CV-TRICKS . COM {
}
Detected CMS Systems:
- Wordpress (251 occurrences)
Title:
Zero to Hero: Guide to Object Detection using Deep Learning: Faster R-CNN,YOLO,SSD – CV-Tricks.com
Description:
In this post, I shall explain object detection and various algorithms like Faster R-CNN, YOLO, SSD. We shall start from beginners
Website Age:
8 years and 6 months (reg. 2016-12-21).
Matching Content Categories {📚}
- Photography
- TV
- Virtual Reality
Content Management System {📝}
What CMS is cv-tricks.com built with?
Cv-tricks.com operates using WORDPRESS.
Traffic Estimate {📈}
What is the average monthly size of cv-tricks.com audience?
🚦 Initial Traffic: less than 1k visitors per month
Based on our best estimate, this website will receive around 119 visitors per month in the current month.
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How Does Cv-tricks.com Make Money? {💸}
We're unsure if the website is profiting.
Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Cv-tricks.com might have a hidden revenue stream, but it's not something we can detect.
Wordpress Themes and Plugins {🎨}
What WordPress theme does this site use?
What WordPress plugins does this website use?
Keywords {🔍}
object, detection, image, rcnn, class, boxes, problem, bounding, yolo, ssd, classification, box, size, network, window, convolutional, accuracy, cnn, classifier, location, fasterrcnn, faster, objects, run, region, learning, predicts, predict, aspect, selective, search, layer, features, popular, methods, sizes, detector, ratio, generated, regression, pooling, understanding, takes, called, single, neural, fixed, input, simple, small,
Topics {✒️}
regression-based object detectors simple back-propagation calculation max-pooling gradient calculation multi-label classifier convolutional neural network faster r-cnn introduces real-world problems called object detection neural network training called bounding box perform back-propagation single shot detector run real time convolutional layer operates bounding box regression cnn based classifier haar cascades proposed support vector machine avoiding false positives multiple convolutional layers fixed sized input longer requires training selective search takes apply spatial pooling fully connected layers explain object detection object detection starting implementing object detection fully connected part contextual information helps sliding window detector run selective search r-cnn solves learn machine learning pooling regions overlap classification/regression heads low confidence scores spatial pooling layer quick complete tutorial real time train object detectors modeled object detection object detection method simple regression problem preserving aspect ratio bounding box coordinates faster r-cnn constant size vector spp-net paved chosen window size
Questions {❓}
- And How does it achieve that?
- How do you know the size of the window so that it always contains the image?
- So, what did Faster RCNN improve?
- Sounds simple?
- What is Image Classification?
- What size do you choose for your sliding window detector?
- What would our model predict?
- Which one should you use?
- Why do we have so many methods and what are the salient features of each of these?
Social Networks {👍}(3)
Libraries {📚}
- AOS
- Bootstrap
- jQuery
- jQuery module (jquery)
- jQuery module (jquery-migrate)
- Parallax.js
Emails and Hosting {✉️}
Mail Servers:
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- yrusomf75us7t7y6rnw62isbcvjly4brstda3m5tjct2bkekt3ta.mx-verification.google.com
Name Servers:
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