v0.46.1
Browse filesSee https://github.com/quic/ai-hub-models/releases/v0.46.1 for changelog.
- README.md +115 -267
- Track-Anything_TrackAnythingEncodeKeyWithShrinkage_float.onnx.zip +0 -3
- Track-Anything_TrackAnythingEncodeKeyWithShrinkage_float.tflite +0 -3
- Track-Anything_TrackAnythingEncodeKeyWithoutShrinkage_float.onnx.zip +0 -3
- Track-Anything_TrackAnythingEncodeKeyWithoutShrinkage_float.tflite +0 -3
- Track-Anything_TrackAnythingEncodeValue_float.onnx.zip +0 -3
- Track-Anything_TrackAnythingEncodeValue_float.tflite +0 -3
- Track-Anything_TrackAnythingSegment_float.onnx.zip +0 -3
- Track-Anything_TrackAnythingSegment_float.tflite +0 -3
- tool-versions.yaml +0 -4
README.md
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# Track-Anything: Optimized for
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## High-performance interactive tracking and segmentation in videos
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Track-Anything is a video based machine learning model to track an object in a video.
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This
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| TrackAnythingEncodeKeyWithoutShrinkage |
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| TrackAnythingEncodeKeyWithoutShrinkage |
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| TrackAnythingEncodeKeyWithoutShrinkage |
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| TrackAnythingEncodeKeyWithoutShrinkage |
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| TrackAnythingEncodeKeyWithoutShrinkage |
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## Demo off target
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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.track_anything.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.track_anything.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.track_anything.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/track_anything/qai_hub_models/models/Track-Anything/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.track_anything 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.track_anything.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.track_anything.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 Track-Anything's performance across various devices [here](https://aihub.qualcomm.com/models/track_anything).
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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 Track-Anything can be found
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[here](https://github.com/gaomingqi/Track-Anything/blob/master/LICENSE).
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## References
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* [Track Anything: Segment Anything Meets Videos](https://arxiv.org/abs/2304.11968)
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* [Source Model Implementation](https://github.com/gaomingqi/Track-Anything)
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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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# Track-Anything: Optimized for Qualcomm Devices
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Track-Anything is a video based machine learning model to track an object in a video.
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This is based on the implementation of Track-Anything found [here](https://github.com/gaomingqi/Track-Anything).
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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/track_anything) 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/track_anything/releases/v0.46.1/track_anything-onnx-float.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/track_anything/releases/v0.46.1/track_anything-tflite-float.zip)
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For more device-specific assets and performance metrics, visit **[Track-Anything on Qualcomm® AI Hub](https://aihub.qualcomm.com/models/track_anything)**.
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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/track_anything) 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 [Track-Anything on GitHub](https://github.com/quic/ai-hub-models/blob/main/qai_hub_models/models/track_anything) for usage instructions.
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## Model Details
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**Model Type:** Model_use_case.video_object_tracking
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**Model Stats:**
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- Model checkpoint: xmem
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- Input resolution: 320x568
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- Number of parameters (TrackAnythingEncodeKeyWithShrinkage): 9.72M
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- Model size (TrackAnythingEncodeKeyWithShrinkage) (float): 37.1 MB
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- Number of parameters (TrackAnythingEncodeValue): 23.3M
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- Model size (TrackAnythingEncodeValue) (float): 88.8 MB
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- Number of parameters (TrackAnythingEncodeKeyWithoutShrinkage): 9.71M
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- Model size (TrackAnythingEncodeKeyWithoutShrinkage) (float): 37.1 MB
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- Number of parameters (TrackAnythingSegment): 30.1M
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- Model size (TrackAnythingSegment) (float): 115 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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|---|---|---|---|---|---|---
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| TrackAnythingEncodeKeyWithShrinkage | ONNX | float | Snapdragon® X Elite | 5.562 ms | 19 - 19 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 3.893 ms | 10 - 152 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | ONNX | float | Qualcomm® QCS8550 (Proxy) | 5.415 ms | 0 - 20 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | ONNX | float | Qualcomm® QCS9075 | 9.595 ms | 10 - 13 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.175 ms | 0 - 115 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.572 ms | 0 - 117 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 4.098 ms | 0 - 217 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 31.289 ms | 3 - 181 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 5.889 ms | 3 - 34 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® SA8775P | 41.8 ms | 3 - 181 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® QCS9075 | 11.161 ms | 2 - 29 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 11.69 ms | 3 - 201 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® SA7255P | 31.289 ms | 3 - 181 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Qualcomm® SA8295P | 10.203 ms | 3 - 164 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 3.32 ms | 0 - 187 MB | NPU
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| TrackAnythingEncodeKeyWithShrinkage | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 2.45 ms | 0 - 191 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | ONNX | float | Snapdragon® X Elite | 6.877 ms | 66 - 66 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 5.172 ms | 64 - 212 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | ONNX | float | Qualcomm® QCS8550 (Proxy) | 7.005 ms | 58 - 78 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | ONNX | float | Qualcomm® QCS9075 | 11.772 ms | 62 - 64 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.611 ms | 61 - 208 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.76 ms | 0 - 155 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 5.302 ms | 20 - 239 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 33.117 ms | 20 - 205 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 7.511 ms | 18 - 20 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® SA8775P | 11.533 ms | 20 - 206 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® QCS9075 | 12.674 ms | 20 - 63 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 14.205 ms | 20 - 221 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® SA7255P | 33.117 ms | 20 - 205 MB | NPU
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| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Qualcomm® SA8295P | 12.482 ms | 20 - 184 MB | NPU
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| 96 |
+
| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 4.413 ms | 19 - 205 MB | NPU
|
| 97 |
+
| TrackAnythingEncodeKeyWithoutShrinkage | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 3.729 ms | 0 - 229 MB | NPU
|
| 98 |
+
| TrackAnythingEncodeValue | ONNX | float | Snapdragon® X Elite | 12.251 ms | 38 - 38 MB | NPU
|
| 99 |
+
| TrackAnythingEncodeValue | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 10.039 ms | 0 - 150 MB | NPU
|
| 100 |
+
| TrackAnythingEncodeValue | ONNX | float | Qualcomm® QCS8550 (Proxy) | 15.664 ms | 0 - 52 MB | NPU
|
| 101 |
+
| TrackAnythingEncodeValue | ONNX | float | Qualcomm® QCS9075 | 19.384 ms | 6 - 14 MB | NPU
|
| 102 |
+
| TrackAnythingEncodeValue | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 8.45 ms | 7 - 127 MB | NPU
|
| 103 |
+
| TrackAnythingEncodeValue | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.443 ms | 4 - 125 MB | NPU
|
| 104 |
+
| TrackAnythingEncodeValue | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 8.276 ms | 2 - 290 MB | NPU
|
| 105 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 51.6 ms | 3 - 224 MB | NPU
|
| 106 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 11.859 ms | 3 - 6 MB | NPU
|
| 107 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® SA8775P | 17.544 ms | 3 - 218 MB | NPU
|
| 108 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® QCS9075 | 18.977 ms | 0 - 55 MB | NPU
|
| 109 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 22.669 ms | 3 - 285 MB | NPU
|
| 110 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® SA7255P | 51.6 ms | 3 - 224 MB | NPU
|
| 111 |
+
| TrackAnythingEncodeValue | TFLITE | float | Qualcomm® SA8295P | 19.664 ms | 0 - 223 MB | NPU
|
| 112 |
+
| TrackAnythingEncodeValue | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 7.178 ms | 2 - 197 MB | NPU
|
| 113 |
+
| TrackAnythingEncodeValue | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 6.542 ms | 2 - 213 MB | NPU
|
| 114 |
+
| TrackAnythingSegment | ONNX | float | Snapdragon® X Elite | 23.784 ms | 38 - 38 MB | NPU
|
| 115 |
+
| TrackAnythingSegment | ONNX | float | Snapdragon® 8 Gen 3 Mobile | 17.9 ms | 4 - 191 MB | NPU
|
| 116 |
+
| TrackAnythingSegment | ONNX | float | Qualcomm® QCS8550 (Proxy) | 24.373 ms | 17 - 28 MB | NPU
|
| 117 |
+
| TrackAnythingSegment | ONNX | float | Qualcomm® QCS9075 | 39.271 ms | 21 - 45 MB | NPU
|
| 118 |
+
| TrackAnythingSegment | ONNX | float | Snapdragon® 8 Elite For Galaxy Mobile | 14.036 ms | 5 - 166 MB | NPU
|
| 119 |
+
| TrackAnythingSegment | ONNX | float | Snapdragon® 8 Elite Gen 5 Mobile | 10.769 ms | 6 - 170 MB | NPU
|
| 120 |
+
| TrackAnythingSegment | TFLITE | float | Snapdragon® 8 Gen 3 Mobile | 17.532 ms | 0 - 283 MB | NPU
|
| 121 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® QCS8275 (Proxy) | 521.551 ms | 38 - 56 MB | GPU
|
| 122 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® QCS8550 (Proxy) | 23.497 ms | 0 - 239 MB | NPU
|
| 123 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® SA8775P | 33.249 ms | 0 - 196 MB | NPU
|
| 124 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® QCS9075 | 38.367 ms | 0 - 83 MB | NPU
|
| 125 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® QCS8450 (Proxy) | 54.61 ms | 2 - 285 MB | NPU
|
| 126 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® SA7255P | 521.551 ms | 38 - 56 MB | GPU
|
| 127 |
+
| TrackAnythingSegment | TFLITE | float | Qualcomm® SA8295P | 37.307 ms | 2 - 209 MB | NPU
|
| 128 |
+
| TrackAnythingSegment | TFLITE | float | Snapdragon® 8 Elite For Galaxy Mobile | 13.587 ms | 1 - 204 MB | NPU
|
| 129 |
+
| TrackAnythingSegment | TFLITE | float | Snapdragon® 8 Elite Gen 5 Mobile | 10.892 ms | 2 - 227 MB | NPU
|
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| 130 |
|
| 131 |
## License
|
| 132 |
* The license for the original implementation of Track-Anything can be found
|
| 133 |
[here](https://github.com/gaomingqi/Track-Anything/blob/master/LICENSE).
|
| 134 |
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| 135 |
## References
|
| 136 |
* [Track Anything: Segment Anything Meets Videos](https://arxiv.org/abs/2304.11968)
|
| 137 |
* [Source Model Implementation](https://github.com/gaomingqi/Track-Anything)
|
| 138 |
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| 139 |
## Community
|
| 140 |
* Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI.
|
| 141 |
* For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).
|
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Track-Anything_TrackAnythingEncodeKeyWithShrinkage_float.onnx.zip
DELETED
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|
| 2 |
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| 3 |
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Track-Anything_TrackAnythingEncodeKeyWithShrinkage_float.tflite
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Track-Anything_TrackAnythingEncodeKeyWithoutShrinkage_float.tflite
DELETED
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Track-Anything_TrackAnythingEncodeValue_float.onnx.zip
DELETED
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Track-Anything_TrackAnythingEncodeValue_float.tflite
DELETED
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Track-Anything_TrackAnythingSegment_float.onnx.zip
DELETED
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size 110771794
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Track-Anything_TrackAnythingSegment_float.tflite
DELETED
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size 120533856
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tool-versions.yaml
DELETED
|
@@ -1,4 +0,0 @@
|
|
| 1 |
-
tool_versions:
|
| 2 |
-
onnx:
|
| 3 |
-
qairt: 2.37.1.250807093845_124904
|
| 4 |
-
onnx_runtime: 1.23.0
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