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59 lines
1.6 KiB
Markdown
59 lines
1.6 KiB
Markdown
### Introduction
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Here we provide some lightweight facial landmark models with fast coordinate regression.
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The input of these models is loose cropped face image while the output is the direct landmark coordinates.
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### Pretrained Models
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- **Model ``2d106det``**
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**2021.07: We now support model inference by our `insightface` python package, please check [image_infer.py](image_infer.py) for detail.**
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Given face detection bounding box, predict 2d-106 landmarks. Mainly used for static image inference.
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Backbone: MobileNet-0.5, size 5MB.
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Input: size 192x192, loose cropped detection bounding-box.
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Download link:
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[baidu cloud](https://pan.baidu.com/s/10m5GmtNV5snynDrq3KqIdg) (code: ``lqvv``)
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[google drive](https://drive.google.com/file/d/13Pz8mH-a1s7RXpq_jFUXxaqCpDUE0oSr/view?usp=sharing)
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- **Model ``2d106track``**
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Given landmarks bounding box, predict 2d-106 landmarks. Used for video landmarks tracking.
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Download link: coming soon
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### Visualization
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<p align="center">Points mark-up(ordered by point names):</p>
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<div align="center">
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<img src="https://github.com/nttstar/insightface-resources/blob/master/alignment/images/2d106markup.jpg" alt="markup" width="320">
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</div>
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<p align="center">Image result:</p>
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<div align="center">
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<img src="https://github.com/nttstar/insightface-resources/blob/master/alignment/images/t1_out.jpg" alt="imagevis" width="800">
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</div>
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<p align="center">Video result:</p>
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<div align="center">
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<img src="https://github.com/nttstar/insightface-resources/blob/master/alignment/images/C_jiaguo.gif" alt="videovis" width="240">
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</div>
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### FAQ
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