How to analyze The COCO Dataset for Pose Estimation

You need a dataset for training pose estimation models, but what’s the best choice?
There are public datasets like COCO, MPII, and CrowdPose. Not much, if we compare it to the number of publicly available datasets for different computer vision tasks, like object detection or classification.
The pose estimation problem belongs to a category of rather complex problems. Building a suitable dataset for neural network models is hard. Every joint of every person in the image has to be located and tagged. That’s a mundane and time-consuming task.
The most popular dataset for pose estimation is the COCO dataset. It has around 80 categories of images and around 250,000 instances of people.
If you check some random images from this dataset, you may come across instances that are irrelevant to the problem you are going to solve. Reaching the highest level of precision is desirable in academia, but not always in real-world, production environments.
In the real world, we may be more interested in training models that work well in very specific environments, such as pedestrians, basketball players, gym sessions, etc.

#sklearn #coco-dataset #deep-learning #pose-estimation #pandas

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How to analyze The COCO Dataset for Pose Estimation

Dance on Human Pose Estimation Using Artificial Intelligence

Dance on Human Pose Estimation Using Artificial Intelligence with Complete Tutorial & Source Code Download Free.

A Human Pose Skeleton speaks to the direction of an individual in a graphical organization. Basically, it is a bunch of directions that can be associated with depict the posture of the individual. Every co-ordinate within the skeleton is understood as a neighborhood or a joint, or a keypoint. A substantial association between two sections is known as a couple.

#projects #artificial intelligence #ai based dance on human pose estimation #dance on human pose estimation #dance on human pose estimation ai project #dance on human pose estimation using artificial intelligence #download code dance on human pose estimation ai project

How to analyze The COCO Dataset for Pose Estimation

You need a dataset for training pose estimation models, but what’s the best choice?
There are public datasets like COCO, MPII, and CrowdPose. Not much, if we compare it to the number of publicly available datasets for different computer vision tasks, like object detection or classification.
The pose estimation problem belongs to a category of rather complex problems. Building a suitable dataset for neural network models is hard. Every joint of every person in the image has to be located and tagged. That’s a mundane and time-consuming task.
The most popular dataset for pose estimation is the COCO dataset. It has around 80 categories of images and around 250,000 instances of people.
If you check some random images from this dataset, you may come across instances that are irrelevant to the problem you are going to solve. Reaching the highest level of precision is desirable in academia, but not always in real-world, production environments.
In the real world, we may be more interested in training models that work well in very specific environments, such as pedestrians, basketball players, gym sessions, etc.

#sklearn #coco-dataset #deep-learning #pose-estimation #pandas

Inside ABCD, A Dataset To Build In-Depth Task-Oriented Dialogue Systems

According to a recent study, call centre agents’ spend approximately 82 percent of their total time looking at step-by-step guides, customer data, and knowledge base articles.

Traditionally, dialogue state tracking (DST) has served as a way to determine what a caller wants at a given point in a conversation. Unfortunately, these aspects are not accounted for in popular DST benchmarks. DST is the core part of a spoken dialogue system. It estimates the beliefs of possible user’s goals at every dialogue turn.

To reduce the burden on call centre agents and improve the SOTA of task-oriented dialogue systems, AI-powered customer service company ASAPP recently launched an action-based conversations dataset (ABCD). The dataset is designed to help develop task-oriented dialogue systems for customer service applications. ABCD consists of a fully labelled dataset with over 10,000 human dialogues containing 55 distinct user intents requiring sequences of actions constrained by company policies to accomplish tasks.

https://twitter.com/asapp/status/1397928363923177472

The dataset is currently available on GitHub.

#developers corner #asapp abcd dataset #asapp new dataset #build enterprise chatbot #chatbot datasets latest #customer support datasets #customer support model training #dataset for chatbots #dataset for customer datasets

Kolby  Wyman

Kolby Wyman

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Getting started with COCO dataset

Introduction

COCO (official website) dataset, meaning “Common Objects In Context”, is a set of challenging, high quality datasets for computer vision, mostly state-of-the-art neural networks. This name is also used to name a format used by those datasets.

Quoting COCO creators:

COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features:

- Object segmentation

- Recognition in context

- Superpixel stuff segmentation

- 330K images (>200K labeled)

- 1.5 million object instances

- 80 object categories

Format of this dataset is automatically understood by advanced neural network libraries, e. g. Facebook’s Detectron2 (link). There are even tools built specifically to work with datasets in COCO format, e. g. COCO-annotator and COCOapi. Understanding how this dataset is represented will help with using and modifying the existing datasets and also with creating the custom ones. Specifically, we are interested in annotationsfiles, since complete dataset consists of images directory and annotation file, providing metadata used by machine learning algorithms.

What can you do with COCO?

There are actually multiple COCO datasets, each one made for a specific machine learning task, with additional data. The 3 most popular tasks are:

  • object detection — model should get bounding boxes for objects, i. e. return list of object classes and coordinates of rectangles around them; objects (also called “things”) are discrete, separate objects, often with parts, like humans and cars; the official dataset for this task also contains additional data for object segmentation (see below)

#machine-learning #ai #coco #dataset #computer-vision

How to do Pose Estimation With MoveNet

Using computer vision, we can understand how the images and videos are stored and manipulated, and also it helps us retrieve data from images and videos. Computer vision is part of artificial intelligence; it plays a major role in autonomous vehiclesobject detection, robotics, and application. It is an open-source library mainly used for image processing and machine learning. It gives better output for real-time data. We can process images and videos so that implemented algorithms can identify objects such as statues, pedestrians, animals, vehicles, human faces and so on. Moreover, with the help of other data analysis libraries, it can process images and videos according to one’s desires.

Today in this article, we will use OpenCV for pose estimation and the newly launched google model for pose estimation, i.e., MoveNet.

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What is Pose Estimation?

Human pose estimation is a CV technique used to predict a person’s body parts or joints position. This can be done by defining the human body joints like wrist, shoulder, knees, eyes, ears, ankles, arms, also called key points in images and videos. Then, when a picture or video comes in as input to the pose estimator model, it identifies the coordinates of those detected body parts as output and a confidence score indicating continuity of the estimations.

At this time, we have two types of pose estimation, i.e. 2D and 3D. 2D involves the extraction of X, Y coordinates for each key point in the RGB image, whereas 3D involves X, Y, Z coordinates of each key point. Google’s MoveNet model is based on 3D estimation. The operation takes place in a phase-wise manner like; first, the RGB image is fed to convolutional network as input, then pose model is applied to detect the poses, key points, pose confidence score and key point confidence score from the model outputs.

Let’s see briefly what exactly the estimator returns when the inference takes place;

Poses:

The estimator returns a pose object with a complete list of key points and an instance-level confidence score for a detected person.

Key point:

It contains the estimated parts of a person: nose, eyes, ears with coordinate position, and key point confidence score.

Confidence score:

This value indicates the overall confidence in the estimated person’s pose and key points from the image with values between 0 and 1 based on which model decides which one is to be shown and which one is hidden.

The below shows the 17 points that the pose estimator can identify.

#developers corner #deep learning #gesture recognition #movenet #opencv #pose estimation #transfer learning