v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- README.md +110 -258
- Segformer-Base_float.dlc +0 -3
- Segformer-Base_float.onnx.zip +0 -3
- Segformer-Base_float.tflite +0 -3
- Segformer-Base_w8a16.onnx.zip +0 -3
- Segformer-Base_w8a8.onnx.zip +0 -3
- Segformer-Base_w8a8.tflite +0 -3
- tool-versions.yaml +0 -4
README.md
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# Segformer-Base: Optimized for
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## Real-time object segmentation
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Segformer Base is a machine learning model that predicts masks and classes of objects in an image.
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| Segformer-Base |
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Qualcomm®
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```bash
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python -m qai_hub_models.models.segformer_base.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.segformer_base.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.segformer_base.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/segformer_base/qai_hub_models/models/Segformer-Base/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.segformer_base 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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model=target_model,
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device=device,
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)
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```
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Step 3: **Verify on-device accuracy**
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To verify the accuracy of the model on-device, you can run on-device inference
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on sample input data on the same cloud hosted device.
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```python
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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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With the output of the model, you can compute like PSNR, relative errors or
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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.segformer_base.demo --eval-mode on-device
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```
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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.segformer_base.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 Segformer-Base's performance across various devices [here](https://aihub.qualcomm.com/models/segformer_base).
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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 Segformer-Base can be found
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[here](https://github.com/huggingface/transformers/blob/main/LICENSE).
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## References
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* [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
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* [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/segformer)
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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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# Segformer-Base: Optimized for Qualcomm Devices
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Segformer Base is a machine learning model that predicts masks and classes of objects in an image.
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This is based on the implementation of Segformer-Base found [here](https://github.com/huggingface/transformers/tree/main/src/transformers/models/segformer).
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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/segformer_base) 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/segformer_base/releases/v0.46.1/segformer_base-onnx-float.zip)
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| ONNX | w8a16 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/segformer_base/releases/v0.46.1/segformer_base-onnx-w8a16.zip)
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| ONNX | w8a8 | Universal | QAIRT 2.37, ONNX Runtime 1.23.0 | [Download](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/models/segformer_base/releases/v0.46.1/segformer_base-onnx-w8a8.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/segformer_base/releases/v0.46.1/segformer_base-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/segformer_base/releases/v0.46.1/segformer_base-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/segformer_base/releases/v0.46.1/segformer_base-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/segformer_base/releases/v0.46.1/segformer_base-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/segformer_base/releases/v0.46.1/segformer_base-tflite-w8a8.zip)
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For more device-specific assets and performance metrics, visit **[Segformer-Base on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/segformer_base)**.
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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/segformer_base) 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 [Segformer-Base on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/segformer_base) for usage instructions.
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## Model Details
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**Model Type:** Model_use_case.semantic_segmentation
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**Model Stats:**
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- Model checkpoint: nvidia/segformer-b0-finetuned-ade-512-512
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- Input resolution: 512x512
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- Number of output classes: 150
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- Number of parameters: 3.75M
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- Model size (float): 14.4 MB
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- Model size (w8a16): 4.57 MB
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- Model size (w8a8): 3.90 MB
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## Performance Summary
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| Model | Runtime | Precision | Chipset | Inference Time (ms) | Peak Memory Range (MB) | Primary Compute Unit
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| Segformer-Base | ONNX | float | Snapdragon® X Elite | 112.756 ms | 33 - 33 MB | NPU
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| Segformer-Base | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 84.305 ms | 24 - 201 MB | NPU
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| Segformer-Base | ONNX | float | Qualcomm® QCS8550 (Proxy) | 108.769 ms | 19 - 28 MB | NPU
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| Segformer-Base | ONNX | float | Qualcomm® QCS9075 | 120.239 ms | 23 - 26 MB | NPU
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| Segformer-Base | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 74.648 ms | 21 - 160 MB | NPU
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| Segformer-Base | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 73.716 ms | 27 - 175 MB | NPU
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| Segformer-Base | ONNX | w8a16 | Snapdragon® X Elite | 51.865 ms | 131 - 131 MB | NPU
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| Segformer-Base | ONNX | w8a16 | Snapdragon® 8 Gen 3 Mobile | 49.215 ms | 90 - 271 MB | NPU
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| Segformer-Base | ONNX | w8a16 | Qualcomm® QCS6490 | 841.672 ms | 379 - 385 MB | CPU
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| Segformer-Base | ONNX | w8a16 | Qualcomm® QCS8550 (Proxy) | 61.921 ms | 83 - 87 MB | NPU
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| Segformer-Base | ONNX | w8a16 | Qualcomm® QCS9075 | 68.104 ms | 89 - 92 MB | NPU
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| Segformer-Base | ONNX | w8a16 | Qualcomm® QCM6690 | 574.842 ms | 324 - 334 MB | CPU
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| Segformer-Base | ONNX | w8a16 | Snapdragon® 8 Elite For Galaxy Mobile | 45.505 ms | 90 - 228 MB | NPU
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| Segformer-Base | ONNX | w8a16 | Snapdragon® 7 Gen 4 Mobile | 412.489 ms | 323 - 333 MB | CPU
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| Segformer-Base | ONNX | w8a16 | Snapdragon® 8 Elite Gen 5 Mobile | 39.672 ms | 90 - 227 MB | NPU
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| Segformer-Base | ONNX | w8a8 | Snapdragon® 8 Gen 3 Mobile | 42.463 ms | 58 - 219 MB | NPU
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| Segformer-Base | ONNX | w8a8 | Qualcomm® QCS6490 | 352.803 ms | 200 - 207 MB | CPU
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| Segformer-Base | ONNX | w8a8 | Qualcomm® QCS8550 (Proxy) | 51.706 ms | 54 - 59 MB | NPU
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| Segformer-Base | ONNX | w8a8 | Qualcomm® QCS9075 | 56.372 ms | 58 - 61 MB | NPU
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| Segformer-Base | ONNX | w8a8 | Qualcomm® QCM6690 | 214.217 ms | 203 - 213 MB | CPU
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| Segformer-Base | ONNX | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 39.85 ms | 57 - 181 MB | NPU
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| Segformer-Base | ONNX | w8a8 | Snapdragon® 7 Gen 4 Mobile | 191.097 ms | 201 - 211 MB | CPU
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| Segformer-Base | ONNX | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 34.78 ms | 58 - 187 MB | NPU
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| 93 |
+
| Segformer-Base | QNN_DLC | float | Snapdragon® X Elite | 114.563 ms | 3 - 3 MB | NPU
|
| 94 |
+
| Segformer-Base | QNN_DLC | float | Snapdragon® 8 Gen 3 Mobile | 83.817 ms | 2 - 228 MB | NPU
|
| 95 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® QCS8275 (Proxy) | 214.645 ms | 1 - 183 MB | NPU
|
| 96 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® QCS8550 (Proxy) | 110.097 ms | 3 - 5 MB | NPU
|
| 97 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® SA8775P | 472.472 ms | 1 - 189 MB | NPU
|
| 98 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® QCS9075 | 113.605 ms | 3 - 17 MB | NPU
|
| 99 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® QCS8450 (Proxy) | 121.904 ms | 2 - 225 MB | NPU
|
| 100 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® SA7255P | 214.645 ms | 1 - 183 MB | NPU
|
| 101 |
+
| Segformer-Base | QNN_DLC | float | Qualcomm® SA8295P | 122.206 ms | 3 - 181 MB | NPU
|
| 102 |
+
| Segformer-Base | QNN_DLC | float | Snapdragon® 8 Elite For Galaxy Mobile | 75.008 ms | 3 - 197 MB | NPU
|
| 103 |
+
| Segformer-Base | QNN_DLC | float | Snapdragon® 8 Elite Gen 5 Mobile | 74.055 ms | 3 - 195 MB | NPU
|
| 104 |
+
| Segformer-Base | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 83.556 ms | 9 - 241 MB | NPU
|
| 105 |
+
| Segformer-Base | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 172.201 ms | 10 - 39 MB | GPU
|
| 106 |
+
| Segformer-Base | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 110.21 ms | 9 - 12 MB | NPU
|
| 107 |
+
| Segformer-Base | TFLITE | float | Qualcomm® SA8775P | 110.706 ms | 0 - 193 MB | NPU
|
| 108 |
+
| Segformer-Base | TFLITE | float | Qualcomm® QCS9075 | 114.107 ms | 8 - 30 MB | NPU
|
| 109 |
+
| Segformer-Base | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 122.524 ms | 9 - 235 MB | NPU
|
| 110 |
+
| Segformer-Base | TFLITE | float | Qualcomm® SA7255P | 172.201 ms | 10 - 39 MB | GPU
|
| 111 |
+
| Segformer-Base | TFLITE | float | Qualcomm® SA8295P | 122.282 ms | 10 - 198 MB | NPU
|
| 112 |
+
| Segformer-Base | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 74.86 ms | 9 - 198 MB | NPU
|
| 113 |
+
| Segformer-Base | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 74.212 ms | 14 - 213 MB | NPU
|
| 114 |
+
| Segformer-Base | TFLITE | w8a8 | Snapdragon® 8 Gen 3 Mobile | 10.304 ms | 1 - 214 MB | NPU
|
| 115 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® QCS6490 | 134.981 ms | 13 - 48 MB | NPU
|
| 116 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® QCS8275 (Proxy) | 170.87 ms | 15 - 45 MB | GPU
|
| 117 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® QCS8550 (Proxy) | 14.171 ms | 2 - 5 MB | NPU
|
| 118 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® SA8775P | 14.948 ms | 2 - 180 MB | NPU
|
| 119 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® QCS9075 | 12.574 ms | 2 - 12 MB | NPU
|
| 120 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® QCM6690 | 148.583 ms | 13 - 190 MB | NPU
|
| 121 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® QCS8450 (Proxy) | 18.904 ms | 2 - 213 MB | NPU
|
| 122 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® SA7255P | 170.87 ms | 15 - 45 MB | GPU
|
| 123 |
+
| Segformer-Base | TFLITE | w8a8 | Qualcomm® SA8295P | 17.819 ms | 2 - 184 MB | NPU
|
| 124 |
+
| Segformer-Base | TFLITE | w8a8 | Snapdragon® 8 Elite For Galaxy Mobile | 7.856 ms | 2 - 173 MB | NPU
|
| 125 |
+
| Segformer-Base | TFLITE | w8a8 | Snapdragon® 8 Elite Gen 5 Mobile | 7.214 ms | 2 - 188 MB | NPU
|
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| 126 |
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| 127 |
## License
|
| 128 |
* The license for the original implementation of Segformer-Base can be found
|
| 129 |
[here](https://github.com/huggingface/transformers/blob/main/LICENSE).
|
| 130 |
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| 131 |
## References
|
| 132 |
* [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
|
| 133 |
* [Source Model Implementation](https://github.com/huggingface/transformers/tree/main/src/transformers/models/segformer)
|
| 134 |
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| 135 |
## Community
|
| 136 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 137 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
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Segformer-Base_float.dlc
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| 3 |
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Segformer-Base_float.onnx.zip
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tool-versions.yaml
DELETED
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tool_versions:
|
| 2 |
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onnx:
|
| 3 |
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qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
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