 0 - 323 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8450 | 41.717 ms | 0 - 327 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® Dragonwing™ IQ-9075 | 27.256 ms | 0 - 88 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® Dragonwing™ Q-8750 | 10.497 ms | 0 - 302 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 86.011 ms | 0 - 293 MB | NPU
| DETR-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 31.092 ms | 0 - 247 MB | NPU
| DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Mobile | 10.497 ms | 0 - 302 MB | NPU
| DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.047 ms | 0 - 313 MB | NPU

## License
* The license for the original implementation of DETR-ResNet50 can be found
  [here](https://github.com/facebookresearch/detr/blob/main/LICENSE).

## References
* [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872)
* [Source Model Implementation](https://github.com/facebookresearch/detr)

## Community
* For questions or feedback, chat with our engineers in [Qualcomm® AI Hub Workbench](https://workbench.aihub.qualcomm.cn).