Oleta  Becker

Oleta Becker

1601517600

Feature pyramid network for image classification

Object detection is one of the main problems in computer vision that may fail when there are multi-scale objects in images. Using feature pyramids helps to solve this problem.

Some previous studies tried to use different kinds of feature pyramids to improve object detection. One method fed various sizes of the input image to the deep network to see objects with different scales. This way also helped improve object detection but increases computational costs and processing time so much that it is not efficient.

Feature pyramid network(FPN) was introduced by Tsung-Yi Lin et al., which enhanced object detection accuracy for deep convolutional object detectors. FPN solves this problem by generating a bottom-up and a top-down feature hierarchy with lateral connections from the network’s generated features at different scales. This helps the network generate more semantic features, so using FPN helps increase detection accuracy when there are objects with various scales in the image while not changing detection speed.

_Here, I aim to introduce a new architecture based on FPN to improve classification accuracy. This architecture is proposed in my _paper.

As described, FPN helps extract multi-scale features from the input image, which better presents objects with different scales. We have designed an architecture that utilizes FPN to understand better the important parts of the image that could exist in different sizes.

In the next figure, you can see our proposed architecture. This architecture was developed for classifying the patient CT scan images into normal and COVID-19. Researchers can modify this architecture for using on different datasets and classes.

Image for post

#image-classification #neural-networks #classification #deep-learning #machine-learning

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Feature pyramid network for image classification
Oleta  Becker

Oleta Becker

1601517600

Feature pyramid network for image classification

Object detection is one of the main problems in computer vision that may fail when there are multi-scale objects in images. Using feature pyramids helps to solve this problem.

Some previous studies tried to use different kinds of feature pyramids to improve object detection. One method fed various sizes of the input image to the deep network to see objects with different scales. This way also helped improve object detection but increases computational costs and processing time so much that it is not efficient.

Feature pyramid network(FPN) was introduced by Tsung-Yi Lin et al., which enhanced object detection accuracy for deep convolutional object detectors. FPN solves this problem by generating a bottom-up and a top-down feature hierarchy with lateral connections from the network’s generated features at different scales. This helps the network generate more semantic features, so using FPN helps increase detection accuracy when there are objects with various scales in the image while not changing detection speed.

_Here, I aim to introduce a new architecture based on FPN to improve classification accuracy. This architecture is proposed in my _paper.

As described, FPN helps extract multi-scale features from the input image, which better presents objects with different scales. We have designed an architecture that utilizes FPN to understand better the important parts of the image that could exist in different sizes.

In the next figure, you can see our proposed architecture. This architecture was developed for classifying the patient CT scan images into normal and COVID-19. Researchers can modify this architecture for using on different datasets and classes.

Image for post

#image-classification #neural-networks #classification #deep-learning #machine-learning

Lane  Sanford

Lane Sanford

1591903440

Preprocessing your images for machine learning (image recognition)

During my studies at JKU there was a task for preprocessing images for a machine learning project. It is necessary to clean the raw images before using them in a learning algorithm, so thats why we create a pre-processing function. I think it can be quite useful for others as well so I want to share a bit of my approach. The file is structured in a way that it is easy to understand and also should have a tutorial-like effect.

#image-recognition #image #image-classification #machine-learning #image-processing

Angela  Dickens

Angela Dickens

1598554500

Satellite image classification with a convolutional neural network.

My latest project at Flatiron was to use neural networks to classify satellite image tiles. I chose to use a convolutional neural network (CNN) and create a dataset of webscraped images to train the model with. This will just be a quick rundown of what went into the project with additional links to my articles to more of the technical parts. This way, it can help to familiarize you with the topics or help to share more about my work with those who have similar interests in computer vision and machine learning.

I chose to use a CNN because I read some school lessons on computer vision about how a CNN has advantages with image classification. A CNN uses pooling layers that filter through patches of the image pixels, finding common patterns, which develop into more complex patterns in order to help determine image class. I chose to work on a computer vision project with satellite images because there are possible use cases for solutions on Earth as well as use cases on other planets. I’ve read articles about organizations looking at different geological patterns on the Mars surface in search of the possible presence of water or perhaps its pre-existence on the planet. This led me to try and build the model to recognize river delta patterns here on Earth, with the next step being to train the model and locate delta patterns on Mars. The model could also eventually be useful for looking at changes to river deltas on Earth, for possible use in agriculture, climate change or even real estate. For now, the project is ongoing as of my writing this blog post, with training and testing performed on the Earth images. The Mars images will be the next part I’ll begin after graduation.

A land image tile.

A river delta image tile.

A Mars delta image tile.

In order to obtain the images for a dataset, I looked into some different API’s and webscraping with Beautiful Soup. Afterwards, I decided to use Selenium to scrape some images from an image search on Google. This method was able to scroll through the page interactively, which was necessary in order to have access to all of the images. I wrote a separate article about that process here. This method was useful as a starting point, in order to go through the process of building the dataset, creating the model, training, testing and just getting everything to work. The disadvantage was that there were a lot of images that were not clean, contained pieces of text or other image artifacts and overall led to less accurate results. There are some example images below so you can see what I mean. I do not claim copyright to any of the used images as they were used for an educational project for school and will remove them if anyone objects to their display on my article.

#convolutional-network #computer-vision #python #image-classification #machine-learning #neural networks

I am Developer

1597565398

Laravel 7/6 Image Validation

In this image validation in laravel 7/6, i will share with you how validate image and image file mime type like like jpeg, png, bmp, gif, svg, or webp before uploading image into database and server folder in laravel app.

https://www.tutsmake.com/image-validation-in-laravel/

#laravel image validation #image validation in laravel 7 #laravel image size validation #laravel image upload #laravel image validation max #laravel 6 image validation

Ahebwe  Oscar

Ahebwe Oscar

1620200340

how to integrate CKEditor in Django

how to integrate CKEditor in Django

Welcome to my Blog, in this article we learn about how to integrate CKEditor in Django and inside this, we enable the image upload button to add an image in the blog from local. When I add a CKEditor first time in my project then it was very difficult for me but now I can easily implement it in my project so you can learn and implement CKEditor in your project easily.

how to integrate CKEditor in Django

#django #add image upload in ckeditor #add image upload option ckeditor #ckeditor image upload #ckeditor image upload from local #how to add ckeditor in django #how to add image upload plugin in ckeditor #how to install ckeditor in django #how to integrate ckeditor in django #image upload in ckeditor #image upload option in ckeditor