PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Overview

Unbiased Teacher for Semi-Supervised Object Detection

License: MIT

This is the PyTorch implementation of our paper:
Unbiased Teacher for Semi-Supervised Object Detection
Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, Peter Vajda
International Conference on Learning Representations (ICLR), 2021

[arXiv] [OpenReview] [Project]

Installation

Prerequisites

  • Linux or macOS with Python ≥ 3.6
  • PyTorch ≥ 1.5 and torchvision that matches the PyTorch installation.

Install PyTorch in Conda env

# create conda env
conda create -n detectron2 python=3.6
# activate the enviorment
conda activate detectron2
# install PyTorch >=1.5 with GPU
conda install pytorch torchvision -c pytorch

Build Detectron2 from Source

Follow the INSTALL.md to install Detectron2.

Dataset download

  1. Download COCO dataset
# download images
wget http://images.cocodataset.org/zips/train2017.zip
wget http://images.cocodataset.org/zips/val2017.zip

# download annotations
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
  1. Organize the dataset as following:
unbiased_teacher/
└── datasets/
    └── coco/
        ├── train2017/
        ├── val2017/
        └── annotations/
        	├── instances_train2017.json
        	└── instances_val2017.json

Training

  • Train the Unbiased Teacher under 1% COCO-supervision
python train_net.py \
      --num-gpus 8 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup1_run1.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 16 SOLVER.IMG_PER_BATCH_UNLABEL 16
  • Train the Unbiased Teacher under 2% COCO-supervision
python train_net.py \
      --num-gpus 8 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup2_run1.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 16 SOLVER.IMG_PER_BATCH_UNLABEL 16
  • Train the Unbiased Teacher under 5% COCO-supervision
python train_net.py \
      --num-gpus 8 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup5_run1.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 16 SOLVER.IMG_PER_BATCH_UNLABEL 16
  • Train the Unbiased Teacher under 10% COCO-supervision
python train_net.py \
      --num-gpus 8 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup10_run1.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 16 SOLVER.IMG_PER_BATCH_UNLABEL 16

Resume the training

python train_net.py \
      --resume \
      --num-gpus 8 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup10_run1.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 16 SOLVER.IMG_PER_BATCH_UNLABEL 16 MODEL.WEIGHTS <your weight>.pth

Evaluation

python train_net.py \
      --eval-only \
      --num-gpus 8 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup10_run1.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 16 SOLVER.IMG_PER_BATCH_UNLABEL 16 MODEL.WEIGHTS <your weight>.pth

Model Zoo

Coming soon

FAQ

  1. Q: Using the lower batch size and fewer GPUs cannot achieve the results presented in the paper?
  • A: We train the model with 32 labeled images + 32 unlabeled images per batch for the results presented in the paper, and using the lower batch size leads to lower accuracy. For example, in the 1% COCO-supervision setting, the model trained with 16 labeled images + 16 unlabeled images achieves 19.9 AP as shown in the following table.
Experiment GPUs Batch size per node Batch size AP
8 GPUs/node; 4 nodes 8 labeled imgs + 8 unlabeled imgs 32 labeled img + 32 unlabeled imgs 20.75
8 GPUs/node; 1 node 16 labeled imgs + 16 unlabeled imgs 16 labeled imgs + 16 unlabeled imgs 19.9

Citing Unbiased Teacher

If you use Unbiased Teacher in your research or wish to refer to the results published in the paper, please use the following BibTeX entry.

@inproceedings{liu2021unbiased,
    title={Unbiased Teacher for Semi-Supervised Object Detection},
    author={Liu, Yen-Cheng and Ma, Chih-Yao and He, Zijian and Kuo, Chia-Wen and Chen, Kan and Zhang, Peizhao and Wu, Bichen and Kira, Zsolt and Vajda, Peter},
    booktitle={Proceedings of the International Conference on Learning Representations (ICLR)},
    year={2021},
}

Also, if you use Detectron2 in your research, please use the following BibTeX entry.

@misc{wu2019detectron2,
  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
                  Wan-Yen Lo and Ross Girshick},
  title =        {Detectron2},
  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
  year =         {2019}
}

License

This project is licensed under MIT License, as found in the LICENSE file.

Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models.

WECHSEL Code for WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models. arXiv: https://arx

Institute of Computational Perception 45 Dec 29, 2022
Reinforcement Learning with Q-Learning Algorithm on gym's frozen lake environment implemented in python

Reinforcement Learning with Q Learning Algorithm Q learning algorithm is trained on the gym's frozen lake environment. Libraries Used gym Numpy tqdm P

1 Nov 10, 2021
OpenVisionAPI server

🚀 Quick start An instance of ova-server is free and publicly available here: https://api.openvisionapi.com Checkout ova-client for a quick demo. Inst

Open Vision API 93 Nov 24, 2022
On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition

On the Analysis of French Phonetic Idiosyncrasies for Accent Recognition With the spirit of reproducible research, this repository contains codes requ

0 Feb 24, 2022
pytorch implementation of trDesign

trdesign-pytorch This repository is a PyTorch implementation of the trDesign paper based on the official TensorFlow implementation. The initial port o

Learn Ventures Inc. 41 Dec 29, 2022
A Python package for generating concise, high-quality summaries of a probability distribution

GoodPoints A Python package for generating concise, high-quality summaries of a probability distribution GoodPoints is a collection of tools for compr

Microsoft 28 Oct 10, 2022
g2o: A General Framework for Graph Optimization

g2o - General Graph Optimization Linux: Windows: g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has bee

Rainer Kümmerle 2.5k Dec 30, 2022
Mind the Trade-off: Debiasing NLU Models without Degrading the In-distribution Performance

Models for natural language understanding (NLU) tasks often rely on the idiosyncratic biases of the dataset, which make them brittle against test cases outside the training distribution.

Ubiquitous Knowledge Processing Lab 22 Jan 02, 2023
This repository contains a toolkit for collecting, labeling and tracking object keypoints

This repository contains a toolkit for collecting, labeling and tracking object keypoints. Object keypoints are semantic points in an object's coordinate frame.

ETHZ ASL 13 Dec 12, 2022
Dataset and Code for the paper "DepthTrack: Unveiling the Power of RGBD Tracking" (ICCV2021), and "Depth-only Object Tracking" (BMVC2021)

DeT and DOT Code and datasets for "DepthTrack: Unveiling the Power of RGBD Tracking" (ICCV2021) "Depth-only Object Tracking" (BMVC2021) @InProceedings

Yan Song 55 Dec 15, 2022
Python Implementation of algorithms in Graph Mining, e.g., Recommendation, Collaborative Filtering, Community Detection, Spectral Clustering, Modularity Maximization, co-authorship networks.

Graph Mining Author: Jiayi Chen Time: April 2021 Implemented Algorithms: Network: Scrabing Data, Network Construbtion and Network Measurement (e.g., P

Jiayi Chen 3 Mar 03, 2022
ICCV2021 - Mining Contextual Information Beyond Image for Semantic Segmentation

Introduction The official repository for "Mining Contextual Information Beyond Image for Semantic Segmentation". Our full code has been merged into ss

55 Nov 09, 2022
This folder contains the implementation of the multi-relational attribute propagation algorithm.

MrAP This folder contains the implementation of the multi-relational attribute propagation algorithm. It requires the package pytorch-scatter. Please

6 Dec 06, 2022
Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation (ICCV 2021) This repository contains the implementation of th

Yuhang Zang 21 Dec 17, 2022
A hybrid SOTA solution of LiDAR panoptic segmentation with C++ implementations of point cloud clustering algorithms. ICCV21, Workshop on Traditional Computer Vision in the Age of Deep Learning

ICCVW21-TradiCV-Survey-of-LiDAR-Cluster Motivation In contrast to popular end-to-end deep learning LiDAR panoptic segmentation solutions, we propose a

YimingZhao 103 Nov 22, 2022
Causal Imitative Model for Autonomous Driving

Causal Imitative Model for Autonomous Driving Mohammad Reza Samsami, Mohammadhossein Bahari, Saber Salehkaleybar, Alexandre Alahi. arXiv 2021. [Projec

VITA lab at EPFL 8 Oct 04, 2022
BED: A Real-Time Object Detection System for Edge Devices

BED: A Real-Time Object Detection System for Edge Devices About this project Thi

Data Analytics Lab at Texas A&M University 44 Nov 18, 2022
Orbivator AI - To Determine which features of data (measurements) are most important for diagnosing breast cancer and find out if breast cancer occurs or not.

Orbivator_AI Breast Cancer Wisconsin (Diagnostic) GOAL To Determine which features of data (measurements) are most important for diagnosing breast can

anurag kumar singh 1 Jan 02, 2022
Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021)

Transferable Semantic Augmentation for Domain Adaptation Code release for "Transferable Semantic Augmentation for Domain Adaptation" (CVPR 2021) Paper

66 Dec 16, 2022