A new Computer Vision Model including 1,368 new taxa in 37 days

We released a new computer vision model today. It has 66,214 taxa, up from 64,884.

This new model (v1.3) is the second we’ve trained in about a month using the new faster approach, but it’s the first with a narrow ~1 month interval between the export of the data it was trained on and the export of the data the model it is replacing (v1.2) was trained on. The previous model (v1.2) was replacing a model (v1.1) trained on data exported in April so there was a 4 month interval between these data exports (interval between A and B in the figure below). This 4 month interval is why model 1.2 added ~5,000 new taxa to the model. The new model (v1.3) was trained on data exported just 37 days after the data used to train model 1.2 (interval between B and C in the figure below) and added 1,368 new taxa.

While our goal is to maintain this ~1 month interval, we caution that this is getting more and more challenging as the iNaturalist dataset continues to grow. Expect the interval to lengthen unless we secure improved training hardware or devise improvements to the way we generate a training set or train the models themselves. However, it’s fun to look at this comparison between models 1.3 and 1.2 and imagine what maintaining this pace of a new model and about 1,000 new taxa a month would be like.

Taxa differences to previous model

The charts below summarize these 1,368 new taxa using the same groupings we described in the 1.2 release post.

By category, most of these 1,368 new taxa were insects and plants

Here are species level examples of new species added for each category:

• Plantae
• Other Animalia
• Mollusca
• Reptilia
• Insecta
• Protozoa
• Aves
• Arachnida
• Chromista
• Amphibia
• Fungi
• Mammalia
• Actinopterygii

Click on the links to see these taxa in the Explore page to see these samples rendered as species lists.

Remember, to see if a particular species is included in the currently live computer vision model, you can look at the “About” section of its taxon page.

We couldn't do it without you

Thank you to everyone in the iNaturalist community who makes this work possible! Sometimes the computer vision suggestions feel like magic, but it’s truly not possible without people. None of this would work without the millions of people who have shared their observations and the knowledgeable experts who have added identifications.

In addition to adding observations and identifications, here are other ways you can help:

• Share your Machine Learning knowledge: iNaturalist’s computer vision features wouldn’t be possible without learning from many colleagues in the machine learning community. If you have machine learning expertise, these are two great ways to help:
• Participate in the annual iNaturalist challenges: Our collaborators Grant Van Horn and Oisin Mac Aodha continue to run machine learning challenges with iNaturalist data as part of the annual Computer Vision and Pattern Recognition conference. By participating you can help us all learn new techniques for improving these models.
• Start building your own model with the iNaturalist data now: If you can’t wait for the next CVPR conference, thanks to the Amazon Open Data Program you can start downloading iNaturalist data to train your own models now. Please share with us what you’ve learned by contributing to iNaturalist on Github.
• Donate to iNaturalist: For the rest of us, you can help by donating! Your donations help offset the substantial staff and infrastructure costs associated with training, evaluating, and deploying model updates. Thank you for your support!

Publicado el 14 de octubre de 2022 a las 02:44 AM por loarie