Implementation of DropLoss for Long-Tail Instance Segmentation in Pytorch

Overview

[AAAI 2021]DropLoss for Long-Tail Instance Segmentation

[AAAI 2021] DropLoss for Long-Tail Instance Segmentation
Ting-I Hsieh*, Esther Robb*, Hwann-Tzong Chen, Jia-Bin Huang.
Association for the Advancement of Artificial Intelligence (AAAI), 2021

Image Figure: Measuring the performance tradeoff. Comparison between rare, common, and frequent categories AP for baselines and our method. We visualize the tradeoff for ‘common vs. frequent’ and ‘rare vs. frequent’as a Pareto frontier, where the top-right position indicates an ideal tradeoff between objectives. DropLoss achieves an improved tradeoff between object categories, resulting in higher overall AP.

This project is a pytorch implementation of DropLoss for Long-Tail Instance Segmentation. DropLoss improves long-tail instance segmentation by adaptively removing discouraging gradients to infrequent classes. A majority of the code is modified from facebookresearch/detectron2 and tztztztztz/eql.detectron2.

Progress

  • Training code.
  • Evaluation code.
  • LVIS v1.0 datasets.
  • Provide checkpoint model.

Installation

Requirements

  • Linux or macOS with Python = 3.7
  • PyTorch = 1.4 and torchvision that matches the PyTorch installation. Install them together at pytorch.org to make sure of this
  • OpenCV (optional but needed for demos and visualization)

Build Detectron2 from Source

gcc & g++ ≥ 5 are required. ninja is recommended for faster build.

After installing them, run:

python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'
# (add --user if you don't have permission)

# Or, to install it from a local clone:
git clone https://github.com/facebookresearch/detectron2.git
python -m pip install -e detectron2


# Or if you are on macOS
CC=clang CXX=clang++ ARCHFLAGS="-arch x86_64" python -m pip install ......

Remove the latest fvcore package and install an older version:

pip uninstall fvcore
pip install fvcore==0.1.1.post200513

LVIS Dataset

Following the instructions of README.md to set up the LVIS dataset.

Training

To train a model with 8 GPUs run:

cd /path/to/detectron2/projects/DropLoss
python train_net.py --config-file configs/droploss_mask_rcnn_R_50_FPN_1x.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly:

cd /path/to/detectron2/projects/DropLoss
python train_net.py --config-file configs/droploss_mask_rcnn_R_50_FPN_1x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Citing DropLoss

If you use DropLoss, please use the following BibTeX entry.

@inproceedings{DBLP:conf/aaai/Ting21,
  author 	= {Hsieh, Ting-I and Esther Robb and Chen, Hwann-Tzong and Huang, Jia-Bin},
  title     = {DropLoss for Long-Tail Instance Segmentation},
  booktitle = {Proceedings of the Workshop on Artificial Intelligence Safety 2021
               (SafeAI 2021) co-located with the Thirty-Fifth {AAAI} Conference on
               Artificial Intelligence {(AAAI} 2021), Virtual, February 8, 2021},
  year      = {2021}
  }
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286

Pytorch-DPPO Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286 Using PPO with clip loss (from https

Alexis David Jacq 163 Dec 26, 2022
A library for differentiable nonlinear optimization.

Theseus A library for differentiable nonlinear optimization built on PyTorch to support constructing various problems in robotics and vision as end-to

Meta Research 1.1k Dec 30, 2022
Self-Supervised CNN-GCN Autoencoder

GCNDepth Self-Supervised CNN-GCN Autoencoder GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network To be published

53 Dec 14, 2022
COVINS -- A Framework for Collaborative Visual-Inertial SLAM and Multi-Agent 3D Mapping

COVINS -- A Framework for Collaborative Visual-Inertial SLAM and Multi-Agent 3D Mapping Version 1.0 COVINS is an accurate, scalable, and versatile vis

ETHZ V4RL 183 Dec 27, 2022
Detection of PCBA defect

Detection_of_PCBA_defect Detection_of_PCBA_defect Use yolov5 to train. $pip install -r requirements.txt Detect.py will detect file(jpg,mp4...) in cu

6 Nov 28, 2022
[ICLR 2021] Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization

Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga, Adrien Gaidon, Tengyu Ma

Kaidi Cao 29 Oct 20, 2022
Light-weight network, depth estimation, knowledge distillation, real-time depth estimation, auxiliary data.

light-weight-depth-estimation Boosting Light-Weight Depth Estimation Via Knowledge Distillation, https://arxiv.org/abs/2105.06143 Junjie Hu, Chenyou F

Junjie Hu 13 Dec 10, 2022
A script written in Python that returns a consensus string and profile matrix of a given DNA string(s) in FASTA format.

A script written in Python that returns a consensus string and profile matrix of a given DNA string(s) in FASTA format.

Zain 1 Feb 01, 2022
An efficient and easy-to-use deep learning model compression framework

TinyNeuralNetwork 简体中文 TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework, which contains features like neura

Alibaba 441 Dec 25, 2022
GEA - Code for Guided Evolution for Neural Architecture Search

Efficient Guided Evolution for Neural Architecture Search Usage Create a conda e

6 Jan 03, 2023
Detectorch - detectron for PyTorch

Detectorch - detectron for PyTorch (Disclaimer: this is work in progress and does not feature all the functionalities of detectron. Currently only inf

Ignacio Rocco 558 Dec 23, 2022
2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup)智能人机交互自然语言理解赛道第二名参赛解决方案

2021 CCF BDCI 全国信息检索挑战杯(CCIR-Cup) 智能人机交互自然语言理解赛道第二名解决方案 比赛网址: CCIR-Cup-智能人机交互自然语言理解 1.依赖环境: python==3.8 torch==1.7.1+cu110 numpy==1.19.2 transformers=

JinXiang 22 Oct 29, 2022
(ICONIP 2020) MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image

MobileHand: Real-time 3D Hand Shape and Pose Estimation from Color Image This repo contains the source code for MobileHand, real-time estimation of 3D

90 Dec 12, 2022
GndNet: Fast ground plane estimation and point cloud segmentation for autonomous vehicles using deep neural networks.

GndNet: Fast Ground plane Estimation and Point Cloud Segmentation for Autonomous Vehicles. Authors: Anshul Paigwar, Ozgur Erkent, David Sierra Gonzale

Anshul Paigwar 114 Dec 29, 2022
Perform zero-order Hankel Transform for an 1D array (float or real valued).

perform zero-order Hankel Transform for an 1D array (float or real valued). An discrete form of Parseval theorem is guaranteed. Suit for iterative problems.

1 Jan 17, 2022
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
PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more

PyTorch Image Models Sponsors What's New Introduction Models Features Results Getting Started (Documentation) Train, Validation, Inference Scripts Awe

Ross Wightman 22.9k Jan 09, 2023
REGTR: End-to-end Point Cloud Correspondences with Transformers

REGTR: End-to-end Point Cloud Correspondences with Transformers This repository contains the source code for REGTR. REGTR utilizes multiple transforme

Zi Jian Yew 108 Dec 17, 2022