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.gitignore

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# mypy
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.mypy_cache/
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.DS_Store

README.md

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alignment/README.md

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You can now find heatmap based approaches under ``heatmapReg`` directory.
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## Face Alignment
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<div align="left">
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<img src="https://insightface.ai/assets/img/custom/logo3.jpg" width="240"/>
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</div>
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## Introduction
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These are the face alignment methods of [InsightFace](https://insightface.ai)
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<div align="left">
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<img src="https://insightface.ai/assets/img/custom/thumb_sdunet.png" width="600"/>
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</div>
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### Datasets
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Please refer to [datasets](_datasets_) page for the details of face alignment datasets used for training and evaluation.
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### Evaluation
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Please refer to [evaluation](_evaluation_) page for the details of face alignment evaluation.
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## Methods
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Supported methods:
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- [x] [SDUNets (BMVC'2018)](heatmap)
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- [x] [SimpleRegression](coordinate_reg)
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## Contributing
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We appreciate all contributions to improve the face alignment model zoo of InsightFace.
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You can now find coordinate regression approaches under ``coordinateReg`` directory.
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attribute/README.md

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## Face Alignment
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<div align="left">
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<img src="https://insightface.ai/assets/img/custom/logo3.jpg" width="320"/>
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</div>
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## Introduction
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These are the face attribute methods of [InsightFace](https://insightface.ai)
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<div align="left">
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<img src="https://insightface.ai/assets/img/github/t1_genderage.jpg" width="600"/>
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</div>
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### Datasets
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Please refer to [datasets](_datasets_) page for the details of face attribute datasets used for training and evaluation.
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### Evaluation
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Please refer to [evaluation](_evaluation_) page for the details of face attribute evaluation.
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## Methods
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Supported methods:
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- [x] [Gender_Age](gender_age)
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## Contributing
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We appreciate all contributions to improve the face attribute model zoo of InsightFace.
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deploy/test.py attribute/gender_age/test.py

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import numpy as np
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import insightface
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from insightface.app import FaceAnalysis
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from insightface.data import get_image as ins_get_image
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assert insightface.__version__>='0.2'
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parser = argparse.ArgumentParser(description='insightface test')
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parser = argparse.ArgumentParser(description='insightface gender-age test')
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# general
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parser.add_argument('--ctx', default=0, type=int, help='ctx id, <0 means using cpu')
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args = parser.parse_args()
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app = FaceAnalysis(name='antelope')
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app = FaceAnalysis(allowed_modules=['detection', 'genderage'])
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app.prepare(ctx_id=args.ctx, det_size=(640,640))
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img = cv2.imread('../sample-images/t1.jpg')
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img = ins_get_image('t1')
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faces = app.get(img)
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assert len(faces)==6
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rimg = app.draw_on(img, faces)
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cv2.imwrite("./t1_output.jpg", rimg)
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print(len(faces))
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for face in faces:
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print(face.bbox)
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print(face.kps)
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print(face.embedding.shape)
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print(face.sex, face.age)
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challenges/README.md

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## Challenges
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<div align="left">
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<img src="https://insightface.ai/assets/img/custom/logo3.jpg" width="240"/>
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</div>
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## Introduction
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These are challenges hold by [InsightFace](https://insightface.ai)
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<div align="left">
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<img src="https://insightface.ai/assets/img/custom/thumb_ifrt.png" width="480"/>
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</div>
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## List
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Supported methods:
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- [LFR19 (ICCVW'2019)](iccv19-lfr)
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- [MFR21 (ICCVW'2021)](iccv21-mfr)
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- [IFRT](ifrt)
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challenges/iccv19-lfr/README.md

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*For Chinese:*
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![wechat](https://github.com/deepinsight/insightface/blob/master/resources/lfr19_wechat1.jpg)
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![wechat](https://insightface.ai/assets/img/github/lfr19_wechat1.jpg)
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*For English:*
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deploy/README.md

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deploy/convert_onnx.py

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deploy/face_model.py

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deploy/model_slim.py

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deploy/mtcnn-model/det1-0001.params

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