What is the COCO Dataset? What you need to know in 2023 - viso.ai (2023)

FAQs

What is the COCO Dataset? What you need to know in 2023 - viso.ai? ›

The large dataset comprises annotated photos of everyday scenes of common objects in their natural context. Those objects are labeled using pre-defined classes such as “chair” or “banana”. The process of labeling, also named image annotation and is a very popular technique in computer vision.

What is the COCO dataset? The COCO (Common Objects in Context) dataset is a large-scale image recognition dataset for object detection, segmentation, and captioning tasks. It contains over 330,000 images, each annotated with 80 object categories and 5 captions describing the scene.

MS COCO (Microsoft Common Objects in Context) is a large-scale image dataset containing 328,000 images of everyday objects and humans. The dataset contains annotations you can use to train machine learning models to recognize, label, and describe objects.

COCO Captions contains over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions are be provided for each image.

COCO is a large-scale object detection, segmentation, and captioning dataset. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students.

* Coco defines 91 classes but the data only uses 80 classes.

The dataset consists of three main parts: (1) Metadata; (2) UI events; (3) Network traces.

The Microsoft Common Objects in Context (COCO) dataset is the gold standard benchmark for evaluating the performance of state of the art computer vision models. COCO contains over 330,000 images, of which more than 200,000 are labelled, across dozens of categories of objects.

How to build COCO dataset? ›

The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is based on the MS COCO dataset, which contains images of complex everyday scenes. The images were not collected with text in mind and thus contain a broad variety of text instances.

COCO is a format for specifying large-scale object detection, segmentation, and captioning datasets. This Python example shows you how to transform a COCO object detection format dataset into an Amazon Rekognition Custom Labels bounding box format manifest file.

The COCO animals dataset has 800 training images and 200 test images of 8 classes of animals: bear, bird, cat, dog, giraffe, horse, sheep, and zebra.

The features of the COCO dataset are – object segmentation, context recognition, stuff segmentation, three hundred thirty thousand images, 1.5 million instances of the object, eighty categories of object, ninety-one categories of staff, five per image captions, 250,000 keynotes people. The size of the dataset is 25 GB.

Five COCO Annotation Types

According to cocodataset.org/#format-data: COCO has five annotation types: for object detection, keypoint detection, stuff segmentation, panoptic segmentation, and image captioning. The annotations are stored using JSON.

Generally speaking, the rule of thumb regarding machine learning is that you need at least ten times as many rows (data points) as there are features (columns) in your dataset. This means that if your dataset has 10 columns (i.e., features), you should have at least 100 rows for optimal results.

COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features: Object segmentation. Recognition in context. Superpixel stuff segmentation.

Finally, coming on the types of Data Sets, we define them into three categories namely, Record Data, Graph-based Data, and Ordered Data.

What are the 4 areas of data analysis? ›

Modern analytics tend to fall in four distinct categories: descriptive, diagnostic, predictive, and prescriptive.

COCO Annotator is a web-based image annotation tool designed for versatility and efficiently label images to create training data for image localization and object detection.

Image resolution: 640×480.

Objects in COCO

As written in the original research paper, there are 91 object categories in COCO.

Visualizing data in Three Dimensions (3-D)

Considering three attributes or dimensions in the data, we can visualize them by considering a pair-wise scatter plot and introducing the notion of color or hue to separate out values in a categorical dimension.

The MS COCO dataset has 81 classes, out of which only five are useful to us; these include carrot, broccoli, orange, apple and banana. We retrained the model by adding Pineapple to our database. Note: In order to implement the task, you will have to reproduce the Mask R CNN model.

Train the model

It takes ~6 minutes to train 300 iterations on Colab's K80 GPU. In case you switch to your own datasets, change the number of classes, learning rate, or max iterations accordingly.

How can I create my own dataset? ›

The COCO dataset and benchmark are used in a wide range of AI vision tasks and disciplines. Models trained on COCO are used for object detection, people detection, face detection, pose estimation, and many more computer vision tasks.

Optical Character Recognition (OCR) is used to analyze text in images.

COCO (Microsoft Common Objects in Context)

Splits: The first version of MS COCO dataset was released in 2014.

It is widely used to benchmark the performance of computer vision methods. Due to the popularity of the dataset, the format that COCO uses to store annotations is often the go-to format when creating a new custom object detection dataset.

Appearance. Coco is a light brown rabbit with dark speckles and darker brown legs. She has empty black eyes and a mouth with an apparently hollow head, referencing the appearance of gyroids.

One of the most charming (and funny) characters in Pixar's new movie “Coco” is Dante the dog, a stray who accompanies the main character, Miguel on his journey into the Land of the Dead. Dante is a Xolo dog—short for Xoloitzcuintli—the national dog of Mexico.

Coco & Eve is cruelty-free. Coco & Eve has confirmed that it is truly cruelty-free. They don't test finished products or ingredients on animals, and neither do their suppliers or any third-parties. They also don't sell their products where animal testing is required by law.

The most common way to define whether a data set is sufficient is to apply a 10 times rule. This rule means that the amount of input data (i.e., the number of examples) should be ten times more than the number of degrees of freedom a model has. Usually, degrees of freedom mean parameters in your data set.

Description – Digi-Face 1M is the largest scale synthetic dataset for face recognition that is free from privacy violations and lack of consent. Licensing – The Digi-Face 1M dataset is available for non-commercial research purposes only.

Does the size of dataset matter? ›

The Size of a Data Set

As a rough rule of thumb, your model should train on at least an order of magnitude more examples than trainable parameters. Simple models on large data sets generally beat fancy models on small data sets. Google has had great success training simple linear regression models on large data sets.

The data annotation process involves a series of well-defined steps to ensure high-quality and accurate data labeling for machine learning applications. These steps cover every aspect of the process, from data collection to exporting the annotated data for further use.

A data set consists of roughly two components. The two components are rows and columns. Additionally, a key feature of a data set is that it is organized so that each row contains one observation.

A data set is an ordered collection of data. As we know, a collection of information obtained through observations, measurements, study, or analysis is referred to as data. It could include information such as facts, numbers, figures, names, or even basic descriptions of objects.

The “COCO format” is a specific JSON structure dictating how labels and metadata are saved for an image dataset. Many blog posts exist that describe the basic format of COCO, but they often lack detailed examples of loading and working with your COCO formatted data.

Hence, information systems can be viewed as having six major components: hardware, software, network communications, data, people, and processes. Each has a specific role, and all roles must work together to have a working information system.

Four Elements of Data: Volume, velocity, variety, and veracity – Effective Database Management.

There are two types of categorical data sets: dichotomous and polytomous.