Explore and download labeled image datasets for AI, ML, and computer vision. Find datasets for object detection, image classification, and image segmentation.

Given a blurred image, image deblurring aims to produce a clear, high-quality image that accurately represents the original scene. Blurring can be caused by various factors such as camera shake, fast motion, out-of-focus objects, etc. making it a particularly challenging computer vision problem. This has led to the recent development of a large spectrum of deblurring models and unique datasets.

Despite the rapid advancement in image deblurring, the process of finding and pre-processing a number of datasets for training and testing purposes has been both time exhaustive and unnecessarily complicated for both experts and non-experts alike. Moreover, there is a serious lack of ready-to-use domain-specific datasets such as face and text deblurring datasets.

To this end, the following card contains a curated list of ready-to-use image deblurring datasets for training and testing various deblurring models. Additionally, we have created an extensive, highly customizable python package for single image deblurring called DBlur that can be used to train and test various SOTA models on the given datasets just with 2-3 lines of code.

Following is a list of the datasets that are currently provided: - GoPro: The GoPro dataset for deblurring consists of 3,214 blurred images with a size of 1,280×720 that are divided into 2,103 training images and 1,111 test images. - HIDE: HIDE is a motion-blurred dataset that includes 2025 blurred images for testing. It mainly focus on pedestrians and street scenes. - RealBlur: The RealBlur testing dataset consists of two subsets. The first is RealBlur-J, consisting of 1900 camera JPEG outputs. The second is RealBlur-R, consisting of 1900 RAW images. The RAW images are generated by using white balance, demosaicking, and denoising operations. - CelebA: A face deblurring dataset created using the CelebA dataset which consists of 2 000 000 training images, 1299 validation images, and 1300 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018 - Helen: A face deblurring dataset created using the Helen dataset which consists of 2 000 training images, 155 validation images, and 155 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018 - Wider-Face: A face deblurring dataset created using the Wider-Face dataset which consists of 4080 training images, 567 validation images, and 567 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018
- TextOCR: A text deblurring dataset created using the TextOCR dataset which consists of 5000 training images, 500 validation images, and 500 testing images. The blurred images were created using the blurred kernels provided by Shent et al. 2018

Dataset Card for "new-image-dataset"

More Information needed

Open Images is a dataset of ~9M images that have been annotated with image-level labels and object bounding boxes.

The training set of V4 contains 14.6M bounding boxes for 600 object classes on 1.74M images, making it the largest existing dataset with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8.4 per image on average). Moreover, the dataset is annotated with image-level labels spanning thousands of classes.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('open_images_v4', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/open_images_v4-original-2.0.0.png" alt="Visualization" width="500px">

The initial 8 classes were collected by Oliva and Torralba [1], and then 5 categories were added by Fei-Fei and Perona [2]; finally, 2 additional categories were introduced by Lazebnik et al. [3]. [1] A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” IJCV, 2001. [2] L. Fei-Fei and P. Perona, “A bayesian hierarchical model for learning natural scene categories,” CVPR, 2005. [3] S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” CVPR, 2006.

Explore our Plant Disease Image Dataset, featuring a diverse collection of labeled images for developing and testing machine learning models in agriculture.

Almost 12000 images used in Google Recaptcha V2 collected by category more than 500 of which with manual markup for training object detection model such as YOLO.

If you find this dataset useful, please leave an upvote, that motivates me to collect such datasets✋

Feel free to using this data for your commercial or educational goals. P.S. https://github.com/Artistrazh/recaptcha_v2_solver is my project for solving Google Recaptcha V2 using yolov3, BLIP and this dataset.

What this collection is: A curated, binary-classified image dataset of grayscale (1 band) 400 x 400-pixel size, or image chips, in a JPEG format extracted from processed Sentinel-1 Synthetic Aperture Radar (SAR) satellite scenes acquired over various regions of the world, and featuring clear open ocean chips, look-alikes (wind or biogenic features) and oil slick chips.

This binary dataset contains chips labelled as: - "0" for chips not containing any oil features (look-alikes or clean seas)
- "1" for those containing oil features.

This binary dataset is imbalanced, and biased towards "0" labelled chips (i.e., no oil features), which correspond to 66% of the dataset. Chips containing oil features, labelled "1", correspond to 34% of the dataset.

Why: This dataset can be used for training, validation and/or testing of machine learning, including deep learning, algorithms for the detection of oil features in SAR imagery. Directly applicable for algorithm development for the European Space Agency Sentinel-1 SAR mission (https://sentinel.esa.int/web/sentinel/missions/sentinel-1 ), it may be suitable for the development of detection algorithms for other SAR satellite sensors.

Overview of this dataset: Total number of chips (both classes) is N=5,630 Class 0 1 Total 3,725 1,905

Further information and description is found in the ReadMe file provided (ReadMe_Sentinel1_SAR_OilNoOil_20221215.txt)

DATASET IMAGE

## Overview

DATASET IMAGE is a dataset for object detection tasks - it contains FISH WcSP annotations for 4,230 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

MMID is a large-scale, massively multilingual dataset of images paired with the words they represent collected at the University of Pennsylvania. The dataset is doubly parallel: for each language, words are stored parallel to images that represent the word, and parallel to the word's translation into English (and corresponding images.)

Introduction

Welcome to the Native American Multi-Year Facial Image Dataset, thoughtfully curated to support the development of advanced facial recognition systems, biometric identification models, KYC verification tools, and other computer vision applications. This dataset is ideal for training AI models to recognize individuals over time, track facial changes, and enhance age progression capabilities.

Facial Image Data

This dataset includes over 5,000+ high-quality facial images, organized into individual participant sets, each containing:

Diversity & Representation

Image Quality & Capture Conditions

To ensure model generalization and practical usability, images in this dataset reflect real-world diversity:

Metadata

Each participant’s dataset is accompanied by rich metadata to support advanced model training and analysis, including:

Use Cases & Applications

This dataset is highly valuable for a wide range of AI and computer vision applications:

Secure & Ethical Collection

Dataset Updates & Customization

To keep pace with evolving AI needs, this dataset is regularly updated and customizable. Custom data collection options include:

Overview

The dataset contains two classes: Shells or Pebbles. This dataset can be used to for binary classification tasks to determine whether a certain image constitutes as a shell or a pebble. Cover Image by wirestock on Freepik

Inspiration

I found it cool to create an app with a CV algorithm that could classify whether a certain picture is a shell or image. The next time that I would be visiting a beach, I could just use the app to help me collect either shells or pebbles. 😄

Compilation of python codes for data preprocessing and VegeNet building, as well as image datasets (zip files).

Image datasets:

1. vege_original : Images of vegetables captured manually in data acquisition stage
2. vege_cropped_renamed : Images in (1) cropped to remove background areas and image labels renamed
3. non-vege images : Images of non-vegetable foods for CNN network to recognize other-than-vegetable foods
4. food_image_dataset : Complete set of vege (2) and non-vege (3) images for architecture building.
5. food_image_dataset_split : Image dataset (4) split into train and test sets
6. process : Images created when cropping (pre-processing step) to create dataset (2).

Collection of truck images, from a side point view, used to extract information about truck axles, collected on a highway in the State of São Paulo, Brazil. This is still a work in progress dataset and will be updated regularly, as new images are acquired. More info can be found on: Researchgate Lab Page, OrcID Profiles, or ITS Lab page on Github.

The dataset includes 725 cropped images of trucks, taken with three different cameras, on five different locations.

• 725 images
• Format: JPG
• Resolution: 1920xVarious, 96dpi, 24bits
• Naming pattern:

If this dataset helps in any way your research, please feel free to contact the authors. We really enjoy knowing about other researcher's projects and how everybody is making use of the images on this dataset. We are also open for collaborations and to answer any questions. We also have a paper that uses this dataset, so if you want to officially cite us in your research, please do so! We appreciate it!

Marcomini, Leandro Arab, and André Luiz Cunha. "Truck Axle Detection with Convolutional Neural Networks." arXiv preprint arXiv:2204.01868 (2022).

Images of landmarks within the context of their environment

What’s Included

Introducing the English Newspaper, Books, and Magazine Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the English language.

Containing a total of 5000 images, this English OCR dataset offers an equal distribution across newspapers, books, and magazines. Within, you'll find a diverse collection of content, including articles, advertisements, cover pages, headlines, call outs, and author sections from a variety of newspapers, books, and magazines. Images in this dataset showcases distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personal identifiable information (PII), and in each image a minimum of 80% space is contain visible English text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, further enhancing dataset diversity. The collection features images in portrait and landscape modes.

All these images were captured by native English Speaking people to ensure the text quality, avoid toxic content and PII text. We used latest iOS and android mobile devices above 5MP camera to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data you will also receive detailed structured metadata in CSV format. For each image it includes metadata like device information, source type like newspaper, magazine or book image, and image type like portrait or landscape etc. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of English text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English language crowd community.

If you require a custom dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this image dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the English language. Your journey to enhanced language understanding and processing starts here.

What’s Included

Introducing the Finnish Product Image Dataset - a diverse and comprehensive collection of images meticulously curated to propel the advancement of text recognition and optical character recognition (OCR) models designed specifically for the Finnish language.

Containing a total of 2000 images, this Finnish OCR dataset offers diverse distribution across different types of front images of Products. In this dataset, you'll find a variety of text that includes product names, taglines, logos, company names, addresses, product content, etc. Images in this dataset showcase distinct fonts, writing formats, colors, designs, and layouts.

To ensure the diversity of the dataset and to build a robust text recognition model we allow limited (less than five) unique images from a single resource. Stringent measures have been taken to exclude any personally identifiable information (PII) and to ensure that in each image a minimum of 80% of space contains visible Finnish text.

Images have been captured under varying lighting conditions – both day and night – along with different capture angles and backgrounds, to build a balanced OCR dataset. The collection features images in portrait and landscape modes.

All these images were captured by native Finnish people to ensure the text quality, avoid toxic content and PII text. We used the latest iOS and Android mobile devices above 5MP cameras to click all these images to maintain the image quality. In this training dataset images are available in both JPEG and HEIC formats.

Along with the image data, you will also receive detailed structured metadata in CSV format. For each image, it includes metadata like image orientation, county, language, and device information. Each image is properly renamed corresponding to the metadata.

The metadata serves as a valuable tool for understanding and characterizing the data, facilitating informed decision-making in the development of Finnish text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native Finnish crowd community.

If you require a custom product image OCR dataset tailored to your guidelines or specific device distribution, feel free to contact us. We're equipped to curate specialized data to meet your unique needs.

Furthermore, we can annotate or label the images with bounding box or transcribe the text in the image to align with your specific project requirements using our crowd community.

This Image dataset, created by FutureBeeAI, is now available for commercial use.

Leverage the power of this product image OCR dataset to elevate the training and performance of text recognition, text detection, and optical character recognition models within the realm of the Finnish language. Your journey to enhanced language understanding and processing starts here.

The “Fused Image dataset for convolutional neural Network-based crack Detection” (FIND) is a large-scale image dataset with pixel-level ground truth crack data for deep learning-based crack segmentation analysis. It features four types of image data including raw intensity image, raw range (i.e., elevation) image, filtered range image, and fused raw image. The FIND dataset consists of 2500 image patches (dimension: 256x256 pixels) and their ground truth crack maps for each of the four data types. The images contained in this dataset were collected from multiple bridge decks and roadways under real-world conditions. A laser scanning device was adopted for data acquisition such that the captured raw intensity and raw range images have pixel-to-pixel location correspondence (i.e., spatial co-registration feature). The filtered range data were generated by applying frequency domain filtering to eliminate image disturbances (e.g., surface variations, and grooved patterns) from the raw range data [1]. The fused image data were obtained by combining the raw range and raw intensity data to achieve cross-domain feature correlation [2,3]. Please refer to [4] for a comprehensive benchmark study performed using the FIND dataset to investigate the impact from different types of image data on deep convolutional neural network (DCNN) performance. If you share or use this dataset, please cite [4] and [5] in any relevant documentation. In addition, an image dataset for crack classification has also been published at [6]. References: [1] Shanglian Zhou, & Wei Song. (2020). Robust Image-Based Surface Crack Detection Using Range Data. Journal of Computing in Civil Engineering, 34(2), 04019054. https://doi.org/10.1061/(asce)cp.1943-5487.0000873 [2] Shanglian Zhou, & Wei Song. (2021). Crack segmentation through deep convolutional neural networks and heterogeneous image fusion. Automation in Construction, 125. https://doi.org/10.1016/j.autcon.2021.103605 [3] Shanglian Zhou, & Wei Song. (2020). Deep learning–based roadway crack classification with heterogeneous image data fusion. Structural Health Monitoring, 20(3), 1274-1293. https://doi.org/10.1177/1475921720948434 [4] Shanglian Zhou, Carlos Canchila, & Wei Song. (2023). Deep learning-based crack segmentation for civil infrastructure: data types, architectures, and benchmarked performance. Automation in Construction, 146. https://doi.org/10.1016/j.autcon.2022.104678 5 Shanglian Zhou, Carlos Canchila, & Wei Song. (2022). Fused Image dataset for convolutional neural Network-based crack Detection (FIND) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6383044 [6] Wei Song, & Shanglian Zhou. (2020). Laser-scanned roadway range image dataset (LRRD). Laser-scanned Range Image Dataset from Asphalt and Concrete Roadways for DCNN-based Crack Classification, DesignSafe-CI. https://doi.org/10.17603/ds2-bzv3-nc78

Image Dataset of face images for compuer vision tasks

Dataset comprises 500,600+ images of individuals representing various races, genders, and ages, with each person having a single face image. It is designed for facial recognition and face detection research, supporting the development of advanced recognition systems. By leveraging this dataset, researchers and developers can enhance deep learning models, improve face verification and face identification techniques, and refine… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-recognition-image-dataset.

Curated RGB image dataset for our analysis, splited into training and evalutaion set. Based on ImageNet ILSVRC dataset (Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, 2015).