A python-image-classification web application project, written in Python and served through the Flask Microframework

Overview

Image Classification in Python

Implementing image classification in Flask using Keras.

The VGG16 is a convolution neural network model architecture that is the best classifier for images till today. This project implements the classifier through Keras. This appplication was written in Python. It also utilizes the Flask Microframework, as the server to render a template, and pass the submitted file to the prediction model. It also uses Keras modules to perform image classification.

Requirements

  • Python 3.5+
  • Pip (For installing modules)
  • Flask Download
  • Tensorflow
  • Keras Modules

Installation

(Plain Set Up)

Go ahead an clone this specific repository. You could do this through the command below:

> git clone https://github.com/grayoj/Python-Image-Classification.git

Then navigate to the directory. If you use VSCode, you could avoid interacting with the terminal.

Run the following commands in the directory:

> pythom -m flask run

(Full Set Up)

Go ahead an clone this specific repository. You could do this through the command below:

> git clone https://github.com/grayoj/Python-Image-Classification.git

To install Python, download here. If you already have Python 3.5 installed, you may proceed to the next steps below:

You will notice this line of code in the app.py file:

To ensure the modules would be imported on your system, into the project, run the following command:

> pip install flask

That would install flask on your local machine. Next step is to install the Keras Modules, and packages required. Run the following command:

> pip install keras

If you use Pylance, it should validate the imports above in the app.py. i.e show no errors, of modules missing. Modules being installed:

You would have to install Tensorflow as well.

> pip install tensorflow

You are set. Now let's dial in to a localhost port.

> python -m flask run

Viola, the application should load sucessfully. If there are any errors, ensure you installed the modules properly.

Http://127.0.0.1:5000 

Don't worry if you notice a sudden download process. Tensorflow would begin to download dependencies for the VGG16 Covolutional Neural network model.

Prediction in action

Now, the fun part. This should have loaded open:

Let's see whether our model can predict what this animal is: The picture used is in the repository. You could use other images, and have fun.

Now let's input it

Click on predict

Our model predicted a tiger cat! Epic.

More Information

Reach out to me if you have questions or suggestions. Would love to connect.


Twitter: @geraldabuchi

Python-Image-Classification

Flask, Keras, Tensorflow, VGG16

You might also like...
A real world application of a Recurrent Neural Network on a binary classification of time series data
A real world application of a Recurrent Neural Network on a binary classification of time series data

What is this This is a real world application of a Recurrent Neural Network on a binary classification of time series data. This project includes data

Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)
Web-interface + rest API for classification and regression (https://jeff1evesque.github.io/machine-learning.docs)

Machine Learning This project provides a web-interface, as well as a programmatic-api for various machine learning algorithms. Supported algorithms: S

A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.
A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku.

Automatic_Background_Remover A Web API for automatic background removal using Deep Learning. App is made using Flask and deployed on Heroku. 👉 https:

Music library streaming app written in Flask & VueJS

djtaytay This is a little toy app made to explore Vue, brush up on my Python, and make a remote music collection accessable through a web interface. I

Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches
Framework for joint representation learning, evaluation through multimodal registration and comparison with image translation based approaches

CoMIR: Contrastive Multimodal Image Representation for Registration Framework 🖼 Registration of images in different modalities with Deep Learning 🤖

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

Xcessiv Xcessiv is a tool to help you create the biggest, craziest, and most excessive stacked ensembles you can think of. Stacked ensembles are simpl

This application explain how we can easily integrate Deepface framework with Python Django application

deepface_suite This application explain how we can easily integrate Deepface framework with Python Django application install redis cache install requ

In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.
In this project we use both Resnet and Self-attention layer for cat, dog and flower classification.

cdf_att_classification classes = {0: 'cat', 1: 'dog', 2: 'flower'} In this project we use both Resnet and Self-attention layer for cdf-Classification.

An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)
An end-to-end machine learning web app to predict rugby scores (Pandas, SQLite, Keras, Flask, Docker)

Rugby score prediction An end-to-end machine learning web app to predict rugby scores Overview An demo project to provide a high-level overview of the

Comments
  • Add CodeQL workflow for GitHub code scanning

    Add CodeQL workflow for GitHub code scanning

    Hi grayoj/image-classifier!

    This is a one-off automatically generated pull request from LGTM.com :robot:. You might have heard that we’ve integrated LGTM’s underlying CodeQL analysis engine natively into GitHub. The result is GitHub code scanning!

    With LGTM fully integrated into code scanning, we are focused on improving CodeQL within the native GitHub code scanning experience. In order to take advantage of current and future improvements to our analysis capabilities, we suggest you enable code scanning on your repository. Please take a look at our blog post for more information.

    This pull request enables code scanning by adding an auto-generated codeql.yml workflow file for GitHub Actions to your repository — take a look! We tested it before opening this pull request, so all should be working :heavy_check_mark:. In fact, you might already have seen some alerts appear on this pull request!

    Where needed and if possible, we’ve adjusted the configuration to the needs of your particular repository. But of course, you should feel free to tweak it further! Check this page for detailed documentation.

    Questions? Check out the FAQ below!

    FAQ

    Click here to expand the FAQ section

    How often will the code scanning analysis run?

    By default, code scanning will trigger a scan with the CodeQL engine on the following events:

    • On every pull request — to flag up potential security problems for you to investigate before merging a PR.
    • On every push to your default branch and other protected branches — this keeps the analysis results on your repository’s Security tab up to date.
    • Once a week at a fixed time — to make sure you benefit from the latest updated security analysis even when no code was committed or PRs were opened.

    What will this cost?

    Nothing! The CodeQL engine will run inside GitHub Actions, making use of your unlimited free compute minutes for public repositories.

    What types of problems does CodeQL find?

    The CodeQL engine that powers GitHub code scanning is the exact same engine that powers LGTM.com. The exact set of rules has been tweaked slightly, but you should see almost exactly the same types of alerts as you were used to on LGTM.com: we’ve enabled the security-and-quality query suite for you.

    How do I upgrade my CodeQL engine?

    No need! New versions of the CodeQL analysis are constantly deployed on GitHub.com; your repository will automatically benefit from the most recently released version.

    The analysis doesn’t seem to be working

    If you get an error in GitHub Actions that indicates that CodeQL wasn’t able to analyze your code, please follow the instructions here to debug the analysis.

    How do I disable LGTM.com?

    If you have LGTM’s automatic pull request analysis enabled, then you can follow these steps to disable the LGTM pull request analysis. You don’t actually need to remove your repository from LGTM.com; it will automatically be removed in the next few months as part of the deprecation of LGTM.com (more info here).

    Which source code hosting platforms does code scanning support?

    GitHub code scanning is deeply integrated within GitHub itself. If you’d like to scan source code that is hosted elsewhere, we suggest that you create a mirror of that code on GitHub.

    How do I know this PR is legitimate?

    This PR is filed by the official LGTM.com GitHub App, in line with the deprecation timeline that was announced on the official GitHub Blog. The proposed GitHub Action workflow uses the official open source GitHub CodeQL Action. If you have any other questions or concerns, please join the discussion here in the official GitHub community!

    I have another question / how do I get in touch?

    Please join the discussion here to ask further questions and send us suggestions!

    opened by lgtm-com[bot] 0
Releases(v1.0.0)
Owner
Gerald Maduabuchi
Computer Programmer. ML Enthusiast C++, Python, PHP and JavaScript
Gerald Maduabuchi
Data and analysis code for an MS on SK VOC genomes phenotyping/neutralisation assays

Description Summary of phylogenomic methods and analyses used in "Immunogenicity of convalescent and vaccinated sera against clinical isolates of ance

Finlay Maguire 1 Jan 06, 2022
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation Demo video: CVPR 2021 Oral: Single Channel Manipulation: Localized or attribu

Zongze Wu 267 Dec 30, 2022
This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning

This is the code for Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning It includes /bert, which is the original BERT repos

Mitchell Gordon 11 Nov 15, 2022
JupyterLite demo deployed to GitHub Pages 🚀

JupyterLite Demo JupyterLite deployed as a static site to GitHub Pages, for demo purposes. ✨ Try it in your browser ✨ ➡️ https://jupyterlite.github.io

JupyterLite 223 Jan 04, 2023
Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

Nicely is a real-time Feedback and Intervention Program Depression is a prevalent issue across all age groups, socioeconomic classes, and cultural identities.

1 Jan 16, 2022
Public Models considered for emotion estimation from EEG

Emotion-EEG Set of models for emotion estimation from EEG. Composed by the combination of two deep-learing models learning together (RNN and CNN) with

Victor Delvigne 21 Dec 23, 2022
[ICML 2021] A fast algorithm for fitting robust decision trees.

GROOT: Growing Robust Trees Growing Robust Trees (GROOT) is an algorithm that fits binary classification decision trees such that they are robust agai

Cyber Analytics Lab 17 Nov 21, 2022
Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy

Creating a Linear Program Solver by Implementing the Simplex Method in Python with NumPy Simplex Algorithm is a popular algorithm for linear programmi

Reda BELHAJ 2 Oct 12, 2022
3rd place solution for the Weather4cast 2021 Stage 1 Challenge

weather4cast2021_Stage1 3rd place solution for the Weather4cast 2021 Stage 1 Challenge Dependencies The code can be executed from a fresh environment

5 Aug 14, 2022
Official Pytorch implementation of "Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021)

Unbiased Classification Through Bias-Contrastive and Bias-Balanced Learning (NeurIPS 2021) Official Pytorch implementation of Unbiased Classification

Youngkyu 17 Jan 01, 2023
A toolkit for developing and comparing reinforcement learning algorithms.

Status: Maintenance (expect bug fixes and minor updates) OpenAI Gym OpenAI Gym is a toolkit for developing and comparing reinforcement learning algori

OpenAI 29.6k Jan 08, 2023
Tzer: TVM Implementation of "Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation (OOPSLA'22)“.

Artifact • Reproduce Bugs • Quick Start • Installation • Extend Tzer Coverage-Guided Tensor Compiler Fuzzing with Joint IR-Pass Mutation This is the s

12 Dec 29, 2022
A simple software for capturing human body movements using the Kinect camera.

KinectMotionCapture A simple software for capturing human body movements using the Kinect camera. The software can seamlessly save joints and bones po

Aleksander Palkowski 5 Aug 13, 2022
An official source code for "Augmentation-Free Self-Supervised Learning on Graphs"

Augmentation-Free Self-Supervised Learning on Graphs An official source code for Augmentation-Free Self-Supervised Learning on Graphs paper, accepted

Namkyeong Lee 59 Dec 01, 2022
Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training.

Updates (2020/06/21) Code of PVTv2 is released! PVTv2 largely improves PVTv1 and works better than Swin Transformer with ImageNet-1K pre-training. Pyr

1.3k Jan 04, 2023
PyTorch implementation of "Transparency by Design: Closing the Gap Between Performance and Interpretability in Visual Reasoning"

Transparency-by-Design networks (TbD-nets) This repository contains code for replicating the experiments and visualizations from the paper Transparenc

David Mascharka 351 Nov 18, 2022
Includes PyTorch -> Keras model porting code for ConvNeXt family of models with fine-tuning and inference notebooks.

ConvNeXt-TF This repository provides TensorFlow / Keras implementations of different ConvNeXt [1] variants. It also provides the TensorFlow / Keras mo

Sayak Paul 87 Dec 06, 2022
Official implementation of Neural Bellman-Ford Networks (NeurIPS 2021)

NBFNet: Neural Bellman-Ford Networks This is the official codebase of the paper Neural Bellman-Ford Networks: A General Graph Neural Network Framework

MilaGraph 136 Dec 21, 2022
Image-popularity-score - A novel deep regression method for image scoring.

Image-popularity-score - A novel deep regression method for image scoring.

Shoaib ahmed 1 Dec 26, 2021
Phy-Q: A Benchmark for Physical Reasoning

Phy-Q: A Benchmark for Physical Reasoning Cheng Xue*, Vimukthini Pinto*, Chathura Gamage* Ekaterina Nikonova, Peng Zhang, Jochen Renz School of Comput

29 Dec 19, 2022