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README.md

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+---
+task_categories:
+- image-classification
+pretty_name: MS-Celeb-1M
+size_categories:
+- 1M<n<10M
+---
+
+# MS-Celeb-1M (v3)
+
+This is a copy of [gaunernst/ms1mv3-wds](https://huggingface.co/datasets/gaunernst/ms1mv3-wds) with gzip compression. Thus, the shards (after decompression) are identical. The compression ratio is around 50%, indicating that the original JPEG images were not compressed much.
+
+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 original dataset format is MXNet RecordIO. It was converted to WebDataset in this copy here. There are 100 shards in total.
+
+## Usage
+
+```python
+import webdataset as wds
+
+url = "https://huggingface.co/datasets/gaunernst/ms1mv3-wds-gz/resolve/main/ms1mv3-{{0000..0099}}.tar.gz"
+ds = wds.WebDataset(url).decode("pil").to_tuple("jpg", "cls")
+
+img, label = next(iter(ds))
+```
+

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