---
language: en
license: mit
library_name: pytorch
model-index:
- name: baseline
  results:
  - task:
      type: Geoscore
    dataset:
      name: OSV-5M
      type: geolocation
    metrics:
    - type: ranking
      value: 3361
  - task:
      type: Haversine Distance
    dataset:
      name: OSV-5M
      type: geolocation
    metrics:
    - type: distance
      value: 1814
  - task:
      type: Country classification
    dataset:
      name: OSV-5M
      type: geolocation
    metrics:
    - type: accuracy
      value: 68
  - task:
      type: Region classification
    dataset:
      name: OSV-5M
      type: geolocation
    metrics:
    - type: accuracy
      value: 39.4
  - task:
      type: Area classification
    dataset:
      name: OSV-5M
      type: geolocation
    metrics:
    - type: accuracy
      value: 10.3
  - task:
      type: City classification
    dataset:
      name: OSV-5M
      type: geolocation
    metrics:
    - type: accuracy
      value: 5.9
---

# Model Card for baseline

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## Model Details

### Model Description

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Geolocation benchmark on OpenStreetView-5M dataset

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- **Language(s) (NLP):** en
- **License:** mit
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### Model Sources [optional]

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## Uses

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### Direct Use

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

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## Training Details

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#### Training Hyperparameters

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## Evaluation

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#### Summary



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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

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## Technical Specifications [optional]

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