Potato Disease Classification - Training, Rest APIs, and Frontend to test.

Overview

Potato Disease Classification

Setup for Python:

  1. Install Python (Setup instructions)

  2. Install Python packages

pip3 install -r training/requirements.txt
pip3 install -r api/requirements.txt
  1. Install Tensorflow Serving (Setup instructions)

Setup for ReactJS

  1. Install Nodejs (Setup instructions)
  2. Install NPM (Setup instructions)
  3. Install dependencies
cd frontend
npm install --from-lock-json
npm audit fix
  1. Copy .env.example as .env.

  2. Change API url in .env.

Setup for React-Native app

  1. Go to the React Native environment setup, then select React Native CLI Quickstart tab.

  2. Install dependencies

cd mobile-app
yarn install
  • 2.1 Only for mac users
cd ios && pod install && cd ../
  1. Copy .env.example as .env.

  2. Change API url in .env.

Training the Model

  1. Download the data from kaggle.
  2. Only keep folders related to Potatoes.
  3. Run Jupyter Notebook in Browser.
jupyter notebook
  1. Open training/potato-disease-training.ipynb in Jupyter Notebook.
  2. In cell #2, update the path to dataset.
  3. Run all the Cells one by one.
  4. Copy the model generated and save it with the version number in the models folder.

Running the API

Using FastAPI

  1. Get inside api folder
cd api
  1. Run the FastAPI Server using uvicorn
uvicorn main:app --reload --host 0.0.0.0
  1. Your API is now running at 0.0.0.0:8000

Using FastAPI & TF Serve

  1. Get inside api folder
cd api
  1. Copy the models.config.example as models.config and update the paths in file.
  2. Run the TF Serve (Update config file path below)
docker run -t --rm -p 8501:8501 -v C:/Code/potato-disease-classification:/potato-disease-classification tensorflow/serving --rest_api_port=8501 --model_config_file=/potato-disease-classification/models.config
  1. Run the FastAPI Server using uvicorn For this you can directly run it from your main.py or main-tf-serving.py using pycharm run option (as shown in the video tutorial) OR you can run it from command prompt as shown below,
uvicorn main-tf-serving:app --reload --host 0.0.0.0
  1. Your API is now running at 0.0.0.0:8000

Running the Frontend

  1. Get inside api folder
cd frontend
  1. Copy the .env.example as .env and update REACT_APP_API_URL to API URL if needed.
  2. Run the frontend
npm run start

Running the app

  1. Get inside mobile-app folder
cd mobile-app
  1. Copy the .env.example as .env and update URL to API URL if needed.

  2. Run the app (android/iOS)

npm run android

or

npm run ios
  1. Creating public (signed APK)

Creating the TF Lite Model

  1. Run Jupyter Notebook in Browser.
jupyter notebook
  1. Open training/tf-lite-converter.ipynb in Jupyter Notebook.
  2. In cell #2, update the path to dataset.
  3. Run all the Cells one by one.
  4. Model would be saved in tf-lite-models folder.

Deploying the TF Lite on GCP

  1. Create a GCP account.
  2. Create a Project on GCP (Keep note of the project id).
  3. Create a GCP bucket.
  4. Upload the potatoes.h5 model in the bucket in the path models/potatos.h5.
  5. Install Google Cloud SDK (Setup instructions).
  6. Authenticate with Google Cloud SDK.
gcloud auth login
  1. Run the deployment script.
cd gcp
gcloud functions deploy predict_lite --runtime python38 --trigger-http --memory 512 --project project_id
  1. Your model is now deployed.
  2. Use Postman to test the GCF using the Trigger URL.

Inspiration: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions

Deploying the TF Model (.h5) on GCP

  1. Create a GCP account.
  2. Create a Project on GCP (Keep note of the project id).
  3. Create a GCP bucket.
  4. Upload the tf .h5 model generate in the bucket in the path models/potato-model.h5.
  5. Install Google Cloud SDK (Setup instructions).
  6. Authenticate with Google Cloud SDK.
gcloud auth login
  1. Run the deployment script.
cd gcp
gcloud functions deploy predict --runtime python38 --trigger-http --memory 512 --project project_id
  1. Your model is now deployed.
  2. Use Postman to test the GCF using the Trigger URL.

Inspiration: https://cloud.google.com/blog/products/ai-machine-learning/how-to-serve-deep-learning-models-using-tensorflow-2-0-with-cloud-functions

Owner
codebasics
codebasics
Re-TACRED: Addressing Shortcomings of the TACRED Dataset

Re-TACRED Re-TACRED: Addressing Shortcomings of the TACRED Dataset

George Stoica 40 Dec 10, 2022
Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

VITA 77 Oct 05, 2022
This is an official implementation for "SimMIM: A Simple Framework for Masked Image Modeling".

Project This repo has been populated by an initial template to help get you started. Please make sure to update the content to build a great experienc

Microsoft 674 Dec 26, 2022
Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning

Machine_Learning Election Exit Poll Prediction and U.S.A Presidential Speech Analysis using Machine Learning This project is based on 2 case-studies:

Avnika Mehta 1 Jan 27, 2022
A Re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"

What is This This is a simple re-implementation of the paper "A Deep Learning Framework for Character Motion Synthesis and Editing"(1). Only Sections

102 Dec 14, 2022
CVNets: A library for training computer vision networks

CVNets: A library for training computer vision networks This repository contains the source code for training computer vision models. Specifically, it

Apple 1.1k Jan 03, 2023
TrackFormer: Multi-Object Tracking with Transformers

TrackFormer: Multi-Object Tracking with Transformers This repository provides the official implementation of the TrackFormer: Multi-Object Tracking wi

Tim Meinhardt 321 Dec 29, 2022
商品推荐系统

商品top50推荐系统 问题建模 本项目的数据集给出了15万左右的用户以及12万左右的商品, 以及对应的经过脱敏处理的用户特征和经过预处理的商品特征,旨在为用户推荐50个其可能购买的商品。 推荐系统架构方案 本项目采用传统的召回+排序的方案。

107 Dec 29, 2022
A Self-Supervised Contrastive Learning Framework for Aspect Detection

AspDecSSCL A Self-Supervised Contrastive Learning Framework for Aspect Detection This repository is a pytorch implementation for the following AAAI'21

Tian Shi 30 Dec 28, 2022
SatelliteSfM - A library for solving the satellite structure from motion problem

Satellite Structure from Motion Maintained by Kai Zhang. Overview This is a libr

Kai Zhang 190 Dec 08, 2022
A model that attempts to learn and benefit from data collected on card counting.

A model that attempts to learn and benefit from data collected on card counting. A decision tree like model is built to win more often than loose and increase the bet of the player appropriately to c

1 Dec 17, 2021
EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks

EncT5 (Unofficial) Pytorch Implementation of EncT5: Fine-tuning T5 Encoder for Non-autoregressive Tasks About Finetune T5 model for classification & r

Jangwon Park 34 Jan 01, 2023
The devkit of the nuScenes dataset.

nuScenes devkit Welcome to the devkit of the nuScenes and nuImages datasets. Overview Changelog Devkit setup nuImages nuImages setup Getting started w

Motional 1.6k Jan 05, 2023
Graph Attention Networks

GAT Graph Attention Networks (Veličković et al., ICLR 2018): https://arxiv.org/abs/1710.10903 GAT layer t-SNE + Attention coefficients on Cora Overvie

Petar Veličković 2.6k Jan 05, 2023
Torch code for our CVPR 2018 paper "Residual Dense Network for Image Super-Resolution" (Spotlight)

Residual Dense Network for Image Super-Resolution This repository is for RDN introduced in the following paper Yulun Zhang, Yapeng Tian, Yu Kong, Bine

Yulun Zhang 494 Dec 30, 2022
official implementation for the paper "Simplifying Graph Convolutional Networks"

Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After

Tianyi 727 Jan 01, 2023
Code for our CVPR 2022 Paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection"

GEN-VLKT Code for our CVPR 2022 paper "GEN-VLKT: Simplify Association and Enhance Interaction Understanding for HOI Detection". Contributed by Yue Lia

Yue Liao 47 Dec 04, 2022
fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

Ali Abdalla 34 Jan 05, 2023
Reference code for the paper CAMS: Color-Aware Multi-Style Transfer.

CAMS: Color-Aware Multi-Style Transfer Mahmoud Afifi1, Abdullah Abuolaim*1, Mostafa Hussien*2, Marcus A. Brubaker1, Michael S. Brown1 1York University

Mahmoud Afifi 36 Dec 04, 2022
Setup freqtrade/freqUI on Heroku

UNMAINTAINED - REPO MOVED TO https://github.com/p-zombie/freqtrade Creating the app git clone https://github.com/joaorafaelm/freqtrade.git && cd freqt

João 51 Aug 29, 2022