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The source code of machine learning model's API of Travens smart guide in order to complete Bangkit Capstone Project

License: MIT License

Shell 0.09% Python 99.75% Dockerfile 0.16%

bangkit-machine-learning's Introduction

bangkit-machine-learning

The source code of machine learning model's API of Travens smart guide in order to complete Bangkit Capstone Project

Landmark_Detection (1)

Model Building's Notebook

Landmark Detection Notebook

API URL

Flask API

Dataset Resources

Detection Labels

Label Map

API Endpoint

Endpoint Method Body Sent (JSON) Description
/ GET None HTTP GET REQUEST Testing Endpoint
/post POST Anything HTTP POST REQUEST Testing Endpoint
/404 GET & POST Anything 404 NOT FOUND Endpoint
/predict POST file: Image file HTTP POST REQUEST Prediction Endpoint

The flow of Machine Learning Service

Travens Machine Learning-Page-1 drawio

How to run this Flask app

  • Clone this repo
  • Open terminal and go to this project's root directory
  • Type python -m venv .venv and hit enter
  • In Linux, type source .venv/bin/activate
  • In Windows, type .venv\Scripts\activate
  • Type pip install -r requirements.txt
  • Serve the Flask app by typing flask run
  • It will run on http://127.0.0.1:5000

How to predict image with Postman

  • Open Postman
  • Enter URL request bar with http://127.0.0.1:5000/predict
  • Select method POST
  • Go to Body tab and select form-data
  • Change key from form-data with file (it must be named file)
  • Input the image that you want predict as a value of the key
  • Send the request

image

image

Architecture of SSD MobileNet V2 for Landmark Detection

SSD_MobileNetV2

References

  • Cao B, Araujo A, Sim J. Unifying Deep Local and Global Features for Image Search. 2020. Arxiv
  • Weyand T, Araujo A, Cao B, Sim J. Google Landmarks Dataset v2-A Large-Scale Benchmark for Instance-Level Recognition and Retrieval. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. Arxiv
  • A Howard, A Zhmoginov, LC Chen, M Sandler, M Zhu. MobileNetV2: Inverted Residuals and Linear Bottlenecks. The IEEE Conference on Computer Vision and Pattern Recognition. 2018. Arxiv
  • Collection of models for landmark recognition TensorFlow Hub

bangkit-machine-learning's People

Contributors

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Watchers

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