Computer vision posts

Explore the latest in our mission to build a better world using data science and AI.

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Meet the winners of the Pose Bowl challenge

Learn about the top solutions submitted for the Pose Bowl: Spacecraft Detection and Pose Estimation Challenge.

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NASA Pose Bowl - Benchmark

An introduction to the NASA Pose Bowl competition, with a benchmark solution for the object detection track

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What's a takahē?

Although Zamba's models are trained with animals from Africa and Europe, they can be used with videos from other locations that show species the models have never seen. We demonstrate with a dataset from New Zealand.

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Standing on the Threshold

Using probabilistic classifications from Zamba, we can automatically remove a large majority of blank videos while controlling the fraction of wildlife videos we lose. But how do we know where to draw the line?

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Find all the pangolins

We can use Zamba's probablistic classifications to search for videos containing specific animals. Particularly for small animals, this strategy can be highly effective.

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You can stop watching blank videos

Using Zamba's probablistic classifications, you can identify and remove blank videos -- saving viewing time, storage space, and data transfer costs -- while minimizing the loss of videos that contain animals.

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Meet the winners of the Where's Whale-do Challenge

Meet the winners of the Where's Whale-do challenge, and learn about the deep learning models they developed to identify individual Cook Inlet beluga whales from images.

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Where's Whale-Do? Data Exploration and Benchmark

How to get started with the Where's Whale-do beluga photo-identification challenge!

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Introduction to Image Classification using Camera Trap Images

We'll show you how to get started identifying animal species from camera trap images!

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Meet the winners of the Deep Chimpact: Depth Estimation for Wildlife Conservation Challenge

Meet the minds behind the top models for measuring wildlife depth! Accurate depth estimations help ecologists track wildlife populations and protect the ecosystems that depend on them.

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Meet the winners of the Image Similarity Challenge

Introducing the winners of the Facebook AI Image Similarity Challenge! Meet the top teams who matched manipulated images with their source images.

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Deep Chimpact: Depth Estimation for Wildlife Conservation - Benchmark

In this guest post by MathWorks, we'll show you how to start working with camera trap videos to estimate the distance to an animal seen in the wild.

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Facebook AI Image Similarity Challenge - Getting Started

In this post, we will introduce the Facebook AI Image Similarity Challenge and highlight some resources to help you get started.

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Meet the winners of the Hateful Memes Challenge

Hear from the winners who were best able to detect hate speech in memes using multi-modal models.

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Meet the winners of the Clog Loss Challenge for Alzheimer's Research

Meet the winners who were best able to detect clogged blood vessels and advance Alzheimer's research from 3D image stacks.

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Advance Alzheimer’s Research with Stall Catchers - Benchmark

In this guest post by MathWorks, we'll show you how to start working with videos to determine if a blood vessel is stalled or flowing.

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How to build a multimodal deep learning model to detect hateful memes

We're launching a new competition to detect hateful memes. In this post, we'll show you how to get started!

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Meet the winners of the Hakuna Ma-Data Challenge

Meet the winners who built the best wildlife identifiers!

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How to Use Deep Learning to Identify Wildlife

We show you how to get off to a wild start on our animal identification competition using a neural network!

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Results from Pri-Matrix Factorization and a New Open Source Tool for Wildlife Research and Conservation

Using AI to study the natural world: check out the results!

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Meet the winners of the N+1 Fish, N+2 Fish challenge

See how DrivenData's top modelers managed to predict length, species, and count from videos of fish captured on fishing vessels.

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Pri-matrix Factorization - Benchmark

How about some deep learning to identify wild animals in camera traps? Here's a benchmark post to get contributors started in our newest challenge.

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