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Explore our curated list of AI models designed for cattle data analysis, feedlot monitoring, and disease detection. These models are available through a number of libraries and frameworks, which contain a number of tools for every level of data processing, from pre-processing and annotation to post-processing and result visualization. Whether you're looking to optimize feed efficiency, monitor animal health, or detect diseases early, these models and tools are here to support your research and practical applications.
Frameworks and Libraries
Frameworks and Libraries
MMPose Library
MMPose Library
MMPose is an open-source library for pose estimation, featuring a robust model zoo with numerous models trained on animal pose estimation datasets such as AP-10K. While some coding knowledge is required to get started, MMPose offers pre-trained models that can work on various datasets without fine-tuning, making it an excellent choice for researchers looking for reliable and versatile solutions.
DeepLabCut
DeepLabCut
DeepLabCut is the best choice for anyone without coding knowledge! This library offers a variety of models, along with tools for annotation and model training, all through an easy-to-use graphical interface. The new SuperAnimal model introduced by DeepLabCut shows great promise for various animal pose estimation applications, including cattle.
Tools and Repositories
Tools and Repositories
OpenCattleHub Kit (Coming soon)
OpenCattleHub Kit (Coming soon)
Sources: GitHub
This project contains all the libraries and tools developed by our own team. From automatic synthetic data generation addons, to annotation converters (e.g. MS COCO to DeepLabCut) and scripts for working with image data, you can find a tool for every need. This repository is open to suggestions and pull requests, hoping to provide a rich support section for working on cattle datasets.
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