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About
Intro to COCO Annotation
COCO (Common Objects in Context) is a massive dataset for image segmentation and captioning. Machine learning and computer vision engineers widely use the COCO dataset for various computer vision labeling projects such as object recognition, people identification, face detection, motion analysis, and many others.
The COCO dataset object detection includes pictures of over 80 “entity” and 91 generic “material” categories, implying that it can benchmark general-purpose models more than small-scale datasets. Furthermore, the COCO dataset includes 121,408 photographs and 883,331 annotations of objects.
The COCO dataset can train machine learning models using a carefully monitored training method. After training on it, the model can be smoothly taught other tasks using a custom dataset. This one is not unlike many other datasets, where pictures are centered on specific contexts.
what we do
COCO Annotation Services You Can Get With Us
Object detection and instance segmentation
The annotator draws shapes around objects in an image. COCO's classification and bounding boxes span 80 categories, providing opportunities to experiment with annotation forms and image varieties and get the best results.
Image captioning
COCO image captioning helps to convert an input image into a text format description. The collection comprises around 500,000 captions that define over 330 thousand photos. This feature accelerates and creates good conditions for a high-quality job.
Keypoint recognition
COCO keypoint detection provides additional details about a segmented entity. They define a set of points of interest, the connections between them, the location of those points within the edge detection, and if they are visible or not. For instance of a person, "keypoints" denote various body parts.
Panoptic segmentation
The COCO panoptic segmentation annotator prepares an entire picture classification, highlighting objects in the image based on 80 categories of "things" (pillow, chair, washing machine, etc.) and 91 categories of "stuff" (water, roadway, etc.).
Dense pose
The dataset collection contains about 39,000 photos with over 56,000 tagged people, each with an individual identifier and a mapping across pixels indicating that person's body and a pattern 3D model. It gives the resources for annotators to work effectively and fast on your project.
Stuff image segmentation
Whereas Panoptic segmentation covers both “thing”(object) and “stuff” detection, Stuff image COCO segmentation focuses only on “stuff,” using those 91 categories pre-made in the COCO dataset.
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How to Outsource COCO Annotator Specialist
- Use the form below to leave your contacts. We’ll get back to you the same day.
- Share your project details with our team to define all goals and peculiarities of the needed annotation services for computer vision.
- Get an estimate regarding the work completion time and an offer from us.
- Pay the bill, and begin the cooperation with a trustworthy COCO annotator!
Our major strengths are developing long-term partnerships with our clients, taking an innovative approach to recruitment, and working hard every day to retain outstanding people.
Outsourcing object detection COCO providers understand the importance of delivering high-quality work for annotation projects as their reputation is at stake.
Our company is a reliable partner for more than 40 clients worldwide. We are proficient in recruiting and hiring qualified individuals for computer vision data labeling jobs. Reach us immediately, and we will provide the best specialist for your company’s needs!
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our benefits
Advantages of Using Our COCO Annotators
Our company has been a reliable source for brilliant remote IT specialists. We have more than 1,000 projects completed for clients from 15 countries. With this experience, we provide several benefits for our partners.
Reliability
We have a wide talent pool of world-class object labeling and annotation experts available to work on projects and tasks for large and small enterprises, whether it’s an image or video annotation for computer vision. That’s why if you need to find an expert in the short term, we’ll solve this challenge successfully.
Cost-effectiveness
In-house personnel is costly as, in addition to the higher salary, additional office space, and equipment, your company may require more paid supervisors to oversee the project’s team. We’re here to help you save money by providing low-cost yet highly-qualified remote specialists from Ukraine.
Сonfidence in an expected result
Our team provides exceptional annotation and data labeling services and support from the beginning to the end of the partnership. We assist all of our clients with onboarding and continuing assistance. This guarantees that partnerships are sustainable and productive.
HOW IT WORKS
Choose Your Cooperation Model For The Best Experience
When hiring in-house annotators, you have to ensure they have enough work and tasks. Otherwise, you’ll lose your money.
We can offer different collaboration formats based on your project needs, such as completion time, budget, and complexity.
You’ll be able to prolong any of the abovementioned cooperation if needed or finish it when the project is completed successfully.
Managed COCO annotator
You get a skillful specialist and fully rely on us to manage his work. Instead of directing an annotator, you get more time to execute tasks with higher priority.
Part-time COCO annotator specialist dedicated to your project
If your project requires solving the problem of burnout and overloading of your main team, a part-time specialist will be a perfect choice.
Full-time (dedicated) COCO annotator specialist
When your project is complex and has to be done in a strict and short time frame, it’d be better to hire a full-time specialist.
MORE INFO
Frequently Asked Questions:
Our project management will estimate deadlines based on your project requirements.
COCO is a visual dataset with many pictures displaying everyday items in various everyday scenarios. This distinguishes COCO from other object classification datasets that may focus on narrowly focused areas of artificial intelligence.
A COCO dataset comprises five formats that give information for the full dataset.
◉ info – information on the dataset in general.
◉ licensing – information about the licenses for the photos in the dataset.
◉ pictures – a list of visuals from the dataset.
◉ annotations – a list of annotations (including bounding boxes) found in all photos in the dataset.
- categories – a collection of label classifications.
COCO is a large-scale dataset for object tracking, classification, key-point detection, and captioning. There are 328 thousand of photos in the dataset.
Not only do you save money on office renting, paid vacation, and sick leave fees, you get highly-qualified and motivated professionals who work on a performance basis and have no communication barriers.