Semi-supervised learning for object detection

Last update: Dec 25, 2022

Related tags

Overview

Source code for STAC: A Simple Semi-Supervised Learning Framework for Object Detection

STAC is a simple yet effective SSL framework for visual object detection along with a data augmentation strategy. STAC deploys highly confident pseudo labels of localized objects from an unlabeled image and updates the model by enforcing consistency via strong augmentation.

This code is only used for research. This is not an official Google product.

Instruction

Install dependencies

Set global enviroment variables.

export PRJROOT=/path/to/your/project/directory/STAC
export DATAROOT=/path/to/your/dataroot
export COCODIR=$DATAROOT/coco
export VOCDIR=$DATAROOT/voc
export PYTHONPATH=$PYTHONPATH:${PRJROOT}/third_party/FasterRCNN:${PRJROOT}/third_party/auto_augment:${PRJROOT}/third_party/tensorpack

Install virtual environment in the root folder of the project

cd ${PRJROOT}

sudo apt install python3-dev python3-virtualenv python3-tk imagemagick
virtualenv -p python3 --system-site-packages env3
. env3/bin/activate
pip install -r requirements.txt

# Make sure your tensorflow version is 1.14 not only in virtual environment but also in
# your machine, 1.15 can cause OOM issues.
python -c 'import tensorflow as tf; print(tf.__version__)'

# install coco apis
pip3 install 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'

(Optional) Install tensorpack

tensorpack with a compatible version is already included at third_party/tensorpack. bash cd ${PRJROOT}/third_party pip install --upgrade git+https://github.com/tensorpack/tensorpack.git

Download COCO/PASCAL VOC data and pre-trained models

Download data

See DATA.md

Download backbone model

cd ${COCODIR}
wget http://models.tensorpack.com/FasterRCNN/ImageNet-R50-AlignPadding.npz

Training

There are three steps:

1. Train a standard detector on labeled data (detection/scripts/coco/train_stg1.sh).
2. Predict pseudo boxes and labels of unlabeled data using the trained detector (detection/scripts/coco/eval_stg1.sh).
3. Use labeled data and unlabeled data with pseudo labels to train a STAC detector (detection/scripts/coco/train_stg2.sh).

Besides instruction at here, detection/scripts/coco/train_stac.sh provides a combined script to train STAC.

detection/scripts/voc/train_stac.sh is a combined script to train STAC on PASCAL VOC.

The following example use labeled data as 10% train2017 and rest 90% train2017 data as unlabeled data.

Step 0: Set variables

cd ${PRJROOT}/detection

# Labeled and Unlabeled datasets
[email protected]
UNLABELED_DATASET=${DATASET}-unlabeled

# PATH to save trained models
CKPT_PATH=result/${DATASET}

# PATH to save pseudo labels for unlabeled data
PSEUDO_PATH=${CKPT_PATH}/PSEUDO_DATA

# Train with 8 GPUs
export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7

Step 1: Train FasterRCNN on labeled data

. scripts/coco/train_stg1.sh.

Set TRAIN.AUGTYPE_LAB=strong to apply strong data augmentation.

# --simple_path makes train_log/${DATASET}/${EXPNAME} as exact location to save
python3 train_stg1.py \
    --logdir ${CKPT_PATH} --simple_path --config \
    BACKBONE.WEIGHTS=${COCODIR}/ImageNet-R50-AlignPadding.npz \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${DATASET}',)" \
    MODE_MASK=False \
    FRCNN.BATCH_PER_IM=64 \
    PREPROC.TRAIN_SHORT_EDGE_SIZE="[500,800]" \
    TRAIN.EVAL_PERIOD=20 \
    TRAIN.AUGTYPE_LAB='default'

Step 2: Generate pseudo labels of unlabeled data

. scripts/coco/eval_stg1.sh.

Evaluate using COCO metrics and save eval.json

# Check pseudo path
if [ ! -d ${PSEUDO_PATH} ]; then
    mkdir -p ${PSEUDO_PATH}
fi

# Evaluate the model for sanity check
# model-180000 is the last checkpoint
# save eval.json at $PSEUDO_PATH

python3 predict.py \
    --evaluate ${PSEUDO_PATH}/eval.json \
    --load "${CKPT_PATH}"/model-180000 \
    --config \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${UNLABELED_DATASET}',)"

Generate pseudo labels for unlabeled data

Set EVAL.PSEUDO_INFERENCE=True to use original images rather than resized ones for inference.

# Extract pseudo label
python3 predict.py \
    --predict_unlabeled ${PSEUDO_PATH} \
    --load "${CKPT_PATH}"/model-180000 \
    --config \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${UNLABELED_DATASET}',)" \
    EVAL.PSEUDO_INFERENCE=True

Step 3: Train STAC

. scripts/coco/train_stg2.sh.

The dataloader loads pseudo labels from ${PSEUDO_PATH}/pseudo_data.npy.

Apply default augmentation on labeled data and strong augmentation on unlabeled data.

TRAIN.CONFIDENCE and TRAIN.WU are two major parameters of the method.

python3 train_stg2.py \
    --logdir=${CKPT_PATH}/STAC --simple_path \
    --pseudo_path=${PSEUDO_PATH} \
    --config \
    BACKBONE.WEIGHTS=${COCODIR}/ImageNet-R50-AlignPadding.npz \
    DATA.BASEDIR=${COCODIR} \
    DATA.TRAIN="('${DATASET}',)" \
    DATA.UNLABEL="('${UNLABELED_DATASET}',)" \
    MODE_MASK=False \
    FRCNN.BATCH_PER_IM=64 \
    PREPROC.TRAIN_SHORT_EDGE_SIZE="[500,800]" \
    TRAIN.EVAL_PERIOD=20 \
    TRAIN.AUGTYPE_LAB='default' \
    TRAIN.AUGTYPE='strong' \
    TRAIN.CONFIDENCE=0.9 \
    TRAIN.WU=2

Tensorboard

All training logs and tensorboard info are under ${PRJROOT}/detection/train_log. Visualize using

tensorboard --logdir=${PRJROOT}/detection/train_log

Citation

@inproceedings{sohn2020detection,
  title={A Simple Semi-Supervised Learning Framework for Object Detection},
  author={Kihyuk Sohn and Zizhao Zhang and Chun-Liang Li and Han Zhang and Chen-Yu Lee and Tomas Pfister},
  year={2020},
  booktitle={arXiv:2005.04757}
}

Semi-supervised learning for object detection

Related tags

Overview

Source code for STAC: A Simple Semi-Supervised Learning Framework for Object Detection

Instruction

Install dependencies

Set global enviroment variables.

Install virtual environment in the root folder of the project

(Optional) Install tensorpack

Download COCO/PASCAL VOC data and pre-trained models

Download data

Download backbone model

Training

Step 0: Set variables

Step 1: Train FasterRCNN on labeled data

Step 2: Generate pseudo labels of unlabeled data

Evaluate using COCO metrics and save eval.json

Generate pseudo labels for unlabeled data

Step 3: Train STAC

Tensorboard

Citation

Acknowledgement

Owner

Google Research

VolumeGAN - 3D-aware Image Synthesis via Learning Structural and Textural Representations

codes for Self-paced Deep Regression Forests with Consideration on Ranking Fairness

Grounding Representation Similarity with Statistical Testing

Detectorch - detectron for PyTorch

Consecutive-Subsequence - Simple software to calculate susequence with highest sum

This repository contains pre-trained models and some evaluation code for our paper Towards Unsupervised Dense Information Retrieval with Contrastive Learning

Python scripts to detect faces in Python with the BlazeFace Tensorflow Lite models

Runtime type annotations for the shape, dtype etc. of PyTorch Tensors.

A simple Neural Network that predicts the label for a series of handwritten digits

This repo provides the source code & data of our paper "GreaseLM: Graph REASoning Enhanced Language Models"

Code for CVPR 2021 paper TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture Search.

A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch

Robust & Reliable Route Recommendation on Road Networks

Code release for Convolutional Two-Stream Network Fusion for Video Action Recognition

Evolution Strategies in PyTorch

PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

Analysis of Smiles through reservoir sampling & RDkit

Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style Transfer

HairCLIP: Design Your Hair by Text and Reference Image

Unofficial PyTorch implementation of the Adaptive Convolution architecture for image style transfer