Boundary IoU API (Beta version)

Overview

Boundary IoU API (Beta version)

Bowen Cheng, Ross Girshick, Piotr Dollár, Alexander C. Berg, Alexander Kirillov

[arXiv] [Project] [BibTeX]

This API is an experimental version of Boundary IoU for 5 datasets:

To install Boundary IoU API, run:

pip install git+https://github.com/bowenc0221/boundary-iou-api.git

or

git clone [email protected]:bowenc0221/boundary-iou-api.git
cd boundary_iou_api
pip install -e .

Summary of usage

We provide two ways to use this api, you can either replace imports with our api or do offline evaluation.

Replacing imports

Our Boundary IoU API supports both evaluation with Mask IoU and Boundary IoU with the same interface as original ones. Thus, you only need to change the import, without worried about breaking your existing code.

  1. COCO instance segmentation
    replace

    from pycocotools.coco import COCO
    from pycocotools.cocoeval import COCOeval

    with

    from boundary_iou.coco_instance_api.coco import COCO
    from boundary_iou.coco_instance_api.cocoeval import COCOeval

    and set

    COCOeval(..., iouType="boundary")
  2. LVIS instance segmentation
    replace

    from lvis import LVISEval

    with

    from boundary_iou.lvis_instance_api.eval import LVISEval

    and set

    LVISEval(..., iou_type="boundary")
  3. Cityscapes instance segmentation
    replace

    import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval

    with

    import boundary_iou.cityscapes_instance_api.evalInstanceLevelSemanticLabeling as cityscapes_eval

    and set

    cityscapes_eval.args.iou_type = "boundary"
  4. COCO panoptic segmentation
    replace

    from panopticapi.evaluation import pq_compute

    with

    from boundary_iou.coco_panoptic_api.evaluation import pq_compute

    and set

    pq_compute(..., iou_type="boundary")
  5. Cityscapes panoptic segmentation
    replace

    from cityscapesscripts.evaluation.evalPanopticSemanticLabeling as evaluatePanoptic

    with

    from boundary_iou.cityscapes_panoptic_api.evalPanopticSemanticLabeling import evaluatePanoptic

    and set

    evaluatePanoptic(..., iou_type="boundary")

Offline evaluation

We also provide evaluation code that can evaluates your prediction files for each dataset.

  1. COCO instance segmentation

    python ./tools/coco_instance_evaluation.py \
        --gt-json-file COCO_GT_JSON \
        --dt-json-file COCO_DT_JSON \
        --iou-type boundary
  2. LVIS instance segmentation

    python ./tools/lvis_instance_evaluation.py \
        --gt-json-file LVIS_GT_JSON \
        --dt-json-file LVIS_DT_JSON \
        --iou-type boundary
  3. Cityscapes instance segmentation

    python ./tools/cityscapes_instance_evaluation.py \
        --gt_dir GT_DIR \
        --result_dir RESULT_DIR \
        --iou-type boundary
  4. COCO panoptic segmentation

    python ./tools/coco_panoptic_evaluation.py \
        --gt_json_file PANOPTIC_GT_JSON \
        --gt_folder PANOPTIC_GT_DIR \
        --pred_json_file PANOPTIC_PRED_JSON \
        --pred_folder PANOPTIC_PRED_DIR \
        --iou-type boundary
  5. Cityscapes panoptic segmentation

    python ./tools/cityscapes_panoptic_evaluation.py \
        --gt_json_file PANOPTIC_GT_JSON \
        --gt_folder PANOPTIC_GT_DIR \
        --pred_json_file PANOPTIC_PRED_JSON \
        --pred_folder PANOPTIC_PRED_DIR \
        --iou-type boundary

Citing Boundary IoU

If you find Boundary IoU helpful in your research or wish to refer to the referenced results, please use the following BibTeX entry.

@inproceedings{cheng2021boundary,
  title={Boundary {IoU}: Improving Object-Centric Image Segmentation Evaluation},
  author={Bowen Cheng and Ross Girshick and Piotr Doll{\'a}r and Alexander C. Berg and Alexander Kirillov},
  booktitle={CVPR},
  year={2021}
}

Contact

If you have any questions regarding this API, please contact us at bcheng9 AT illinois.edu

Owner
Bowen Cheng
Ph.D. at University of Illinois Urbana-Champaign
Bowen Cheng
Image Recognition using Pytorch

PyTorch Project Template A simple and well designed structure is essential for any Deep Learning project, so after a lot practice and contributing in

Sarat Chinni 1 Nov 02, 2021
Synthesize photos from PhotoDNA using machine learning 🌱

Ribosome Synthesize photos from PhotoDNA. See the blog post for more information. Installation Dependencies You can install Python dependencies using

Anish Athalye 112 Nov 23, 2022
Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Chris Donahue 98 Dec 14, 2022
This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

README This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et a

Raghav 42 Dec 15, 2022
Learning What and Where to Draw

###Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee This is the code for our NIPS 201

Scott Ellison Reed 337 Nov 18, 2022
Artifacts for paper "MMO: Meta Multi-Objectivization for Software Configuration Tuning"

MMO: Meta Multi-Objectivization for Software Configuration Tuning This repository contains the data and code for the following paper that is currently

0 Nov 17, 2021
House_prices_kaggle - Predict sales prices and practice feature engineering, RFs, and gradient boosting

House Prices - Advanced Regression Techniques Predicting House Prices with Machine Learning This project is build to enhance my knowledge about machin

Gurpreet Singh 1 Jan 01, 2022
JstDoS - HTTP Protocol Stack Remote Code Execution Vulnerability

jstDoS If you are going to skid that, please give credits ! ^^ ¿How works? This

apolo 4 Feb 11, 2022
Official implementation of the article "Unsupervised JPEG Domain Adaptation For Practical Digital Forensics"

Unsupervised JPEG Domain Adaptation for Practical Digital Image Forensics @WIFS2021 (Montpellier, France) Rony Abecidan, Vincent Itier, Jeremie Boulan

Rony Abecidan 6 Jan 06, 2023
Visual Memorability for Robotic Interestingness via Unsupervised Online Learning (ECCV 2020 Oral and TRO)

Visual Interestingness Refer to the project description for more details. This code based on the following paper. Chen Wang, Yuheng Qiu, Wenshan Wang,

Chen Wang 36 Sep 08, 2022
Training Very Deep Neural Networks Without Skip-Connections

DiracNets v2 update (January 2018): The code was updated for DiracNets-v2 in which we removed NCReLU by adding per-channel a and b multipliers without

Sergey Zagoruyko 585 Oct 12, 2022
Pretraining Representations For Data-Efficient Reinforcement Learning

Pretraining Representations For Data-Efficient Reinforcement Learning Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Ch

Mila 40 Dec 11, 2022
BuildingNet: Learning to Label 3D Buildings

BuildingNet This is the implementation of the BuildingNet architecture described in this paper: Paper: BuildingNet: Learning to Label 3D Buildings Arx

16 Nov 07, 2022
Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Demetri Pananos 9 Oct 04, 2022
Latex code for making neural networks diagrams

PlotNeuralNet Latex code for drawing neural networks for reports and presentation. Have a look into examples to see how they are made. Additionally, l

Haris Iqbal 18.6k Jan 01, 2023
This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust.

Demo BERT ONNX pipeline written in rust This demo showcase the use of onnxruntime-rs with a GPU on CUDA 11 to run Bert in a data pipeline with Rust. R

Xavier Tao 14 Dec 17, 2022
Resources related to our paper "CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain"

CLIN-X (CLIN-X-ES) & (CLIN-X-EN) This repository holds the companion code for the system reported in the paper: "CLIN-X: pre-trained language models a

Bosch Research 4 Dec 05, 2022
ComputerVision - This repository aims at realized easy network architecture

ComputerVision This repository aims at realized easy network architecture Colori

DongDong 4 Dec 14, 2022
Neural-net-from-scratch - A simple Neural Network from scratch in Python using the Pymathrix library

A Simple Neural Network from scratch A Simple Neural Network from scratch in Pyt

Youssef Chafiqui 2 Jan 07, 2022
The Python ensemble sampling toolkit for affine-invariant MCMC

emcee The Python ensemble sampling toolkit for affine-invariant MCMC emcee is a stable, well tested Python implementation of the affine-invariant ense

Dan Foreman-Mackey 1.3k Dec 31, 2022