Introduction to Camera Trap Recognition with Deep Learning

The greatest logistical barrier to long-term wildlife monitoring with camera traps is the overwhelming amount of human labor needed to annotate thousands or millions of images for ecological analysis. In this tutorial, learn how to train a deep learning model to automatically identify animals in camera trap images.

Author:

• Zhongqi Miao: zhongqimiao@microsoft.com

Presented at Climate Change AI Summer School 2023

Access this tutorial

We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.

Estimated time to execute end-to-end: 10 minutes

Contribute to this tutorial

Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.

Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.

Climate Change AI Tutorials

Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.

License

Usage of this tutorial is subject to the MIT License.

Cite

Plain Text

Miao, Z. (2023). Introduction to Camera Trap Recognition with Deep Learning [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.11619529

BibTeX

@misc{miao2023introduction,
  title={Introduction to Camera Trap Recognition with Deep Learning},
  author={Miao, Zhongqi},
  year={2023},
  organization={Climate Change AI},
  type={Tutorial},
  doi={https://doi.org/10.5281/zenodo.11619529},
  booktitle={Climate Change AI Summer School},
  howpublished={\url{https://github.com/climatechange-ai-tutorials/camera-trap-recognition}}
}