Image Recognition

## Overview

Image Recognition is a dataset for object detection tasks - it contains Nav Infrastruktura annotations for 238 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 [MIT license](https://creativecommons.org/licenses/MIT).

Overview

This dataset is a comprehensive collection of scientific images curated for the advancement of image classification algorithms in the scientific domain. It comprises a diverse set of images across six distinct classes, providing a unique challenge for machine learning enthusiasts and researchers. The base source of the data is derived from the Biofors dataset, with additional images incorporated to enhance variety and complexity. All images are either in .JPG or .PNG formats.

Dataset Description

The dataset is organized into six primary classes, each representing a different aspect of scientific imaging:

Blot-Gel: Images of various blotting techniques and gel electrophoresis results used in molecular biology.

FACS (Fluorescence-Activated Cell Sorting): Flow cytometry images showcasing cell populations based on fluorescent labeling.

Histopathology: High-resolution images of tissue sections stained to reveal cellular structures and patterns indicative of pathological states.

Macroscopy: Images captured without magnification, highlighting the gross features and details of biological specimens.

Microscopy: A collection of microscopic images that reveal the intricate details of cells and microorganisms.

Non-scientific: A control group of images unrelated to scientific inquiry, included to test the robustness of classification models. It mainly consists images from ImageNet dataset.

Use Cases

This dataset is ideal for developing and benchmarking image classification models that can be applied to:

Image Falsification and Fabrication Detection: This dataset serves as a foundation for developing forensic tools to combat image falsification and fabrication in scientific publications. With the Biofors dataset as a base, participants have the opportunity to create models that can detect unethical manipulations, thereby safeguarding the credibility of scientific research. The challenge lies in identifying subtle alterations that may indicate misconduct, such as duplicated, spliced, or artificially enhanced images. Success in this area has far-reaching implications, potentially preventing the spread of misinformation and preserving the integrity of scientific literature.

Automated Analysis of Scientific Experiments: The dataset facilitates the development of models for automated analysis in scientific experiments, which can significantly accelerate the pace of discovery. Automated research workflows, integrating computation, laboratory automation, and AI tools, are transforming how experiments are designed, conducted, and analyzed.

Diagnostic Tools in Medicine: In the medical field, diagnostic tools are essential for achieving diagnostic excellence, which involves making correct and timely diagnoses while maximizing patient experience and managing uncertainty. AI in healthcare is revolutionizing diagnostics, from analyzing medical images to identifying disease patterns and predicting patient outcomes.

References

[1] https://ieeexplore.ieee.org/document/9710731

[2] https://github.com/vimal-isi-edu/BioFors

[3] https://link.springer.com/chapter/10.1007/978-3-031-53085-2_26

[4] https://www.nationalacademies.org/news/2022/05/automated-research-workflows-are-speeding-pace-of-scientific-discovery-new-report-offers-recommendations-to-advance-their-development

[5] https://warwick.ac.uk/fac/cross_fac/tia/data/pannuke (Histopathology images)

[6] https://www.kaggle.com/datasets/chopinforest/esophageal-endoscopy-images (Macroscopy)

[7] https://www.kaggle.com/datasets/safurahajiheidari/kidney-stone-images (Macroscopy)

[8] https://www.kaggle.com/datasets/alifrahman/covid19-chest-xray-image-dataset (Macroscopy)

[9] https://www.kaggle.com/datasets/vitaliykinakh/stable-imagenet1k (Non-scientific images)

[10] https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic (Macroscopy)

[11] https://www.kaggle.com/datasets/sunedition/graphs-dataset (Non–scientific images)

A Dataset comprised of two parts, images generated by AI image generation models such as DALL-E and Midjourney, and real images known to be made by humans. The majority of AI generated images are artistic works of some type and not photorealistic because it was found that having more artistic works than photos in the human generated set yielded better test results. One major issue found when trying to train classifiers on this set is while a test accuracy as high as 94% was achieved, if the image (regardless of source AI or human) contained noise such as a film grain or fur there was a higher error rate and the image was more likely to be mislabeled as AI generated. My theory is because diffusion image generation models (DALL-E etc.) start with random noise and turn it into an image based on the prompt, so the classifier could be using the noise of the image as a way to detect Ai generated art and by adding noise the model is getting confused. One possible solution to this is using image denoising on the image or edge detection however I have yet to test either.

Benefits over other datasets

The benefit of this dataset compared to other artificially generated image datasets (such as CIFAKE) is that all images are in there original size and aspect ratio.

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.

Worktool Image Recognition

## Overview

Worktool Image Recognition is a dataset for classification tasks - it contains Tools annotations for 2,354 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Labeled Cicada images suitable for training and evaluating computer vision and deep learning models.

Mdp Image Recognition

## Overview

Mdp Image Recognition is a dataset for object detection tasks - it contains Letters annotations for 5,541 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).

1) Data Introduction • The Shells or Pebbles: An Image Classification Dataset is a computer vision dataset designed for a binary classification task that distinguishes between shells and pebbles. The dataset consists of two classes (Shells and Pebbles), and each image is used to determine whether the object is a shell or a pebble.

2) Data Utilization (1) Characteristics of the Shells or Pebbles: An Image Classification Dataset: • The dataset is designed to help models learn and distinguish subtle visual differences between shells and pebbles, which often share similar shapes and textures. • It contains images captured under varied backgrounds and conditions, making it suitable for training models with strong generalization capabilities.

(2) Applications of the Shells or Pebbles: An Image Classification Dataset: • Development of binary classification models (Shell vs. Pebble): The dataset can be used to train deep learning models that classify images as either shell or pebble. • Educational use for visual recognition tasks: This dataset is also suitable for training in shape-, texture-, and edge-based feature extraction and pattern recognition, making it a valuable resource for teaching and experimentation in computer vision.

Multi-class weather dataset(MWD) for image classification is a valuable dataset used in the research paper entitled “Multi-class weather recognition from still image using heterogeneous ensemble method”. The dataset provides a platform for outdoor weather analysis by extracting various features for recognizing different weather conditions.

About the Dataset

Context

This dataset encompasses images of various fruits and vegetables, providing a diverse collection for image recognition tasks. The included food items are:

• Fruits: Banana, Apple, Pear, Grapes, Orange, Kiwi, Watermelon, Pomegranate, Pineapple, Mango
• Vegetables: Cucumber, Carrot, Capsicum, Onion, Potato, Lemon, Tomato, Radish, Beetroot, Cabbage, Lettuce, Spinach, Soybean, Cauliflower, Bell Pepper, Chilli Pepper, Turnip, Corn, Sweetcorn, Sweet Potato, Paprika, Jalapeño, Ginger, Garlic, Peas, Eggplant

Content

The dataset is organized into three main folders: - Train: Contains 100 images per category. - Test: Contains 10 images per category. - Validation: Contains 10 images per category.

Each of these folders is subdivided into specific folders for each type of fruit and vegetable, containing respective images.

Data Collection

The images in this dataset were sourced using Bing Image Search for a personal project focused on image recognition of food items. The creator does not hold the rights to any of the images included in this dataset. If you are the owner of any image and have concerns regarding its use, please contact the creator to request its removal. The creator will promptly comply with any such requests to ensure all legal obligations are met.

Disclaimer: Users of this dataset are responsible for ensuring that their use of the images complies with applicable copyright laws and regulations. The creator assumes no responsibility for any legal issues that may arise from the use of this dataset. It is recommended to use the dataset for educational and non-commercial purposes only and to seek legal counsel if you have specific concerns about copyright compliance.

Inspiration

The primary motivation behind creating this dataset was to develop an application capable of recognizing food items from photographs. The application aims to suggest various recipes that can be prepared using the identified ingredients.

Citation

Kritik Seth, "Fruits and Vegetables Image Recognition Dataset," Kaggle 2020 [https://www.kaggle.com/kritikseth/fruit-and-vegetable-image-recognition]

Image Recognition

## Overview

Image Recognition is a dataset for object detection tasks - it contains Objects annotations for 757 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).

A dataset combining AI-generated and web-scraped images across subjects like people, animals, portraits, scenery, and psychedelics. Designed for image classification, recognition, and creative AI research.

Utensil Image Recognition

## Overview

Utensil Image Recognition is a dataset for object detection tasks - it contains Utensils annotations for 379 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

1) Data Introduction • The Intel Image Classification Dataset contains natural scene images from various locations around the world and is labeled across six distinct categories.

2) Data Utilization (1) Characteristics of the Intel Image Classification Dataset: • The dataset features a diverse range of scenes, including buildings, forests, glaciers, mountains, seas, and streets, allowing for testing model generalization across multiple real-world environments. • The data is organized into separate sets for training, testing, and prediction, making it straightforward to use for supervised learning tasks.

(2) Applications of the Intel Image Classification Dataset: • Development of scene classification models: This dataset is suitable for training and evaluating deep learning models that can automatically classify different types of natural scenes, supporting applications in automated photo organization, environmental monitoring, and geolocation tasks.

Labeled Classroom images suitable for training and evaluating computer vision and deep learning models.

The Image Recognition Solutions market is booming, projected to reach [estimated 2033 value based on CAGR] by 2033, driven by AI advancements and cloud adoption. Explore market trends, key players (Amazon, Google, etc.), and regional insights in this comprehensive analysis.

A diverse image dataset containing clock faces with varying styles, angles, and hand positions, split into training, testing, and validation subsets for accurate time recognition and image classification tasks.

1) Data Introduction • The Sports balls - multiclass image classification Dataset is a computer vision dataset for multi-class image classification, designed to classify images of balls used in various sports. The dataset consists of 15 categories, including basketballs, footballs (soccer), rugby balls, table tennis balls, and more.

2) Data Utilization (1) Characteristics of the Sports balls - multiclass image classification Dataset: • Some balls in the dataset feature intentional visual alterations (e.g., balls painted to resemble other types), enabling a precise evaluation of a model’s generalization and discrimination capabilities.

(2) Applications of the Sports balls - multiclass image classification Dataset: • Sports Ball Classification Model Development: This dataset can be used to train deep learning-based image classification models that automatically recognize and categorize various types of sports equipment. • Development of Sports-related Applications: The dataset is suitable for building sports equipment recognition systems, AR-based educational tools, and video-based sports analysis systems.

1) Data Introduction • The Satellite Image Classification Dataset is a benchmark image classification dataset constructed using satellite remote sensing imagery. It includes a total of four land surface classes—cloudy, desert, green_area, and water—collected from various sensor-based images and Google Maps snapshots. The dataset is designed for training and evaluating image-based scene recognition models.

2) Data Utilization (1) Characteristics of the Satellite Image Classification Dataset: • The dataset was collected with the aim of automatic interpretation of satellite imagery and consists of a combination of sensor-based images and map snapshots, offering a realistic representation of real-world conditions. • All images are of fixed resolution and include diverse landform features, making the dataset suitable for classification experiments across different environments and for evaluating model generalization performance.

(2) Applications of the Satellite Image Classification Dataset: • Land surface classification model training: Can be used in experiments to classify various types of terrain such as buildings, farmland, and roads. • Research and application in geospatial information analysis: Useful for developing models that support spatial decision-making through tasks such as land use monitoring, urban structure analysis, and land surface inference.

AI IMAGE RECOGNITION

## Overview

AI IMAGE RECOGNITION is a dataset for object detection tasks - it contains Objects annotations for 10,000 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 [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).