logasja/VGGFace2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by yu zhang zhang

Contents

RichardErkhov/VGGFace2 dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by Dalton Omondi

Released under MIT

Contents

Dataset

This dataset was created by Frontman

Contents

FAU detection results of the VGGFace2 model after retraining with the EmotioNet database.

Context

Pretrained weights for face detection and recognition.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F224054%2F9c4899c27d4cfbb1bbdb28f11f50e68a%2Ftracked.gif?generation=1583983309257134&alt=media" alt="">

Content

For package compatibility reasons, the weights are split into two components: the feature weights and the logit weights. The facenet-pytorch package can load them automatically in that format once they are placed in the pytorch cache directory.

License

MIT License

Copyright (c) 2019 Timothy Esler

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Acknowledgements

See https://github.com/timesler/facenet-pytorch

WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('wider_face', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/wider_face-0.1.0.png" alt="Visualization" width="500px">