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Autonomous Driving 3D Keypoints

We introduce a new large-scale 2D dataset, named SODA10M, which contains 10M unlabeled images and 20k labeled images wit......

数据结构 ? 2T

     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. It is the largest 2D autonomous driving dataset until now and will serve as a more challenging benchmark for the community.

    Self-supervised Learning for Next-generation Industry-level Autonomous driving refers to a variety of studies that attempt to refresh the solutions on challenging real-world perception tasks by learning from unlabeled or semi-supervised large-scale collected data to incrementally self-train powerful recognition models. Thanks to the rise of large-scale annotated data sets and the advance in computing hardware, various supervised learning methods have significantly improved the performance in many problems (e.g. 2D detection, instance segmentation and 3D Lidar Detection) in the field of self-driving. However, these supervised learning approaches are notorious "data hungry", especially in the current autonomous driving fields.

    The performance of self-driving perception systems highly relies on the annotation scale of labeled bounding boxes and IDs, which makes them not practical in many real-world industrial applications. The intuition is that a human driver can keep accumulating experiences from self-exploring the roads without any tutor’s guidance, instead current CV solutions are still baby-sitted with extensive annotation efforts on every new scenario.

    To facilitate an industry-level autonomous driving system in the future, the desired visual recognition model should be equipped with the ability of self-exploring, self-training and self-adapting across diverse new-appearing geographies, streets, cities, weather conditions, object labels, viewpoints or abnormal scenarios. To address this problem, many recent efforts in self-supervised learning, large-scale pretraining, weakly supervised learning and incremental/continual learning have been made to improve the perception systems to deviate from traditional paths of supervised learning for self-driving solutions.

    The aim of releasing this dataset is let the public to explore methods that utilizing both labeled data and unlabled data to achieve industry-level autonomous driving solutions. The benchmark paper has been released at Arxiv and it will be used to hold the ICCV2021 SSLAD chanllege.

    If you have any questions about SODA10M, please contact or for further help. 

    Data Format

    The annotation file keeps consistent with COCO format and contains three keys: "images", "categories" and "annotations".

    "images": {
            "file_name": <str> -- File name.
            "id": <int>  -- Unique image id.
            "height": <float>  -- Height of the image.
            "width": <float> -- Width of the image.
            "city": <str> -- City tag.
            "location": <str> -- Location tag.
            "period": <str> -- Period tag.
            "weather": <str> -- Weather tag.}


    "annotations": {
            "image_id": <int> -- The image id for this annotation.
            "category_id": <int> -- The category id.
            "bbox": <list> -- Coordinate of boundingbox [x, y, w, h].
            "area": <float> -- Area of this annotation (w * h).
            "id": <int> -- Unique annotation id.
            "iscrowd": <int> -- Whether this annotation is crowd. Note that iscrowd is always 0.}


    "categories": {
            "name": <str> -- Unique category name.
            "id": <int> Unique category id.
            "supercategory": <str> The supercategory for this category.}


    Data Annotation

    Image tags (i.e., weather conditions, location scenes, periods) for all images and 2D bounding boxes for labeled parts are annotated for SODA10M.


    - The SODA10M dataset has been released! (2021/6/8)
                   - The SODA10M paper has been released on Arxiv! (2021/6/21)
                   - The challenge website has been released at CodaLab! (2021/7/1)
                   - The challenge results and technical reports have been released on Challenge page! (2021/10/21)
                   - The SSLAD2021 workshop record video (including challenge report) has been released on YouTube! (2021/10/21)
                   - The evaluation server has been re-opened at CodaLab! (2021/11/9)


          title={SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving}, 
          author={Jianhua Han and Xiwen Liang and Hang Xu and Kai Chen and Lanqing Hong and Jiageng Mao and Chaoqiang Ye and Wei Zhang and Zhenguo Li and Xiaodan Liang and Chunjing Xu},

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