We introduce a new large-scale 2D dataset, named SODA10M, which contains 10M unlabeled images and 20k labeled images with 6 representative object categories. SODA10M is designed for promoting significant progress of self-supervised learning and domain adaptation in autonomous driving.

In order to make downloading easier, we divide the total unlabeled files into 10 splits, and each split contains 1 million pictures.

Jun 21, 2021 · Here, we release a Large-Scale 2D Self/semi-supervised Object Detection dataset for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories.

Here, we release a Large-Scale 2D Self/semi-supervised Object Detection dataset for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories.

SODA10M dataset is collected from two differenct suppliers, named Testin and Speechocean. The image collection task is distributed to the tens of thousands taxi drivers in the form of crowdsourcing. They are required to use the mobile phone or driving recorder (1080P+) to obtain images every ten seconds per frame.

Here, we release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories.

**SODA10M** is a large-scale object detection benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data. SODA10M contains 10 million unlabeled images and 20K images labeled …

因此， 华为诺亚方舟实验室联合中山大学发布了新一代半/自监督的2D基准数据集SODA10M，其主要包含从32个城市采集的一千万张多样性丰富的无标签道路场景图片以及两万张带标签图片。 SODA10M 将同时被用于举办 ICCV 2021 SSLAD Challenge。 该数据集的发布旨在提升自动驾驶领域研究人员对于半监督、自监督相关方向上的研究，共同促进自动驾驶生态的搭建。 SODA10M是通过众包分发模式采集的高质量（1080P+）行车场景数据集，其中相关隐私信 …

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