shreeram0206/faster_rcnn

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Faster R-CNN for Object Detection and Segmentation

This is an implementation of Faster R-CNN on Python 3, Keras, and TensorFlow. The model generates bounding boxes for each instance of an object in the image. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.

Getting Started

• demo.ipynb Is the easiest way to start. It shows an example of using a model pre-trained on MS COCO to segment objects in your own images. It includes code to run object detection and instance segmentation on arbitrary images.

• train_shapes.ipynb shows how to train Mask R-CNN on your own dataset. This notebook introduces a toy dataset (Shapes) to demonstrate training on a new dataset.

• (model.py, utils.py, config.py): These files contain the main Mask RCNN implementation.

• inspect_data.ipynb. This notebook visualizes the different pre-processing steps to prepare the training data.

• inspect_model.ipynb This notebook goes in depth into the steps performed to detect and segment objects. It provides visualizations of every step of the pipeline.

• inspect_weights.ipynb This notebooks inspects the weights of a trained model and looks for anomalies and odd patterns.

Training on MS COCO

We're providing pre-trained weights for MS COCO to make it easier to start. You can use those weights as a starting point to train your own variation on the network. Training and evaluation code is in samples/coco/coco.py. You can import this module in Jupyter notebook (see the provided notebooks for examples) or you can run it directly from the command line as such:

# Train a new model starting from pre-trained COCO weights
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=coco

# Train a new model starting from ImageNet weights
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=imagenet

# Continue training a model that you had trained earlier
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5

# Continue training the last model you trained. This will find
# the last trained weights in the model directory.
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=last

You can also run the COCO evaluation code with:

# Run COCO evaluation on the last trained model
python3 samples/coco/coco.py evaluate --dataset=/path/to/coco/ --model=last

The training schedule, learning rate, and other parameters should be set in samples/coco/coco.py.

Training on Your Own Dataset

Start by reading this blog post about the balloon color splash sample. It covers the process starting from annotating images to training to using the results in a sample application.

In summary, to train the model on your own dataset you'll need to extend two classes:

Config This class contains the default configuration. Subclass it and modify the attributes you need to change.

Dataset This class provides a consistent way to work with any dataset. It allows you to use new datasets for training without having to change the code of the model. It also supports loading multiple datasets at the same time, which is useful if the objects you want to detect are not all available in one dataset.

See examples in samples/shapes/train_shapes.ipynb, samples/coco/coco.py, samples/balloon/balloon.py, and samples/nucleus/nucleus.py.

Citation

Use this bibtex to cite this repository:

@misc{matterport_maskrcnn_2017,
  title={Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow},
  author={Waleed Abdulla},
  year={2017},
  publisher={Github},
  journal={GitHub repository},
  howpublished={\url{https://github.com/matterport/Mask_RCNN}},
}

Requirements

Python 3.6, TensorFlow 2.0, and other common packages listed in requirements.txt.

MS COCO Requirements:

To train or test on MS COCO, you'll also need:

• pycocotools (installation instructions below)
• MS COCO Dataset
• Download the 5K minival and the 35K validation-minus-minival subsets. More details in the original Faster R-CNN implementation.

Installation

1. Clone this repository

2. Install dependencies

   pip3 install -r requirements.txt

3. Run setup from the repository root directory

   python3 setup.py install

4. Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page.

5. (Optional) To train or test on MS COCO install pycocotools from one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore).

   • Linux: https://github.com/waleedka/coco
   • Windows: https://github.com/philferriere/cocoapi. You must have the Visual C++ 2015 build tools on your path (see the repo for additional details)