深度学习领域yolox-inference算法在深海海星目标检测（带数据集）-10、yolox-inference-track

httpswww.kaggle.comcompetitionstensorflow-great-barrier-reefdata 在这场比赛中，您将通过在大屏障礁周围的不同时间和位置拍摄的一系列水下图像来预测thorns海星的存在和位置。预测以一个边界盒的形式以及每个已确定的海星的置信度得分。图像可能包含零或更多海星。 该竞赛使用一个隐藏的测试集，该测试集将由API提供，以确保您以每个视频中记录的顺序评估图像。评分您提交的笔记本时，将在笔记本电脑上可用的实际测试数据（包括示例提交）。 In this competition, you will predict the presence and position of crown-of-thorns starfish in sequences of underwater images taken at various times and locations around the Great Barrier Reef. Predictions take the form of a bounding box together with a confidence score for each identified starfish. An image may contain zero or more starfish. This competition uses a hidden test set that will be served by an API to ensure you evaluate the images in the same order they were recorded within each video. When your submitted notebook is scored, the actual test data (including a sample submission) will be availabe to your notebook. Files train/ - Folder containing training set photos of the form video_{video_id}/{video_frame_number}.jpg. [train/test].csv - Metadata for the images. As with other test files, most of the test metadata data is only available to your notebook upon submission. Just the first few rows available for download. video_id - ID number of the video the image was part of. The video ids are not meaningfully ordered. video_frame - The frame number of the image within the video. Expect to see occasional gaps in the frame number from when the diver surfaced. sequence - ID of a gap-free subset of a given video. The sequence ids are not meaningfully ordered. sequence_frame - The frame number within a given sequence. image_id - ID code for the image, in the format '{video_id}-{video_frame}' annotations - The bounding boxes of any starfish detections in a string format that can be evaluated directly with Python. Does not use the same format as the predictions you will submit. Not available in test.csv. A bounding box is described by the pixel coordinate (x_min, y_min) of its upper left corner within the image together with its width and height in pixels. example_sample_submission.csv - A sample submission file in the correct format. The actual sample submission will be provided by the API; this is only provided to illustrate how to properly format predictions. The submission format is further described on the Evaluation page. example_test.npy - Sample data that will be served by the example API. greatbarrierreef - The image delivery API that will serve the test set pixel arrays. You may need Python 3.7 and a Linux environment to run the example offline without errors.