Awesome

Implementation

1. Model training and inference

We recommend creating a python 3.6+ virtual environment for this project. You can use pyenv-virtualenv to do so.

Install these Tensorflow versions in the activated environment.

tensorboard==1.14.0
tensorboard-plugin-wit==1.6.0.post3
tensorflow-estimator==1.14.0
tensorflow-gpu==1.14.0

2. Setup TensorFlow Object Detection API

2a. Install TensorFlow object detection:

Download the necessary scripts with git clone https://github.com/tensorflow/models.git
Install TensorFlow Object Detection API by strictly following these instructions. Once you've successfully run python object_detection/builders/model_builder_test.py you are ready for the next step.
To access the necessary utility scripts, you'll need to run all the following commands from the models/research/object_detection directory from the cloned repo. From here on we will refer the TensorFlow Object Detection directory models/research/object_detection/ as the TOD directory.

You could also work from this codebase as a stable implementation with the above listed TF library versions. Just ensure that repo folder is set as models/research/object_detection/.

3. Create TFRecords for model training

Tensorflow API supports a variety of file formats. The TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have example code in this repo which converts the labels.npz file to a TFRecords file:

Copy utils_convert_tfrecords.py from this repo to the TOD directory, .
Your $folder will be the data path containing your labels.npz file and tiles.
From the TOD directory run:

python3 utils_convert_tfrecords.py    \
        --label_input=$folder/labels.npz   \
        --data_dir=tf_records   \
        --tiles_dir=$folder/tiles    \
        --pbtxt=classes.pbtxt

This will create train.record, val.record and test.record files in a folder called tf_records in the TOD directory. Each record file contains different and non-overlapping partitions of the data (86,7,7 percents, respectively).

4. Object detection model setup

Now we're ready to set up the model architecture. For this walkthrough, we'll download a pre-trained model from the TensorFlow model zoo. We'll demonstrate using ssd_resnet_101_fpn_oidv4 (download link):

Download the model, unzip, and move the folder to the TOD directory
Create a new folder training in the TOD directory.
Copy a model configuration file to the training directory.
Copy a class definitions file to the data directory.

Now your current directory should be models/research/object_detection/ and in addition to the files included in that repo originally, your folder structure should look like this:

models/research/object_detection/
├── ssd_resnet101_v1_fpn_multilabel/
├── training/
│   └── ssd_resnet101_v1_fpn_marine_debris.config
├── data/
│   ├── train.record
│   ├── val.record
│   ├── test.record
│   ├── marine_debris.pbtxt
└───

5. Train the TensorFlow object detection model

You are now ready to train the model. From the models/research/ directory, run:

#!/usr/bin/env bash
pyenv activate tf114_od
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cd object_detection
export CUDA_VISIBLE_DEVICES=0
python model_main.py --alsologtostderr --model_dir=training/ --pipeline_config_path=training/ssd_resnet101_v1_fpn_multilabel.config

The model checkpoints and outputs for this task will save in the training folder.

6. Visualize the Model

Using this script, create the marine debris detection model inference graph with:

python export_inference_graph.py --input_type image_tensor \
              --pipeline_config_path training/ssd_resnet101_v1_fpn_multilabel.config \
              --trained_checkpoint_prefix training/model.ckpt-500000 \
              --output_directory model_50k

We can visualize this graph using tensorboard:

tensorboard --logdir='training'

Go to http://127.0.0.1:6006/ in your web browser and you will see:

7. Prediction

Now let's run the model over our test tiles to predict where marine debris patches are. Copy this script to the TOD directory then run:

python tf_od_predict_image_aug_to_geo_corrected.py --model_name=model_50k \
                        --path_to_label=data/marine_debris.pbtxt \
                        --test_image_path=path/to/test/image/tiles

This code will read through all your test images in path/to/test/image/tiles folder and output the final prediction into the same folder. You will find new images in test_image_path with the _test suffixed to the end of the file basenames. The are images with the predicted bounding boxes and confidence scores plotted on top. As well, you will find a multipolygon geojson of predicted bounding boxes in the test_image_path.

Option for flags:

export base_dir=models/research/object_detection
export EXPORT_DIR=models/research/object_detection/model_50k
python3 ${base_dir}/tf_od_predict_image_aug_to_geo_corrected.py --model_name=${EXPORT_DIR} --path_to_label=${base_dir}/marine_debris.pbtxt --test_image_path=${base_dir}/test/

Detections geo-registered and vectorized to GeoJSON format: <img src="assets/detections_geo.png" width="800px" height="auto">

8. Evaluation

You can use the code in this folder to compute standard evaluation metrics with your model. Runtime and background instructions live here.