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❎Ships/Vessels in Aerial Images

https://t.me/datasets1💯

📝Ships/Vessels in Aerial Images

❎ 26900 ANNOTATED images - Detect ships in Aerial/satellite imagery

🔍
This dataset contains a vast collection of 26.9k images, which have been carefully annotated for the specific purpose of ship detection. The bounding box annotations are presented in the YOLO format, which allows for accurate and efficient detection of the ships in the images. The dataset has been curated to include images of only one class - "ship" - thus enabling streamlined and precise analysis.

The detection of ships or vessels within an image is a vital task that has significant practical applications. Maritime safety is one such application, as the detection of ships can help prevent accidents at sea by providing early warnings of potential collisions or obstacles. Fisheries management is another important use case, where the detection of fishing vessels can aid in monitoring fishing activities and preventing overfishing. In addition, ship detection can be used for marine pollution monitoring, defense and maritime security, protection from piracy, illegal immigration, and a range of other purposes.
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https://t.me/datasets1💯

❎Ships/Vessels in Aerial Images

https://t.me/datasets1💯

🌟Human Action Recognition (HAR) Dataset

https://t.me/datasets1💯

📝Human Action Recognition (HAR) Dataset

❎ The dataset features 15 different classes of Human Activities.

🔍
he Human Activity Recognition (HAR) dataset consists of over 12,000 labeled images spanning 15 distinct human activity classes, including actions such as calling, dancing, running, and sleeping. Each image represents a single activity and is organized into separate folders corresponding to its class label. The objective is to develop a convolutional neural network (CNN)-based image classification model capable of accurately predicting the activity being performed in each image. A separate test set of 5,400 unlabeled images is provided for evaluation, along with a submission template that specifies the required format for output predictions. This task falls under the broader domain of computer vision and has practical applications in surveillance, healthcare monitoring, human-computer interaction, and behavior analysis.

#AI #ArtificialIntelligence #MachineLearning #DeepLearning #Python #DataScience #ComputerVision #TensorFlow #PyTorch #CNN #VisionAI #OpenCV #ImageClassification #HumanActivityRecognition #HAR #BehaviorAnalysis #SmartSurveillance #PoseEstimation #MLProjects #AIChallenge💯



https://t.me/datasets1💯

🌟Human Action Recognition (HAR) Dataset

https://t.me/datasets1💯

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📝Age Detection - Face Recognition Dataset

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📝Age Detection - Face Recognition Dataset

❎ otos of people from 18 to 60 for face detection and age determination

🔍
The Age Detection dataset is built upon a collection of selfies and ID card images, featuring high-quality facial photographs of individuals between the ages of 18 and 60. The dataset is divided into five distinct age groups: 18–20, 21–30, 31–40, 41–50, and 51–60, with separate folders for training and testing purposes. Each image is accompanied by a CSV file containing rich metadata, including the individual’s exact age, true gender, country, ethnicity, as well as the file extension and resolution for each photo. The demographic diversity within the dataset—covering various ethnicities, genders, and nationalities—makes it highly suitable for developing and evaluating deep learning models for age estimation, facial recognition, and biometric analysis. The full commercial version contains over 95,000 photos and is available for purchase via the TrainingData platform. In addition, several supplementary datasets are offered, including selfie-video datasets, bald-person datasets, and anti-spoofing datasets, making this a comprehensive resource for advanced biometric system development.
#AgeDetectionDataset#FacialAnalysis#AgeEstimation#FaceRecognitionDataset#BiometricData#DeepLearning#MachineLearningDataset#AgeGroupClassification#SelfieDataset#IDPhotoDataset

https://t.me/datasets1💯

📝Age Detection - Face Recognition Dataset

https://t.me/datasets1💯

📝Age Detection - Face Recognition Dataset

❎ otos of people from 18 to 60 for face detection and age determination

🔍
The Age Detection dataset is built upon a collection of selfies and ID card images, featuring high-quality facial photographs of individuals between the ages of 18 and 60. The dataset is divided into five distinct age groups: 18–20, 21–30, 31–40, 41–50, and 51–60, with separate folders for training and testing purposes. Each image is accompanied by a CSV file containing rich metadata, including the individual’s exact age, true gender, country, ethnicity, as well as the file extension and resolution for each photo. The demographic diversity within the dataset—covering various ethnicities, genders, and nationalities—makes it highly suitable for developing and evaluating deep learning models for age estimation, facial recognition, and biometric analysis. The full commercial version contains over 95,000 photos and is available for purchase via the TrainingData platform. In addition, several supplementary datasets are offered, including selfie-video datasets, bald-person datasets, and anti-spoofing datasets, making this a comprehensive resource for advanced biometric system development.
#AgeDetectionDataset#FacialAnalysis#AgeEstimation#FaceRecognitionDataset#BiometricData#DeepLearning#MachineLearningDataset#AgeGroupClassification#SelfieDataset#IDPhotoDataset

https://t.me/datasets1💯

❎ Ai Generated Dogs.jpg VS Real Dogs.jpg

https://t.me/datasets1💯

📝 Ai Generated Dogs.jpg VS Real Dogs.jpg

❎ Reality vs. AI – A Comparative Exploration of Authentic and Synthetic Img.

This fascinating dataset focuses on distinguishing between real dog images and those generated by AI models. With over 26,000 images in the full version, it’s neatly organized into Train, Validation, and Test sets, each containing both images and label files (0: real dog, 1: AI-generated dog). Whether you're working on image classification, evaluating generative model quality, exploring data augmentation, or conducting advanced computer vision research, this dataset offers a rich and versatile resource. Perfect for anyone exploring the intersection of AI and visual perception!.

https://t.me/datasets1 💯

Datasets Guide 📚

A practical and beginner-friendly guide that walks you through everything you need to know about datasets in machine learning and deep learning. This guide explains how to load, preprocess, and use datasets effectively for training models. It's an essential resource for anyone working with LLMs or custom training workflows, especially with tools like Unsloth.

Importance:
Understanding how to properly handle datasets is a critical step in building accurate and efficient AI models. This guide simplifies the process, helping you avoid common pitfalls and optimize your data pipeline for better performance.

Link: https://docs.unsloth.ai/basics/datasets-guide

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❎Bone Fracture Detection

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📝Bone Fracture Detection: Computer Vision Project

❎ Object Detection By YOLO

🔍
A comprehensive X-ray image dataset for bone fracture detection has been created to support computer vision projects. The dataset includes images categorized by fracture types such as Elbow Positive, Fingers Positive, Forearm Fracture, Humerus Fracture, Shoulder Fracture, and Wrist Positive. Each image is annotated with bounding boxes or pixel-level segmentation masks to indicate fracture locations. This dataset is ideal for training and evaluating machine learning models, particularly for object detection algorithms aimed at automated fracture detection. It accelerates the development of computer vision solutions for medical diagnostics, enhancing patient care.
https://t.me/datasets1💯

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❓Geometric Shapes Mathematics

https://t.me/datasets1💯

📝Geometric Shapes Mathematics

❎ Eight shapes of class; New version support hand-drawn plane shape synthesis.

🔍

Collection methodology: using software Processing with Python Mode. To generate a plane shapes like a hand-drawn, the Perlin Noise method is used (More info: https://github.com/reevald/FlatShapeNet). The dataset is used for the educational game Ariga.Currently the dataset (Version 4) is constructed by choosing 8 largest classes : "Circle", "Kite", "Parallelogram", "Square", "Rectangle", "Rhombus", "Trapezoid", and "Triangle". Each class contains 1,500 training samples, 500 validation samples, and 500 test samples. The total number of training samples is 12,000, validation samples 4,000, and testing 4,000. Each sample is an image measuring (224 x 224 x 3) (RGB).
https://t.me/datasets1💯

📝Geometric Shapes Mathematics

❎ Eight shapes of class; New version support hand-drawn plane shape synthesis.

🔍

Collection methodology: using software Processing with Python Mode. To generate a plane shapes like a hand-drawn, the Perlin Noise method is used (More info: https://github.com/reevald/FlatShapeNet). The dataset is used for the educational game Ariga.Currently the dataset (Version 4) is constructed by choosing 8 largest classes : "Circle", "Kite", "Parallelogram", "Square", "Rectangle", "Rhombus", "Trapezoid", and "Triangle". Each class contains 1,500 training samples, 500 validation samples, and 500 test samples. The total number of training samples is 12,000, validation samples 4,000, and testing 4,000. Each sample is an image measuring (224 x 224 x 3) (RGB).
https://t.me/datasets1💯

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This channels is for Programmers, Coders, Software Engineers.

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❓VITON-HD

https://t.me/datasets1💯

🔍
The task of image-based virtual try-on aims to transfer a target clothing item onto the corresponding region of a person, which is commonly tackled by fitting the item to the desired body part and fusing the warped item with the person. While an increasing number of studies have been conducted, the resolution of synthesized images is still limited to low (e.g., 256x192), which acts as the critical limitation against satisfying online consumers. We argue that the limitation stems from several challenges: as the resolution increases, the artifacts in the misaligned areas between the warped clothes and the desired clothing regions become noticeable in the final results; the architectures used in existing methods have low performance in generating high-quality body parts and maintaining the texture sharpness of the clothes. To address the challenges, we propose a novel virtual try-on method called VITON-HD that successfully synthesizes 1024x768 virtual try-on images. Specifically, we first prepare the segmentation map to guide our virtual try-on synthesis, and then roughly fit the target clothing item to a given person's body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS generator to handle the misaligned areas and preserve the details of 1024x768 inputs. Through rigorous comparison with existing methods, we demonstrate that VITON-HD highly surpasses the baselines in terms of synthesized image quality both qualitatively and quantitatively.
https://t.me/datasets1💯

📝VITON-HD

❎ High-Resolution VITON-Zalando Dataset.

❓WLASL (World Level American Sign Language) Video

https://t.me/datasets1💯

📝WLASL (World Level American Sign Language) Video

❎ 12k processed videos of Word-Level American Sign Language glossary performance.

🔍
WLASL is the largest video dataset for Word-Level American Sign Language (ASL) recognition, which features 2,000 common different words in ASL. We hope WLASL will facilitate the research in sign language understanding and eventually benefit the communication between deaf and hearing communities.
https://t.me/datasets1💯

❎ AI vs. Human-Generated Images

https://t.me/datasets1💯

📝 AI vs. Human-Generated Images

❎ A Curated Dataset of AI-Generated and Authentic Images.

The dataset consists of authentic images sampled from the Shutterstock platform across various categories, including a balanced selection where one-third of the images feature humans. These authentic images are paired with their equivalents generated using state-of-the-art generative models. This structured pairing enables a direct comparison between real and AI-generated content, providing a robust foundation for developing and evaluating image authenticity detection systems.

https://t.me/datasets1💯

❓ Ghibli Dataset

https://t.me/datasets1 💯🌟

📝 Ghibli Dataset

❓Original to #Ghibli art image generator and classifier dataset.

The Ghibli Art Image Dataset is a prototype designed for generating Ghibli-style images using machine learning. It includes three subsets—training, testing, and validation—each containing directories with two PNG images: one original (o.png) and one generated in Ghibli style (g.png). This dataset supports tasks like image classification and Ghibli-style image generation. As a small-scale version of a larger dataset, it provides essential resources like model code and a pre-trained Generator.pth model. The images were collected from platforms such as Meta, Google, and Instagram for research and experimentation purposes.

https://t.me/datasets1 💯🌟

This channels is for Programmers, Coders, Software Engineers.

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❓ Real to Ghibli Image Dataset

https://t.me/datasets1 📜

📝 Real to Ghibli Image Dataset (5K High-Quality Images)

❓ A high-quality dataset of 5,000 images for training AI models to transfor

The Real to #Ghibli Image Dataset is a high-quality collection of 5,000 images designed for AI-driven style transfer and artistic transformations. This dataset is ideal for training GANs, CycleGAN, diffusion models, and other deep learning applications in image-to-image translation.It consists of two separate subsets:trainA (2,500 Real-World Images) → A diverse collection of human faces, landscapes, rivers, mountains, forests, buildings, vehicles, and more.
trainB_ghibli (2,500 Ghibli-Style Images) → Stylized images inspired by Studio Ghibli movies, including animated characters, landscapes, and artistic compositions.
Unlike paired datasets, this collection contains independent images in each subset, making it suitable for unsupervised learning approaches.

https://t.me/datasets1 🌟

❓ Micro Expression Dataset for Lie Detection

⭐️ https://t.me/datasets1

📝 Micro Expression Dataset for Lie Detection

❓Detailed Facial Mirco-expression Image Dataset for Lie Detection

🔎
Lie detection is the process of determining whether someone is being truthful or deceptive
Micro-expressions are very brief & tiny, facial expressions that occur in response to emotions. They typically last for only a fraction of a second, making them difficult to detect without careful observation. These expressions can reveal genuine emotions that a person might be trying to conceal or manage consciously.The brain’s limbic system, which is involved in emotional processing, plays a key role in generating micro-expressions. When a person experiences an emotion, the brain sends signals to facial muscles to express that emotion. Sometimes, these signals are so rapid and sensitive that they are not consciously controlled.Detecting micro-expressions requires careful observation and often specialized training.

⭐️ https://t.me/datasets1

❓Labelled Faces in the Wild (LFW) Dataset

⭐️https://t.me/datasets1

📝Labelled Faces in the Wild (LFW) Dataset

❓Over 13,000 images of faces collected from the web

🔎
The Labeled Faces in the Wild (LFW) dataset contains 13,233 images of faces from 5,749 different individuals, created for research in unconstrained face recognition. These images were gathered from the web and detected and centered using the Viola-Jones algorithm. The version used in this dataset is the deep-funneled version, in which the images are aligned in a specific way, and according to reports, it performs better in face verification algorithms. This dataset includes images and ten metadata files, which allow for training and testing models in two different modes (pairs of images or individual people). This collection was created by the University of Massachusetts, Amherst, and is considered one of the most widely used resources in the field of face recognition.


⭐️https://t.me/datasets1

❓Labelled Faces in the Wild (LFW) Dataset

⭐️https://t.me/datasets1

❓Emotions

⭐️ https://t.me/datasets1

📝Emotions dataset

❓Emotions dataset for NLP classification tasks

🔎
This dataset contains a collection of documents and their associated emotions, specifically designed for classification tasks in Natural Language Processing (NLP). The dataset includes a list of documents, each associated with a specific emotion label. It helps you develop machine learning models for identifying various emotions in text.

Dataset Contents:
A list of text documents with emotion labels
The dataset is split into three parts: training (Train), validation (Validation), and testing (Test) for building machine learning models.
⭐️https://t.me/datasets1

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If you've worked on an interesting project with this dataset, we’d be happy to share your notebook or GitHub link with us. We’d love to feature your project on the DataScienceN channel so others can benefit from it and give you stars or feedback. This is a great opportunity for your project to get more visibility and be useful to everyone!

❓Bird vs Drone

⭐️ https://t.me/datasets1

📝Bird vs Drone

❓Distinguishing the Skies: A Dataset for Drone vs Bird Classification

🔎
YOLO-based Segmented Dataset for Drone vs. Bird Detection for Deep and Machine Learning Algorithms


Formatted in accordance with the YOLOv7 PyTorch specification, the dataset is organized into three folders: Test, Train, and Valid. Each folder contains two sub-folders—Images and Labels—with the Labels folder including the associated metadata in plaintext format. This metadata provides valuable information about the detected objects within each image, allowing the model to accurately learn and detect drones and birds in varying circumstances. The dataset contains a total of 20,925 images, all with a resolution of 640 x 640 pixels in JPEG format, providing comprehensive training and validation opportunities for machine learning models.

⭐️ https://t.me/datasets1

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❓Eye Disease Image Dataset

⭐️ https://t.me/datasets1

📝 Eye Disease Image Dataset

❓A dataset of color fundus images of eye diseases

🔎
A total of 5335 images of healthy and affected eye images were collected from Anwara Hamida Eye Hospital in Faridpur and BNS Zahrul Haque Eye Hospital in Faridpur district with the help of the hospital authorities. Then from these original images, a total of 16242 augmented images are produced by using Rotation, Width shifting, Height shifting, Translation, Flipping, and Zooming techniques to increase the number of data.
Worldwide, eye ailments are recognized as significant contributors to nonfatal disabling conditions. In Bangladesh, 1.5% of adults suffer from blindness, while 21.6% experience low vision. Therefore, eye disease detection is crucial for preserving vision, preventing blindness, and maintaining overall health. Early detection allows for prompt intervention and treatment, preventing irreversible damage and preserving quality of life.
⭐️ https://t.me/datasets1

Person-Collecting-Waste COCO Dataset

https://t.me/datasets1

Person-Collecting-Waste COCO Dataset

https://t.me/datasets1

Person-Collecting-Waste COCO Dataset

COCO dataset of Person Collecting Garbage

The "Person-Collecting-Waste COCO Dataset," provided by the user ashu009 on Kaggle, is designed for object detection tasks and follows the COCO format. This dataset includes 300 images along with corresponding annotation files in JSON format. The primary goal of this dataset is to identify and detect individuals collecting waste, which can be useful for projects related to environmental protection and waste management.

Person-Collecting-Waste COCO Dataset

https://t.me/datasets1

archive.zip.002

archive.zip.001

❓Eye Disease Image Dataset

⭐️ https://t.me/datasets1

📝 Eye Disease Image Dataset

❓A dataset of color fundus images of eye diseases

🔎
A total of 5335 images of healthy and affected eye images were collected from Anwara Hamida Eye Hospital in Faridpur and BNS Zahrul Haque Eye Hospital in Faridpur district with the help of the hospital authorities. Then from these original images, a total of 16242 augmented images are produced by using Rotation, Width shifting, Height shifting, Translation, Flipping, and Zooming techniques to increase the number of data.
Worldwide, eye ailments are recognized as significant contributors to nonfatal disabling conditions. In Bangladesh, 1.5% of adults suffer from blindness, while 21.6% experience low vision. Therefore, eye disease detection is crucial for preserving vision, preventing blindness, and maintaining overall health. Early detection allows for prompt intervention and treatment, preventing irreversible damage and preserving quality of life.
⭐️ https://t.me/datasets1

Gaza Before and After

https://t.me/datasets1

Gaza Before and After

Gaza Strip Satellite Imagery: Before & After The Conflict

About Dataset
This dataset is part of a project aimed at collecting high-resolution satellite imagery of the Gaza Strip before and after the recent conflict. The images were retrieved using Sentinel Hub API and Planet.com API, covering weekly snapshots from January 2023 to the present.
With over 3,500 images, each accompanied by a metadata JSON file, this dataset enables researchers, analysts, and humanitarian organizations to study urban damage assessment, environmental changes, and infrastructure impact.
Key Features:
Weekly satellite images from multiple sources (Sentinel-2, Landsat-8, PlanetScope)
Configurable grid-based coverage of the entire Gaza Strip
Each image includes metadata (timestamps, coordinates, satellite source, etc.)
Supports before & after visualizations for damage assessment
Open-source processing pipeline available on GitHub

Tomato Leaf Disease Detection - YOLOv8 Dataset

https://t.me/datasets1 ⭐

Tomato Leaf Disease Detection - YOLOv8 Dataset

Annotated Tomato Leaf Disease Dataset for YOLOv8 Model Training & Detection

About Dataset
Overview
This dataset is designed for Tomato Leaf Disease Detection using YOLOv8. It contains 10,853 labeled images spanning 10 different classes of tomato leaf conditions, including viral, bacterial, and fungal infections, as well as healthy leaves.
Dataset Details
Total Images: 10,853
Train Set: 7,842 images (72%)
Validation Set: 1,960 images (18%)
Test Set: 1,051 images (10%)
Image Resolution: Resized to 640x640 (stretched)
Annotation Format: YOLOv8
Classes (10 Categories)
Tomato Bacterial Spot
Tomato Early Blight
Tomato Late Blight
Tomato Leaf Mold
Tomato Septoria Leaf Spot
Tomato Spider Mites (Two-Spotted Spider Mite)
Tomato Target Spot
Tomato Yellow Leaf Curl Virus
Tomato Healthy
Tomato Mosaic Virus
Preprocessing Applied
Auto-orientation of pixel data (EXIF metadata stripped)
Images resized to 640x640 (stretched)
No augmentation applied

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Mammogram Mass Analyzer Desktop App

https://t.me/datasets1 👾

Mammogram Mass Analyzer Desktop App

A free desktop breast cancer detection app that accepts dicom files.

Mammogram Mass Analyzer

This is a free desktop computer aided diagnosis (CAD) tool that uses computer vision to detect and localize masses on full field digital mammograms. It's a flask app that's running on the desktop. Internally there are two Yolov5L ensembled models that were trained on data from the VinDr-Mammo dataset. The model ensemble has a validation accuracy of 0.65 and a validation recall of 0.63.

My aim was to create a proof of concept for a free desktop computer aided diagnosis (CAD) system that could be used as an aid when diagnosing breast cancer. Unlike a web app, this tool does not need an internet connection and there are no monthly costs for hosting and web server rental. I think a desktop tool could be helpful to radiologists in private practice and to medical non-profits that work in remote areas.

The complete project folder, including the trained models, is stored in this Kaggle dataset.

Alzheimer's Disease Multiclass Images Dataset

https://t.me/datasets1 👾

Alzheimer's Disease Multiclass Images Dataset

https://t.me/datasets1 👾

Alzheimer's Disease Multiclass Images Dataset

Alzheimer's Disease dataset split into 4 classes

About Dataset

The Alzheimer's Disease Multiclass Dataset contains approximately 44,000 MRI images categorized into four distinct classes based on the severity of Alzheimer's disease. This dataset is intended for use in machine learning model training and testing. All images are skull-stripped and clean of non-brain tissue.

Dataset Structure
The dataset is organized into the following four directories, each representing a different class of disease severity:
NonDemented: Contains 12,800 MRI images of subjects with no signs of dementia.
VeryMildDemented: Contains 11,200 MRI images of subjects with very mild symptoms of dementia.
MildDemented: Contains 10,000 MRI images of subjects with mild dementia.
ModerateDemented: Contains 10,000 MRI images of subjects with moderate dementia.

Image Details
Total Number of Images: 44,000
Image Format: MRI scans as .JPG files
Image Usage: Suitable for training and testing machine learning models focused on classifying Alzheimer's disease stages.

Disease Severity Classification
The dataset follows a severity ranking system for Alzheimer's disease:
NonDemented: No dementia.
Very Mild Demented: Early signs of dementia, very mild symptoms.
Mild Demented: Clear signs of dementia, but still mild.
Moderate Demented: More pronounced symptoms of dementia, moderate severity.

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A Large Scale Fish Dataset

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A Large Scale Fish Dataset

A Large-Scale Dataset for Fish Segmentation and Classification

The dataset contains 9 different seafood types. For each class, there are 1000 augmented images and their pair-wise augmented ground truths.
Each class can be found in the "Fish_Dataset" file with their ground truth labels. All images for each class are ordered from "00000.png" to "01000.png".

For example, if you want to access the ground truth images of the shrimp in the dataset, the order should be followed is "Fish->Shrimp->Shrimp GT".