COCO is a visual data set that plays an important role in computer vision. In this article, we'll cover everything you need to know about Microsoft's popular COCO dataset, which is widely used for machine learning projects. Learn what you can do with MS COCO and what makes it different from COCO alternatives like Google's OID (Open Images Dataset).
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The COCO data set
The MS COCO dataset is a large-scale datasetobject detection,image segmentationand closed captioning data set published by Microsoft.machine learningand the COCO data set is popularly used by computer vision engineers for various computer vision projects.
Understanding visual scenes is the main goal of computer vision; It involves recognizing which objects are present, locating objects in 2D and 3D, determining the attributes of objects, and characterizing the relationship between objects. Therefore, the algorithms forobject detectionand the object classification can be trained using the data set.
What is COCO?
COCO stands for Common Objects in Context as the image dataset was created with the goal of advancingimage recognition. The COCO dataset contains high-quality and challenging visual datasets for computer vision, primarily state-of-the-art neural networks.
For example, COCO is often used to benchmark algorithms to compare real-time object detection performance. The format of the COCO data set is automatically interpreted by advancedneural networkslibraries
Characteristics of the COCO data set
- Object segmentation with detailed instance annotations
- Recognition in context
- Segmentation of superpixel things
- More than 200,000 images out of a total of 330,000 images are tagged
- 1.5 million object instances
- 80 categories of objects, the “COCO classes”, which include “things” for which individual instances can be easily tagged (person, car, chair, etc.)
- 91 categories of things, where “COCO things” include materials and objects without clear boundaries (sky, street, grass, etc.) that provide meaningful contextual information.
- 5 captions per image
- 250,000 people with 17 different key points, popularly used forpose estimation
List of COCO object classes
The COCO dataset classes for object detection and tracking include the following 80 pretrained objects:
'person', 'bicycle', 'car', 'motorcycle', 'plane', 'bus', 'train', 'truck', 'ship', 'traffic light', 'fire hydrant', 'stop sign ' , 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'purse', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'sports glove' 'baseball', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'glass', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'sofa', ' potty', 'bed', 'dining table', 'bathroom', 'tv', 'laptop', 'mouse', 'remote control', 'keyboard', 'mobile phone', 'microwave', 'oven ', 'toaster', 'sink', 'fridge', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair dryer', 'toothbrush'
COCO Key Points List
COCO keypoints include 17 different pretrained keypoints (classes) that are annotated with three values (x, y, v). The x and y values mark the coordinates and v indicates the visibility of the key point (visible, not visible).
"nose", "left_eye", "right_eye", "left_ear", "right_ear", "left_shoulder", "right_shoulder", "left_elbow", "right_elbow", "left_wrist", "right_wrist", "left_hip", "right_hip" ", "joelho_esquerdo", "joelho_direito", "tornozelo_esquerdo", "tornozelo_direito"
Annotated COCO images
The large data set includes annotated photos of everyday scenes of common objects in their natural context. These objects are tagged using predefined classes such as "chair" or "banana". The labeling process, also calledimage annotationand it is a very popular technique in artificial vision.
While other object recognition datasets focus on 1) image classification, 2) object bounding box placement, or 3) semantic segmentation at the pixel level, the mscoco dataset focuses on 4 ) segmentation of individual object instances.
Why common objects in natural context?
For many categories of objects, iconic views are available. For example, when doing a web-based image search for a specific object category (eg "chair"), the top-ranked examples appear in the profile, unobstructed and near the center of a well-arranged photo. See example images below.
While image recognition systems generally work well on these iconic views, they have difficulty recognizing objects in real-life scenes that show a complex scene or partially occlude the object. Therefore, it is an essential aspect of coconut images that contain natural images that contain multiple objects.
How to use the COCO dataset
Is the COCO dataset free to use?
Yes, the MS COCO image dataset is licensed under a Creative Commons Attribution 4.0 license. So this license allows youdistribute, remix, modify and develop your work, even commercially, as long as you credit the original creator.
How to download the COCO dataset
There are different divisions of data sets available for free download. Each year's images are associated with different tasks, such as object detection, keypoint tracking, image captions, and more.
To download and view the latest Microsoft COCO 2020 challenges, visit theMS COCO official site. To download COCO images efficiently, it is recommended to usegsutil rsyncto avoid downloading large zip files. you can use theCOCONUT APIto configure the downloaded COCO data.
COCO recommends using the open source toolFifty-oneto access the MSCOCO dataset to build computer vision models.
COCO vs. open image data set (OID)
A popular alternative to the COCO dataset is the Open Image Dataset (OID), created by Google. It is essential to understand and compare COCO and OID visual data sets with their differences before using one for projects to optimize all available resources.
Open Image Data Set (OID)
What makes it unique?Googleannotated all the picturesin the OID dataset with image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives. This allows it to be used for slightly more computer vision tasks compared to COCO due to its slightly broader annotation system. EITHERhomepage of OIDit also claims that it is the largest existing dataset with object location annotations.
Data.Open Images is a data set of approximately 9 million previously annotated images. Most, if not all, of the images in Google's Open Image Dataset were manually annotated by professional image annotators. This ensures the accuracy and consistency of each image and leads to higher accuracy rates forcomputer vision applicationswhen in use.
Common objects in context (COCO)
What makes it unique?With COCO, Microsoft introduced a visual dataset containing a large number of photos that represent common objects in complex everyday scenes. This sets COCO apart from other object recognition data sets that may be AI-specific sectors. Such sectors includeimage rating, location of the object's bounding box, or semantic segmentation at the pixel level.
Meanwhile, COCO annotations are mainly focused on targeting multiple instances of individual objects. This broader approach allows COCO to be used in more instances than other popular datasets likeCIFAR-10 is CIFAR-100. However, compared to the OID dataset, COCO doesn't stand out much, and in most cases, both can be used.
Data.With 2.5 million tagged instances in 328k images, COCO is a very large and expansive dataset that allows for many uses. However, this value doesn't compare to Google's OID, which contains a whopping 9 million annotated images.
Google's 9 million annotated images weremanually annotated, while the OID reveals that it generated object bounding boxes and segmentation masks using automated and computerized methods. Both COCO and OID do not reveal the precision of the bounding box, so it is up to the user to decide whether to assume that automated bounding boxes would be more accurate than manual ones.
The COCO dataset and benchmark are used in a wide range of AI vision disciplines and tasks. COCO-trained models are used for object detection, person detection,face detection,pose estimate, and many other machine vision tasks.
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