v0.40.0

See https://github.com/quic/ai-hub-models/releases/v0.40.0 for changelog.

.gitattributes

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+DEPLOYMENT_MODEL_LICENSE.pdf filter=lfs diff=lfs merge=lfs -text

DEPLOYMENT_MODEL_LICENSE.pdf

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LICENSE

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+The license of the original trained model can be found at https://github.com/w-hc/torch_audioset/blob/master/LICENSE.
+The license for the deployable model files (.tflite, .onnx, .dlc, .bin, etc.) can be found in DEPLOYMENT_MODEL_LICENSE.pdf.

README.md

@@ -0,0 +1,243 @@
+---
+library_name: pytorch
+license: other
+tags:
+- android
+pipeline_tag: other
+
+---
+
+![](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/bevfusion_det/web-assets/model_demo.png)
+
+# BEVFusion: Optimized for Mobile Deployment
+## Construct a bird’s eye view from sensors mounted on a vehicle
+
+
+BeVFusion is a machine learning model for generating a birds eye view represenation from the sensors(cameras) mounted on a vehicle.
+
+This model is an implementation of BEVFusion found [here](https://github.com/mit-han-lab/bevfusion).
+
+
+This repository provides scripts to run BEVFusion on Qualcomm® devices.
+More details on model performance across various devices, can be found
+[here](https://aihub.qualcomm.com/models/bevfusion_det).
+
+
+
+### Model Details
+
+- **Model Type:** Model_use_case.driver_assistance
+- **Model Stats:**
+  - Model checkpoint: camera-only-det.pth
+  - Input resolution: 1 x 6 x 3 x 256 x 704
+  - Number of parameters: 44M
+  - Model size: 171 MB
+
+| Model | Precision | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit | Target Model
+|---|---|---|---|---|---|---|---|---|
+| BEVFusionEncoder1 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 1078.859 ms | 4 - 14 MB | NPU | Use Export Script |
+| BEVFusionEncoder1 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 732.856 ms | 13 - 16 MB | NPU | Use Export Script |
+| BEVFusionEncoder1 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 547.855 ms | 13 - 31 MB | NPU | Use Export Script |
+| BEVFusionEncoder1 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 440.393 ms | 12 - 29 MB | NPU | Use Export Script |
+| BEVFusionEncoder1 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 361.452 ms | 12 - 23 MB | NPU | Use Export Script |
+| BEVFusionEncoder1 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 691.022 ms | 12 - 12 MB | NPU | Use Export Script |
+| BEVFusionEncoder2 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 5445.765 ms | 17 - 27 MB | NPU | Use Export Script |
+| BEVFusionEncoder2 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 3543.695 ms | 17 - 20 MB | NPU | Use Export Script |
+| BEVFusionEncoder2 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 2635.488 ms | 17 - 35 MB | NPU | Use Export Script |
+| BEVFusionEncoder2 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 2345.316 ms | 17 - 33 MB | NPU | Use Export Script |
+| BEVFusionEncoder2 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 2280.661 ms | 17 - 27 MB | NPU | Use Export Script |
+| BEVFusionEncoder2 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 3337.453 ms | 17 - 17 MB | NPU | Use Export Script |
+| BEVFusionEncoder3 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 994.618 ms | 609 - 619 MB | NPU | Use Export Script |
+| BEVFusionEncoder3 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 648.913 ms | 611 - 614 MB | NPU | Use Export Script |
+| BEVFusionEncoder3 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 523.718 ms | 608 - 627 MB | NPU | Use Export Script |
+| BEVFusionEncoder3 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 445.664 ms | 609 - 630 MB | NPU | Use Export Script |
+| BEVFusionEncoder3 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 367.939 ms | 609 - 624 MB | NPU | Use Export Script |
+| BEVFusionEncoder3 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 605.931 ms | 610 - 610 MB | NPU | Use Export Script |
+| BEVFusionEncoder4 | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 25.458 ms | 18 - 28 MB | NPU | Use Export Script |
+| BEVFusionEncoder4 | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 12.57 ms | 19 - 21 MB | NPU | Use Export Script |
+| BEVFusionEncoder4 | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 9.191 ms | 14 - 33 MB | NPU | Use Export Script |
+| BEVFusionEncoder4 | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 7.772 ms | 13 - 30 MB | NPU | Use Export Script |
+| BEVFusionEncoder4 | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 6.806 ms | 18 - 30 MB | NPU | Use Export Script |
+| BEVFusionEncoder4 | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 12.112 ms | 19 - 19 MB | NPU | Use Export Script |
+| BEVFusionDecoder | float | QCS8275 (Proxy) | Qualcomm® QCS8275 (Proxy) | QNN_CONTEXT_BINARY | 92.613 ms | 4 - 14 MB | NPU | Use Export Script |
+| BEVFusionDecoder | float | QCS8550 (Proxy) | Qualcomm® QCS8550 (Proxy) | QNN_CONTEXT_BINARY | 12.479 ms | 5 - 8 MB | NPU | Use Export Script |
+| BEVFusionDecoder | float | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 Mobile | QNN_CONTEXT_BINARY | 9.692 ms | 5 - 23 MB | NPU | Use Export Script |
+| BEVFusionDecoder | float | Samsung Galaxy S25 | Snapdragon® 8 Elite For Galaxy Mobile | QNN_CONTEXT_BINARY | 7.682 ms | 5 - 18 MB | NPU | Use Export Script |
+| BEVFusionDecoder | float | Snapdragon 8 Elite Gen 5 QRD | Snapdragon® 8 Elite Gen5 Mobile | QNN_CONTEXT_BINARY | 5.949 ms | 5 - 16 MB | NPU | Use Export Script |
+| BEVFusionDecoder | float | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN_CONTEXT_BINARY | 13.126 ms | 5 - 5 MB | NPU | Use Export Script |
+
+
+
+
+## Installation
+
+
+Install the package via pip:
+```bash
+pip install "qai-hub-models[bevfusion-det]" torch==2.4.1 --trusted-host download.openmmlab.com -f https://download.openmmlab.com/mmcv/dist/cpu/torch2.4/index.html -f https://qaihub-public-python-wheels.s3.us-west-2.amazonaws.com/index.html
+```
+
+
+## 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.bevfusion_det.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.bevfusion_det.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.bevfusion_det.export
+```
+
+
+
+## How does this work?
+
+This [export script](https://aihub.qualcomm.com/models/bevfusion_det/qai_hub_models/models/BEVFusion/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.bevfusion_det 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. [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 BEVFusion's performance across various devices [here](https://aihub.qualcomm.com/models/bevfusion_det).
+Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
+
+
+## License
+* The license for the original implementation of BEVFusion can be found
+  [here](https://github.com/w-hc/torch_audioset/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
+* [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://arxiv.org/abs/2205.13542)
+* [Source Model Implementation](https://github.com/mit-han-lab/bevfusion)
+
+
+
+## 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).
+
+

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