Tensorflow_Object_detection_API-Custom_Faster_RCNN

A sample project to detect the custom object using Tensorflow object detection API

Folder Structure

• Tensorflow_API-Custom_object_detection
  • pre_trained_models
    • downloaded files for the choosen pre-trained model will come here
  • dataset
    • Annotations
      • Annotations for your training images will come here
    • JPEGImages
      • all of your images for training will come here
    • testImages
      • all your images for testing will come here
    • lable.pbtxt
    • train.record
  • IG
    • inference graph of the trained model will be saved here
  • CP
    • checkpoints of the trained model will be saved here
  • eval.ipynb
  • train.ipynb
  • config file for the choosen model

Steps

Create folders

Create the folders following the structure given above (You could use a different name for any of the folders if you want)

Prepare train and test images

This repository contains train and test images for detection of "UE Roll" blue bluetooth speaker but I will highly recommend you to create your own dataset. Pick up an object you want to detect and take some pics of it with varying backgrounds, angles and distances. Some of the sample images used in this sample project are given below:

Once you have captured images, transfer it to your PC and resize it to a smaller size (given images have the size of 605 x 454) so that your training will go smoothly without running out of memory. Now rename (for better referencing later) and divide your captured images into two chunks, one chunk for training(80%) and other for testing(20%). Finally, move training images into JPEGImages folder and testing images into testImages folder.

Label the data

Now its time to label your training data. We will be doing it using the labelImg library. To download this library along with its dependencies go to THIS LINK. Once you have the labelImg library downloaded on your PC, run lableImg.py. Select JPEGImages directory by clicking on Open Dir and change the save directory to Annotations by clicking on Change Save Dir. Now all you need to do is to draw rectangles around the object you are planning to detect. You will need to click on Create RectBox and then you will get the cursor to label the objects. After drawing rectangles around objects, give the name for the label and save it so that Annotations will get saved as the .xml file in Annotations folder.

Setup Tensorflow models repository

Now it's time when we will start using Tensorflow object detection API so go ahead and clone it using the following command

git clone https://github.com/tensorflow/models.git

Once you have cloned this repository, change your present working directory to models/reserarch/ and add it to your python path. If you want to add it permanently then you will have to make the change in your .bashrc file or you could add it temporarily for current session using the following command:

export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim

You also need to run following command in order to get rid of the string_int_label_map_pb2 issue (more details HERE)

protoc object_detection/protos/*.proto --python_out=.

Now your Environment is all set to use Tensorlow object detection API

Convert the data to Tensorflow record format

In order to use Tensorflow API, you need to feed data in Tensorflow record format. Thankfully Tensorflow gives python script to convert Pascal VOC format dataset to Tensorflow record format. Path for the file I mentioned in last line is given below: models/research/object_detection/dataset_tools/create_pascal_tf_record.py Now you have two options, either follow Pascal VOC dataset format or modify the Tesorflow script as per your need. I modified the script and I have placed same in this repository inside the folder named as extra. All you need to do is to take this script and replace the original script with this. If you do so, you don't need to follow any specific format. After doing all this circus, one last thing is still remaining before we get our Tensorflow record file. You need to create a file for label map, in this repo its label.pbtxt, with the dictionary of the label and the id of objects. Check label.pbtxt given in the repository to undestand the format, its pretty simple (Note: name of the label should be same as what you had given while labeling object using the labelImg). Now it time to create record file. From models/research as present working directory run the following command to create Tensorflow record:

python object_detection/dataset_tools/create_pascal_tf_record.py --data_dir=<path_to_your_dataset_directory> --annotations_dir=<name_of_annotations_directory> --output_path=<path_where_you_want_record_file_to_be_saved> --label_map_path=<path_of_label_map_file>

For more help run the following command:

python object_detection/dataset_tools/create_pascal_tf_record.py -h

An example will be:

Python object_detection/dataset_tools/create_pascal_tf_record.py --data_dir=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/dataset --annotations_dir=Annotations --output_path=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/dataset/train.record --label_map_path=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/dataset/label.pbtxt

Training

Now that we have data in the right format to feed, we could go ahead with training our model. The first thing you need to do is to select the pre-trained model you would like to use. You could check and download a pret-rained model from Tensorflow detection model zoo Github page. Once downloaded, extract all file to the folder you had created for saving the pre-trained model files. Next you need to copy models/research/sample/configs/<your_model_name.config> and paste it in the project repo. You need to configure 5 paths in this file. Just open this file and search for PATH_TO_BE_CONFIGURED and replace it with the required path. I used pre-trained faster RCNN trained on COCO dataset and I have added modified config file (along with PATH_TO_BE_CONFIGURED as comment above lines which has been modified) for same in this repo. You could also play with other hyperparameters if you want. Now you are all set to train your model, just run th following command with models/research as present working directory

python object_detection/legacy/train.py --train_dir=<path_to_the folder_for_saving_checkpoints> --pipeline_config_path=<path_to_config_file>

An example will be

python object_detection/legacy/train.py --train_dir=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/CP --pipeline_config_path=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/faster_rcnn_resnet101_coco.config

Let it train till loss will be below 0.1 or even lesser. once you see that loss is as low as you want then give keyboard interrupt. Checkpoints will be saved in CP folder. Now its time to generate inference graph from saved checkpoints

python object_detection/export_inference_graph.py --input_type=image_tensor --pipeline_config_path=<path_to_config_file> --trained_checkpoint_prefix=<path to saved checkpoint> --output_directory=<path_to_the_folder_for_saving_inference_graph>

An example will be

python object_detection/export_inference_graph.py --input_type=image_tensor --pipeline_config_path=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/faster_rcnn_resnet101_coco.config --trained_checkpoint_prefix=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/CP/model.ckpt-1691 --output_directory=/Users/vijendra1125/Documents/tensorflow/object_detection/speaker_detection/IG

Bonus: If you want to train your model using Google Colab then check out the train.ipynb file

Test the trained model

Finally, it's time to check the result of all the hard work you did. All you need to do is to copy model/research/object_detection/object_detection_tutorial.ipynb and modify it to work with you inference graph. A modified file is already given as eval.ipynb with this repo, you just need to change the path, number of classes and the number of images you have given as test image. Below is the result of the model trained for detecting "UE Roll" blue bluetooth speaker.