CNTK/Examples/Image/Detection/FasterRCNN

CNTK Examples: Image/Detection/Faster R-CNN

Overview

This folder contains an end-to-end solution for using Faster R-CNN to perform object detection. The original research paper for Faster R-CNN can be found at https://arxiv.org/abs/1506.01497. 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 utils/configs and importing it in run_faster_rcnn.py.

Running the example

Setup

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

pip install opencv-python easydict pyyaml

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 to be able to plot the CNTK graphs (the GraphViz executable has to be in the system’s PATH).

Getting the data and AlexNet model

We use a toy dataset of images captured from a refrigerator to demonstrate Faster R-CNN (the same one is used in the Fast R-CNN example). 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 Image/DataSets/Grocery folder. The AlexNet model will be downloaded to the Image/PretrainedModels folder. We recommend you to keep the downloaded data in the respective folder while downloading, as the configuration files expect that by default.

Running Faster R-CNN on the example data

To train and evaluate Faster R-CNN run

python run_faster_rcnn.py

The results for end-to-end training on Grocery using AlexNet as the base model should look similar to these:

AP for          eggBox = 1.0000
AP for          tomato = 1.0000
AP for     orangeJuice = 1.0000
AP for         ketchup = 0.6667
AP for         mustard = 1.0000
AP for           water = 0.5000
AP for       champagne = 1.0000
AP for         joghurt = 1.0000
AP for          pepper = 1.0000
AP for         avocado = 1.0000
AP for           onion = 1.0000
AP for         tabasco = 1.0000
AP for            milk = 1.0000
AP for          orange = 1.0000
AP for          gerkin = 1.0000
AP for          butter = 1.0000
Mean AP = 0.9479

Running Faster 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_faster_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_faster_rcnn.py. Beware that training might take a while.

Running Faster 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"
__C.CNTK.NUM_TRAIN_IMAGES = 500
__C.CNTK.NUM_TEST_IMAGES = 200
__C.CNTK.PROPOSAL_LAYER_SCALES = [8, 16, 32]

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

from utils.configs.MyDataSet_config import cfg as dataset_cfg

and run python run_faster_rcnn.py to train and evaluate Faster R-CNN on your data.

Technical details

Parameters

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

# E2E or 4-stage training
__C.CNTK.TRAIN_E2E = True
# If set to 'True' conv layers weights from the base model will be trained, too
__C.TRAIN_CONV_LAYERS = True

# E2E learning parameters
__C.CNTK.E2E_MAX_EPOCHS = 20
__C.CNTK.E2E_LR_PER_SAMPLE = [0.001] * 10 + [0.0001] * 10 + [0.00001]

# NMS threshold used to discard overlapping predicted bounding boxes
__C.RESULTS_NMS_THRESHOLD = 0.5

Faster R-CNN CNTK code

Most of the code is in FasterRCNN_train.py and FasterRCNN_eval.py (and Examples/Image/Detection/utils/rpn/rpn_helpers.py for the region proposal network). Please see those files for details.

Algorithm

All details regarding the Faster R-CNN algorithm can be found in the original research paper: https://arxiv.org/abs/1506.01497.