Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
| license: apache-2.0 | |
| task_categories: | |
| - image-classification | |
| language: | |
| - en | |
| tags: | |
| - Scene-Detection | |
| - buildings | |
| - forest | |
| - glacier | |
| - mountain | |
| - sea | |
| - street | |
| - climate | |
| 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: | |
| - `buildings` | |
| - `forest` | |
| - `glacier` | |
| - `mountain` | |
| - `sea` | |
| - `street` | |
| 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 scene | |
| - `label`: 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 |