Custom image detector using Tensorflow object detection API

The aim of this tutorial to use tensorflow object detection API to detect custom objects. Here in this tutorial, we will try to train the network to recognize battery charging image (Why battery charging ? later, this trained net can be used in a robot to detect the charging point from a picture). This is basically an excerpt of sentdex tensorflow tutorial series. I have listed out the steps which I have done to train custom image for quick access.

Download files here

battery charging image detection
Image to detect

To train the model, first we need to collect training data. This can be done by collecting images from google images. I used a chrome extension ‘Fatkun Batch Download Image’ for saving bulk images. Once the images are downloaded, download and install labelImg to annotate the training data.

git clone
sudo apt-get install pyqt5-dev-tools
sudo pip3 install lxml
make qt5py3

Browse to the image folder that contains downloaded  images. The idea is to create xml label for all the images. Select the image one by one, Click create rectangle box, give the label as ‘charging sign’ and save as xml file(default). labelImg-tensorflow 

Create train and test directory. Copy 10% of images with respective xml label file to test directory and remaining 90% to train directory.

Run modified from datitran’s github  to create ‘train/test_labels.csv’. The directory structure is as follows.

Next step is to generate tfrecord for test and train data from generated csv data. Use modified for this step and generate tfrecord for test and train data.

python3 --csv_input=data/train_labels.csv  --output_path=data/train.record

python3 --csv_input=data/test_labels.csv  --output_path=data/test.record

If you are getting error saying object_detection folder does not exist, export the below path. This tutorial needs tensor flow Object detection preinstalled.   Please follow this link for more information

# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

Copy data, training, images and  ssd_mobilenet_v1_coco_11_06_2017 directories to tensorflow object_detection folder and start training.


python --logtostderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config

ssd_mobilenet_v1_pets.config will have paths to both tf records, graph and pbtxt file which contain the classes to detect. The checkpoint files will be created inside training directory.

Next we need to create a frozen inference graph from the latest checkpoint file created. Once done, use the inference program to detect the charging sign.

python --input_type image_tensor 
--pipeline_config_path training/ssd_mobilenet_v1_pets.config 
--trained_checkpoint_prefix training/model.ckpt-9871 
--output_directory charging_spot_det__inference_graph


Since my training data set was small( less than 100) and there was only one class, the inference is buggy. It identifies almost everything as charging sign. but this can be extended with multiple classes and more training data to get accurate results.