--- library_name: pytorch license: other tags: - backbone - android pipeline_tag: video-classification --- ![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/resnet_3d/web-assets/model_demo.png) # ResNet-3D: Optimized for Mobile Deployment ## Sports and human action recognition in videos ResNet 3D is a network with 3D convolutions used for video understanding. This model is an implementation of ResNet-3D found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py). This repository provides scripts to run ResNet-3D on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/resnet_3d). ### Model Details - **Model Type:** Model_use_case.video_classification - **Model Stats:** - Model checkpoint: Kinetics-400 - Input resolution: 112x112 - Number of parameters: 33.4M - Model size (float): 127 MB - Model size (w8a8): 32.1 MB | Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | ResNet-3D | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 571.504 ms | 0 - 184 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 91.333 ms | 1 - 172 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 324.437 ms | 0 - 238 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 26.414 ms | 2 - 216 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 303.707 ms | 0 - 3 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 13.463 ms | 2 - 5 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 13.33 ms | 0 - 80 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.onnx.zip) | | ResNet-3D | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 1317.716 ms | 0 - 185 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 24.215 ms | 2 - 193 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 571.504 ms | 0 - 184 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 91.333 ms | 1 - 172 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 329.574 ms | 0 - 182 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 25.789 ms | 2 - 178 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 1317.716 ms | 0 - 185 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 24.215 ms | 2 - 193 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 217.844 ms | 0 - 248 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 9.462 ms | 2 - 238 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 9.675 ms | 2 - 189 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.onnx.zip) | | ResNet-3D | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 210.267 ms | 0 - 186 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 7.611 ms | 0 - 180 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 7.991 ms | 1 - 130 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.onnx.zip) | | ResNet-3D | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 190.76 ms | 0 - 184 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.tflite) | | ResNet-3D | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 5.848 ms | 2 - 186 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 6.004 ms | 2 - 135 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.onnx.zip) | | ResNet-3D | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 13.895 ms | 2 - 2 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.dlc) | | ResNet-3D | float | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 13.527 ms | 64 - 64 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D.onnx.zip) | | ResNet-3D | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | TFLITE | 1515.831 ms | 678 - 812 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | QNN_DLC | 79.864 ms | 1 - 161 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Dragonwing Q-6690 MTP | Qualcomm® QCM6690 | ONNX | 330.263 ms | 59 - 72 MB | CPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | TFLITE | 1740.677 ms | 695 - 1060 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | QNN_DLC | 18.3 ms | 1 - 3 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Dragonwing RB3 Gen 2 Vision Kit | Qualcomm® QCS6490 | ONNX | 266.615 ms | 56 - 128 MB | CPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | TFLITE | 479.474 ms | 0 - 211 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_DLC | 14.692 ms | 1 - 147 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | TFLITE | 248.814 ms | 0 - 275 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | QCS8450 (Proxy) | Qualcomm® QCS8450 (Proxy) | QNN_DLC | 6.255 ms | 1 - 204 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | TFLITE | 225.312 ms | 0 - 3 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_DLC | 4.45 ms | 1 - 3 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | ONNX | 4.773 ms | 0 - 42 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | TFLITE | 233.199 ms | 0 - 209 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | QCS9075 (Proxy) | Qualcomm® QCS9075 (Proxy) | QNN_DLC | 4.558 ms | 1 - 147 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | SA7255P ADP | Qualcomm® SA7255P | TFLITE | 479.474 ms | 0 - 211 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | SA7255P ADP | Qualcomm® SA7255P | QNN_DLC | 14.692 ms | 1 - 147 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | SA8295P ADP | Qualcomm® SA8295P | TFLITE | 255.582 ms | 0 - 269 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | SA8295P ADP | Qualcomm® SA8295P | QNN_DLC | 8.195 ms | 1 - 152 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | SA8775P ADP | Qualcomm® SA8775P | TFLITE | 233.199 ms | 0 - 209 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | SA8775P ADP | Qualcomm® SA8775P | QNN_DLC | 4.558 ms | 1 - 147 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | TFLITE | 172.818 ms | 0 - 282 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_DLC | 3.305 ms | 1 - 215 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | ONNX | 3.414 ms | 0 - 190 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | TFLITE | 127.751 ms | 0 - 217 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_DLC | 2.583 ms | 1 - 150 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | ONNX | 2.824 ms | 0 - 123 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | TFLITE | 1184.654 ms | 679 - 841 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | QNN_DLC | 7.52 ms | 1 - 143 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Snapdragon 7 Gen 4 QRD | Snapdragon® 7 Gen 4 Mobile | ONNX | 305.592 ms | 43 - 58 MB | CPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | TFLITE | 148.393 ms | 0 - 257 MB | NPU | [ResNet-3D.tflite](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.tflite) | | ResNet-3D | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | QNN_DLC | 1.837 ms | 1 - 154 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen 5 Mobile | ONNX | 2.134 ms | 1 - 119 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | | ResNet-3D | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_DLC | 4.753 ms | 1 - 1 MB | NPU | [ResNet-3D.dlc](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.dlc) | | ResNet-3D | w8a8 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 4.778 ms | 33 - 33 MB | NPU | [ResNet-3D.onnx.zip](https://huggingface.co/qualcomm/ResNet-3D/blob/main/ResNet-3D_w8a8.onnx.zip) | ## Installation Install the package via pip: ```bash # NOTE: 3.10 <= PYTHON_VERSION < 3.14 is supported. pip install "qai-hub-models[resnet-3d]" ``` ## 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.resnet_3d.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.resnet_3d.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.resnet_3d.export ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/resnet_3d/qai_hub_models/models/ResNet-3D/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.resnet_3d 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). ## 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 ResNet-3D's performance across various devices [here](https://aihub.qualcomm.com/models/resnet_3d). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of ResNet-3D can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE). ## References * [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://arxiv.org/abs/1711.11248) * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/video/resnet.py) ## 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).