It is modified Tensorflow 2.x version of Mask R-CNN

Overview

[TF 2.X] Mask R-CNN for Object Detection and Segmentation

[Notice] : The original mask-rcnn uses the tensorflow 1.X version. I modified it for tensorflow 2.X version.

Development Environment

  • OS : Ubuntu 20.04.2 LTS
  • GPU : Geforce RTX 3090
  • CUDA : 11.2
  • Tensorflow : 2.5.0
  • Keras : 2.5.0 (tensorflow backend)
  • Python 3.8

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.

Instance Segmentation Sample

The repository includes:

  • Source code of Mask R-CNN built on FPN and ResNet101.
  • Training code for MS COCO
  • Pre-trained weights for MS COCO
  • Jupyter notebooks to visualize the detection pipeline at every step
  • ParallelModel class for multi-GPU training
  • Evaluation on MS COCO metrics (AP)
  • Example of training on your own dataset

The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released Matterport3D dataset useful as well. This dataset was created from 3D-reconstructed spaces captured by our customers who agreed to make them publicly available for academic use. You can see more examples here.

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.

Step by Step Detection

To help with debugging and understanding the model, there are 3 notebooks (inspect_data.ipynb, inspect_model.ipynb, inspect_weights.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.

6. Composing the different pieces into a final result

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.

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

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}},
}

Contributing

Contributions to this repository are welcome. Examples of things you can contribute:

  • Speed Improvements. Like re-writing some Python code in TensorFlow or Cython.
  • Training on other datasets.
  • Accuracy Improvements.
  • Visualizations and examples.

You can also join our team and help us build even more projects like this one.

Requirements

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

MS COCO Requirements:

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

If you use Docker, the code has been verified to work on this Docker container.

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).

Projects Using this Model

If you extend this model to other datasets or build projects that use it, we'd love to hear from you.

4K Video Demo by Karol Majek.

Mask RCNN on 4K Video

Images to OSM: Improve OpenStreetMap by adding baseball, soccer, tennis, football, and basketball fields.

Identify sport fields in satellite images

Splash of Color. A blog post explaining how to train this model from scratch and use it to implement a color splash effect.

Balloon Color Splash

Segmenting Nuclei in Microscopy Images. Built for the 2018 Data Science Bowl

Code is in the samples/nucleus directory.

Nucleus Segmentation

Detection and Segmentation for Surgery Robots by the NUS Control & Mechatronics Lab.

Surgery Robot Detection and Segmentation

Reconstructing 3D buildings from aerial LiDAR

A proof of concept project by Esri, in collaboration with Nvidia and Miami-Dade County. Along with a great write up and code by Dmitry Kudinov, Daniel Hedges, and Omar Maher. 3D Building Reconstruction

Usiigaci: Label-free Cell Tracking in Phase Contrast Microscopy

A project from Japan to automatically track cells in a microfluidics platform. Paper is pending, but the source code is released.

Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery

Research project to understand the complex processes between degradations in the Arctic and climate change. By Weixing Zhang, Chandi Witharana, Anna Liljedahl, and Mikhail Kanevskiy. image

Mask-RCNN Shiny

A computer vision class project by HU Shiyu to apply the color pop effect on people with beautiful results.

Mapping Challenge: Convert satellite imagery to maps for use by humanitarian organisations.

Mapping Challenge

GRASS GIS Addon to generate vector masks from geospatial imagery. Based on a Master's thesis by Ondřej Pešek.

GRASS GIS Image

Owner
Milner
UNIST AI, Graduate Student
Milner
iBOT: Image BERT Pre-Training with Online Tokenizer

Image BERT Pre-Training with iBOT Official PyTorch implementation and pretrained models for paper iBOT: Image BERT Pre-Training with Online Tokenizer.

Bytedance Inc. 435 Jan 06, 2023
Label-Free Model Evaluation with Semi-Structured Dataset Representations

Label-Free Model Evaluation with Semi-Structured Dataset Representations Prerequisites This code uses the following libraries Python 3.7 NumPy PyTorch

8 Oct 06, 2022
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis

Readme File for "Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis" by Ham, Imai, and Janson. (2022) All scripts were written and

0 Jan 27, 2022
Deploy optimized transformer based models on Nvidia Triton server

Deploy optimized transformer based models on Nvidia Triton server

Lefebvre Sarrut Services 1.2k Jan 05, 2023
Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

CDAN Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018) New version: https://github.com/thuml/Transfer-Learning-Library Dataset

THUML @ Tsinghua University 363 Dec 20, 2022
FG-transformer-TTS Fine-grained style control in transformer-based text-to-speech synthesis

LST-TTS Official implementation for the paper Fine-grained style control in transformer-based text-to-speech synthesis. Submitted to ICASSP 2022. Audi

Li-Wei Chen 64 Dec 30, 2022
Convert onnx models to pytorch.

onnx2torch onnx2torch is an ONNX to PyTorch converter. Our converter: Is easy to use – Convert the ONNX model with the function call convert; Is easy

ENOT 264 Dec 30, 2022
Asynchronous Advantage Actor-Critic in PyTorch

Asynchronous Advantage Actor-Critic in PyTorch This is PyTorch implementation of A3C as described in Asynchronous Methods for Deep Reinforcement Learn

Reiji Hatsugai 38 Dec 12, 2022
The official PyTorch code for NeurIPS 2021 ML4AD Paper, "Does Thermal data make the detection systems more reliable?"

MultiModal-Collaborative (MMC) Learning Framework for integrating RGB and Thermal spectral modalities This is the official code for NeurIPS 2021 Machi

NeurAI 12 Nov 02, 2022
Official Pytorch implementation of C3-GAN

Official pytorch implemenation of C3-GAN Contrastive Fine-grained Class Clustering via Generative Adversarial Networks [Paper] Authors: Yunji Kim, Jun

NAVER AI 114 Dec 02, 2022
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

Super Resolution Examples We run this script under TensorFlow 2.0 and the TensorLayer2.0+. For TensorLayer 1.4 version, please check release. 🚀 🚀 🚀

TensorLayer Community 2.9k Jan 08, 2023
(ICCV 2021) Official code of "Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit Editing."

Dressing in Order (DiOr) 👚 [Paper] 👖 [Webpage] 👗 [Running this code] The official implementation of "Dressing in Order: Recurrent Person Image Gene

Aiyu Cui 277 Dec 28, 2022
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

eXtreme Gradient Boosting Community | Documentation | Resources | Contributors | Release Notes XGBoost is an optimized distributed gradient boosting l

Distributed (Deep) Machine Learning Community 23.6k Dec 31, 2022
Benchmark for evaluating open-ended generation

OpenMEVA Contributed by Jian Guan, Zhexin Zhang. Thank Jiaxin Wen for DeBugging. OpenMEVA is a benchmark for evaluating open-ended story generation me

25 Nov 15, 2022
GraPE is a Rust/Python library for high-performance Graph Processing and Embedding.

GraPE GraPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both of

AnacletoLab 194 Dec 29, 2022
GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

If you are using this code in your own project, please cite our paper: @inproceedings{awiszus2020toadgan, title={TOAD-GAN: Coherent Style Level Gene

Maren A. 13 Dec 14, 2022
A TensorFlow Implementation of "Deep Multi-Scale Video Prediction Beyond Mean Square Error" by Mathieu, Couprie & LeCun.

Adversarial Video Generation This project implements a generative adversarial network to predict future frames of video, as detailed in "Deep Multi-Sc

Matt Cooper 704 Nov 26, 2022
Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

Pano3D A Holistic Benchmark and a Solid Baseline for 360o Depth Estimation Pano3D is a new benchmark for depth estimation from spherical panoramas. We

Visual Computing Lab, Information Technologies Institute, Centre for Reseach and Technology Hellas 50 Dec 29, 2022
Automatic differentiation with weighted finite-state transducers.

GTN: Automatic Differentiation with WFSTs Quickstart | Installation | Documentation What is GTN? GTN is a framework for automatic differentiation with

100 Dec 29, 2022
Official implementation of deep-multi-trajectory-based single object tracking (IEEE T-CSVT 2021).

DeepMTA_PyTorch Officical PyTorch Implementation of "Dynamic Attention-guided Multi-TrajectoryAnalysis for Single Object Tracking", Xiao Wang, Zhe Che

Xiao Wang(王逍) 7 Dec 03, 2022