{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/chainercv-a-library-for-deep-learning-in","title":"ChainerCV: a Library for Deep Learning in Computer Vision","arxiv_id":"1708.08169","date":"2017-08-28","proceeding":null,"authors":["Yusuke Niitani","Toru Ogawa","Shunta Saito","Masaki Saito"],"abstract":"Despite significant progress of deep learning in the field of computer\nvision, there has not been a software library that covers these methods in a\nunifying manner. We introduce ChainerCV, a software library that is intended to\nfill this gap. ChainerCV supports numerous neural network models as well as\nsoftware components needed to conduct research in computer vision. These\nimplementations emphasize simplicity, flexibility and good software engineering\npractices. The library is designed to perform on par with the results reported\nin published papers and its tools can be used as a baseline for future research\nin computer vision. Our implementation includes sophisticated models like\nFaster R-CNN and SSD, and covers tasks such as object detection and semantic\nsegmentation.","url_abs":"http://arxiv.org/abs/1708.08169v1","url_pdf":"http://arxiv.org/pdf/1708.08169v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"chainercv-a-library-for-deep-learning-in","repo_url":"https://github.com/pfnet/chainercv","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"chainercv-a-library-for-deep-learning-in","repo_url":"https://github.com/chainer/chainercv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bottleneck-residual-block","method_name":"Bottleneck Residual Block"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"faster-r-cnn","method_name":"Faster R-CNN"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roipool","method_name":"RoIPool"},{"method_slug":"ssd","method_name":"SSD"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-coco","task":"Object Detection","dataset":"COCO test-dev","model":"FPN (ResNet101 backbone)","rank_in_archive_order":203,"of":225,"metrics":{"box mAP":"39.5"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}