Hack the North 2019

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

The repository includes:

Source code of Mask R-CNN built on FPN and ResNet101.
Jupyter notebooks to visualize the detection pipeline at every step
ParallelModel class for multi-GPU training

Getting Started

(model.py, utils.py, config.py): These files contain the main Mask RCNN implementation.
inspect_treefall_data.ipynb. This notebook visualizes the different pre-processing steps to prepare the training data.
inspect_treefall_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.

Step by Step Detection

To help with debugging and understanding the model, there are 2 notebooks (inspect_treefall_data.ipynb and inspect_treefall_model.ipynb that provide a lot of visualizations and allow running the model step by step to inspect the output at each point. Here are a few examples:

1. Anchor sorting and filtering

Visualizes every step of the first stage Region Proposal Network and displays positive and negative anchors along with anchor box refinement.

2. Bounding Box Refinement

This is an example of final detection boxes (dotted lines) and the refinement applied to them (solid lines) in the second stage.

3. Mask Generation

Examples of generated masks. These then get scaled and placed on the image in the right location.

4.Layer activations

Often it's useful to inspect the activations at different layers to look for signs of trouble (all zeros or random noise).

5. Weight Histograms

Another useful debugging tool is to inspect the weight histograms. These are included in the inspect_weights.ipynb notebook.

6. Logging to TensorBoard

TensorBoard is another great debugging and visualization tool. The model is configured to log losses and save weights at the end of every epoch.

7. Composing the different pieces into a final result

Differences from the Official Paper

This implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do let us know.

Image Resizing: To support training multiple images per batch we resize all images to the same size. For example, 1024x1024px on MS COCO. We preserve the aspect ratio, so if an image is not square we pad it with zeros. In the paper the resizing is done such that the smallest side is 800px and the largest is trimmed at 1000px.
Bounding Boxes: Some datasets provide bounding boxes and some provide masks only. To support training on multiple datasets we opted to ignore the bounding boxes that come with the dataset and generate them on the fly instead. We pick the smallest box that encapsulates all the pixels of the mask as the bounding box. This simplifies the implementation and also makes it easy to apply image augmentations that would otherwise be harder to apply to bounding boxes, such as image rotation.

To validate this approach, we compared our computed bounding boxes to those provided by the COCO dataset. We found that ~2% of bounding boxes differed by 1px or more, ~0.05% differed by 5px or more, and only 0.01% differed by 10px or more.
Learning Rate: The paper uses a learning rate of 0.02, but we found that to be too high, and often causes the weights to explode, especially when using a small batch size. It might be related to differences between how Caffe and TensorFlow compute gradients (sum vs mean across batches and GPUs). Or, maybe the official model uses gradient clipping to avoid this issue. We do use gradient clipping, but don't set it too aggressively. We found that smaller learning rates converge faster anyway so we go with that.

Citation

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

Requirements

Python 3.4, TensorFlow 1.3, Keras 2.0.8 and other common packages listed in requirements.txt.

Installation

Install dependencies
```
pip3 install -r requirements.txt
```
Clone this repository
Run setup from the repository root directory
```
python3 setup.py install
```
Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page.

sanaasy/htn2019