/TensorFlow.js-Custom-Object-Detection

An E2E custom object detection browser-based application using TensorFlow.js.

Primary LanguageJupyter Notebook

Custom Object Detection on the browser using TensorFlow.js

Create your own custom object detection model and deploy it on the browser using TensorFlow.js

Note: TF 1.x is no longer supported; refer to the TFJS-TFLite Object Detection repository to create and deploy an object detection model on the browser.

Steps:

  1. Clone the repository on your local machine.

  2. Upload your dataset on Google Drive in the following directory structure ONLY; to avoid any errors as the notebook is created which is compatible to this format.

    TFJS-Custom-Detection.zip
|__ images (contains all training and validation *.jpg files)
|__ annotations (contains all training and validation *.xml files)
|__ train (contains only training *.jpg and *.xml files)
|__ val (contains only validation *.jpg and *.xml files)

  3. Sign in to your Google account and upload the Custom_Object_Detection_using_TensorFlow_js.ipynb notebook on Colab.

  4. Run the notebook cells one-by-one by following the instructions.

  5. Once the TFJS model is downloaded, copy the model_web folder inside TensorFlow.js-Custom-Object-Detection/React_Web_App/public directory.

  6. Run the following commands:

    • cd TensorFlow.js-Custom-Object-Detection/React_Web_App
    • npm install
    • npm start

  7. Open localhost:3000 on your web browser and test the model for yourself.

Output:

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