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license: gpl-3.0 |
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pipeline_tag: image-classification |
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--- |
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K-Nearest-Neighbour model of human perception of Amsterdam street view imagery. data.npz is a compressed numpy file with 10 records, it can be loaded with numpy.load and the resulting object can be indexed by any of the following keys: |
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* walkability_vecs, walkability_scores |
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* bikeability_vecs, bikeability_scores |
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* pleasantness_vecs, pleasantness_scores |
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* greenness_vecs, greenness_scores |
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* safety_vecs, safety_scores |
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The _vecs entries are matrices of size Nx1024 and the _scores entries are vectors of size N. |
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The _vecs are encoded by OpenCLIP ViT-H-14-378-quickgelu (pretrained: dfn5b). |
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The _scores are the given rating scores (1 to 5) for the corresponding vector. For example, the vector given by walkability_vecs[10,:] has a corresponding score in walkability_scores[10]. |
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This can be used to model a score for any given vector from an image encoded by the above CLIP model. |
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# Example spaces |
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* [Percept-map](https://huggingface.co/spaces/Spatial-Data-Science-and-GEO-AI-Lab/percept-map): pick a point on a map and evaluate perception ratings for Mapillary imagery from that location |
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* [Percept-image](https://huggingface.co/spaces/Spatial-Data-Science-and-GEO-AI-Lab/percept-image): evaluation perception ratings for a given image |
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See also: [svi_percept](https://github.com/Spatial-Data-Science-and-GEO-AI-Lab/svi_percept) Python package. |
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