|Model||Mobile SSD||Faster RCNN|
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
- 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
- python image_resizer.py -input=input_folder_images -output=output_folder -height=800 -width=600
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.
- Modified version of xml_to_csv.py from racoon github repo.
- generate_tfrecord.py from racoon github repo.