657 Sightings
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Wild North Wildbook for is a free, online research platform that assists in the conservation of mountain lions (cougars), bobcats, Canada lynx, skunks, and more by automating individual identification and providing a common data model for collaboration.
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Wild North Wildbook Partners
Algorithms
MIEW-ID
MIEW-ID (µID) is a modern deep learning algorithm used to identify individuals. MIEW-ID can learn what makes images similar or dissimilar (or what differentiates one animal from another) across a wide range of species. Distinguishing individuals through their unique body markings is a key concept for wildlife conservation. MIEW-ID learns embeddings for images from the database. Embeddings are the unique markings that represent individuals. When new images are analyzed, their embeddings are matched against clusters of those in the database. As an added benefit, MIEW-ID is able generate visualizations of matched features, providing import inspectability inside its neural network. Learn more about MIEW-ID.
PIE v2
Pose Invariant Embeddings (or PIE) is a deep learning approach to individual ID. PIE is trained to learn embeddings that are useful for distinguishing among individuals in a wildlife population. Unlike HotSpotter, which is a “static” pattern matcher (i.e. a fixed algorithm not trained for each separate species), PIE can be trained on a per-species basis. Wild Me has generated separate PIE models optimized for manta rays, humpback whales, orcas, right whales, and so on. And unlike fixed-catalog classifiers like Deepsense or Kaggle7, PIE can gracefully add new individuals to its catalog without being retrained: it learns the general task of mapping images into embeddings that represent individuals, rather than the specific task of sorting images into a fixed number of IDs. PIE strikes a lovely balance between a flexible general-purpose identifier and one that can be trained and refined on a given problem. In summary, PIE is a very powerful, reusable machine learning technique that can be trained to identify individuals across many species. Learn more about PIE.
HOTSPOTTER
Hotspotter is a SIFT-based computer vision algorithm. It analyzes the textures in an image to find distinct patterning, or "hot spots", and then compares those against other images in the database. Unlike machine learning-based approaches, HotSpotter can help build new catalogs for new species that need to match individuals but don’t have the training data yet for machine learning-based approaches. Hotspotter can also match new individuals without the need for network retraining. Hotspotter produces a ranked list of potential matches, increasing match scores with increasing similarity. Learn more about Hotspotter.
MIEW-ID (µID) is a modern deep learning algorithm used to identify individuals. MIEW-ID can learn what makes images similar or dissimilar (or what differentiates one animal from another) across a wide range of species. Distinguishing individuals through their unique body markings is a key concept for wildlife conservation. MIEW-ID learns embeddings for images from the database. Embeddings are the unique markings that represent individuals. When new images are analyzed, their embeddings are matched against clusters of those in the database. As an added benefit, MIEW-ID is able generate visualizations of matched features, providing import inspectability inside its neural network. Learn more about MIEW-ID.
PIE v2
Pose Invariant Embeddings (or PIE) is a deep learning approach to individual ID. PIE is trained to learn embeddings that are useful for distinguishing among individuals in a wildlife population. Unlike HotSpotter, which is a “static” pattern matcher (i.e. a fixed algorithm not trained for each separate species), PIE can be trained on a per-species basis. Wild Me has generated separate PIE models optimized for manta rays, humpback whales, orcas, right whales, and so on. And unlike fixed-catalog classifiers like Deepsense or Kaggle7, PIE can gracefully add new individuals to its catalog without being retrained: it learns the general task of mapping images into embeddings that represent individuals, rather than the specific task of sorting images into a fixed number of IDs. PIE strikes a lovely balance between a flexible general-purpose identifier and one that can be trained and refined on a given problem. In summary, PIE is a very powerful, reusable machine learning technique that can be trained to identify individuals across many species. Learn more about PIE.
HOTSPOTTER
Hotspotter is a SIFT-based computer vision algorithm. It analyzes the textures in an image to find distinct patterning, or "hot spots", and then compares those against other images in the database. Unlike machine learning-based approaches, HotSpotter can help build new catalogs for new species that need to match individuals but don’t have the training data yet for machine learning-based approaches. Hotspotter can also match new individuals without the need for network retraining. Hotspotter produces a ranked list of potential matches, increasing match scores with increasing similarity. Learn more about Hotspotter.
Species
PUMA CONCOLOR
Cougar
LYNX RUFUS
Bobcat
LYNX CANADENSIS
Canada lynx
SPILOGALE GRACILIS
Western spotted skunk
Cougar
LYNX RUFUS
Bobcat
LYNX CANADENSIS
Canada lynx
SPILOGALE GRACILIS
Western spotted skunk
