Why is Object Detection so Messy?

Why is Object Detection so Messy?

Why is Object Detection so Messy? The downside is its high memory cost and lower detection accuracy. Each box consumes memory proportional to the number of classes, and the number of boxes grows quadratically with the image resolution. This hunger can be quite costly when there are many classes and a high input resolution

Those working with Neural Networks know how complicated Object Detection techniques can be. It is no wonder there is no straight forward resource for training them. You are always required to convert your data to a COCO-like JSON or some other unwanted format. It is never a plug and play experience. Moreover, no diagram thoroughly explains Faster R-CNN or YOLO as there is for U-Net or ResNet. There are just too many details.

While these models are quite messy, the explanation for their lack of simplicity is quite straight forward. It fits in a single sentence:

Neural Networks have fixed-sized outputs

In object detection, you can’t know _a priori _how many objects there are in a scene. There might be one, two, twelve, or none. The following images all have the same resolution but feature different numbers of objects.

The one million dollar question is: _How can we build variable-sized outputs out of fixed-sized networks? _Plus, how are we supposed to train a variable number of answers and loss terms? How can we penalize wrong predictions?

Implementing Variable Sized Predictions

To create outputs that vary in size, two approaches dominate the literature: the “one size fits all” approach, an output so broad that it suffices for all applications, and the “look-ahead” idea, we search for regions-of-interest, and then we classify them.

I just made up those terms 😄. In practice, they are known as “one-stage” and “two-stage” approaches, which is a tad less self-explanatory.

One Stage Approaches

Overfeat, YOLO, SSD, RetinaNet, etc.

If we can’t have variable-sized outputs, we shall return an output so large that it will always be larger than what we need, then we can prune the excess

neural-networks machine-learning data-science artificial-intelligence object-detection

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

How to Find Ulimit For user on Linux

Explains how to find ulimit values of currently running process or given user account under Linux using the 'ulimit -a' builtin command.

Most popular Data Science and Machine Learning courses — July 2020

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Artificial Intelligence vs Machine Learning vs Data Science

Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.

Emotion Detection Model with Machine Learning | Data Science | Machine Learning | Python

In this article, I will take you through am Emotion Detection Model with Machine Learning. Detection of emotions means recognizing the

AI(Artificial Intelligence): The Business Benefits of Machine Learning

Enroll now at CETPA, the best Institute in India for Artificial Intelligence Online Training Course and Certification for students & working professionals & avail 50% instant discount.