A python-image-classification web application project, written in Python and served through the Flask Microframework. This Project implements the VGG16 covolutional neural network, through Keras and Tensorflow wrappers, to make predictions on uploaded images.

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...
Using python and scikit-learn to make stock predictions

MachineLearningStocks in python: a starter project and guide EDIT as of Feb 2021: MachineLearningStocks is no longer actively maintained MachineLearni

🔮 Execution time predictions for deep neural network training iterations across different GPUs.

Habitat: A Runtime-Based Computational Performance Predictor for Deep Neural Network Training Habitat is a tool that predicts a deep neural network's

Framework that uses artificial intelligence applied to mathematical models to make predictions
Framework that uses artificial intelligence applied to mathematical models to make predictions

LiconIA Framework that uses artificial intelligence applied to mathematical models to make predictions Interface Overview Table of contents [TOC] 1 Ar

Some code of the implements of Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network

3D-GMPDCNN Geological Modeling Using 3D Pixel-Adaptive and Deformable Convolutional Neural Network PyTorch implementation of "Geological Modeling Usin

This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons
This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong Poisons

Adversarial poison generation and evaluation. This framework implements the data poisoning method found in the paper Adversarial Examples Make Strong

A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.
A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon.

PokeGAN A tensorflow/keras implementation of StyleGAN to generate images of new Pokemon. Dataset The model has been trained on dataset that includes 8

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

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

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
Attempt at implementation of a simple GAN using Keras

Simple GAN This is my attempt to make a wrapper class for a GAN in keras which can be used to abstract the whole architecture process. Simple GAN Over

Deven96 7 May 23, 2019
MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution

Octave Convolution MXNet implementation for: Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution Imag

Meta Research 549 Dec 28, 2022
Tweesent-back - Tweesent backend uses fastAPI as the web framework

TweeSent Backend Tweesent backend. This repo uses fastAPI as the web framework.

0 Mar 26, 2022
pytorch, hand(object) detect ,yolo v5,手检测

YOLO V5 物体检测,包括手部检测。 项目介绍 手部检测 手部检测示例如下 : 视频示例: 项目配置 作者开发环境: Python 3.7 PyTorch = 1.5.1 数据集 手部检测数据集 该项目数据集采用 TV-Hand 和 COCO-Hand (COCO-Hand-Big 部分) 进

Eric.Lee 11 Dec 20, 2022
A Dataset for Direct Quotation Extraction and Attribution in News Articles.

DirectQuote - A Dataset for Direct Quotation Extraction and Attribution in News Articles DirectQuote is a corpus containing 19,760 paragraphs and 10,3

THUNLP-MT 9 Sep 23, 2022
10th place solution for Google Smartphone Decimeter Challenge at kaggle.

Under refactoring 10th place solution for Google Smartphone Decimeter Challenge at kaggle. Google Smartphone Decimeter Challenge Global Navigation Sat

12 Oct 25, 2022
Ranking Models in Unlabeled New Environments (iccv21)

Ranking Models in Unlabeled New Environments Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch 1.7.0 + torchivision 0.8.1

14 Dec 17, 2021
Source code for the paper: Variance-Aware Machine Translation Test Sets (NeurIPS 2021 Datasets and Benchmarks Track)

Variance-Aware-MT-Test-Sets Variance-Aware Machine Translation Test Sets License See LICENSE. We follow the data licensing plan as the same as the WMT

NLP2CT Lab, University of Macau 5 Dec 21, 2021
DECAF: Deep Extreme Classification with Label Features

DECAF DECAF: Deep Extreme Classification with Label Features @InProceedings{Mittal21, author = "Mittal, A. and Dahiya, K. and Agrawal, S. and Sain

46 Nov 06, 2022
PyTorch implementation for View-Guided Point Cloud Completion

PyTorch implementation for View-Guided Point Cloud Completion

22 Jan 04, 2023
Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment

Python implementation of MULTIseq barcode alignment using fuzzy string matching and GMM barcode assignment.

MT Schmitz 2 Feb 11, 2022
OpenDelta - An Open-Source Framework for Paramter Efficient Tuning.

OpenDelta is a toolkit for parameter efficient methods (we dub it as delta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most paramters

THUNLP 386 Dec 26, 2022
Conditional Generative Adversarial Networks (CGAN) for Mobility Data Fusion

This code implements the paper, Kim et al. (2021). Imputing Qualitative Attributes for Trip Chains Extracted from Smart Card Data Using a Conditional Generative Adversarial Network. Transportation Re

Eui-Jin Kim 2 Feb 03, 2022
Official pytorch implementation of the AAAI 2021 paper Semantic Grouping Network for Video Captioning

Semantic Grouping Network for Video Captioning Hobin Ryu, Sunghun Kang, Haeyong Kang, and Chang D. Yoo. AAAI 2021. [arxiv] Environment Ubuntu 16.04 CU

Hobin Ryu 43 Nov 25, 2022
Code release for NeurIPS 2020 paper "Co-Tuning for Transfer Learning"

CoTuning Official implementation for NeurIPS 2020 paper Co-Tuning for Transfer Learning. [News] 2021/01/13 The COCO 70 dataset used in the paper is av

THUML @ Tsinghua University 35 Sep 23, 2022
Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis in JAX

SYMPAIS: Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis Overview | Installation | Documentation | Examples | Notebo

Yicheng Luo 4 Sep 13, 2022
Code for reproducible experiments presented in KSD Aggregated Goodness-of-fit Test.

Code for KSDAgg: a KSD aggregated goodness-of-fit test This GitHub repository contains the code for the reproducible experiments presented in our pape

Antonin Schrab 5 Dec 15, 2022
Repository for the COLING 2020 paper "Explainable Automated Fact-Checking: A Survey."

Explainable Fact Checking: A Survey This repository and the accompanying webpage contain resources for the paper "Explainable Fact Checking: A Survey"

Neema Kotonya 42 Nov 17, 2022
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022)

Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR2022)[paper] Authors: Chenhang He, Ruihuang Li, Shuai Li, L

Billy HE 141 Dec 30, 2022
Automatic Differentiation Multipole Moment Molecular Forcefield

Automatic Differentiation Multipole Moment Molecular Forcefield Performance notes On a single gpu, using waterbox_31ang.pdb example from MPIDplugin wh

4 Jan 07, 2022