https://github.com/suhani1209/mobile-gallery-image-classifier

Distinguishes the mobile gallery's images into various sets such as whatsapp documents, nature, cars, screenshots, the machine learns itself and predicts the results with an accuracy of 97.6%
https://github.com/suhani1209/mobile-gallery-image-classifier

cnn deep-learning deep-neural-networks image-classification object-classification python

Last synced: about 12 hours ago
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Distinguishes the mobile gallery's images into various sets such as whatsapp documents, nature, cars, screenshots, the machine learns itself and predicts the results with an accuracy of 97.6%

• Host: GitHub
• URL: https://github.com/suhani1209/mobile-gallery-image-classifier
• Owner: suhani1209
• Created: 2020-10-21T12:52:13.000Z (about 4 years ago)
• Default Branch: main
• Last Pushed: 2021-09-03T08:46:04.000Z (about 3 years ago)
• Last Synced: 2024-03-23T17:33:46.513Z (8 months ago)
• Topics: cnn, deep-learning, deep-neural-networks, image-classification, object-classification, python
• Language: Jupyter Notebook
• Homepage:
• Size: 1.46 MB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Mobile gallery image classifier

Everyone often finds their gallery to be messed up, with all the images mixed up together ,whether  it’s any kind of information or any important documents,images or notes etc. This becomes a  problem when ever you want any image then you have to look through the whole gallery and  search out for that image and if you found it on time it’s your luck .This often poses a problem  when anyone by chance delete any important images thinking it to be of waste. Everyone wants  their images to be sorted and organized in such a way that whenever they search a particular  image they can get it easily in fraction of time. In this project a Deep Learning (DL) based gallery  image classification method is provided which can classify a given set of images into different  categories such as trees, machines, memes etc. & hence make our phone gallery a better place to  visit. The DL method used in this project is the Convolutional Neural Network (CNN). The dataset used for training consists of 6 categories –Whatsapp Screenshots, Memes, Selfies, Cars,  Mountains and trees. The accuracy of the prediction is 97.6%.