Datasets
Standard Dataset
CSMambaDataSets
- Citation Author(s):
- Submitted by:
- Yufeng Zhou
- Last updated:
- Tue, 12/03/2024 - 02:27
- DOI:
- 10.21227/654k-eg58
- License:
15 Views- Categories:
- Keywords:
Abstract
Set5, Set11, and Set14 are classic small-scale benchmark datasets widely used for image super-resolution tasks. BSD100 and BSD500 feature complex natural scenes, commonly used for denoising and segmentation research. McM18 is a medical imaging dataset focused on medical image reconstruction. Urban100 emphasizes urban scenes, ideal for evaluating models on high-frequency details and structural textures. These datasets span diverse applications, serving as valuable benchmarks in computer vision research.
This guide provides instructions for utilizing benchmark datasets, including Set5, Set11, Set14, BSD100, BSD500, McM18, and Urban100, commonly employed in computer vision tasks such as super-resolution, denoising, and segmentation.
1. Dataset Overview
- Set5, Set11, Set14: Small-scale datasets for super-resolution, containing diverse image categories for testing reconstruction performance.
- BSD100, BSD500: Natural scene images for denoising, segmentation, or feature extraction studies, with varying resolutions and complexities.
- McM18: Medical image dataset for tasks like reconstruction and enhancement. Suitable for domain-specific testing in healthcare applications.
- Urban100: Urban scene dataset, ideal for super-resolution models focusing on high-frequency textures and structural details.
2. General Instructions
- Download: Ensure datasets are downloaded from their official sources to maintain integrity.
- Preprocessing: Use standard preprocessing techniques (e.g., resizing, normalization) as per the specific research requirement.
- Partitioning: Split datasets into training, validation, and testing sets if not predefined.
- Usage: Apply appropriate data augmentations (e.g., flipping, rotation) to enhance model robustness.
3. Evaluation Metrics
- Common metrics include PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index) for assessing reconstruction and image quality.
- For segmentation, use IoU (Intersection over Union) or Dice Coefficient.
4. Best Practices
- Maintain consistent preprocessing pipelines across datasets for comparable results.
- Document dataset configurations and processing steps in publications or experiments for reproducibility.
- Cite the datasets appropriately in academic work.
By following these instructions, you can maximize the utility of these datasets in your research and ensure rigorous evaluation.
