TensorFlow Object Detection API tutorial¶
This tutorial is intended for TensorFlow 1.14, which (at the time of writing this tutorial) is the latest stable version before TensorFlow 2.x.
Tensorflow 1.15 has also been released, but seems to be exhibiting instability issues.
A version for TensorFlow 1.9 can be found here.
At the time of righting this tutorial, Object Detection model training and evaluation was not migrated to TensorFlow 2.x (see here). From personal tests, it seems that detection using pre-trained models works, however it is not yet possible to train and evaluate models. Once the migration has been completed, a version for TensorFlow 2.x will be produced.
This is a step-by-step tutorial/guide to setting up and using TensorFlow’s Object Detection API to perform, namely, object detection in images/video.
The software tools which we shall use throughout this tutorial are listed in the table below:
|Target Software versions|
|Anaconda||Python 3.7 (Optional)|
- General Remarks
- Install Anaconda Python 3.7 (Optional)
- TensorFlow Installation
- TensorFlow CPU
- TensorFlow GPU
- TensorFlow Models Installation
- LabelImg Installation
- Detect Objects Using Your Webcam
- Training Custom Object Detector
- Preparing workspace
- Annotating images
- Partitioning the images
- Creating Label Map
- Creating TensorFlow Records
- Configuring a Training Pipeline
- Training the Model
- Evaluating the Model (Optional)
- Monitor Training Job Progress using TensorBoard
- Exporting a Trained Inference Graph
- Common issues