v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
DeepLabV3-Plus-MobileNet_float.dlc
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README.md
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# DeepLabV3-Plus-MobileNet: Optimized for
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## Deep Convolutional Neural Network model for semantic segmentation
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The package contains a simple end-to-end demo that downloads pre-trained
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weights and runs this model on a sample input.
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```bash
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python -m qai_hub_models.models.deeplabv3_plus_mobilenet.demo
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```
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The above demo runs a reference implementation of pre-processing, model
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inference, and post processing.
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**NOTE**: If you want running in a Jupyter Notebook or Google Colab like
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environment, please add the following to your cell (instead of the above).
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```
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%run -m qai_hub_models.models.deeplabv3_plus_mobilenet.demo
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```
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### Run model on a cloud-hosted device
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In addition to the demo, you can also run the model on a cloud-hosted Qualcomm®
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device. This script does the following:
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* Performance check on-device on a cloud-hosted device
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* Downloads compiled assets that can be deployed on-device for Android.
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* Accuracy check between PyTorch and on-device outputs.
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```bash
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python -m qai_hub_models.models.deeplabv3_plus_mobilenet.export
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```
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## How does this work?
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This [export script](https://aihub.qualcomm.com/models/deeplabv3_plus_mobilenet/qai_hub_models/models/DeepLabV3-Plus-MobileNet/export.py)
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leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model
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on-device. Lets go through each step below in detail:
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Step 1: **Compile model for on-device deployment**
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To compile a PyTorch model for on-device deployment, we first trace the model
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in memory using the `jit.trace` and then call the `submit_compile_job` API.
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```python
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import torch
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import qai_hub as hub
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from qai_hub_models.models.deeplabv3_plus_mobilenet import Model
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# Load the model
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torch_model = Model.from_pretrained()
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# Device
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device = hub.Device("Samsung Galaxy S25")
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# Trace model
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input_shape = torch_model.get_input_spec()
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sample_inputs = torch_model.sample_inputs()
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pt_model = torch.jit.trace(torch_model, [torch.tensor(data[0]) for _, data in sample_inputs.items()])
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# Compile model on a specific device
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compile_job = hub.submit_compile_job(
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model=pt_model,
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device=device,
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input_specs=torch_model.get_input_spec(),
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)
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# Get target model to run on-device
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target_model = compile_job.get_target_model()
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```
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Step 2: **Performance profiling on cloud-hosted device**
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After compiling models from step 1. Models can be profiled model on-device using the
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`target_model`. Note that this scripts runs the model on a device automatically
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provisioned in the cloud. Once the job is submitted, you can navigate to a
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provided job URL to view a variety of on-device performance metrics.
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```python
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profile_job = hub.submit_profile_job(
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)
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```
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on sample input data on the same cloud hosted device.
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input_data = torch_model.sample_inputs()
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inference_job = hub.submit_inference_job(
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model=target_model,
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device=device,
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inputs=input_data,
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)
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on_device_output = inference_job.download_output_data()
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```
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spot check the output with expected output.
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**Note**: This on-device profiling and inference requires access to Qualcomm®
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AI Hub Workbench. [Sign up for access](https://myaccount.qualcomm.com/signup).
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## Run demo on a cloud-hosted device
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You can also run the demo on-device.
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```bash
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python -m qai_hub_models.models.deeplabv3_plus_mobilenet.demo --eval-mode on-device
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```
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environment, please add the following to your cell (instead of the above).
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%run -m qai_hub_models.models.deeplabv3_plus_mobilenet.demo -- --eval-mode on-device
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```
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## Deploying compiled model to Android
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The models can be deployed using multiple runtimes:
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- TensorFlow Lite (`.tflite` export): [This
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tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a
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guide to deploy the .tflite model in an Android application.
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- QNN (`.so` export ): This [sample
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app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html)
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provides instructions on how to use the `.so` shared library in an Android application.
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## View on Qualcomm® AI Hub
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Get more details on DeepLabV3-Plus-MobileNet's performance across various devices [here](https://aihub.qualcomm.com/models/deeplabv3_plus_mobilenet).
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Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/)
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## License
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* The license for the original implementation of DeepLabV3-Plus-MobileNet can be found
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[here](https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/LICENSE).
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## References
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* [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)
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* [Source Model Implementation](https://github.com/jfzhang95/pytorch-deeplab-xception)
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## Community
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* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
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* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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# DeepLabV3-Plus-MobileNet: Optimized for Qualcomm Devices
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DeepLabV3 is designed for semantic segmentation at multiple scales, trained on the various datasets. It uses MobileNet as a backbone.
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This is based on the implementation of DeepLabV3-Plus-MobileNet found [here](https://github.com/jfzhang95/pytorch-deeplab-xception).
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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/deeplabv3_plus_mobilenet) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
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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.
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## Getting Started
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There are two ways to deploy this model on your device:
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### Option 1: Download Pre-Exported Models
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Below are pre-exported model assets ready for deployment.
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| Runtime | Precision | Chipset | SDK Versions | Download |
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|---|---|---|---|---|
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| 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/deeplabv3_plus_mobilenet/releases/v0.46.1/deeplabv3_plus_mobilenet-onnx-float.zip)
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| QNN_DLC | float | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplabv3_plus_mobilenet/releases/v0.46.1/deeplabv3_plus_mobilenet-qnn_dlc-float.zip)
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| QNN_DLC | w8a16 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplabv3_plus_mobilenet/releases/v0.46.1/deeplabv3_plus_mobilenet-qnn_dlc-w8a16.zip)
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| QNN_DLC | w8a8 | Universal | QAIRT 2.42 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplabv3_plus_mobilenet/releases/v0.46.1/deeplabv3_plus_mobilenet-qnn_dlc-w8a8.zip)
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| 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/deeplabv3_plus_mobilenet/releases/v0.46.1/deeplabv3_plus_mobilenet-tflite-float.zip)
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| TFLITE | w8a8 | Universal | QAIRT 2.42, TFLite 2.17.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/deeplabv3_plus_mobilenet/releases/v0.46.1/deeplabv3_plus_mobilenet-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[DeepLabV3-Plus-MobileNet on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/deeplabv3_plus_mobilenet)**.
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### Option 2: Export with Custom Configurations
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Use the [Qualcomm® AI Hub Models](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/deeplabv3_plus_mobilenet) Python library to compile and export the model with your own:
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- Custom weights (e.g., fine-tuned checkpoints)
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- Custom input shapes
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- Target device and runtime configurations
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This option is ideal if you need to customize the model beyond the default configuration provided here.
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See our repository for [DeepLabV3-Plus-MobileNet on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/deeplabv3_plus_mobilenet) for usage instructions.
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## Model Details
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**Model Type:** Model_use_case.semantic_segmentation
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| 55 |
+
**Model Stats:**
|
| 56 |
+
- Model checkpoint: VOC2012
|
| 57 |
+
- Input resolution: 513x513
|
| 58 |
+
- Number of output classes: 21
|
| 59 |
+
- Number of parameters: 5.80M
|
| 60 |
+
- Model size (float): 22.2 MB
|
| 61 |
+
- Model size (w8a16): 6.67 MB
|
| 62 |
+
|
| 63 |
+
## Performance Summary
|
| 64 |
+
| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
|
| 65 |
+
|---|---|---|---|---|---|---
|
| 66 |
+
| DeepLabV3-Plus-MobileNet | ONNX | float | Snapdragon® X Elite | 10.692 ms | 10 - 10 MB | NPU
|
| 67 |
+
| DeepLabV3-Plus-MobileNet | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 7.435 ms | 4 - 160 MB | NPU
|
| 68 |
+
| DeepLabV3-Plus-MobileNet | ONNX | float | Qualcomm® QCS8550 (Proxy) | 10.182 ms | 0 - 146 MB | NPU
|
| 69 |
+
| DeepLabV3-Plus-MobileNet | ONNX | float | Qualcomm® QCS9075 | 17.954 ms | 3 - 6 MB | NPU
|
| 70 |
+
| DeepLabV3-Plus-MobileNet | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 5.875 ms | 1 - 119 MB | NPU
|
| 71 |
+
| DeepLabV3-Plus-MobileNet | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.63 ms | 0 - 122 MB | NPU
|
| 72 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Snapdragon® X Elite | 12.301 ms | 3 - 3 MB | NPU
|
| 73 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 8.058 ms | 3 - 211 MB | NPU
|
| 74 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 58.563 ms | 0 - 171 MB | NPU
|
| 75 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 11.532 ms | 3 - 148 MB | NPU
|
| 76 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® SA8775P | 17.648 ms | 2 - 175 MB | NPU
|
| 77 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® QCS9075 | 20.203 ms | 3 - 8 MB | NPU
|
| 78 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 19.04 ms | 3 - 212 MB | NPU
|
| 79 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® SA7255P | 58.563 ms | 0 - 171 MB | NPU
|
| 80 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Qualcomm® SA8295P | 19.465 ms | 0 - 169 MB | NPU
|
| 81 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.249 ms | 3 - 202 MB | NPU
|
| 82 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.323 ms | 3 - 187 MB | NPU
|
| 83 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Snapdragon® X Elite | 9.151 ms | 2 - 2 MB | NPU
|
| 84 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Snapdragon® 8 Gen 3 Mobile | 6.134 ms | 0 - 219 MB | NPU
|
| 85 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® QCS6490 | 33.609 ms | 1 - 4 MB | NPU
|
| 86 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® QCS8275 (Proxy) | 21.97 ms | 2 - 182 MB | NPU
|
| 87 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® QCS8550 (Proxy) | 8.455 ms | 2 - 106 MB | NPU
|
| 88 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® SA8775P | 8.927 ms | 2 - 184 MB | NPU
|
| 89 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® QCS9075 | 9.779 ms | 2 - 5 MB | NPU
|
| 90 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® QCM6690 | 110.772 ms | 2 - 232 MB | NPU
|
| 91 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® QCS8450 (Proxy) | 12.218 ms | 2 - 221 MB | NPU
|
| 92 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® SA7255P | 21.97 ms | 2 - 182 MB | NPU
|
| 93 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Qualcomm® SA8295P | 13.386 ms | 2 - 195 MB | NPU
|
| 94 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 4.374 ms | 2 - 181 MB | NPU
|
| 95 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Snapdragon® 7 Gen 4 Mobile | 11.923 ms | 2 - 183 MB | NPU
|
| 96 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 3.382 ms | 2 - 197 MB | NPU
|
| 97 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Snapdragon® X Elite | 4.587 ms | 1 - 1 MB | NPU
|
| 98 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Snapdragon® 8 Gen 3 Mobile | 2.866 ms | 1 - 196 MB | NPU
|
| 99 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® QCS6490 | 15.682 ms | 1 - 3 MB | NPU
|
| 100 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® QCS8275 (Proxy) | 10.639 ms | 1 - 169 MB | NPU
|
| 101 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® QCS8550 (Proxy) | 4.136 ms | 1 - 79 MB | NPU
|
| 102 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® SA8775P | 4.598 ms | 1 - 172 MB | NPU
|
| 103 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® QCS9075 | 4.998 ms | 1 - 3 MB | NPU
|
| 104 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® QCM6690 | 48.763 ms | 1 - 200 MB | NPU
|
| 105 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® QCS8450 (Proxy) | 6.358 ms | 1 - 198 MB | NPU
|
| 106 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® SA7255P | 10.639 ms | 1 - 169 MB | NPU
|
| 107 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Qualcomm® SA8295P | 6.411 ms | 1 - 168 MB | NPU
|
| 108 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 2.14 ms | 0 - 175 MB | NPU
|
| 109 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Snapdragon® 7 Gen 4 Mobile | 6.034 ms | 1 - 172 MB | NPU
|
| 110 |
+
| DeepLabV3-Plus-MobileNet | QNN_DLC | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.643 ms | 1 - 175 MB | NPU
|
| 111 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 8.065 ms | 0 - 216 MB | NPU
|
| 112 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 58.632 ms | 0 - 173 MB | NPU
|
| 113 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 11.486 ms | 0 - 3 MB | NPU
|
| 114 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® SA8775P | 80.711 ms | 0 - 173 MB | NPU
|
| 115 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® QCS9075 | 19.652 ms | 0 - 18 MB | NPU
|
| 116 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 18.991 ms | 0 - 216 MB | NPU
|
| 117 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® SA7255P | 58.632 ms | 0 - 173 MB | NPU
|
| 118 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Qualcomm® SA8295P | 19.443 ms | 2 - 171 MB | NPU
|
| 119 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 6.254 ms | 0 - 200 MB | NPU
|
| 120 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 4.325 ms | 0 - 185 MB | NPU
|
| 121 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 3.017 ms | 0 - 208 MB | NPU
|
| 122 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® QCS6490 | 15.852 ms | 0 - 11 MB | NPU
|
| 123 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 10.832 ms | 0 - 174 MB | NPU
|
| 124 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 4.405 ms | 1 - 3 MB | NPU
|
| 125 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® SA8775P | 4.554 ms | 0 - 175 MB | NPU
|
| 126 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® QCS9075 | 5.164 ms | 0 - 10 MB | NPU
|
| 127 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® QCM6690 | 51.702 ms | 0 - 195 MB | NPU
|
| 128 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 5.715 ms | 0 - 206 MB | NPU
|
| 129 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® SA7255P | 10.832 ms | 0 - 174 MB | NPU
|
| 130 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Qualcomm® SA8295P | 6.135 ms | 2 - 174 MB | NPU
|
| 131 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 2.057 ms | 0 - 178 MB | NPU
|
| 132 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Snapdragon® 7 Gen 4 Mobile | 6.414 ms | 0 - 178 MB | NPU
|
| 133 |
+
| DeepLabV3-Plus-MobileNet | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 1.659 ms | 0 - 183 MB | NPU
|
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|
| 134 |
|
| 135 |
## License
|
| 136 |
* The license for the original implementation of DeepLabV3-Plus-MobileNet can be found
|
| 137 |
[here](https://github.com/jfzhang95/pytorch-deeplab-xception/blob/master/LICENSE).
|
| 138 |
|
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|
| 139 |
## References
|
| 140 |
* [Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)
|
| 141 |
* [Source Model Implementation](https://github.com/jfzhang95/pytorch-deeplab-xception)
|
| 142 |
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|
| 143 |
## Community
|
| 144 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 145 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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tool-versions.yaml
DELETED
|
@@ -1,3 +0,0 @@
|
|
| 1 |
-
tool_versions:
|
| 2 |
-
qnn_dlc:
|
| 3 |
-
qairt: 2.41.0.251128145156_191518
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