Computer Vision AI tutorial

1. Tensorflow object detection needs some dependent libraries to install before it can run, including Tensorflow itself. Follow the instructions here to install and test these libraries:

 

https://github.com/tensorflow/models/blob/v1.13.0/research/object_detection/g3doc/installation.md

   
    ** Be sure to install tensorflow 1.13.0 by issuing the following command in the terminal:

 

pip install tensorflow==1.13.0

  

Otherwise you will install a later version of TensorFlow that is incompatible

 

2. Now that tensorflow 1.13.0 is installed, clone the Tensorflow models library version 1.13.0 from GitHub: 

 

 

With Tensorflow 2 officially released, we want to make sure we clone the 1.13.0 models repository. You can clone the specific version of the models repository using the following commands in terminal:

 

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

cd models

git checkout v1.13.0

cd research

 

Note: All commands from now on must be done from the models/research folder. 

4. First, download the sample dataset from the PlantVillage Dropbox using the following link:

    https://www.dropbox.com/sh/e5a4q0fz89zd9uv/AAC8sGa_254gi7ko7CWygJU9a?dl=0

5. Create a text file called label_map.pbtxt using the following content:

item {

  id: 1

  name: 'CassavaBLS'

}

6. Create a text file called trainval.txt and list all of the image filenames (without extensions). Your trainval file should be structured like this: 

CassavaBLS_TZ.8.16_FS_Cheroke_5782
CassavaBLS_TZ.8.16_FS_Cheroke_5783
CassavaBLS_TZ.8.16_FS_Cheroke_5784
...
CassavaBLS_TZ.8.16_FS_Cheroke_5802
CassavaBLS_TZ.8.16_FS_Cheroke_5803
CassavaBLS_TZ.8.16_FS_Cheroke_5804

7. Set up folder structure as follows:

+ data: contains the the label_map.pbtxt file, annotations folder, and images folder
+ data / images: contains the image files in jpg format
+ data / annotations contains a folder xmls and a text file ‘trainval.txt’
+ data / annotations / xmls contains all of the annotation files created in LabelImg, ‘trainval.txt’ contains the list of files and corresponding object label ID
+ model: contains the folder ckpt, eval, and the config file

 

 

Now we're ready to create our TensorFlow Record files!

 

8. Run the following command to create your own TF record files:

** Note make sure to run the command from the /models/research directory

 

python object_detection/dataset_tools/create_pet_tf_record.py \

--label_map_path=/path/to/label_map.pbtxt \
--data_dir=/path/to/data \
--output_dir=/path/to/save/records

9. Download a pre-trained object detection model checkpoint from Tensorflow model zoo. For example, you can download the ssd_mobilenet model v2 pre-trained on the coco dataset.
     http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz
 

10. Download the corresponding model config file and update the paths within the file (change PATHS_TO_BE_CONFIGURED) to match the local paths to your data.
    https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/ssd_mobilenet_v2_quantized_300x300_coco.config

11. Change directory into the models/research directory:

cd /path/to/models/research

 

12. Generate the protobuf files:

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

 

13. Export the python path:

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

 

Now we can start training!

 

14. Start a training session by running the following command:

** Be sure to replace /path/to/.. with the actual path to the directory or file

python object_detection/model_main.py --alsologtostderr \
    --pipeline_config_path=/path/to/config/dir/ssd_mobilenet_v2_quantized_300x300_coco.config \
    --model_dir=/path/to/save/checkpoints/during/training

 

If you would like to monitor the progress of the training session, you can use TensorBoard by issuing the following command in Terminal:

 

tensorboard --logdir=/path/to/ckpt/

 

** As always, be sure to replace /path/to/ckpt/ with the actual path to the directory with the ckpt event log(s)