Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
|
Download README.md from prithivMLmods/OpenScene-Classification: direct link, hf CLI and curl.
- Browser
- Download file 1.87 kB
-
https://huggingface.co/datasets/prithivMLmods/OpenScene-Classification/resolve/main/README.md
- Command line
-
hf download hf://datasets/prithivMLmods/OpenScene-Classification/README.md
-
curl -L -o README.md https://huggingface.co/datasets/prithivMLmods/OpenScene-Classification/resolve/main/README.md
1.87 kB
metadata
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- Scene-Detection
- buildings
- glacier
- forest
- mountain
- sea
- climate
- street
size_categories:
- 10K<n<100K
OpenScene-Classification Dataset
A high-quality image classification dataset curated for scene detection tasks, particularly useful in training and evaluating models for recognizing various natural and man-made environments.
Dataset Summary
The OpenScene-Classification dataset contains labeled images categorized into six distinct scene types:
buildingsforestglaciermountainseastreet
This dataset is structured for supervised image classification, suitable for deep learning models aiming to identify and classify real-world scenes.
Dataset Structure
- Split:
train(currently only one split) - Format:
parquet - Modality:
Image - Labels Type: Integer class indices with corresponding string names
- License: Apache-2.0
Each entry in the dataset includes:
image: the image of the scenelabel: the class index (e.g., 0 for buildings)label_name: the class name (e.g., "buildings")
Note: The dataset viewer on Hugging Face may take a moment to load all samples.
Label Mapping
| Class Index | Label |
|---|---|
| 0 | buildings |
| 1 | forest |
| 2 | glacier |
| 3 | mountain |
| 4 | sea |
| 5 | street |
Dataset Stats
- Size: 10K - 100K images
- Language: English (tags, metadata)
- Tags:
Scene-Detection,buildings,forest,glacier,mountain,sea,street
Intended Use
This dataset is ideal for:
- Scene classification model training
- Benchmarking computer vision algorithms
- Educational purposes in machine learning and AI