how i can use this in dota datasets

🧑‍🏫 Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection 🧑‍🏫

This repository contains the offical implementation for our CVPR-2023 paper.

✨We are now able to train detector on 10% MS-COCO to 40 mAP✨

Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection

Xinjiang Wang*, Xingyi Yang*, Shilong Zhang, Yijiang Li, Litong Feng, Shijie Fang, Chengqi Lyu, Kai Chen, Wayne Zhang

(*: Co-first Author)

Selected as Hightligh for CVPR2023🔥 (235/2360, top 10% accepted paper)

In this paper, we systematically investigate the inconsistency problems in semi-supervised object detection, where the pseudo boxes may be highly inaccurate and vary greatly at different stages of training. To alleviate the aforementioned problem, we present a holistic semi-supervised object detector termed Consistent-Teacher. Consistent-Teacher achieves compelling improvement on a wide range of evaluations and serves as a new solid baseline for SSOD.

Main Results

All results, logs, configs and checkpoints are listed here. Enjoy 👀!

MS-COCO 1%/2%/5/%/10% Labeled Data

Method	Data	mAP	config	Links	Google Drive	Baidu Drive
ConsistentTeacher	MS-COCO 1%	25.50	config	log/ckpt	log/ckpt	log/ckpt
ConsistentTeacher	MS-COCO 2%	30.70	config	log/ckpt	log/ckpt	log/ckpt
ConsistentTeacher	MS-COCO 5%	36.60	config	log/ckpt	log/ckpt	log/ckpt
ConsistentTeacher	MS-COCO 10%	40.20	config	log/ckpt	log/ckpt	log/ckpt
ConsistentTeacher 2x8	MS-COCO 10%	38.00	config	log/ckpt	log/ckpt	log/ckpt
ConsistentTeacher 2x8 (FP16)	MS-COCO 10%	37.90	config	log/ckpt	log/ckpt	log/ckpt

MS-COCO100% Labeled + Unlabeled Data

Method	Data	mAP	config	Links	Google Drive	Baidu Drive
ConsistentTeacher 5x8	MS-COCO 100% + unlabeled	48.20	config	log/ckpt	log/ckpt	log/ckpt

PASCAL VOC07 Label + VOC12 Unlabel

Method	Data	mAP	AP50	config	Links
ConsistentTeacher	PASCAL VOC07 Label + VOC12 Unlabel	59.00	81.00	config	log/ckpt

Notes

Defaultly, all models are trained on 8*V100 GPUs with 5 images per GPU.
Additionally, we support the 2x8 and fp16 training setting to ensure everyone is able to run the code, even with only 12G graphic cards.
With 8x2+fp16, the total training time for MS-COCO is less than 1 day.
We carefully tuned the hyper-parameters after submitting the paper, which is why the results in the repository are slightly higher than those reported in the paper.

Visualizations

Zoom in for better View.

File Orgnizations

├── configs              
    ├── baseline
    │   |-- mean_teacher_retinanet_r50_fpn_coco_180k_10p.py       
    |       # Mean Teacher COCO 10% config
    |   |-- mean_teacher_retinanet_r50_fpn_voc0712_72k.py      
    |       # Mean Teacher VOC0712 config
    ├── consistent-teacher
    |   |-- consistent_teacher_r50_fpn_coco_360k_fulldata.py           
    |       # Consistent Teacher COCO label+unlabel config
    |
    |   |-- consistent_teacher_r50_fpn_coco_180k_1/2/5/10p.py           
    |       # Consistent Teacher COCO 1%/2%/5%/10% config
    |   |-- consistent_teacher_r50_fpn_coco_180k_10p_2x8.py     
    |       # Consistent Teacher COCO 10% config with 8x2 GPU
    |   |-- consistent_teacher_r50_fpn_voc0712_72k.py             
    |       # Consistent Teacher VOC0712 config
├── ssod
    |-- models/mean_teacher.py                           
    |   # Consistent Teacher Class file
    |-- models/consistent_teacher.py                     
    |   # Consistent Teacher Class file
    |-- models/dense_heads/fam3d.py                      
    |   # FAM-3D Class file
    |-- models/dense_heads/improved_retinanet.py                      
    |   # ImprovedRetinaNet baseline file
    |-- core/bbox/assigners/dynamic_assigner.py
    |   # Aadaptive Sample Assignment Class file
├── tools
    |-- dataset/semi_coco.py
    |   # COCO data preprocessing
    |-- train.py/test.py
    |   # Main file for train and evaluate the models

Usage

Requirements

Pytorch=1.9.0
mmdetection=2.25.0
mmcv=1.3.9
wandb=0.10.31

or

mmdetection=2.28.1
mmcv=1.7.1

Notes

We use wandb for visualization, if you don't want to use it, just comment line 328-339 in configs/consistent-teacher/consistent_teacher_r50_fpn_coco_180k_10p.py.

Installation

Install all the requirements INSTALL, then git pull the mmdetecton repo and ConsistentTeacher under the same folder

git clone https://github.com/open-mmlab/mmdetection.git
git clone https://github.com/Adamdad/ConsistentTeacher.git
cd ConsistentTeacher/
make install

Data Preparation

COCO Dataset

Download the COCO dataset
Execute the following command to generate data set splits:

# YOUR_DATA should be a directory contains coco dataset.
# For eg.:
# YOUR_DATA/
#  coco_semi/
#     instances_train2017.${fold}@${percent}.json
#  coco/
#     train2017/
#     val2017/
#     unlabeled2017/
#     annotations/
ln -s ${YOUR_DATA} data
bash tools/dataset/prepare_coco_data.sh conduct

For concrete instructions of what should be downloaded, please refer to tools/dataset/prepare_coco_data.sh line 11-24

VOC0712 Dataset

Download JSON files for unlabeled images PASCAL VOC data in COCO format

cd ${DATAROOT}

wget https://storage.cloud.google.com/gresearch/ssl_detection/STAC_JSON.tar
tar -xf STAC_JSON.tar.gz
# voc/VOCdevkit/VOC2007/instances_test.json
# voc/VOCdevkit/VOC2007/instances_trainval.json
# voc/VOCdevkit/VOC2012/instances_trainval.json

Training

To train model on the partial labeled data and full labeled data setting:

# CONFIG_FILE_PATH: the config file for experiment.
# GPU_NUM: number of gpus to run the job
bash tools/dist_train.sh <CONFIG_FILE_PATH> <NUM_GPUS>

For example, to train ours R50 model with 8 GPUs:

bash tools/dist_train.sh configs/consistent-teacher/consistent_teacher_r50_fpn_coco_180k_10p.py 8

To train model on new dataset:

The core idea is to convert a new dataset to coco format. Details about it can be found in the adding new dataset.

Inference and Demo

To inference with the pretrained models on images and videos and plot the bounding boxes, we add two scripts
- tools/inference.py for image inference
- tools/inference_vido.py for video inference

License

This project is released under the Apache 2.0 license.

Citation

@article{wang2023consistent,
    author    = {Xinjiang Wang, Xingyi Yang, Shilong Zhang, Yijiang Li, Litong Feng, Shijie Fang, Chengqi Lyu, Kai Chen, Wayne Zhang },
    title     = {Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection},
    journal   = {The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR)},
    year      = {2023},
}

Acknowledgement

This code pattern was inspired from a SoftTeacher and mmdet

adamdad / consistentteacher Goto Github PK

consistentteacher's Introduction

🧑‍🏫 Consistent-Teacher: Towards Reducing Inconsistent Pseudo-targets in Semi-supervised Object Detection 🧑‍🏫

Main Results

Notes

Visualizations

File Orgnizations

Usage

Requirements

Notes

Installation

Data Preparation

COCO Dataset

VOC0712 Dataset

Training

Inference and Demo

License

Citation

Acknowledgement

consistentteacher's People

Contributors

Stargazers

Watchers

Forkers

consistentteacher's Issues

`2023-04-23 12:49:22,302 - mmdet.ssod - INFO - [<StreamHandler (INFO)>, <FileHandler E:\Object-Detection\Github\consist\ConsistentTeacher\work_dirs\consistent_teacher_r50_fpn_coco_180k_1p\20230423_124922.log (INFO)>] 2023-04-23 12:49:22,303 - mmdet.ssod - INFO - Environment info:

TorchVision: 0.10.0+cu111 OpenCV: 4.5.5 MMCV: 1.4.2 MMCV Compiler: MSVC 192930137 MMCV CUDA Compiler: 11.1 MMDetection: 2.25.0+1fa6477

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`2023-04-23 12:49:22,302 - mmdet.ssod - INFO - [<StreamHandler (INFO)>, <FileHandler E:\Object-Detection\Github\consist\ConsistentTeacher\work_dirs\consistent_teacher_r50_fpn_coco_180k_1p\20230423_124922.log (INFO)>]
2023-04-23 12:49:22,303 - mmdet.ssod - INFO - Environment info:

TorchVision: 0.10.0+cu111
OpenCV: 4.5.5
MMCV: 1.4.2
MMCV Compiler: MSVC 192930137
MMCV CUDA Compiler: 11.1
MMDetection: 2.25.0+1fa6477