Dataset Overview

This page contains a modified Cocos dataset along with details about the dataset used.

File Descriptions

imgs.zip - Train: 🚂 This folder contains the training set, which can be split into train/validation data for model training. - Test: 🧪 Your trained models should be used to produce predictions on the test set.

labels.zip - categories.csv: 📝 This file lists all the object classes in the dataset, ordered according to the column ordering in the train labels file. - train_labels.csv: 📊 This file contains data regarding which image contains which categories.

Image Label

## Overview

Image Label is a dataset for object detection tasks - it contains Text annotations for 995 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).

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

LABEL IMAGE

## Overview

LABEL IMAGE is a dataset for object detection tasks - it contains 2 annotations for 6,800 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).

This dataset contains ILSVRC-2012 (ImageNet) validation images annotated with multi-class labels from "Evaluating Machine Accuracy on ImageNet", ICML, 2020. The multi-class labels were reviewed by a panel of experts extensively trained in the intricacies of fine-grained class distinctions in the ImageNet class hierarchy (see paper for more details). Compared to the original labels, these expert-reviewed multi-class labels enable a more semantically coherent evaluation of accuracy.

Version 3.0.0 of this dataset contains more corrected labels from "When does dough become a bagel? Analyzing the remaining mistakes on ImageNet as well as the ImageNet-Major (ImageNet-M) 68-example split under 'imagenet-m'.

Only 20,000 of the 50,000 ImageNet validation images have multi-label annotations. The set of multi-labels was first generated by a testbed of 67 trained ImageNet models, and then each individual model prediction was manually annotated by the experts as either correct (the label is correct for the image),wrong (the label is incorrect for the image), or unclear (no consensus was reached among the experts).

Additionally, during annotation, the expert panel identified a set of problematic images. An image was problematic if it met any of the below criteria:

• The original ImageNet label (top-1 label) was incorrect or unclear
• Image was a drawing, painting, sketch, cartoon, or computer-rendered
• Image was excessively edited
• Image had inappropriate content

The problematic images are included in this dataset but should be ignored when computing multi-label accuracy. Additionally, since the initial set of 20,000 annotations is class-balanced, but the set of problematic images is not, we recommend computing the per-class accuracies and then averaging them. We also recommend counting a prediction as correct if it is marked as correct or unclear (i.e., being lenient with the unclear labels).

One possible way of doing this is with the following NumPy code:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012_multilabel', split='validation')

# We assume that predictions is a dictionary from file_name to a class index between 0 and 999

num_correct_per_class = {}
num_images_per_class = {}

for example in ds:
  # We ignore all problematic images
  if example[‘is_problematic’].numpy():
    continue

  # The label of the image in ImageNet
  cur_class = example['original_label'].numpy()

  # If we haven't processed this class yet, set the counters to 0
  if cur_class not in num_correct_per_class:
    num_correct_per_class[cur_class] = 0
    assert cur_class not in num_images_per_class
    num_images_per_class[cur_class] = 0

  num_images_per_class[cur_class] += 1

  # Get the predictions for this image
  cur_pred = predictions[example['file_name'].numpy()]

  # We count a prediction as correct if it is marked as correct or unclear
  # (i.e., we are lenient with the unclear labels)
  if cur_pred is in example['correct_multi_labels'].numpy() or cur_pred is in example['unclear_multi_labels'].numpy():
    num_correct_per_class[cur_class] += 1

# Check that we have collected accuracy data for each of the 1,000 classes
num_classes = 1000
assert len(num_correct_per_class) == num_classes
assert len(num_images_per_class) == num_classes

# Compute the per-class accuracies and then average them
final_avg = 0
for cid in range(num_classes):
 assert cid in num_correct_per_class
 assert cid in num_images_per_class
 final_avg += num_correct_per_class[cid] / num_images_per_class[cid]
final_avg /= num_classes

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('imagenet2012_multilabel', 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/imagenet2012_multilabel-3.0.0.png" alt="Visualization" width="500px">

The Inria Aerial Image Labeling addresses a core topic in remote sensing: the automatic pixelwise labeling of aerial imagery. Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two semantic classes: building and not building (publicly disclosed only for the training subset) The images cover dissimilar urban settlements, ranging from densely populated areas (e.g., San Francisco’s financial district) to alpine towns (e.g,. Lienz in Austrian Tyrol). Instead of splitting adjacent portions of the same images into the training and test subsets, different cities are included in each of the subsets. For example, images over Chicago are included in the training set (and not on the test set) and images over San Francisco are included on the test set (and not on the training set). The ultimate goal of this dataset is to assess the generalization power of the techniqu

Pm06/my-image-label-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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">

Image Data Labeling Service Market Outlook

The global image data labeling service market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach around USD 6.1 billion by 2032, exhibiting a robust CAGR of 17.1% during the forecast period. The exponential growth of this market is driven by the increasing demand for high-quality labeled data for machine learning and artificial intelligence applications across various industries.

One of the primary growth factors of the image data labeling service market is the surge in the adoption of artificial intelligence (AI) and machine learning (ML) technologies across multiple sectors. Organizations are increasingly relying on AI and ML to enhance operational efficiency, improve customer experience, and gain competitive advantages. As a result, there is a rising need for accurately labeled data to train these AI and ML models, driving the demand for image data labeling services. Furthermore, advancements in computer vision technology have expanded the scope of image data labeling, making it essential for applications such as autonomous vehicles, facial recognition, and medical imaging.

Another significant factor contributing to market growth is the proliferation of big data. The massive volume of data generated from various sources, including social media, surveillance cameras, and IoT devices, necessitates the need for effective data labeling solutions. Companies are leveraging image data labeling services to manage and analyze these vast datasets efficiently. Additionally, the growing focus on personalized customer experiences in sectors like retail and e-commerce is fueling the demand for labeled data, which helps in understanding customer preferences and behaviors.

Investment in research and development (R&D) activities by key players in the market is also a crucial growth driver. Companies are continuously innovating and developing new techniques to enhance the accuracy and efficiency of image data labeling processes. These advancements not only improve the quality of labeled data but also reduce the time and cost associated with manual labeling. The integration of AI and machine learning algorithms in the labeling process is further boosting the market growth by automating repetitive tasks and minimizing human errors.

From a regional perspective, North America holds the largest market share due to early adoption of advanced technologies and the presence of major AI and ML companies. The region is expected to maintain its dominance during the forecast period, driven by continuous technological advancements and substantial investments in AI research. Asia Pacific is anticipated to witness the highest growth rate due to the rising adoption of AI technologies in countries like China, Japan, and India. The increasing focus on digital transformation and government initiatives to promote AI adoption are significant factors contributing to the regional market growth.

Type Analysis

The image data labeling service market is segmented into three primary types: manual labeling, semi-automatic labeling, and automatic labeling. Manual labeling, which involves human annotators tagging images, is essential for ensuring high accuracy, especially in complex tasks. Despite being time-consuming and labor-intensive, manual labeling is widely used in applications where nuanced understanding and precision are paramount. This segment continues to hold a significant market share due to the reliability it offers. However, the cost and time constraints associated with manual labeling are driving the growth of more advanced labeling techniques.

Semi-automatic labeling combines human intervention with automated processes, providing a balance between accuracy and efficiency. In this approach, algorithms perform initial labeling, and human annotators refine and validate the results. This method significantly reduces the time required for data labeling while maintaining high accuracy levels. The semi-automatic labeling segment is gaining traction as it offers a scalable and cost-effective solution, particularly beneficial for industries dealing with large volumes of data, such as retail and IT.

Automatic labeling, driven by AI and machine learning algorithms, represents the most advanced segment of the market. This approach leverages sophisticated models to autonomously label image data with minimal human intervention. The continuous improvement in AI algorithms, along with the availability of large datasets for training, has enhanced the accuracy and reliability of automatic lab

What’s Included

Introducing the French 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 French language.

Containing a total of 5000 images, this French 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 French 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 French 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 French text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native French 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 French language. Your journey to enhanced language understanding and processing starts here.

Here are a few use cases for this project:

1. Library Management: CLARA book label image detection can simplify the process of sorting and organizing books in libraries by identifying book labels, streamlining inventory management, and helping librarians to properly categorize and locate books on the correct shelves.

2. Retail Bookstores: The model can be used in retail bookstores to quickly identify and locate books based on their labels, optimizing shelf organization, and improving the customer experience by allowing staff to easily find and retrieve books requested by customers.

3. Book Warehouse Management: In warehouses where large quantities of books are stored, the CLARA book label image detection model can help automate the process of book sorting, organization, and tracking, thereby reducing the chance of errors and increasing efficiency.

4. Accessible Reading Support: The model can be integrated into an app that assists visually impaired individuals by identifying book labels and providing audio feedback, enabling users to more easily locate and select their desired books in libraries or bookstores.

5. Automatic Book Recommendation Systems: CLARA book label image detection model can be incorporated into an intelligent book recommendation system that recommends books based on similar labels. By analyzing the labels of users' favorite books, it can suggest similar reads, thus enhancing personalized book discovery.

This dataset is designed for training and evaluating object detection models, specifically for detecting plastic bottles and classifying them based on the presence or absence of a label. It is structured to work seamlessly with YOLOv8 and follows the standard YOLO format.

🔍 Classes: 0: Bottle with Label

1: Bottle without Label

📁 Folder Structure: images/: Contains all image files

labels/: Corresponding YOLO-format annotation files

data.yaml: Configuration file for training with YOLOv8

🛠 Use Case: This dataset is ideal for real-time detection systems, quality control applications, recycling automation, and projects focused on object classification in cluttered or real-world environments.

Dataset

This dataset was created by shen

Contents

Building segmentation image data set

This dataset is publish by the research "A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image"

Detail:

OCTA image dataset with label annotation for quality assessment. sOCTA-3x3-10k: 10,480 3 × 3 mm² superficial vascular layer OCTA (sOCTA) images divided into three classes; sOCTA-6x6-14k: 14,042 6 × 6 mm² sOCTA images divided into three classes.

GitHub: https://github.com/shanzha09/COIPS

These datasets are public available, if you use the dataset or our system in your research, please cite our paper: A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image.

This dataset contains 4,599 high-quality, annotated images of 25 commonly used chemistry lab apparatuses. The images, each containing structures in real-world settings, have been captured from different angles, backgrounds, and distances, while also undergoing variations in lighting to aid in the robustness of object detection models. Every image has been labeled using bounding box annotation in YOLO and COCO format, alongside the class IDs and normalized bounding box coordinates making object detection more precise. The annotations and bounding boxes have been built using the Roboflow platform.To achieve a better learning procedure, the dataset has been split into three sub-datasets: training, validation, and testing. The training dataset constitutes 70% of the entire dataset, with validation and testing at 20% and 10% respectively. In addition, all images undergo scaling to a standard of 640x640 pixels while being auto-oriented to rectify rotation discrepancies brought about by the EXIF metadata. The dataset is structured in three main folders - train, valid, and test, and each contains images/ and labels/ subfolders. Every image contains a label file containing class and bounding box data corresponding to each detected object.The whole dataset features 6,960 labeled instances per 25 apparatus categories including beakers, conical flasks, measuring cylinders, test tubes, among others. The dataset can be utilized for the development of automation systems, real-time monitoring and tracking systems, tools for safety monitoring, alongside AI educational tools.

MLRSNet provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

The Dataset includes: 1. Images folder: 46 categories, 109,161 high-spatial resolution remote sensing images. 2. Labels folders: each category has a .csv file. 3. Categories_names. xlsx: Sheet1 lists the names of 46 categories, and the Sheet2 shows the associated multi-label to each category.

What’s Included

Introducing the English 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 English language.

Containing a total of 2000 images, this English 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 English 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 English 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 English text recognition models.

We're committed to expanding this dataset by continuously adding more images with the assistance of our native English 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 English language. Your journey to enhanced language understanding and processing starts here.

This data set contains the raw data from Figures 3 and 4 of the manuscript "Automated Image Label Extraction from Radiology Reports - A Review" The data used to create Figure 3 (co.autorships) provides Co-authorship links among authors of the studies included in the synthesis. The "nodes" tab contains information on each author, such as their modularity class and their degree (the number of colaborations with other authors). The "edges" tab contains information on the links between authors, such as the weight (the number of colaborations) of each link between two authors. A modularity algorithm (https://dx.doi.org/10.1088/1742-5468/2008/10/P10008) was used for community detection. This data was used to create Figure 3 of the manuscript. The data used to create Figure 4 of the manuscript (wordcloud_counts) provides the number of occurences of named entities across the abstracts of the included studies. The entities were extracted using the spaCy model en_core_sci_lg, which is appropriate for processing biomedical, scientific and clinical text (https://spacy.io/). This data was used to create Figure 4 of the manuscript.

Accident And Non Accident Label Image Dataset

## Overview

Accident And Non Accident Label Image Dataset is a dataset for object detection tasks - it contains Accident annotations for 1,485 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).