mrmamon / tensorflow-gun-detection

tensorflow-gun-detection

Scores

Model	Mobile SSD	Faster RCNN
Test Loss	1.5	0.1016
Trained Rounds	5114	7167

Instructions

• Uncompress the train & test images

  • Images inside images are compressed into train.tar.gz and test.tar.gz folders.
  • Navigate to images folder and type "bash uncompress.sh" in your terminal.

• Create csv records for the xml files

  • Navigate to images and run xml_to_csv.py file

• Create tfrecords

  • Navigate to images and see the generate_tfrecords.py script for instructions.

• Once you generate xml files and tfrecords, the files should be available under images/data

• Create a folder called training in the root folder.

• If you want to do transfer learning pull the model & config files from tensorflow zoo and put the in the root folder.

• Move the config file to training/ folder and change the config file to match the paths.

• To train the model:

  • python train.py --logstderr --train_dir=training/ --pipeline_config_path=training/ssd_mobilenet_v1_pets.config

• Export inference graphs

• Paste the graph folders to outputs/ folder

• Count the no.of bounding boxes

  • python image_detection.py

utils/image_resizer.py

• usage
  • python image_resizer.py -input=input_folder_images -output=output_folder -height=800 -width=600

Notes:

• To compress the images which are inside train & test folders inside data/images/processed

  • tar -cvzf train.tar.gz train
  • tar -cvzf test.tar.gz test

• Rectlabel App in Mac / LabelImg open source:

  • The bounding boxes around the images were created using Rectlabel tool available for MAC.
  • We can also use Labelimg open source tool for this task.

References:

• Modified version of xml_to_csv.py from racoon github repo.
• generate_tfrecord.py from racoon github repo.