DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Related tags

Deep Learningweakly-supervised-learninginstance-segmentationsemantic-correspondence
Overview

NVIDIA Source Code License Python 3.8

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Paper | Project page | Demo (Youtube) | Demo (Bilibili)

DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision.
Shiyi Lan, Zhiding Yu, Chris Choy, Subhashree Radhakrishnan, Guilin Liu, Yuke Zhu, Larry Davis, Anima Anandkumar
International Conference on Computer Vision (ICCV) 2021

This repository contains the official Pytorch implementation of training & evaluation code and pretrained models for DiscoBox. DiscoBox is a state of the art framework that can jointly predict high quality instance segmentation and semantic correspondence from box annotations.

We use MMDetection v2.10.0 as the codebase.

All of our models are trained and tested using automatic mixed precision, which leverages float16 for speedup and less GPU memory consumption.

Installation

This implementation is based on PyTorch==1.9.0, mmcv==2.13.0, and mmdetection==2.10.0

Please refer to get_started.md for installation.

Or you can download the docker image from our dockerhub repository.

Models

Results on COCO val 2017

Backbone Weights AP [email protected] [email protected] [email protected] [email protected] [email protected]
ResNet-50 download 30.7 52.6 30.6 13.3 34.1 45.6
ResNet-101-DCN download 35.3 59.1 35.4 16.9 39.2 53.0
ResNeXt-101-DCN download 37.3 60.4 39.1 17.8 41.1 55.4

Results on COCO test-dev

We also evaluate the models in the section Results on COCO val 2017 with the same weights on COCO test-dev.

Backbone Weights AP [email protected] [email protected] [email protected] [email protected] [email protected]
ResNet-50 download 32.0 53.6 32.6 11.7 33.7 48.4
ResNet-101-DCN download 35.8 59.8 36.4 16.9 38.7 52.1
ResNeXt-101-DCN download 37.9 61.4 40.0 18.0 41.1 53.9

Training

COCO

ResNet-50 (8 GPUs):

bash tools/dist_train.sh \
     configs/discobox/discobox_solov2_r50_fpn_3x.py 8

ResNet-101-DCN (8 GPUs):

bash tools/dist_train.sh \
     configs/discobox/discobox_solov2_r101_dcn_fpn_3x.py 8

ResNeXt-101-DCN (8 GPUs):

bash tools/dist_train.sh \
     configs/discobox/discobox_solov2_x101_dcn_fpn_3x.py 8

Pascal VOC 2012

ResNet-50 (4 GPUs):

bash tools/dist_train.sh \
     configs/discobox/discobox_solov2_voc_r50_fpn_6x.py 4

ResNet-101 (4 GPUs):

bash tools/dist_train.sh \
     configs/discobox/discobox_solov2_voc_r101_fpn_6x.py 4

Testing

COCO

ResNet-50 (8 GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_r50_fpn_3x.py \
     work_dirs/coco_r50_fpn_3x.pth 8 --eval segm

ResNet-101-DCN (8 GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_r101_dcn_fpn_3x.py \
     work_dirs/coco_r101_dcn_fpn_3x.pth 8 --eval segm

ResNeXt-101-DCN (GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_x101_dcn_fpn_3x_fp16.py \
     work_dirs/coco_x101_dcn_fpn_3x.pth 8 --eval segm

Pascal VOC 2012 (COCO API)

ResNet-50 (4 GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_voc_r50_fpn_3x_fp16.py \
     work_dirs/voc_r50_6x.pth 4 --eval segm

ResNet-101 (4 GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_voc_r101_fpn_3x_fp16.py \
     work_dirs/voc_r101_6x.pth 4 --eval segm

Pascal VOC 2012 (Matlab)

Step 1: generate results

ResNet-50 (4 GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_voc_r50_fpn_3x_fp16.py \
     work_dirs/voc_r50_6x.pth 4 \
     --format-only \
     --options "jsonfile_prefix=work_dirs/voc_r50_results.json"

ResNet-101 (4 GPUs):

bash tools/dist_test.sh \
     configs/discobox/discobox_solov2_voc_r101_fpn_3x_fp16.py \
     work_dirs/voc_r101_6x.pth 4 \
     --format-only \
     --options "jsonfile_prefix=work_dirs/voc_r101_results.json"

Step 2: format conversion

ResNet-50:

python tools/json2mat.pywork_dirs/voc_r50_results.json work_dirs/voc_r50_results.mat

ResNet-101:

python tools/json2mat.pywork_dirs/voc_r101_results.json work_dirs/voc_r101_results.mat

Step 3: evaluation

Please visit BBTP for the evaluation code written in Matlab.

PF-Pascal

Please visit this repository.

LICENSE

Please check the LICENSE file. DiscoBox may be used non-commercially, meaning for research or evaluation purposes only. For business inquiries, please contact [email protected].

Citation

@article{lan2021discobox,
  title={DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision},
  author={Lan, Shiyi and Yu, Zhiding and Choy, Christopher and Radhakrishnan, Subhashree and Liu, Guilin and Zhu, Yuke and Davis, Larry S and Anandkumar, Anima},
  journal={arXiv preprint arXiv:2105.06464},
  year={2021}
}
Owner
Shiyi Lan
PhD Candidate. Research Interests: Object Detection, Instance segmentation, 3D Object Detection, 3D vehicle trajectory, Weakly/Semi-supervised learning
Shiyi Lan
GitHub Repository https://arxiv.org/abs/2105.06464
Implement A3C for Mujoco gym envs

pytorch-a3c-mujoco Disclaimer: my implementation right now is unstable (you ca refer to the learning curve below), I'm not sure if it's my problems. A

Andrew 70 Dec 12, 2022
RRL: Resnet as representation for Reinforcement Learning

Resnet as representation for Reinforcement Learning (RRL) is a simple yet effective approach for training behaviors directly from visual inputs. We demonstrate that features learned by standard image

Meta Research 21 Dec 07, 2022
Provide partial dates and retain the date precision through processing

Prefix date parser This is a helper class to parse dates with varied degrees of precision. For example, a data source might state a date as 2001, 2001

Friedrich Lindenberg 13 Dec 14, 2022
Iterative Normalization: Beyond Standardization towards Efficient Whitening

IterNorm Code for reproducing the results in the following paper: Iterative Normalization: Beyond Standardization towards Efficient Whitening Lei Huan

Lei Huang 21 Dec 27, 2022
Simulation of moving particles under microscopic imaging

Simulation of moving particles under microscopic imaging Install scipy numpy scikit-image tiffile Run python simulation.py Read result https://imagej

Zehao Wang 2 Dec 14, 2021
Official git repo for the CHIRP project

CHIRP Project This is the official git repository for the CHIRP project. Pull requests are accepted here, but for the moment, the main repository is s

Dan Smith 77 Jan 08, 2023
Bringing Characters to Life with Computer Brains in Unity

AI4Animation: Deep Learning for Character Control This project explores the opportunities of deep learning for character animation and control as part

Sebastian Starke 5.5k Jan 04, 2023
Semantic segmentation models, datasets and losses implemented in PyTorch.

Semantic Segmentation in PyTorch Semantic Segmentation in PyTorch Requirements Main Features Models Datasets Losses Learning rate schedulers Data augm

Yassine 1.3k Jan 07, 2023
WormMovementSimulation - 3D Simulation of Worm Body Movement with Neurons attached to its body

Generate 3D Locomotion Data This module is intended to create 2D video trajector

1 Aug 09, 2022
RL agent to play μRTS with Stable-Baselines3

Gym-μRTS with Stable-Baselines3/PyTorch This repo contains an attempt to reproduce Gridnet PPO with invalid action masking algorithm to play μRTS usin

Oleksii Kachaiev 24 Nov 11, 2022
This is a model made out of Neural Network specifically a Convolutional Neural Network model

This is a model made out of Neural Network specifically a Convolutional Neural Network model. This was done with a pre-built dataset from the tensorflow and keras packages. There are other alternativ

9 Oct 18, 2022
A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented in Python.

Reinforcement-Learning-Notebooks A collection of Reinforcement Learning algorithms from Sutton and Barto's book and other research papers implemented

Pulkit Khandelwal 1k Dec 28, 2022
Concept drift monitoring for HA model servers.

{Fast, Correct, Simple} - pick three Easily compare training and production ML data & model distributions Goals Boxkite is an instrumentation library

98 Dec 15, 2022
Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

258 Dec 29, 2022
Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Time Using Noisy Proxies

Deconfounding Temporal Autoencoder (DTA) This is a repository for the paper "Deconfounding Temporal Autoencoder: Estimating Treatment Effects over Tim

Milan Kuzmanovic 3 Feb 04, 2022
A `Neural = Symbolic` framework for sound and complete weighted real-value logic

Logical Neural Networks LNNs are a novel Neuro = symbolic framework designed to seamlessly provide key properties of both neural nets (learning) and s

International Business Machines 138 Dec 19, 2022
Disagreement-Regularized Imitation Learning

Due to a normalization bug the expert trajectories have lower performance than the rl_baseline_zoo reported experts. Please see the following link in

Kianté Brantley 25 Apr 28, 2022
Deep functional residue identification

DeepFRI Deep functional residue identification Citing @article {Gligorijevic2019, author = {Gligorijevic, Vladimir and Renfrew, P. Douglas and Koscio

Flatiron Institute 156 Dec 25, 2022
Implementation of our paper "DMT: Dynamic Mutual Training for Semi-Supervised Learning"

DMT: Dynamic Mutual Training for Semi-Supervised Learning This repository contains the code for our paper DMT: Dynamic Mutual Training for Semi-Superv

Zhengyang Feng 120 Dec 30, 2022
An implementation of "Learning human behaviors from motion capture by adversarial imitation"

Merel-MoCap-GAIL An implementation of Merel et al.'s paper on generative adversarial imitation learning (GAIL) using motion capture (MoCap) data: Lear

Yu-Wei Chao 34 Nov 12, 2022