boulderspot / README.md
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use ClassLabel for label column
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---
license: apache-2.0
size_categories:
- 10K<n<100K
task_categories:
- image-classification
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': bouldering_area
'1': other
- name: stem
dtype: string
- name: suffix
dtype: string
splits:
- name: train
num_bytes: 2332064084.761
num_examples: 13679
- name: test
num_bytes: 805144625.12
num_examples: 3880
download_size: 3137306204
dataset_size: 3137208709.881
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
tags:
- bouldering
- rock climbing
- Boulderspot
---
# pszemraj/boulderspot
These are aerial images of Switzerland classified into what could be a bouldering area (label: `bouldering_area`) or not (label: `other`). The test set has no labels (i.e. the column is `None`) and is randomly sampled from across the country.
Sources:
- data: [SWISSIMAGE 10 cm](https://www.swisstopo.admin.ch/en/orthoimage-swissimage-10)
- labels: me
Date created: 2021
You can find some example CNN-based models trained on an earlier/smaller version of this dataset in [this repo](https://github.com/pszemraj/BoulderAreaDetector)
If you are a member of **an organization** interested in details of how this was created/similar ideation related to AI applications for the outdoors/climbing, feel free to contact me (info on my [site](https://pszemraj.carrd.co/)).
---
```yml
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype: string
- name: stem
dtype: string
- name: suffix
dtype: string
splits:
- name: train
num_bytes: 2881884752.62
num_examples: 13679
- name: test
num_bytes: 871464803.08
num_examples: 3880
download_size: 3137018901
dataset_size: 3753349555.7
```