Wolfram Neural Net Repository
Immediate Computable Access to Neural Net Models
Determine the geolocation of a photograph
Released in 2017, this geolocation model classifies the location in which a photo was taken among more than 15,000 predefined locations around the world. The classes correspond to cells extracted from Google's S2 Geometry library.
Number of layers: 344 | Parameter count: 74,405,235 | Trained size: 299 MB |
This model correctly localized 82.2% of the IM2GPS test set within 2,500 kilometers.
Get the pre-trained net:
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Obtain an estimate of the latitude and longitude of where a photo was taken:
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Show a map of the area corresponding to the position:
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Mark the position on a world map:
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The net returns a probability distribution over all available locations. Obtain the 50 most probable locations for a given image and plot these locations on the world map, with the size of the location marker proportional to the probability:
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In places with high population density, very fine-grained predictions are possible. Consider the following four landmarks in Paris:
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Predict the locations of the four landmarks and mark the locations on the map:
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Compare with the actual locations:
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Inspect the distribution of the available positions. Display a heat map of the location density on the map:
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Inspect the number of parameters of all arrays in the net:
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Obtain the total number of parameters:
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Obtain the layer type counts:
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Display the summary graphic:
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Wolfram Language 11.2 (September 2017) or above