--- 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/web-assets/model_demo.png) # DETR-ResNet101: 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 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/blob/main/src/qai_hub_models/models/detr_resnet101) 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 2.42, ONNX Runtime 1.24.3 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/detr_resnet101/releases/v0.51.0/detr_resnet101-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/detr_resnet101/releases/v0.51.0/detr_resnet101-qnn_dlc-float.zip) | TFLITE | float | Universal | QAIRT 2.45 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/detr_resnet101/releases/v0.51.0/detr_resnet101-tflite-float.zip) For more device-specific assets and performance metrics, visit **[DETR-ResNet101 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/detr_resnet101)**. ### Option 2: Export with Custom Configurations Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/detr_resnet101) 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 on GitHub](https://github.com/qualcomm/ai-hub-models/blob/main/src/qai_hub_models/models/detr_resnet101) for usage instructions. ## Model Details **Model Type:** Model_use_case.object_detection **Model Stats:** - Model checkpoint: ResNet101 - Input resolution: 480x480 - Number of parameters: 60.3M - Model size (float): 230 MB ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | DETR-ResNet101 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 11.672 ms | 5 - 383 MB | NPU | DETR-ResNet101 | ONNX | float | Snapdragon® X2 Elite | 12.82 ms | 115 - 115 MB | NPU | DETR-ResNet101 | ONNX | float | Snapdragon® X Elite | 25.435 ms | 114 - 114 MB | NPU | DETR-ResNet101 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 18.996 ms | 5 - 497 MB | NPU | DETR-ResNet101 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 25.159 ms | 0 - 130 MB | NPU | DETR-ResNet101 | ONNX | float | Qualcomm® QCS9075 | 40.365 ms | 5 - 12 MB | NPU | DETR-ResNet101 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 14.998 ms | 0 - 318 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 12.379 ms | 5 - 360 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Snapdragon® X2 Elite | 13.884 ms | 5 - 5 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Snapdragon® X Elite | 28.314 ms | 5 - 5 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 20.52 ms | 3 - 463 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 139.859 ms | 2 - 338 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 27.732 ms | 5 - 7 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® SA8775P | 42.301 ms | 1 - 336 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® QCS9075 | 46.805 ms | 5 - 11 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 58.755 ms | 2 - 362 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® SA7255P | 139.859 ms | 2 - 338 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Qualcomm® SA8295P | 44.907 ms | 2 - 250 MB | NPU | DETR-ResNet101 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 16.026 ms | 0 - 338 MB | NPU | DETR-ResNet101 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 10.852 ms | 0 - 388 MB | NPU | DETR-ResNet101 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 19.478 ms | 0 - 489 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 137.596 ms | 0 - 369 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 26.277 ms | 0 - 3 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® SA8775P | 41.001 ms | 0 - 367 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® QCS9075 | 40.67 ms | 0 - 125 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 54.861 ms | 0 - 385 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® SA7255P | 137.596 ms | 0 - 369 MB | NPU | DETR-ResNet101 | TFLITE | float | Qualcomm® SA8295P | 42.969 ms | 0 - 274 MB | NPU | DETR-ResNet101 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 15.188 ms | 0 - 371 MB | NPU ## License * The license for the original implementation of DETR-ResNet101 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).