The dataset was made with a stomatologist surgeon using VoTT for labeling. The export was made under the Tensorflow Pascal VOC format
The project is divided into two tasks:
- Detect tooth restoration, endodontic treatment and implants (models/treatment)
- Detect teeth and identify their ISO Dental Notation (models/index)
- Download the datasets from the google drive (datasets are private at the moment)
- Install tensorflow object detection: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md
- Install Cloud SDK to run on google cloud https://cloud.google.com/sdk/
pip install -r requirements.txt
# Tensorflow Object Detection API
git clone [email protected]:tensorflow/models.git
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
make
cp -r pycocotools <path_to_tensorflow>/models/research/
# From tensorflow/models/research/
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
python <path_to_tensorflow>/models/research/object_detection/model_main.py \
--pipeline_config_path=<path_to_tooth-detection>/tooth-detection/models/treatment/faster_rcnn_resnet50_coco.config \
--model_dir=<path_to_tooth-detection>/tooth-detection/models/treatment/model \
--num_train_steps=100000 \
--alsologtostderr
python inference.py \
--PATH_TO_FROZEN_GRAPH=<path_to_tooth-detection>/<path_to_frozen_graph>/frozen_inference_graph.pb \
--PATH_TO_TEST_IMAGES_DIR=<path_to_tooth-detection>/data/iran_index/JPEGImages \
--PATH_TO_LABELS=<path_to_tooth-detection>/data/pascal_label_map_index.pbtxt
