--- library_name: pytorch license: other tags: - real_time - android pipeline_tag: image-segmentation --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/pidnet/web-assets/model_demo.png) # PidNet: Optimized for Mobile Deployment ## Segment images or video by class in real-time on device PIDNet (Proportional-Integral-Derivative Network) is a real-time semantic segmentation model based on PID controllers This model is an implementation of PidNet found [here](https://github.com/XuJiacong/PIDNet). This repository provides scripts to run PidNet on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/pidnet). ### Model Details - **Model Type:** Model_use_case.semantic_segmentation - **Model Stats:** - Model checkpoint: PIDNet_S_Cityscapes_val.pt - Inference latency: RealTime - Input resolution: 1024x2048 - Number of output classes: 19 - Number of parameters: 8.06M - Model size (float): 29.1 MB - Model size (w8a8): 8.02 MB | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | PidNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 136.7 ms | 0 - 57 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 115.422 ms | 24 - 94 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 58.67 ms | 2 - 70 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 69.219 ms | 24 - 110 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 48.184 ms | 2 - 26 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 36.215 ms | 24 - 50 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 29.548 ms | 24 - 85 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.onnx.zip) | | PidNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 58.213 ms | 2 - 59 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 44.393 ms | 24 - 93 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 136.7 ms | 0 - 57 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 115.422 ms | 24 - 94 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 48.699 ms | 2 - 31 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 35.917 ms | 24 - 51 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 65.89 ms | 2 - 61 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 50.378 ms | 23 - 102 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 48.605 ms | 2 - 33 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 36.219 ms | 24 - 55 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 58.213 ms | 2 - 59 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 44.393 ms | 24 - 93 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 31.461 ms | 1 - 71 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 24.998 ms | 24 - 97 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 20.064 ms | 30 - 105 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.onnx.zip) | | PidNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 25.056 ms | 0 - 61 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 18.638 ms | 24 - 106 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 16.172 ms | 7 - 82 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.onnx.zip) | | PidNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 21.762 ms | 1 - 61 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.tflite) | | PidNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 13.717 ms | 24 - 122 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 11.471 ms | 30 - 147 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.onnx.zip) | | PidNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 36.936 ms | 24 - 24 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.dlc) | | PidNet | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 29.762 ms | 24 - 24 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet.onnx.zip) | | PidNet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | TFLITE | 172.215 ms | 2 - 72 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | ONNX | 390.44 ms | 195 - 216 MB | CPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 103.389 ms | 0 - 46 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 125.244 ms | 6 - 70 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 52.636 ms | 1 - 56 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 77.466 ms | 6 - 85 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 52.448 ms | 0 - 25 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 64.955 ms | 6 - 30 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 61.782 ms | 91 - 113 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 53.101 ms | 1 - 45 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 65.749 ms | 6 - 69 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | RB5 (Proxy) | Qualcomm® QCS8250 (Proxy) | ONNX | 354.579 ms | 190 - 201 MB | CPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 103.389 ms | 0 - 46 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 125.244 ms | 6 - 70 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | TFLITE | 52.584 ms | 0 - 20 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | SA8255 (Proxy) | Qualcomm® SA8255P (Proxy) | QNN_DLC | 64.764 ms | 6 - 31 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 61.026 ms | 1 - 49 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 73.693 ms | 6 - 71 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | TFLITE | 52.222 ms | 0 - 20 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | SA8650 (Proxy) | Qualcomm® SA8650P (Proxy) | QNN_DLC | 65.107 ms | 6 - 24 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 53.101 ms | 1 - 45 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 65.749 ms | 6 - 69 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 39.546 ms | 1 - 57 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 48.901 ms | 6 - 84 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 48.515 ms | 105 - 168 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 38.702 ms | 1 - 50 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 41.3 ms | 6 - 82 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 42.726 ms | 101 - 160 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | TFLITE | 69.393 ms | 1 - 53 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | ONNX | 327.861 ms | 191 - 208 MB | CPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | TFLITE | 24.511 ms | 1 - 50 MB | NPU | [PidNet.tflite](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.tflite) | | PidNet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_DLC | 43.463 ms | 6 - 101 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | ONNX | 43.23 ms | 105 - 169 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | | PidNet | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 68.103 ms | 21 - 21 MB | NPU | [PidNet.dlc](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.dlc) | | PidNet | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 62.524 ms | 131 - 131 MB | NPU | [PidNet.onnx.zip](https://huggingface.co/qualcomm/PidNet/blob/main/PidNet_w8a8.onnx.zip) | ## Installation Install the package via pip: ```bash pip install qai-hub-models ``` ## Configure Qualcomm® AI Hub Workbench to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub Workbench](https://workbench.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://workbench.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.pidnet.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.pidnet.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.pidnet.export ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/pidnet/qai_hub_models/models/PidNet/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.pidnet import Model # Load the model torch_model = Model.from_pretrained() # Device device = hub.Device("Samsung Galaxy S25") # Trace model input_shape = torch_model.get_input_spec() sample_inputs = torch_model.sample_inputs() pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()]) # Compile model on a specific device compile_job = hub.submit_compile_job( model=pt_model, device=device, input_specs=torch_model.get_input_spec(), ) # Get target model to run on-device target_model = 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 profile_job = hub.submit_profile_job( model=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 input_data = torch_model.sample_inputs() inference_job = hub.submit_inference_job( model=target_model, device=device, inputs=input_data, ) on_device_output = 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 Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup). ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.pidnet.demo --eval-mode on-device ``` **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.pidnet.demo -- --eval-mode on-device ``` ## 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 PidNet's performance across various devices [here](https://aihub.qualcomm.com/models/pidnet). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of PidNet can be found [here](https://github.com/XuJiacong/PIDNet/blob/main/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 * [PIDNet A Real-time Semantic Segmentation Network Inspired from PID Controller Segmentation of Road Scenes](https://arxiv.org/abs/2206.02066) * [Source Model Implementation](https://github.com/XuJiacong/PIDNet) ## 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).