The Global Wheat Detection challenge was hosted on Kaggle in the summer of 2020. Many Kagglers used Roboflow to process and augment their datasets for use with common machine learning frameworks as part of their entry into the competition.
The dataset uses a custom CSV format created by the University of Saskatchewan. Roboflow can parse this format and convert it into any other format of your choosing.
Below, learn the structure of Kaggle Wheat CSV.
image_id,width,height,bbox,source
b6ab77fd7,1024,1024,"[834.0, 222.0, 56.0, 36.0]",usask_1
b6ab77fd7,1024,1024,"[226.0, 548.0, 130.0, 58.0]",usask_1
b6ab77fd7,1024,1024,"[377.0, 504.0, 74.0, 160.0]",usask_1
b6ab77fd7,1024,1024,"[834.0, 95.0, 109.0, 107.0]",usask_1
b6ab77fd7,1024,1024,"[26.0, 144.0, 124.0, 117.0]",usask_1
b6ab77fd7,1024,1024,"[569.0, 382.0, 119.0, 111.0]",usask_1
b6ab77fd7,1024,1024,"[52.0, 602.0, 82.0, 45.0]",usask_1
b6ab77fd7,1024,1024,"[627.0, 302.0, 122.0, 75.0]",usask_1
b6ab77fd7,1024,1024,"[412.0, 367.0, 68.0, 82.0]",usask_1With Roboflow supervision, an open source Python package with utilities for completing computer vision tasks, you can merge and split detections in Kaggle Wheat CSV. Read our dedicated guides to learn how to merge and split Kaggle Wheat CSV detections.
Below, see model architectures that require data in the Kaggle Wheat CSV format when training a new model.
On each page below, you can find links to our guides that show how to plot predictions from the model, and complete other common tasks like detecting small objects with the model.