The 4rd Agriculture-Vision Prize Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images. Submissions will be evaluated and ranked by model performance.
Introduction to the Datasets:
For this year's CVPR Challenge, we are introducing a dynamic combination of two distinct datasets:
1. Extended Agriculture Dataset: This is a collection of 105 GB of raw, unprocessed agricultural images. These images are unique as they encompass a range of spectral channels – red, green, blue, and near-infrared (NIR). Crucially, this dataset is unlabeled, providing a rich ground for semi-supervised learning experimentation.
2. Original Agriculture Vision Dataset: This dataset is a compilation of processed and labeled images used in previous CVPR challenges. It serves as a foundational dataset with structured and annotated information.
Download labeled data from AWS
The core of this challenge is to strategically use both the extended, unlabeled agriculture vision dataset and the labeled original agriculture vision dataset. The aim is to enhance model performance by effectively applying semi-supervised learning techniques. Participants are challenged to integrate the depth and variety of the unlabeled dataset with the structured framework of the labeled dataset to achieve superior results.
The performance of the developed models will be evaluated using two key sets from the previous agriculture vision datasets:
We anticipate that the incorporation of the additional unlabeled dataset will significantly enhance the outcomes, leading to more robust and efficient models. This challenge not only tests the limits of semi-supervised learning in agricultural vision but also paves the way for groundbreaking applications in sustainable agriculture practices.
The challenge will be tracked using Codalabs (TBA); this is the platform used in past Agriculture-Vision workshops. Participants for each challenge will be provided with the training and validation set and evaluated on a held-out test set.