CatchZeng / object-detection-api

Make it easy to train and deploy Object Detection(SSD) and Image Segmentation(Mask R-CNN) Model Using TensorFlow Object Detection API.

object-detection-api

Make it easy to train and deploy Object Detection(SSD) and Image Segmentation(Mask R-CNN) Model Using TensorFlow Object Detection API.

Requirements

• conda
• protoc

Use the following command to check if the installation is successful.

$ conda --version
conda 4.9.2
$ protoc --version
libprotoc 3.17.1

Installation

Conda

$ conda create -n  od python=3.8.5 && conda activate od && make install
......
----------------------------------------------------------------------
Ran 24 tests in 21.869s

OK (skipped=1)

Install directly (such as in colab)

$ make install
......
----------------------------------------------------------------------
Ran 24 tests in 21.869s

OK (skipped=1)

For details, please refer to colab demo.

Note: If the installation fails, you can refer to the detailed steps in the official document.

Note: The update of cudaDNN and toolkit may not be as fast as TensorFlow. Therefore, if your machine has a GPU, after the installation is complete, you need to downgrade TensorFlow to the version supported by cudaDNN and toolkit in order to support GPU training. Take 2.8.0 as an example:

$ pip install --upgrade tf-models-official==2.8.0
$ pip install --upgrade tensorflow==2.8.0

Usage

Train

Object Detection

The easiest way to Train a Custom Object Detection Model Using TensorFlow Object Detection API

Image Segmentation

The easiest way to Train a Custom Image Segmentation Model Using TensorFlow Object Detection API Mask R-CNN

Deploy

Deploy image segmentation (Mask R-CNN) model service with TensorFlow Serving & Flask