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--- |
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size_categories: |
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- 1M<n<10M |
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task_categories: |
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- image-classification |
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--- |
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# MS-Celeb-1M (v3) |
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This dataset is introduced in the Lightweight Face Recognition Challenge at ICCV 2019. [Paper](https://openaccess.thecvf.com/content_ICCVW_2019/papers/LSR/Deng_Lightweight_Face_Recognition_Challenge_ICCVW_2019_paper.pdf). |
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There are 5,179,510 images and 93,431 ids. All images are aligned based on facial landmarks predicted by RetinaFace and resized to 112x112. |
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This was downloaded from `https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_` (MS1M-RetinaFace). The dataset is stored in MXNet RecordIO format. |
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## Usage |
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```python |
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import io |
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import numpy as np |
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from PIL import Image |
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np.bool = bool # fix for mxnet |
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from mxnet.recordio import MXIndexedRecordIO, unpack |
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record = MXIndexedRecordIO("ms1m-retinaface-t1/train.idx", "ms1m-retinaface-t1/train.rec", "r") |
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header, _ = unpack(record.read_idx(0)) |
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size = int(header.label[0]) - 1 |
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n_classes = int(open("ms1m-retinaface-t1/property").read().split(",")[0]) |
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sample_idx = 100 # from 0 to size-1 |
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header, raw_img = unpack(self.record.read_idx(sample_idx + 1)) |
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label = header.label |
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if not isinstance(label, (int, float)): |
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label = label[0] |
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label = int(label) |
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img = Image.open(io.BytesIO(raw_img)) # using cv2.imdecode is also possible |
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``` |
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