Classifying Plankton With Deep Neural Networks by Sander Dieleman.
From the post:
The National Data Science Bowl, a data science competition where the goal was to classify images of plankton, has just ended. I participated with six other members of my research lab, the Reservoir lab of prof. Joni Dambre at Ghent University in Belgium. Our team finished 1st! In this post, we’ll explain our approach.
The ≋ Deep Sea ≋ team consisted of Aäron van den Oord, Ira Korshunova, Jeroen Burms, Jonas Degrave, Lionel Pigou, Pieter Buteneers and myself. We are all master students, PhD students and post-docs at Ghent University. We decided to participate together because we are all very interested in deep learning, and a collaborative effort to solve a practical problem is a great way to learn.
There were seven of us, so over the course of three months, we were able to try a plethora of different things, including a bunch of recently published techniques, and a couple of novelties. This blog post was written jointly by the team and will cover all the different ingredients that went into our solution in some detail.
Overview
This blog post is going to be pretty long! Here’s an overview of the different sections. If you want to skip ahead, just click the section title to go there.
- Introduction
- Pre-processing and data augmentation
- Network architecture
- Training
- Unsupervised and semi-supervised approaches
- Model averaging
- Miscellany
- Conclusion
Introduction
The problem
The goal of the competition was to classify grayscale images of plankton into one of 121 classes. They were created using an underwater camera that is towed through an area. The resulting images are then used by scientists to determine which species occur in this area, and how common they are. There are typically a lot of these images, and they need to be annotated before any conclusions can be drawn. Automating this process as much as possible should save a lot of time!
The images obtained using the camera were already processed by a segmentation algorithm to identify and isolate individual organisms, and then cropped accordingly. Interestingly, the size of an organism in the resulting images is proportional to its actual size, and does not depend on the distance to the lens of the camera. This means that size carries useful information for the task of identifying the species. In practice it also means that all the images in the dataset have different sizes.
Participants were expected to build a model that produces a probability distribution across the 121 classes for each image. These predicted distributions were scored using the log loss (which corresponds to the negative log likelihood or equivalently the cross-entropy loss).
This loss function has some interesting properties: for one, it is extremely sensitive to overconfident predictions. If your model predicts a probability of 1 for a certain class, and it happens to be wrong, the loss becomes infinite. It is also differentiable, which means that models trained with gradient-based methods (such as neural networks) can optimize it directly – it is unnecessary to use a surrogate loss function.
Interestingly, optimizing the log loss is not quite the same as optimizing classification accuracy. Although the two are obviously correlated, we paid special attention to this because it was often the case that significant improvements to the log loss would barely affect the classification accuracy of the models.
This rocks!
Code is coming soon to Github!
Certainly of interest to marine scientists but also to anyone in bio-medical imaging.
The problem of too much data and too few experts is a common one.
What I don’t recall seeing are releases of pre-trained classifiers. Is the art developing too quickly for that to be a viable product? Just curious.
I first saw this in a tweet by Angela Zutavern.