---
license: gpl-3.0
pipeline_tag: image-classification
---

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:

* walkability_vecs, walkability_scores
* bikeability_vecs, bikeability_scores
* pleasantness_vecs, pleasantness_scores
* greenness_vecs, greenness_scores
* safety_vecs, safety_scores

The _vecs entries are matrices of size Nx1024 and the _scores entries are vectors of size N.

The _vecs are encoded by OpenCLIP ViT-H-14-378-quickgelu (pretrained: dfn5b).

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].

This can be used to model a score for any given vector from an image encoded by the above CLIP model.

# Example spaces

* [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
* [Percept-image](https://huggingface.co/spaces/Spatial-Data-Science-and-GEO-AI-Lab/percept-image): evaluation perception ratings for a given image

See also: [svi_percept](https://github.com/Spatial-Data-Science-and-GEO-AI-Lab/svi_percept) Python package.