--- library_name: pytorch license: apache-2.0 tags: - real_time - android pipeline_tag: object-detection --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deepbox/web-assets/model_demo.png) # 3D-Deep-BOX: Optimized for Mobile Deployment ## Real-time 3D object detection 3D Deep Box is a machine learning model that predicts 3D bounding boxes and classes of objects in an image. This model is an implementation of 3D-Deep-BOX found [here](https://github.com/skhadem/3D-BoundingBox/). This repository provides scripts to run 3D-Deep-BOX on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/deepbox). ### Model Details - **Model Type:** Object detection - **Model Stats:** - Model checkpoint: YOLOv3-tiny - Input resolution(YOLO): 224x640 - Number of parameters(YOLO): 8.85M - Model size(YOLO): 37.3 MB - Input resolution(VGG): 224x224 - Number of parameters(VGG): 144M - Model size(VGG): 175.9 MB | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | Yolo | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 21.944 ms | 0 - 135 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 2.435 ms | 2 - 4 MB | FP16 | NPU | [3D-Deep-BOX.so](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.so) | | Yolo | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 5.173 ms | 3 - 54 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.onnx) | | Yolo | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 16.556 ms | 0 - 61 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 1.748 ms | 2 - 22 MB | FP16 | NPU | [3D-Deep-BOX.so](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.so) | | Yolo | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 3.528 ms | 1 - 26 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.onnx) | | Yolo | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 14.082 ms | 0 - 33 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 2.087 ms | 2 - 19 MB | FP16 | NPU | Use Export Script | | Yolo | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 4.32 ms | 2 - 15 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.onnx) | | Yolo | SA7255P ADP | SA7255P | TFLITE | 67.784 ms | 0 - 29 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | SA7255P ADP | SA7255P | QNN | 34.599 ms | 2 - 11 MB | FP16 | NPU | Use Export Script | | Yolo | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 22.109 ms | 0 - 141 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | SA8255 (Proxy) | SA8255P Proxy | QNN | 2.436 ms | 2 - 4 MB | FP16 | NPU | Use Export Script | | Yolo | SA8295P ADP | SA8295P | TFLITE | 23.94 ms | 0 - 31 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | SA8295P ADP | SA8295P | QNN | 3.475 ms | 0 - 18 MB | FP16 | NPU | Use Export Script | | Yolo | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 21.84 ms | 0 - 140 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | SA8650 (Proxy) | SA8650P Proxy | QNN | 2.426 ms | 2 - 4 MB | FP16 | NPU | Use Export Script | | Yolo | SA8775P ADP | SA8775P | TFLITE | 27.669 ms | 0 - 29 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | SA8775P ADP | SA8775P | QNN | 3.837 ms | 2 - 11 MB | FP16 | NPU | Use Export Script | | Yolo | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 67.784 ms | 0 - 29 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 34.599 ms | 2 - 11 MB | FP16 | NPU | Use Export Script | | Yolo | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 21.809 ms | 0 - 136 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 2.44 ms | 2 - 5 MB | FP16 | NPU | Use Export Script | | Yolo | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 27.669 ms | 0 - 29 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 3.837 ms | 2 - 11 MB | FP16 | NPU | Use Export Script | | Yolo | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 22.505 ms | 0 - 60 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.tflite) | | Yolo | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 3.531 ms | 2 - 22 MB | FP16 | NPU | Use Export Script | | Yolo | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 2.653 ms | 2 - 2 MB | FP16 | NPU | Use Export Script | | Yolo | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 5.558 ms | 11 - 11 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/Yolo.onnx) | | VGG | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 4.72 ms | 0 - 667 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 4.775 ms | 1 - 2 MB | FP16 | NPU | [3D-Deep-BOX.so](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.so) | | VGG | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 5.323 ms | 0 - 511 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.onnx) | | VGG | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 3.572 ms | 0 - 125 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 3.753 ms | 0 - 18 MB | FP16 | NPU | [3D-Deep-BOX.so](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.so) | | VGG | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 4.057 ms | 0 - 82 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.onnx) | | VGG | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 3.454 ms | 0 - 78 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 3.427 ms | 1 - 76 MB | FP16 | NPU | Use Export Script | | VGG | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 3.811 ms | 1 - 75 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.onnx) | | VGG | SA7255P ADP | SA7255P | TFLITE | 257.893 ms | 0 - 73 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | SA7255P ADP | SA7255P | QNN | 257.859 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | | VGG | SA8255 (Proxy) | SA8255P Proxy | TFLITE | 4.721 ms | 0 - 669 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | SA8255 (Proxy) | SA8255P Proxy | QNN | 4.801 ms | 1 - 3 MB | FP16 | NPU | Use Export Script | | VGG | SA8295P ADP | SA8295P | TFLITE | 9.753 ms | 0 - 76 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | SA8295P ADP | SA8295P | QNN | 9.913 ms | 1 - 19 MB | FP16 | NPU | Use Export Script | | VGG | SA8650 (Proxy) | SA8650P Proxy | TFLITE | 4.729 ms | 0 - 664 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | SA8650 (Proxy) | SA8650P Proxy | QNN | 4.776 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | | VGG | SA8775P ADP | SA8775P | TFLITE | 10.811 ms | 0 - 73 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | SA8775P ADP | SA8775P | QNN | 10.713 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | | VGG | QCS8275 (Proxy) | QCS8275 Proxy | TFLITE | 257.893 ms | 0 - 73 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | QCS8275 (Proxy) | QCS8275 Proxy | QNN | 257.859 ms | 1 - 10 MB | FP16 | NPU | Use Export Script | | VGG | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 4.721 ms | 0 - 668 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 4.772 ms | 1 - 4 MB | FP16 | NPU | Use Export Script | | VGG | QCS9075 (Proxy) | QCS9075 Proxy | TFLITE | 10.811 ms | 0 - 73 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | QCS9075 (Proxy) | QCS9075 Proxy | QNN | 10.713 ms | 0 - 10 MB | FP16 | NPU | Use Export Script | | VGG | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 8.44 ms | 0 - 125 MB | FP16 | NPU | [3D-Deep-BOX.tflite](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.tflite) | | VGG | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 8.649 ms | 1 - 83 MB | FP16 | NPU | Use Export Script | | VGG | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 5.023 ms | 1 - 1 MB | FP16 | NPU | Use Export Script | | VGG | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 5.251 ms | 89 - 89 MB | FP16 | NPU | [3D-Deep-BOX.onnx](https://huggingface.co/qualcomm/3D-Deep-BOX/blob/main/VGG.onnx) | ## Installation Install the package via pip: ```bash pip install "qai-hub-models[deepbox]" ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.deepbox.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.deepbox.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.deepbox.export ``` ``` Profiling Results ------------------------------------------------------------ Yolo Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 21.9 Estimated peak memory usage (MB): [0, 135] Total # Ops : 128 Compute Unit(s) : NPU (128 ops) ------------------------------------------------------------ VGG Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 4.7 Estimated peak memory usage (MB): [0, 667] Total # Ops : 40 Compute Unit(s) : NPU (40 ops) ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/deepbox/qai_hub_models/models/3D-Deep-BOX/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model on-device. Lets go through each step below in detail: Step 1: **Compile model for on-device deployment** To compile a PyTorch model for on-device deployment, we first trace the model in memory using the `jit.trace` and then call the `submit_compile_job` API. ```python import torch import qai_hub as hub from qai_hub_models.models.deepbox import Model # Load the model model = Model.from_pretrained() bbox2D_dectector_model = model.bbox2D_dectector bbox3D_dectector_model = model.bbox3D_dectector # Device device = hub.Device("Samsung Galaxy S23") # Trace model bbox2D_dectector_input_shape = bbox2D_dectector_model.get_input_spec() bbox2D_dectector_sample_inputs = bbox2D_dectector_model.sample_inputs() traced_bbox2D_dectector_model = torch.jit.trace(bbox2D_dectector_model, [torch.tensor(data[0]) for _, data in bbox2D_dectector_sample_inputs.items()]) # Compile model on a specific device bbox2D_dectector_compile_job = hub.submit_compile_job( model=traced_bbox2D_dectector_model , device=device, input_specs=bbox2D_dectector_model.get_input_spec(), ) # Get target model to run on-device bbox2D_dectector_target_model = bbox2D_dectector_compile_job.get_target_model() # Trace model bbox3D_dectector_input_shape = bbox3D_dectector_model.get_input_spec() bbox3D_dectector_sample_inputs = bbox3D_dectector_model.sample_inputs() traced_bbox3D_dectector_model = torch.jit.trace(bbox3D_dectector_model, [torch.tensor(data[0]) for _, data in bbox3D_dectector_sample_inputs.items()]) # Compile model on a specific device bbox3D_dectector_compile_job = hub.submit_compile_job( model=traced_bbox3D_dectector_model , device=device, input_specs=bbox3D_dectector_model.get_input_spec(), ) # Get target model to run on-device bbox3D_dectector_target_model = bbox3D_dectector_compile_job.get_target_model() ``` Step 2: **Performance profiling on cloud-hosted device** After compiling models from step 1. Models can be profiled model on-device using the `target_model`. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics. ```python bbox2D_dectector_profile_job = hub.submit_profile_job( model=bbox2D_dectector_target_model, device=device, ) bbox3D_dectector_profile_job = hub.submit_profile_job( model=bbox3D_dectector_target_model, device=device, ) ``` Step 3: **Verify on-device accuracy** To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device. ```python bbox2D_dectector_input_data = bbox2D_dectector_model.sample_inputs() bbox2D_dectector_inference_job = hub.submit_inference_job( model=bbox2D_dectector_target_model, device=device, inputs=bbox2D_dectector_input_data, ) bbox2D_dectector_inference_job.download_output_data() bbox3D_dectector_input_data = bbox3D_dectector_model.sample_inputs() bbox3D_dectector_inference_job = hub.submit_inference_job( model=bbox3D_dectector_target_model, device=device, inputs=bbox3D_dectector_input_data, ) bbox3D_dectector_inference_job.download_output_data() ``` With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output. **Note**: This on-device profiling and inference requires access to Qualcomm® AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on 3D-Deep-BOX's performance across various devices [here](https://aihub.qualcomm.com/models/deepbox). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of 3D-Deep-BOX can be found [here](https://github.com/skhadem/3D-BoundingBox/blob/master/LICENSE). * The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [3D Bounding Box Estimation Using Deep Learning and Geometry](https://arxiv.org/abs/1612.00496) * [Source Model Implementation](https://github.com/skhadem/3D-BoundingBox/) ## 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).