Dataset Description:
This dataset includes two folders of images of people. One folder contains images of men, and the other contains images of women. The images include faces, upper bodies, and full bodies. This dataset can be used for various projects like gender recognition, human identification, and image classification.

Use Cases:
Gender Recognition: For algorithms that recognize gender based on images.
Human Identification: To improve models for identifying humans in images and videos.
Image Classification: For classifying images into categories of men and women.

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://t.me/datasets1

The data collected for this project were from the following sources:

Fabric Defect Dataset from Kaggle (Ranathunga, 2020)
Fabric Stain Dataset from Kaggle (Pathirana, 2020)
Aitex Fabric Image Database (Silvestre-Blanes et al., 2019)
Dataset from the Author of the Literature Review Paper (Peng, 2020): Only the non-defect images from the dataset were used.

About Dataset
Dataset Information

Image Size (GB): 3.2

Modalities: MR (with some PET/CT)

Number of Images: 22,036

Number of Participants: 26

Number of Series: 182

Number of Studies: 26

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://t.me/datasets1

I created this dataset because I found that many Alzheimer's MRI datasets on Kaggle are highly repetitive (all based on the 6400-image version, with various augmented datasets), and they lack specific data sources. This causes issues for research and citation. This dataset is sourced from OASIS and includes MRI images (axial slices) of 457 individuals (note that there is a data imbalance issue, please perform upsampling as needed). Each image is specifically named to help you locate the corresponding OASIS research phase and individual. I first extracted MRI images from 457*4 NIfTI files (each person has three MRI scan NIfTI files) and converted them to PNG format. Then, I performed skull stripping on the converted MRIs. Finally, I manually removed images with black regions and incomplete brain displays, which took a lot of time. If this dataset is well-received, I will consider releasing a skull-stripped dataset from ADNI. I hope Kagglers can use it to improve the accuracy of Alzheimer's diagnosis using various deep learning frameworks and contribute to Alzheimer's research.

2024-12-1 There are four nii files in the ‘VeryMildDemented’ folder that I forgot to delete. However, this does not affect the images imported using tools like ImageFolder. If you batch convert the images to three channels, it may cause errors. Please search for ‘brain.nii’ and ‘mask.nii’ in the folder and delete them manually.

This dataset contains real-world information about colorectal cancer cases from different countries. It includes patient demographics, lifestyle risks, medical history, cancer stage, treatment types, survival chances, and healthcare costs. The dataset follows global trends in colorectal cancer incidence, mortality, and prevention.

Dataset Structure
Each row represents an individual case, and the columns include:

Patient_ID (Unique identifier)
Country (Based on incidence distribution)
Age (Following colorectal cancer age trends)
Gender (M/F, considering men have 30-40% higher risk)
Cancer_Stage (Localized, Regional, Metastatic)
Tumor_Size_mm (Randomized within medical limits)
Family_History (Yes/No)
Smoking_History (Yes/No)
Alcohol_Consumption (Yes/No)
Obesity_BMI (Normal/Overweight/Obese)
Diet_Risk (Low/Moderate/High)
Physical_Activity (Low/Moderate/High)
Diabetes (Yes/No)
Inflammatory_Bowel_Disease (Yes/No)
Genetic_Mutation (Yes/No)
Screening_History (Regular/Irregular/Never)
Early_Detection (Yes/No)
Treatment_Type (Surgery/Chemotherapy/Radiotherapy/Combination)
Survival_5_years (Yes/No)
Mortality (Yes/No)
Healthcare_Costs (Country-dependent, $25K-$100K+)
Incidence_Rate_per_100K (Country-level prevalence)
Mortality_Rate_per_100K (Country-level mortality)
Urban_or_Rural (Urban/Rural)
Economic_Classification (Developed/Developing)
Healthcare_Access (Low/Moderate/High)
Insurance_Status (Insured/Uninsured)
Survival_Prediction (Yes/No, based on factors)

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://t.me/datasets1

This dataset consists of images of car license plates, paired with their corresponding annotations in YOLO format. It is designed for training and evaluating models focused on detecting car license plates in images. The dataset was derived from the Car License Plate Detection dataset on Kaggle and has been split into training and testing subsets.

Dataset Overview:
Total Images: 433 car license plate images
Image Format: .png
Annotation Format: YOLO (Label, Xc, Yc, Width, Height)
Image Resolution: Varies
Annotations: Bounding box coordinates for car license plates, normalized to image dimensions

Dataset Structure:
The dataset is divided into two main directories: train and test. Each directory contains two subdirectories: images and labels.
train: Contains 346 images and corresponding YOLO annotation files for training
test: Contains 87 images and corresponding YOLO annotation files for testing

Each image file (e.g., Cars0.png) is paired with a corresponding annotation file (e.g., Cars0.txt).
The annotation files contain the following information in YOLO format:
Label: The class of the object (for this dataset, it will always be 0, representing car license plates).
Xc, Yc: Center coordinates of the bounding box, normalized to the width and height of the image.
W, H: Width and height of the bounding box, also normalized.

File Information:
train/images/: 346 .png image files of car license plates.
train/labels/: 346 .txt annotation files in YOLO format.
test/images/: 87 .png image files for testing.
test/labels/: 87 .txt annotation files in YOLO format.
data.yaml: Configuration file with dataset details.

Dataset Splitting:
Training Set: 346 images (80% of total dataset)
Test Set: 87 images (20% of total dataset)

Example:
An example annotation for a license plate might look like this:
0 0.548 0.612 0.432 0.075

Where:
0: Class label (always 0 for license plates).
0.548: X-center (normalized to image width).
0.612: Y-center (normalized to image height).
0.432: Width of the bounding box (normalized to image width).
0.075: Height of the bounding box (normalized to image height).

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://t.me/datasets1

Content
The training data set contains 87,000 images which are 200x200 pixels. There are 29 classes, of which 26 are for the letters A-Z and 3 classes for SPACE, DELETE and NOTHING.
These 3 classes are very helpful in real-time applications, and classification.
The test data set contains a mere 29 images, to encourage the use of real-world test images.

enter image description here
https://www.nidcd.nih.gov/sites/default/files/Content%20Images/NIDCD-ASL-hands-2014.jpg

#KaggleDatasets #DataScience #MachineLearning #DataAnalysis #DataVisualization #OpenData #DataCleaning #TextClassification #NLP #SentimentAnalysis #BigData #APIAutomation #DataLicensing #SocialMediaData #PythonIntegration #DataModeling #kaggle #ComputerVision #python #LLM #DeepLearning #Pytorch #HuggingFace #Dataset

https://t.me/datasets1