The best annotation tool is the one that lets models do most of the labeling for you and then feeds those labels straight into training and deployment, which is why Roboflow Annotate (free, with SAM 3-powered Auto Label and Label Assist) tops the list for most teams. CVAT fits self-hosted or 3D work, Make Sense is the lightest pick for quick solo jobs, Labelbox suits organizations labeling many data types beyond images, and Scale AI is for teams that need a managed labeling workforce at scale.
Your choice of annotation tool for use in your computer vision projects has a big impact. The more efficient you are in your annotation tool, and the more productive the tool makes you, the quicker you can get to your end goal: a working, robust computer vision model.
At Roboflow, we persistently strive toward reducing the amount of time you need to spend labeling data so you can start using a model in production. With that in mind, we have researched several labeling tools on the market today and prepared a review of the five best tools we have found.
In each review, I’ll talk through the features available in each tool, as well as the cost based on publicly-available pricing data. Without further ado, let’s get started!
Image Annotation Tools
The right image annotation tool depends on what you need to label, how much of the process you want to automate, and what you plan to do with your dataset afterward. The table below compares five popular annotation tools across their annotation capabilities, AI-assisted labeling features, pricing, hosting options, and what happens after your data is labeled.
| Tool | Best for | Pricing | Hosted vs. self-hosted | Annotation types | AI-assisted labeling | Ends at |
|---|---|---|---|---|---|---|
| Roboflow Annotate | End-to-end computer vision | Free tier, paid plans | Hosted | Boxes, polygons, masks, keypoints, video | Auto Label, Label Assist, Smart Select, Box Prompting | Trained + deployed model |
| CVAT | Self-hosting and advanced annotation | Free, paid cloud and enterprise | Hosted or self-hosted | Boxes, polygons, masks, keypoints, skeletons, video, 3D | AI agents, automatic annotation | Exported dataset |
| Make Sense | Quick, simple labeling | Free, open source | Browser-based | Boxes, polygons, points, lines | Manual labeling | Exported dataset |
| Labelbox | Enterprise annotation and collaboration | Free options, paid plans | Hosted | Boxes, polygons, masks, keypoints, video, multimodal | Model-assisted labeling, AI workflows | Exported dataset |
| Scale AI | Large-scale managed labeling | Custom pricing | Managed service | Image, video, 3D, multimodal | Human-in-the-loop, AI workflows | Curated dataset |
What to Look for in an Image Annotation Tool
There are several factors to consider when choosing an image annotation tool. The right choice depends on the type of data you’re labeling, how much of the work you want to automate, and how you plan to use the finished dataset.
How much of the labeling does the tool do for you?
Manual annotation can take a significant amount of time, especially when working with large datasets. Some tools only provide the basic tools you need to draw annotations yourself, while others can use AI to generate labels with a single click or automatically annotate entire batches of images.
Annotation types you actually need
Different computer vision tasks require different types of annotations. Object detection typically uses bounding boxes, while segmentation may require polygons or pixel masks. Pose estimation uses keypoints or skeletons. It's also worth checking whether a tool can convert between annotation types when you need to use the same data for different tasks.
Review and approval workflow
AI-generated annotations can save time, but they may still need to be checked before they are used for training. Look for tools that make it easy to review, edit, and approve annotations rather than treating automatically generated labels as finished data.
Hosting, data ownership, and cost model
Consider where your images and annotations will be stored and how you'll pay for the tool. Some platforms are free or open source and can be self-hosted, while others are hosted services that charge based on usage, features, or the amount of labeling work.
What happens after labeling?
Annotation is usually just one step in the computer vision workflow. Some tools focus primarily on creating and exporting a labeled dataset, while others also let you train and deploy a model using that data. If you're building a computer vision application, an integrated workflow can reduce the number of tools you need to manage.
The Best Image Annotation Tools
1. Roboflow Annotate
Roboflow Annotate is a web-based annotation tool for creating datasets for computer vision models. It supports object detection, segmentation, classification, and keypoint detection, with tools for drawing bounding boxes and polygons, creating pixel-precise masks, and adding keypoint annotations. For object detection projects, Roboflow can also automatically convert polygon and mask annotations into bounding boxes when needed.
Where Roboflow stands out is its AI-assisted labeling. Instead of manually annotating every image, you can choose different tools depending on how much of the labeling process you want to automate.
Four ways Roboflow labels for you
- Auto Label: Automatically generates annotations across batches of images using supported foundation models such as SAM 3 and Gemini, your own trained models, or a saved Workflow.
- Label Assist: Uses a model you have already trained to suggest annotations as you label additional images.
- Smart Polygon: Uses AI to generate polygon annotations from an object with just a few clicks, which you can then edit if needed.
- Box Prompting: Lets you draw an example box around an object and uses that example to find other instances of the same object.
These options let you choose between manual labeling, AI-assisted annotation, and bulk automatic labeling depending on your dataset.
Which model should you use?
The best option depends on what you are labeling.
- Gemini 3.7 Flash: A good choice for common, nameable objects when you need bounding boxes.
- SAM 3: Better suited to tasks where the exact shape of an object matters and you need segmentation masks.
- Label Assist: Useful once you have trained your first model and want to use its predictions to speed up additional labeling.
- Workflows: Useful for more specialized labeling pipelines that require custom models or logic.
From labels to a working model
Roboflow connects annotation with the rest of the computer vision workflow. After reviewing and approving your annotations, you can create a dataset version, train a model such as RF-DETR, and deploy it to images, video, or camera streams.
Roboflow Annotate is best for teams that want more than a labeled dataset and need a path from raw images to a trained and deployed computer vision model. Keeping annotation, dataset management, training, and deployment in one platform can also reduce the number of separate tools you need.
Roboflow’s biggest advantage is that it combines annotation, AI-assisted labeling, dataset management, model training, and deployment in one platform. It also offers a free plan and several ways to automate labeling. The main drawback is that AI-assisted labeling uses credits. It may also be more functionality than you need if you're only looking for a simple annotation tool.
Pricing
Roboflow has a free Public plan that includes its data labeling suite, AI-assisted labeling, model training, and Workflow builder. The plan includes a monthly credit allowance, while paid plans provide additional credits and support. AI-assisted labeling uses Roboflow credits, so larger labeling projects may require a paid plan.
2. CVAT
CVAT is an open-source data annotation platform designed for computer vision teams. It can be used through CVAT Online or self-hosted with the open-source Community edition, giving teams control over where their annotation infrastructure and data are hosted. CVAT supports images, video, and 3D point clouds, along with tools for detection, segmentation, tracking, and pose estimation.
Annotation and AI-Assisted Labeling
CVAT supports a wide range of annotation types, including bounding boxes, polygons, masks, points, keypoints, skeletons, and cuboids. It also supports video annotation and tracking, as well as 3D point-cloud annotation, making it one of the more versatile options for teams working with different types of computer vision data.
CVAT also offers several AI-assisted annotation options. Its AI tools can use detectors, interactors, and trackers to automatically or semi-automatically annotate images and videos. Teams can also connect pre-trained or custom models to CVAT for automatic annotation, giving them flexibility over which models they use.
Who should use CVAT?
CVAT is best suited to teams that need self-hosting, advanced annotation capabilities, or support for 3D and video data. Its open-source Community edition is particularly useful when keeping annotation infrastructure under your own control is important.
CVAT primarily focuses on annotation and dataset management rather than providing the same integrated training and deployment workflow as an end-to-end computer vision platform. Its flexibility and broad feature set are major advantages, although they can also make CVAT more complex to set up and use than a lightweight tool like Make Sense.
Pricing: CVAT Community is free and open source for self-hosting. CVAT also offers a managed cloud version with a free plan and paid plans, while Enterprise provides additional self-hosted capabilities and support.
3. Make Sense
Make Sense is a free, open-source annotation tool that runs directly in the browser. It requires no complicated installation or account, and the project states that images are not sent to or stored on its servers. This makes it a convenient option for quickly labeling smaller datasets without setting up a larger annotation platform.
Annotation and AI-Assisted Labeling
Make Sense supports rectangles, polygons, points, and lines, along with image-level labels. It can export annotations in formats including YOLO, Pascal VOC XML, VGG JSON, CSV, and COCO JSON.
While Make Sense is primarily a lightweight manual annotation tool, it also includes local AI integrations. Its project supports models such as SSD, YOLOv5, and PoseNet for tasks including object detection and pose estimation. These models can run in the browser using TensorFlow.js, allowing images to remain on the user's device when using local models.
Make Sense is best for small, one-off labeling jobs where you want to start annotating immediately without creating an account or setting up a larger platform. It is especially appealing when simplicity and local processing are more important than team collaboration or advanced dataset management.
Its main advantages are its simplicity, zero-cost access, browser-based workflow, and privacy-focused local processing. The tradeoff is that its project and collaboration features are relatively basic compared with full-scale annotation platforms, and its AI capabilities are less extensive than the automated labeling systems offered by tools such as Roboflow or CVAT.
Pricing
Make Sense is free to use and open source under the GPLv3 license.
4. Labelbox
Labelbox is a data labeling and data management platform designed for organizations working with large and complex datasets. Its Annotate platform supports collaborative labeling with internal teams, external vendors, or Labelbox's own labeling services, while its broader platform includes dataset management, model workflows, and tools for monitoring labeling performance.
Annotation and AI-Assisted Labeling
Labelbox supports annotation across a wide range of data types, including images, video, text, documents, audio, geospatial data, and multimodal data. Its computer vision tools support common annotation workflows, while the platform can also be used for tasks such as LLM evaluation and prompt-response generation.
Labelbox provides model-assisted labeling through Foundry, which can use foundation models to generate predictions that are then reviewed by humans. Teams can also import their own model predictions into labeling projects. Foundry is integrated with Labelbox's broader Catalog, Annotate, and Model workflows, allowing teams to move from data preparation and labeling into model development.
Who should use Labelbox?
Labelbox is best for large teams and enterprises that need collaborative labeling, quality control, workforce management, and support for multiple data types. Its review workflows, consensus tools, performance monitoring, and labeling services make it better suited to complex production labeling programs than simple one-off annotation jobs.
Labelbox's broader platform is a major advantage for organizations managing large data operations, but its extensive feature set and pricing structure can be more than a small team needs if the goal is simply to label and export a relatively small image dataset.
Pricing
Labelbox offers a free tier with 500 Labelbox Units (LBUs) per month, alongside paid Starter and Enterprise plans. Additional services such as Foundry and professional labeling can incur separate usage-based costs.
5. Scale AI
Scale AI takes a different approach from the other tools in this list. Rather than primarily providing a standalone annotation editor for you to use yourself, Scale offers a broader Data Engine that combines data collection, curation, annotation, evaluation, and other services. Its managed labeling services can provide trained workers and domain experts to annotate data at scale.
Annotation and AI-Assisted Labeling
Scale supports a broad range of data types, including images, video, text, and 3D sensor-fusion data such as LiDAR. Its image annotation tools support geometries including boxes, polygons, points, lines, ellipses, and cuboids, while its video workflows support similar annotations across frames.
Scale uses a human-in-the-loop approach that combines automated labeling and AI tools with human review. Machine learning can be used to accelerate annotation, while subject-matter experts help maintain quality, particularly for complex or specialized datasets.
Who should use Scale AI?
Scale AI is best for organizations that need large-scale, managed data labeling and want to outsource much of the annotation process. It is particularly suited to production programs where specialized annotators, quality control, and the ability to scale a labeling workforce are more important than having a simple self-serve annotation editor.
Scale AI's biggest advantage is that it can provide the workforce, expertise, and infrastructure needed for very large or specialized labeling programs. The tradeoff is that it is generally less suited to an individual developer or small team that simply wants to open an annotation editor and label a few hundred images themselves.
Pricing
Scale offers a self-serve Data Engine with pay-as-you-go pricing and a free allowance for the first 1,000 labeling units when using your own workforce. Enterprise customers can use Scale's managed workforce and receive custom pricing and support.
Free vs. Paid Image Annotation Tools
If you're labeling a small dataset, you don't necessarily need to pay for an annotation platform. Make Sense is completely free and open source, while CVAT Community is free and can be self-hosted. Roboflow also offers a free Public plan that includes AI-assisted labeling, model training, and its Workflow builder.
The main difference between free and paid tools is usually not whether you can draw a bounding box. It's how much of the rest of the workflow the tool can handle for you. Paid plans can provide additional AI labeling capacity, private datasets, team features, managed infrastructure, and other capabilities that become more important as a project grows.
For example, if you're labeling a few hundred images for a personal project, spending hours manually labeling them with a free tool may be perfectly reasonable. But if you're working with tens of thousands of images, paying for AI-assisted labeling or a managed labeling service can be cheaper than the time required to annotate everything by hand.
The right choice depends on the size of your dataset, how much automation you need, and whether you need features such as collaboration, private data, self-hosting, or managed labeling. Free tools can be more than enough for small projects, while paid platforms become more valuable when the time saved by automation and workflow management outweighs the cost.
Auto-Labeling with Roboflow: Step-by-Step Tutorial
One of the easiest ways to see the difference AI-assisted labeling can make is to try it yourself. With Roboflow Auto Label, you can use foundation models such as Gemini 3.7 Flash to generate annotations across a batch of images, then review and correct the results instead of drawing every annotation manually.
For object detection, Gemini 3.7 Flash can generate bounding boxes based on the class names you provide. Here's what the process looks like.
1. Choose Auto Label
Start with an unannotated batch of images in your Roboflow project and open the Annotate tab. Select the batch you want to label and choose Label in minutes to open Auto Label.
Roboflow lets you choose from different models depending on the type of annotation you need. For this example, select Gemini (Boxes) to generate bounding boxes.
2. Enter your class names
Add the objects you want to detect as classes. For example, if you're annotating a dataset of fruits, you might add apple, banana, and orange.
When using Gemini for Auto Label, the class name itself is used to identify the object. The visual description field does not affect Gemini's predictions, so use clear and descriptive class names.
3. Preview the results
Once your classes are set, select Gemini (Boxes). Roboflow runs the model on a small selection of images so you can preview the annotations before labeling the entire batch.
Check whether the model is finding the right objects and assigning the correct classes. This preview gives you an opportunity to change your classes or model before spending credits on the full batch.
4. Run Auto Label on the batch
If the preview looks good, select Auto Label With This Model to run Gemini across the full batch.
Roboflow shows the image count and estimated credit cost before the job starts. Gemini 3.7 Flash can process up to 1,000 images per credit for this Auto Label workflow, so even relatively large batches can be labeled at a low usage cost.
The annotations are generated automatically in the background. Once the job is complete, the images move into the review workflow.
5. Review and approve the annotations
AI-generated annotations should still be reviewed before they're used for training. Open the labeled batch and check the boxes on each image, adding objects that were missed, tightening boxes that are too loose, and correcting any incorrect classes.
Once an image looks correct, approve it. After the annotations have been reviewed, the images are ready to become part of your dataset.
This review step is important because Auto Label is designed to reduce manual work, not eliminate quality control. Gemini can miss objects or produce boxes that need adjustment, particularly in crowded or difficult images.
The full workflow is therefore:
Choose Auto Label → Add class names → Preview → Run the batch → Review and approve
Gemini 3.7 vs. SAM 3
Roboflow also supports SAM 3 for AI-assisted annotation, but the two models are useful for different jobs. Gemini (Boxes) is a good choice when you need bounding boxes for common, nameable objects. SAM 3 is better when you need precise segmentation masks that follow the shape of an object.
For example, if you're labeling cars for an object detection model, Gemini can quickly generate the bounding boxes you need. If you're labeling cracks, scratches, or other defects where the exact shape of the region matters, a segmentation model such as SAM 3 can be more appropriate.
Start Annotating
Choosing an image annotation tool ultimately comes down to how much of the computer vision workflow you want it to handle.
For a small, one-off project, a lightweight tool such as Make Sense may be all you need. CVAT is a strong option when self-hosting, advanced annotation, video, or 3D data are priorities. Labelbox and Scale AI are better suited to larger organizations that need collaboration, quality control, or managed labeling at scale.
For teams that want to minimize the amount of manual labeling and move directly from annotations to a working computer vision model, an integrated platform can offer a different advantage. With Roboflow, you can annotate images, use AI to generate labels, review and version your dataset, train a model such as RF-DETR, and deploy it without moving the dataset between several different tools.
The goal of an annotation tool isn't simply to give you a place to draw boxes and polygons. The best tool is the one that removes as much manual work as possible while still giving you control over the quality of your dataset, then helps you turn those annotations into a model you can actually use.
Check out the following resources to learn more:
Cite this Post
Use the following entry to cite this post in your research:
Yajat Mittal. (May 9, 2026). Best Image Annotation Tools in 2026. Roboflow Blog: https://blog.roboflow.com/best-image-annotation-tools/