Skip to content

Robust Teacher: Self-Correcting Guided Robust Semi-Supervised Learning for Object Detection

Notifications You must be signed in to change notification settings

Complicateddd/RobustT

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Robust Teacher : Self-Correcting Pseudo-Labels Guided Robust Semi-Supervised Learning for Object Detection

(2022.6) PyTorch Implements Early Version Release

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

Clone this repo

git clone https://github.com/Complicateddd/RobustT.git

Build Detectron2 from Source

Follow the INSTALL.md to install Detectron2.

Note: Follow our specific detectron2 components with README.md to modify base detection framework.

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. Download Pascal VOC dataset from VOC challenges website.
  2. Update your project dataset position in 'detectron2/data/datasets/builtin.py'
  3. We provide the whole dataset necessary info file, you can download them from VOC_COCO_info, password: l2h5

Training

  • Train the Robust Teacher under 10% COCO-supervision
python train_net.py \
      --num-gpus 2 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup10_run1_weak.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 4 SOLVER.IMG_PER_BATCH_UNLABEL 4 \
       OUTPUT_DIR ./output10COCO
  • Train the Robust Teacher under VOC07 (as labeled set) and VOC12 (as unlabeled set)
python train_net.py \
      --num-gpus 2 \
      --config configs/voc/weak_super_voc07_voc12.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 4 SOLVER.IMG_PER_BATCH_UNLABEL 4
       OUTPUT_DIR ./outputvoc0712
  • Train the Robust Teacher under VOC07 (as labeled set) and VOC12+COCO20cls (as unlabeled set)
python train_net.py \
      --num-gpus 2 \
      --config configs/voc/weak_super_voc07_voc12coco20.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 4 SOLVER.IMG_PER_BATCH_UNLABEL 4 \
       OUTPUT_DIR ./outputvoc0712cococls

Resume the training

python train_net.py \
      --resume \
      --num-gpus 2 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup10_run1_weak.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 2 \
      --config configs/coco_supervision/faster_rcnn_R_50_FPN_sup10_run1_weak.yaml \
       SOLVER.IMG_PER_BATCH_LABEL 4 SOLVER.IMG_PER_BATCH_UNLABEL 4 \
       MODEL.WEIGHTS <your weight>.pth

Reference

This repository draws on the following excellent works:

Unbiased Teacher

Soft-Teacher

About

Robust Teacher: Self-Correcting Guided Robust Semi-Supervised Learning for Object Detection

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages