深度学习领域yolov8橄榄球比赛NFL目标检测（带数据集）-nfl-yolov8-object-detection-and

下载地址： https://www.kaggle.com/competitions/nfl-player-contact-detection/data In this competition, you are tasked with predicting moments of contact between player pairs, as well as when players make non-foot contact with the ground using game footage and tracking data. Each play has four associated videos. Two videos, showing a sideline and endzone view, are time synced and aligned with each other. Additionally, an All29 view is provided but not guaranteed to be time synced. The training set videos are in train/ with corresponding labels in train_labels.csv, while the videos for which you must predict are in the test/ folder. This year we are also providing baseline helmet detection and assignment boxes for the training and test set. train_baseline_helmets.csv is the output from last year's winning player assignment model. train_player_tracking.csv provides 10 Hz tracking data for each player on the field during the provided plays. train_video_metadata.csv contains timestamps associated with each Sideline and Endzone view for syncing with the player tracking data. This is a code competition. When you submit, your model will be rerun on a set of 61 unseen plays located in a holdout test set. The publicly provided test videos are simply a set of mock plays (copied from the training set) which are not used in scoring. The associated test_baseline_helmets.csv, test_player_tracking.csv, and test_video_metadata.csv are available to your model when submitting. A sample_submission.csv will be available when submitting and will contain all rows required for a valid submission. 在这场比赛中，你的任务是预测球员之间的接触时刻，以及球员使用比赛录像和跟踪数据进行非足部接触地面的时刻。每个剧本有四个相关的视频。两个视频，显示一个边线和端区视图，是时间同步和对齐彼此。此外，还提供了All29视图，但不能保证时间同步。训练集视频在train_labels.csv中有相应的标签，而你必须预测的视频在test/文件夹中。今年，我们还为训练和测试集提供了基线头盔检测和分配盒。csv是去年获胜球员分配模型的输出。train_player_tracking.csv在提供的比赛中为每个球员提供10hz的跟踪数据。csv包含与每个边线和Endzone视图相关联的时间戳，用于与球员跟踪数据同步。这是一场代码竞赛。当您提交时，您的模型将在位于holdout测试集中的一组61个未见过的剧本上重新运行。公开提供的测试视频只是一组模拟游戏(从训练集复制)，不用于评分。相关的test_baseline_helmet .csv、test_player_tracking.csv和test_video_metadata.csv在提交模型时是可用的。在提交时，sample_submit .csv将可用，其中包含有效提交所需的所有行。