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FitBit dataset

Fitness tracker data from smart watch device usage

About Dataset:

This is a Kaggle data set that contains personal fitness tracker data from thirty Fitbit users. Thirty eligible Fitbit users consented to the submission of personal tracker data, including minute-level output for physical activity, heart rate, and sleep monitoring. It includes information about daily activity, steps, and heart rate that can be used to explore users’ habits

ASL Alphabet

Image data set for alphabets in the American Sign Language

About

The data set is a collection of images of alphabets from the American Sign Language, separated in 29 folders which represent the various classes.

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.

Fundus Image Dataset for Vessel Segmentation

High-resolution manually annotated fundus images for vessel segmentation

About Dataset

The FIVES (Fundus Image Vessel Segmentation) dataset comprises 800 high-resolution color fundus photographs manually annotated at the pixel level for retinal vessel segmentation. The images represent a wide range of ages (4–83 years) and include various ocular conditions such as diabetic retinopathy, age-related macular degeneration, and glaucoma. The annotations were standardized via expert crowdsourcing. Each image was further assessed for three quality aspects (illumination and color distortion, blur, and low contrast) using published automatic algorithms. This dataset is currently the largest publicly available collection for retinal vessel segmentation and is designed to facilitate the development and evaluation of AI-based segmentation models.
The dataset supports automated analysis of retinal vasculature for ophthalmological and systemic disease assessment and contributes significantly to advancing the field of AI in medical imaging.

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Pavement Dataset
Synthetic Dataset on Road Pavements (Educational Purposes)

🏗 Pavement Condition Monitoring and Maintenance Prediction
📘 Scenario

You are a data analyst for a city engineering office tasked with identifying which road segments require urgent maintenance. The office has collected inspection data on various roads, including surface conditions, traffic volume, and environmental factors.

Your goal is to analyze this data and build a binary classification model to predict whether a given road segment needs maintenance, based on pavement and environmental indicators.
🔍 Target Variable: Needs_Maintenance

This binary label indicates whether the road segment requires immediate maintenance, defined by the following rule:

Needs_Maintenance = 1
Needs_Maintenance = 0 otherwise

🟢 Name Of Dataset: BAH (Behavioural Ambivalence/Hesitancy)

🟢 Description Of Dataset:
Recognizing complex emotions linked to ambivalence and hesitancy (A/H) can play a critical role in the personalization and effectiveness of digital behaviour change interventions. These subtle and conflicting emotions are manifested by a discord between multiple modalities, such as facial and vocal expressions, and body language. Although experts can be trained to identify A/H, integrating them into digital interventions is costly and less effective. Automatic learning systems provide a cost-effective alternative that can adapt to individual users, and operate seamlessly within real-time, and resource-limited environments. However, there are currently no datasets available for the design of ML models to recognize A/H.This paper introduces a first Behavioural Ambivalence/Hesitancy ( BAH) dataset collected for subject-based multimodal recognition of A/H in videos. It contains videos from 224 participants captured across 9 provinces in Canada, with different age, and ethnicity. Through our web platform, we recruited participants to answer 7 questions, some of which were designed to elicit A/H while recording themselves via webcam with microphone. BAH amounts to 1,118 videos for a total duration of 8.26 hours with 1.5 hours of A/H. Our behavioural team annotated timestamp segments to indicate where A/H occurs, and provide frame- and video-level annotations with the A/H cues. Video transcripts and their timestamps are also included, along with cropped and aligned faces in each frame, and a variety of participants meta-data.Additionally, this paper provides preliminary benchmarking results baseline models for BAH at frame- and video-level recognition with mono- and multi-modal setups. It also includes results on models for zero-shot prediction, and for personalization using unsupervised domain adaptation. The limited performance of baseline models highlights the challenges of recognizing A/H in real-world videos. The data, code, and pretrained weights are available.

🟢 Official Homepage: https://github.com/sbelharbi/bah-dataset

🟢 Number of articles that used this dataset: 1

🟢 Dataset Loaders:
Not found

🟢 Articles related to the dataset:
📝 BAH Dataset for Ambivalence/Hesitancy Recognition in Videos for Behavioural Change

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🔴 For more data science resources:
✓ https://t.me/DataScienceT

🟢 Name Of Dataset: ITDD (Industrial Textile Defect Detection)

🟢 Description Of Dataset:
The Industrial Textile Defect Detection (ITDD) dataset includes 1885 industrial textile images categorized into 4 categories: cotton fabric, dyed fabric, hemp fabric, and plaid fabric. These classes are collected from the industrial production sites of WEIQIAO Textile. ITDD is an upgraded version of WFDD that reorganizes three original classes and adds one new class.

🟢 Official Homepage: https://github.com/cqylunlun/CRAS?tab=readme-ov-file#dataset-release

🟢 Number of articles that used this dataset: 1

🟢 Dataset Loaders:
Not found

🟢 Articles related to the dataset:
📝 Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection

==================================
🔴 For more data science resources:
✓ https://t.me/DataScienceT