The Data Explorer Version 1 dataset is a collection of images organized into two main categories: "stroke_cropped" and "stroke_noncropped." Each category is further subdivided into subsets for testing, training, and validation purposes.

1. stroke_cropped:

   • CROPPED:
     • TEST_CROP
     • TRAIN_CROP
     • VAL_CROP

2. stroke_noncropped:

   • NON_CROPPED:
     • TEST
     • TRAIN
     • VAL

Description:
The dataset primarily focuses on stroke-related images, categorized into cropped and non-cropped versions. In the "stroke_cropped" category, the images have undergone a cropping process, with subsets specifically designated for testing (TEST_CROP), training (TRAIN_CROP), and validation (VAL_CROP) purposes. On the other hand, the "stroke_noncropped" category contains images in their original, non-cropped form, with subsets similarly allocated for testing, training, and validation (TEST, TRAIN, VAL).

The dataset size is approximately 73.4 MB. Researchers, developers, or practitioners interested in stroke-related image analysis and classification tasks may find this dataset useful for training and evaluating machine learning models. The inclusion of both cropped and non-cropped versions allows for a diverse range of experiments and applications, catering to different aspects of stroke-related image processing. It is recommended to review the specific subsets based on the task at hand, whether it be testing, training, or validation, to ensure proper use and interpretation of the dataset.

The key difference between the "cropped" and "non-cropped" versions of the dataset lies in the preprocessing applied to the images.

1. Cropped:

   • Images in the "CROPPED" category have undergone a cropping process, where a portion of the original image has been selected or extracted.
   • This cropping may be performed to focus on specific regions of interest within the image, excluding unnecessary or irrelevant background information.
   • Cropped images are often used to highlight and emphasize particular features, making it potentially easier for machine learning models to learn and classify relevant patterns.

2. Non-Cropped:

   • Images in the "NON_CROPPED" category are presented in their original form without any cropping applied.
   • These images contain the entire scene or object captured by the original image, providing a broader context for analysis.
   • Non-cropped images might contain more background information, and the relevant features for analysis are not isolated or emphasized as they are in the cropped versions.

Use Cases:

• The choice between cropped and non-cropped images depends on the specific goals of a machine learning task. If the objective is to focus on detailed features within a limited region, cropped images might be more suitable.
• On the other hand, if a comprehensive understanding of the entire scene is crucial, non-cropped images may be preferred.

Researchers and practitioners may experiment with both versions based on their specific image analysis objectives and the requirements of their machine learning models. The inclusion of both cropped and non-cropped datasets provides flexibility for different use cases and research scenarios.