--- license: mit task_categories: - image-text-to-text - image-feature-extraction language: - en pretty_name: Colorbar Range Dataset configs: - config_name: default data_files: - split: train path: data/train-* dataset_info: features: - name: image_id dtype: string - name: label struct: - name: Blue sequence: int64 - name: Green sequence: int64 - name: Yellow sequence: int64 - name: Orange sequence: int64 - name: Dark Orange sequence: int64 - name: Red sequence: int64 - name: Dark Red sequence: int64 - name: Magenta sequence: int64 - name: Pink sequence: int64 - name: Purple sequence: int64 - name: image dtype: image splits: - name: train num_bytes: 956621.0 num_examples: 80 download_size: 942355 dataset_size: 956621.0 --- # Colorbar Range Dataset This dataset contains vertical colorbar images annotated with structured numeric ranges for each color segment. These colorbars are commonly used in scientific plots, heatmaps, and machine learning visualizations. Each image is paired with metadata describing the numeric range represented by each color. --- ## Dataset Structure Each sample contains: - `image` : Colorbar image (PNG) - `image_id` : Unique identifier for the image - `label` (or `ranges`) : Dictionary mapping color name → numeric range ### Example Annotation ```json { "Blue": [0, 10], "Green": [10, 15], "Yellow": [15, 20], "Orange": [20, 25], "Red": [25, 30] } ``` --- --- license: mit task_categories: - image-text-to-text - image-feature-extraction language: - en pretty_name: Colorbar Range Dataset --- --- # Colorbar Range Dataset This dataset contains vertical colorbar images annotated with structured numeric ranges for each color segment. These colorbars are commonly used in scientific plots, heatmaps, and machine learning visualizations. --- ## How to Use ### 1. Install Dependencies ```bash pip install datasets pillow ``` ### 2. Load the Dataset from Hugging Face ```bash from datasets import load_dataset dataset = load_dataset( "menasi11/colorbar-range-dataset", split="train" ) sample = dataset[0] sample["image"].show() ```