README.md

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

---

![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/detr_resnet101_dc5/web-assets/model_demo.png)

# DETR-ResNet101-DC5: 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-ResNet101-DC5 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/quic/ai-hub-models/blob/main/qai_hub_models/models/detr_resnet101_dc5) 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 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/detr_resnet101_dc5/releases/v0.46.0/detr_resnet101_dc5-onnx-float.zip)
| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/detr_resnet101_dc5/releases/v0.46.0/detr_resnet101_dc5-qnn_dlc-float.zip)
| TFLITE | float | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/detr_resnet101_dc5/releases/v0.46.0/detr_resnet101_dc5-tflite-float.zip)

For more device-specific assets and performance metrics, visit **[DETR-ResNet101-DC5 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/detr_resnet101_dc5)**.


### Option 2: Export with Custom Configurations

Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/detr_resnet101_dc5) 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-ResNet101-DC5 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/detr_resnet101_dc5) for usage instructions.

## Model Details

**Model Type:** Model_use_case.object_detection

**Model Stats:**
- Model checkpoint: ResNet101-DC5
- Input resolution: 480x480
- Model size (float): 232 MB

## Performance Summary
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|---|---|---|---|---|---|---
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® X Elite | 50.19 ms | 116 - 116 MB | NPU
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 36.327 ms | 7 - 634 MB | NPU
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 49.085 ms | 0 - 126 MB | NPU
| DETR-ResNet101-DC5 | ONNX | float | Qualcomm® QCS9075 | 71.995 ms | 5 - 12 MB | NPU
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 27.503 ms | 2 - 406 MB | NPU
| DETR-ResNet101-DC5 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 21.467 ms | 1 - 494 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® X Elite | 54.381 ms | 5 - 5 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 39.11 ms | 5 - 719 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 219.013 ms | 2 - 531 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 53.499 ms | 5 - 7 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA8775P | 72.159 ms | 2 - 528 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® QCS9075 | 81.376 ms | 5 - 11 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 88.126 ms | 3 - 490 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA7255P | 219.013 ms | 2 - 531 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Qualcomm® SA8295P | 76.367 ms | 0 - 338 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 28.632 ms | 5 - 558 MB | NPU
| DETR-ResNet101-DC5 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 23.02 ms | 5 - 534 MB | NPU

## License
* The license for the original implementation of DETR-ResNet101-DC5 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
* 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).