Try image labeling service
Become the image annotation and start building AI models for your computer vision projects.
- 24/7 Support
- High accuracy
- Fast labeling
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Image annotation service
- Fast labeling
- High accuracy
- 24/7 Support
Rapidly annotate images or photos and export them in any format.
Unlock the full potential of your machine learning, computer vision, and AI endeavors with our image annotation service. Our specialized data labeling service ensures unparalleled accuracy and efficiency in training your models.
How to get started
- Sign up
- Create a task for image annotation
- Submit datasets for annotation via API
- Export the results
Image annotator
Image annotator offers comprehensive solutions for annotating images, catering to various needs across industries such as machine learning, computer vision, and artificial intelligence. With our service tools, you can easily label images for tasks like object detection, image classification, segmentation.
Our service is designed to streamline the annotation process, saving you time and effort while ensuring accurate annotations. Whether you're working on training datasets for ML models or developing computer vision applications, our image annotator service provides the flexibility and precision you need to achieve your goals.
Start annotating your images and unlock the potential of your machine learning and computer vision projects!
Tasks that require annotated data
Various tasks in fields such as machine learning, computer vision, and artificial intelligence require annotated data for training and evaluation purposes. Annotation involves labeling data with relevant information, such as identifying & recognition objects on images or pictures.
Label images for object detection
Labeling images for object detection involves identifying and marking specific objects within images with bounding boxes or masks. This process is essential for training object detection models to accurately recognize and locate objects of interest within images.
Image classification
Image classification involves labeling images with predefined tags or labels using supervised deep learning algorithms. Annotations for this task can be simple text labels, class numbers, or one-hot encodings. These annotations are converted into suitable formats before being used in loss functions during training.
Completely handled dataset annotation services
Easily expand labeling operations by leveraging a skilled workforce, cutting-edge tools, and specialized expertise, ensuring the rapid and accurate annotation of large datasets.
- Scalability: Proficient dataset labeling services enable you to manage extensive datasets without compromising on quality or speed.
- Cost-effectiveness: Whether it's a singular project or an continuous workflow, leverage efficient and consistently available operations to maintain cost-effectiveness.
- Expertise: We are have extensive experience in annotating diverse datasets and possess domain-specific experts across industries.
What is image annotation?
Image annotation refers to the process of labeling images within a given dataset to facilitate the training of machine learning models.
Once manual annotation is finalized, the labeled images are fed into a machine learning or deep learning model, which replicates the annotations without requiring human intervention.
Image annotation establishes the benchmarks that the model endeavors to replicate; consequently, any inaccuracies in the labels are also reproduced. Hence, accurate image annotation forms the cornerstone for training neural networks, rendering annotation one of the pivotal tasks in the realm of computer vision.
The process where in a model autonomously labels images is commonly known as model-assisted labeling.
Image annotations can be conducted manually or through the utilization of automated annotation tools.
Automated annotation tools typically encompass pre-trained algorithms capable of annotating images with a certain level of precision. Their annotations prove invaluable for intricate annotation tasks such as generating segment masks, which are time-intensive to create.
In such scenarios, auto-annotation tools aid manual annotation by furnishing a starting point from which further annotation can proceed.
Moreover, manual annotation is often supplemented by tools that facilitate the recording of key points to simplify data labeling and data storage.
