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
- 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 directorymodels/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/
.
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 $folder will be the
data
path containing yourlabels.npz
file andtiles
. - From the TOD directory run:
python3 utils_convert_tfrecords.py \
--label_input=$folder/labels.npz \
--data_dir=tf_records \
--tiles_dir=$folder/tiles \
--pbtxt=classes.pbtxt
This will create train.record
, val.record
and test.record
files in a folder called tf_records
in the TOD directory. Each record file contains different and non-overlapping partitions of the data (86,7,7 percents, respectively).
Now we're ready to set up the model architecture. For this walkthrough, we'll download a pre-trained model from the TensorFlow model zoo. We'll demonstrate using ssd_resnet_101_fpn_oidv4
(download link):
- Download the model, unzip, and move the folder to the TOD directory
- Create a new folder
training
in the TOD directory. - Copy a model configuration file to the
training directory
. - Copy a class definitions file to the
data
directory.
Now your current directory should be models/research/object_detection/
and in addition to the files included in that repo originally, your folder structure should look like this:
models/research/object_detection/
├── ssd_resnet101_v1_fpn_multilabel/
├── training/
│ └── ssd_resnet101_v1_fpn_marine_debris.config
├── data/
│ ├── train.record
│ ├── val.record
│ ├── test.record
│ ├── marine_debris.pbtxt
└───
You are now ready to train the model. From the models/research/
directory, run:
#!/usr/bin/env bash
pyenv activate tf114_od
export PYTHONPATH=$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.
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:https://127.0.0.1:6006/
in your web browser and you will see:
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:
You can use the code in this folder to compute standard evaluation metrics with your model. Runtime and background instructions live here.