Create README.md

README.md

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+---
+size_categories:
+- 1M<n<10M
+task_categories:
+- image-classification
+---
+# MS-Celeb-1M (v3)
+
+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).
+
+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.
+
+This was downloaded from `https://github.com/deepinsight/insightface/tree/master/recognition/_datasets_` (MS1M-RetinaFace). The dataset is stored in MXNet RecordIO format.
+
+## Usage
+
+```python
+import io
+import numpy as np
+from PIL import Image
+
+np.bool = bool  # fix for mxnet
+from mxnet.recordio import MXIndexedRecordIO, unpack
+
+record = MXIndexedRecordIO("ms1m-retinaface-t1/train.idx", "ms1m-retinaface-t1/train.rec", "r")
+
+header, _ = unpack(record.read_idx(0))
+size = int(header.label[0]) - 1
+n_classes = int(open("ms1m-retinaface-t1/property").read().split(",")[0])
+
+sample_idx = 100  # from 0 to size-1
+header, raw_img = unpack(self.record.read_idx(sample_idx + 1))
+
+label = header.label
+if not isinstance(label, (int, float)):
+    label = label[0]
+label = int(label)
+
+img = Image.open(io.BytesIO(raw_img))  # using cv2.imdecode is also possible
+```