Sadly, today is my last day at DrivenData, so it’s a good time to review one of the projects I’ve been working on, using probabilistic predictions from Zamba to find animals in camera trap videos, like this:

Zamba is one of the longest-running projects at DrivenData. You can read about it in this blog post: Computer vision for wildlife monitoring in a changing climate.

And if you want to know more about my part of it, I wrote this series of articles.

• You can stop watching blank videos
• Find all the pangolins
• Standing on the threshold
• What’s a takahe?

Most recently, I’ve been working on calibrating the predictions from convolutional neural networks (CNNs). I haven’t written about it, but at ODSC East 2023, I’m giving a talk about it:

Don’t Discard That Pangolin, Calibrate Your Deep Learning Classifier 

Suppose you are an ecologist studying a rare species like a pangolin. You can use motion-triggered camera traps to collect data about the presence and abundance of species in the wild, but for every video showing a pangolin, you have 100 that show other species, and 100 more that are blank. You might have to watch hours of video to find one pangolin.

Deep learning can help. Project Zamba provides models that classify camera trap videos and identify the species that appear in them. Of course, the results are not perfect, but we can often remove 80% of the videos we don’t want while losing only 10-20% of the videos we want.

But there’s a problem. The output from deep learning classifiers is generally a “confidence score”, not a probability. If a classifier assigns a label with 80% confidence, that doesn’t mean there is an 80% chance it is correct. However, with a modest number of human-generated labels, we can often calibrate the output to produce more accurate probabilities, and make better predictions.

In this talk, I’ll present use cases based on data from Africa, Guam, and New Zealand, and show how we can use deep learning and calibration to save the pangolin… or at least the pangolin videos. This real-world problem shows how users of ML models can tune the results to improve performance on their applications.  

The ODSC schedule isn’t posted yet, but I’ll fill in the details later.