--- library_name: pytorch license: other tags: - android pipeline_tag: object-detection --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/conditional_detr_resnet50/web-assets/model_demo.png) # Conditional-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 Conditional-DETR-ResNet50 found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_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/conditional_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.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/conditional_detr_resnet50/releases/v0.46.0/conditional_detr_resnet50-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/conditional_detr_resnet50/releases/v0.46.0/conditional_detr_resnet50-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/conditional_detr_resnet50/releases/v0.46.0/conditional_detr_resnet50-tflite-float.zip) For more device-specific assets and performance metrics, visit **[Conditional-DETR-ResNet50 on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/conditional_detr_resnet50)**. ### 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/conditional_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 [Conditional-DETR-ResNet50 on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/conditional_detr_resnet50) for usage instructions. ## Model Details **Model Type:** Model_use_case.object_detection **Model Stats:** - Model checkpoint: ResNet50 - Input resolution: 480x480 - Number of parameters: 43.6M - Model size (float): 166 MB ## Performance Summary | Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit |---|---|---|---|---|---|--- | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® X Elite | 21.551 ms | 83 - 83 MB | NPU | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 15.521 ms | 0 - 379 MB | NPU | Conditional-DETR-ResNet50 | ONNX | float | Qualcomm® QCS8550 (Proxy) | 21.561 ms | 0 - 94 MB | NPU | Conditional-DETR-ResNet50 | ONNX | float | Qualcomm® QCS9075 | 33.407 ms | 4 - 12 MB | NPU | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.775 ms | 0 - 283 MB | NPU | Conditional-DETR-ResNet50 | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.926 ms | 0 - 276 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® X Elite | 23.12 ms | 5 - 5 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 16.723 ms | 3 - 436 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 98.38 ms | 0 - 326 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 22.964 ms | 5 - 7 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8775P | 145.739 ms | 1 - 327 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS9075 | 37.338 ms | 7 - 13 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 46.462 ms | 4 - 372 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA7255P | 98.38 ms | 0 - 326 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Qualcomm® SA8295P | 34.708 ms | 0 - 279 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.53 ms | 5 - 342 MB | NPU | Conditional-DETR-ResNet50 | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 8.878 ms | 5 - 348 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 16.973 ms | 24 - 495 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 92.649 ms | 0 - 367 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 22.1 ms | 0 - 3 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® SA8775P | 29.851 ms | 0 - 426 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS9075 | 33.136 ms | 0 - 93 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 45.579 ms | 0 - 407 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® SA7255P | 92.649 ms | 0 - 367 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Qualcomm® SA8295P | 34.871 ms | 0 - 316 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 11.897 ms | 0 - 361 MB | NPU | Conditional-DETR-ResNet50 | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 9.205 ms | 0 - 462 MB | NPU ## License * The license for the original implementation of Conditional-DETR-ResNet50 can be found [here](https://github.com/huggingface/transformers/blob/main/LICENSE). ## References * [Conditional {DETR} for Fast Training Convergence](https://arxiv.org/abs/2108.06152) * [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/conditional_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).