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CNTK Examples: Image/Detection/Fast R-CNN


This folder contains an end-to-end solution for using Fast R-CNN to perform object detection. The original research paper for Fast R-CNN can be found at https://arxiv.org/abs/1504.08083. Base models that are supported by the current configuration are AlexNet and VGG16. Two image sets that are preconfigured are Pascal VOC 2007 and Grocery. Other base models or image sets can be used by adding a configuration file similar to the examples in Examples/Image/Detection/utils/configs and importing it in run_fast_rcnn.py.

Running the example


To run Fast R-CNN you need a CNTK Python environment. Install the following additional packages:

pip install opencv-python easydict pyyaml dlib

The code uses prebuild Cython modules for parts of the region proposal network. These binaries are contained in the folder (Examples/Image/Detection/utils/cython_modules) for Python 3.5 for Windows and Python 3.5, and 3.6 for Linux. If you require other versions please follow the instructions at https://github.com/rbgirshick/py-faster-rcnn.

If you want to use the debug output you need to run pip install pydot_ng (website) and install graphviz (GraphViz executable has to be in the system’s PATH) to be able to plot the CNTK graphs.

Getting the data and AlexNet model

We use a toy dataset of images captured from a refrigerator to demonstrate Fast R-CNN. Both the dataset and the pre-trained AlexNet model can be downloaded by running the following Python command from the Examples/Image/Detection/FastRCNN folder:

python install_data_and_model.py

After running the script, the toy dataset will be installed under the Examples/Image/DataSets/Grocery folder. The AlexNet model will be downloaded to the PretrainedModels folder in the root CNTK folder. We recommend you to keep the downloaded data in the respective folder while downloading, as the configuration files expect that by default.

Running Fast R-CNN on the example data

To train and evaluate Fast R-CNN run

python run_fast_rcnn.py

Running Fast R-CNN on Pascal VOC data

To download the Pascal data and create the annotation file for Pascal in CNTK format run the following scripts:

python Examples/Image/DataSets/Pascal/install_pascalvoc.py
python Examples/Image/DataSets/Pascal/mappings/create_mappings.py

Change the dataset_cfg in the get_configuration() method of run_fast_rcnn.py to

from utils.configs.Pascal_config import cfg as dataset_cfg

Now you're set to train on the Pascal VOC 2007 data using python run_fast_rcnn.py. Beware that training might take a while.

Running Fast R-CNN on your own data

Preparing your own data and annotating it with ground truth bounding boxes is described here. After storing your images in the described folder structure and annotating them please run

python Examples/Image/Detection/utils/annotations/annotations_helper.py

after changing the folder in that script to your data folder. Finally, create a MyDataSet_config.py in the utils/configs folder following the existing examples:

__C.CNTK.DATASET == "YourDataSet":
__C.CNTK.MAP_FILE_PATH = "../../DataSets/YourDataSet"
__C.CNTK.CLASS_MAP_FILE = "class_map.txt"
__C.CNTK.TRAIN_MAP_FILE = "train_img_file.txt"
__C.CNTK.TEST_MAP_FILE = "test_img_file.txt"
__C.CNTK.TRAIN_ROI_FILE = "train_roi_file.txt"
__C.CNTK.TEST_ROI_FILE = "test_roi_file.txt"

Change the dataset_cfg in the get_configuration() method of run_fast_rcnn.py to

from utils.configs.MyDataSet_config import cfg as dataset_cfg

and run python run_fast_rcnn.py to train and evaluate Fast R-CNN on your data.

Technical details


All options and parameters are in FastRCNN_config.py in the FastRCNN folder and all of them are explained there. These include

# learning parameters
__C.CNTK.LR_PER_SAMPLE = [0.001] * 10 + [0.0001] * 10 + [0.00001]

# Number of regions of interest [ROIs] proposals
# minimum relative width/height of an ROI
__C.roi_min_side_rel = 0.01
# maximum relative width/height of an ROI
__C.roi_max_side_rel = 1.0

Fast R-CNN CNTK code

Most of the code is in FastRCNN_train.py and FastRCNN_eval.py (and Examples/Image/Detection/utils/*.py for helper methods). Please see those files for details.


All details regarding the Fast R-CNN algorithm can be found in the original research paper: https://arxiv.org/abs/1504.08083.