Exploring the best computer vision datasets in 2025

Did you know that data plays a role in almost everything you do daily? Watching a video, taking a photo, or checking Google Maps contributes to the constant flow of information captured by over 75 billion connected devices. These pieces of data form the foundation of artificial intelligence (AI). In fact, advanced computer vision models like Ultralytics YOLO11 rely on visual data to identify patterns, interpret images, and make sense of the world around us.

Interestingly, the value of data isn’t just about quantity. It’s more important how well it’s organized and prepared. If a dataset is messy or incomplete, it can lead to mistakes. However, when datasets are clean and diverse, they help computer vision models perform better, whether it’s recognizing objects in a crowd or analyzing complex visuals. High-quality datasets make all the difference.

In this article, we’ll explore the best computer vision datasets of 2025 and see how they contribute to building more accurate and efficient computer vision models. Let’s get started!

What are computer vision datasets?

A computer vision dataset is a collection of images or videos that help computer vision systems learn to understand and recognize visual information. These datasets come with labels or annotations that help models recognize objects, people, scenes, and patterns within the data.

They can be used to train computer vision models, helping them improve tasks like identifying faces, detecting objects, or analyzing scenes. The better the dataset - well-organized, diverse, and accurate - the better the Vision AI model performs, leading to smarter and more useful technology in everyday life.

How to build a computer vision dataset

Building a computer vision dataset is like preparing study notes to teach someone how to see and understand the world. It all starts with gathering images and videos that match the specific application you’re developing. 

An ideal dataset includes diverse examples of the objects of interest, captured from different angles, under various lighting conditions, and across multiple backgrounds and environments. This variety makes sure that the computer vision model learns to recognize patterns accurately and performs reliably in real-world scenarios.

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After gathering relevant images and videos, the next step is data labeling. This process involves adding tags, annotations, or descriptions to the data so that the AI can understand what each image or video contains. 

Labels can include object names, locations, boundaries, or other relevant details that help train the model to recognize and interpret visual information accurately. Data labeling transforms a simple collection of images into a structured dataset that can be used to train a computer vision model.

Model training requires high-quality data

You might be wondering what makes a dataset high quality. There are many factors involved, like accurate labeling, diversity, and consistency. For example, if multiple annotators are labeling an object detection dataset to identify cat ears, one might label them as part of the head while another labels them separately as ears. This inconsistency can confuse the model and affect its ability to learn correctly.

Here’s a quick overview of the qualities of an ideal computer vision dataset:

• Clear labels: Each image is accurately annotated with consistent and precise labels.
• Diverse data: The dataset includes different objects, backgrounds, lighting conditions, and angles to help the model work well in various situations.
• High-resolution images: Sharp, detailed images make it easier for the model to learn and recognize features.

Ultralytics supports various datasets

Ultralytics YOLO models, like YOLO11, are built to work with datasets in a specific YOLO file format. While it's easy to convert your own data to this format, we also provide a hassle-free option for those who want to start experimenting right away. 

The Ultralytics Python package supports a wide range of computer vision datasets, allowing you to dive into projects using tasks like object detection, instance segmentation, or pose estimation without any extra setup.  

Users can easily access ready-to-use datasets like COCO, DOTA-v2.0, Open Images V7, and ImageNet by specifying the dataset name as one of the parameters in the training function. When you do so, the dataset is automatically downloaded and pre-configured, so you can focus on building and refining your models.

Top 5 computer vision datasets in 2025

Advancements in Vision AI rely on diverse, large-scale datasets that drive innovation and enable breakthroughs. Let’s take a look at some of the most important datasets, supported by Ultralytics, that are influencing computer vision models.

ImageNet dataset 

ImageNet, created by Fei-Fei Li and her team at Princeton University in 2007 and introduced in 2009, is a large dataset with over 14 million labeled images.  It is widely used to train systems to recognize and categorize different objects. Its structured design makes it particularly useful for teaching models to classify images accurately. While well-documented, it primarily focuses on image classification and lacks detailed annotations for tasks like object detection. 

Here’s a look at some of ImageNet’s key strengths:

• Diversity: With images spanning over 20,000 categories, ImageNet offers a vast and varied dataset that enhances model training and generalization.
• Structured organization: Images are meticulously categorized using the WordNet hierarchy, facilitating efficient data retrieval and systematic model training. 
• Comprehensive documentation: Extensive research and years of study make ImageNet accessible to both beginners and experts, providing valuable insights and guidance for computer vision projects.

However, like any dataset, it has its limitations. Here are some of the challenges to consider:

• Computational demands: Its massive size can pose challenges for smaller teams with limited computing resources.
• Lack of temporal data: Since it contains only static images, it may not meet the needs of applications requiring video or time-based data.
• Outdated images: Some images in the dataset are older and may not reflect current objects, styles, or environments, potentially reducing relevance for modern applications.

DOTA-v2.0 dataset

The DOTA-v2.0 dataset, where DOTA stands for Dataset for Object Detection in Aerial Images, is an extensive collection of aerial images created especially for oriented bounding box (OBB) object detection. In OBB detection, rotated bounding boxes are used to align more accurately with the actual orientation of objects in the image. This method works especially well for aerial imagery, where objects often appear at various angles, leading to more precise localization and better detection overall.

This dataset consists of over 11,000 images and more than 1.7 million oriented bounding boxes across 18 object categories. The images range from 800×800 to 20,000×20,000 pixels, and include objects like airplanes, ships, and buildings. 

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Because of its detailed annotations, DOTA-v2.0 has become a popular choice for remote sensing and aerial surveillance projects. Here are some of the key features of DOTA-v2.0:

• Diverse object categories: It covers many different object types, such as vehicles, harbors, and storage tanks, giving models exposure to various real-world objects.
• High-quality annotations: Expert annotators have provided precisely oriented bounding boxes that clearly show object shapes and directions.
• Multiscale images: The dataset includes images of different sizes, helping models to learn how to detect objects at both small and large scales.

While DOTA-v2 has many strengths, here are some limitations users should keep in mind:

• Extra download steps: Due to the way the DOTA dataset is maintained, DOTA-v2.0 requires an extra setup step. You need to first download the DOTA-v1.0 images and then add the extra images and updated annotations for DOTA-v2.0 to complete the dataset.
• Complex annotations: Oriented bounding boxes may require extra effort to handle during model training.
• Limited scope: DOTA-v2 is designed for aerial images, which makes it less useful for general object detection tasks outside of this domain.

Roboflow 100 dataset 

The Roboflow 100 (RF100) dataset was created by Roboflow with support from Intel. It can be used to test and benchmark how well object detection models work. This benchmark dataset includes 100 different datasets chosen from over 90,000 public datasets. It has more than 224,000 images and 800 object classes from areas like healthcare, aerial views, and gaming. 

Here are some of the key advantages of using RF100:

• Broad domain coverage: It includes datasets from seven fields, such as medical imaging, aerial views, and underwater exploration. 
• Encourages model improvement: The variability and domain-specific challenges in RF100 reveal gaps in current models, driving research toward more adaptable and robust object detection solutions.
• Consistent image format: All images are resized to 640x640 pixels. This helps users train models without needing to adjust image sizes.

Despite its strengths, RF100 also comes with certain drawbacks to keep in mind:

• Limited in terms of tasks: RF100 is designed for object detection, so it can’t accommodate tasks like segmentation or classification.
• Benchmark-centric focus: RF100 is primarily designed as a benchmarking tool rather than for training models for real-world applications, so its results may not fully translate to practical deployment scenarios.
• Annotation variability: Since RF100 aggregates crowd-sourced datasets, there can be inconsistencies in annotation quality and labeling practices, which may impact model evaluation and fine-tuning.

COCO (Common objects in context) dataset

The COCO dataset is one of the most widely used computer vision datasets, offering over 330,000 images with detailed image annotations. It’s designed for object detection, segmentation, and image captioning, making it a valuable resource for many projects. Its detailed labels, including bounding boxes and segmentation masks, help systems learn to analyze images precisely.

This dataset is known for its flexibility and is useful for various tasks, from simple to complex projects. It has become a standard in the field of Vision AI, frequently used in challenges and competitions to assess model performance.

Some of its strengths include:

• Diverse and realistic data: The dataset includes images from real-world scenarios with multiple objects, occlusions, and varied lighting conditions.
• Strong community and research adoption: Used in major machine learning competitions and research, the COCO dataset has extensive documentation, pre-trained models, and active community support.
• Rich and detailed annotations: The COCO dataset provides highly detailed annotations, including object segmentation, key points, and captions, making it ideal for projects that require precise visual understanding.

Here are a few limiting factors to be aware of as well:

• High computational requirements: Due to its size and complexity, training models on COCO can require significant computational resources, making it challenging for teams with limited hardware.
• Data imbalance: Some object categories have significantly more images than others, which can lead to bias in model training.
• Complex annotation structure: The dataset’s detailed annotations, while valuable, can be overwhelming for beginners or smaller teams that lack experience in working with structured Vision AI datasets.

Open Images V7 dataset

Open Images V7 is a massive open-source dataset curated by Google, featuring over 9 million images with annotations for 600 object categories. It includes a variety of annotation types and is ideal for tackling complex computer vision tasks. Its scale and depth provide a comprehensive resource for training and testing computer vision models.

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Also, the Open Images V7 dataset’s popularity in research provides plenty of resources and examples for users to learn from. However, its massive size can make downloading and processing time-consuming, especially for smaller teams. Another issue is that some annotations may be inconsistent, requiring extra effort to clean the data, and integration isn’t always seamless, meaning additional preparation may be needed. 

Choosing the right dataset 

Picking the right dataset is a big part of setting your computer vision project up for success. The best choice depends on your specific task - finding a good match helps your model learn the right skills. It should also integrate easily with your tools, so you can focus more on building your model and less on troubleshooting.

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Key takeaways

High-quality datasets are the backbone of any computer vision model, helping systems learn to interpret images accurately. Diverse and well-annotated datasets are especially important, as they enable models to perform reliably in real-world scenarios and reduce errors caused by limited or poor-quality data.

Ultralytics simplifies the process of accessing and working with computer vision datasets, making it easier to find the right data for your project. Choosing the right dataset is a crucial step in building a high-performing model, leading to more precise and impactful results.

Join our community and explore our GitHub repository to learn more about AI. Discover advancements like computer vision for healthcare and AI in self-driving cars on our solutions pages. Check out our licensing options and take the first step toward getting started with computer vision today!