whale1008 / yolo-ms

YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection

🚀 YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection

This repository contains the official implementation of the following paper:

YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-Time Object Detection
Yuming Chen, Xinbin Yuan, Ruiqi Wu, Jiabao Wang, Qibin Hou, Ming-ming Cheng
Under review

[Homepage (TBD)] [Paper] [知乎 (TBD)] [AIWalker] [极市平台] [Poster (TBD)] [Video (TBD)]

📄 Table of Contents

• 📄 Table of Contents
• ✨ News 🔝
• 🛠️ Dependencies and Installation 🔝
• 👼 Quick Demo 🔝
• 🤖 Training and Evaluation 🔝
• 🏡 Model Zoo 🔝
• 🏗️ Supported Tasks 🔝
• 📖 Citation 🔝
• 📜 License 🔝
• 📮 Contact 🔝
• 🤝 Acknowledgement 🔝

✨ News 🔝

Future work can be found in todo.md.

• Aug, 2023: Our code is publicly available!

🛠️ Dependencies and Installation 🔝

We provide a simple scrpit install.sh for installation, or refer to install.md for more details.

1. Clone and enter the repo.

   git clone https://github.com/FishAndWasabi/YOLO-MS.git
   cd YOLO-MS

2. Run install.sh.

   bash install.sh

3. Activate your environment!

   conda activate YOLO-MS

👼 Quick Demo 🔝

python demo/image_demo.py ${IMAGE_PATH} ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

You could run python demo/image_demo.py --help to get detailed information of this scripts.

Detailed arguments

positional arguments:
  img                   Image path, include image file, dir and URL.
  config                Config file
  checkpoint            Checkpoint file

optional arguments:
  -h, --help            show this help message and exit
  --out-dir OUT_DIR     Path to output file
  --device DEVICE       Device used for inference
  --show                Show the detection results
  --deploy              Switch model to deployment mode
  --tta                 Whether to use test time augmentation
  --score-thr SCORE_THR
                        Bbox score threshold
  --class-name CLASS_NAME [CLASS_NAME ...]
                        Only Save those classes if set
  --to-labelme          Output labelme style label file

🤖 Training and Evaluation 🔝

1. Training

   1.1 Single GPU

   python tools/train.py ${CONFIG_FILE} [optional arguments]

   1.2 Multi GPU

   CUDA_VISIBLE_DEVICES=x python tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

   You could run python tools/train.py --help to get detailed information of this scripts.

   Detailed arguments

   positional arguments:
   config                train config file path
   
   optional arguments:
   -h, --help            show this help message and exit
   --work-dir WORK_DIR   the dir to save logs and models
   --amp                 enable automatic-mixed-precision training
   --resume [RESUME]     If specify checkpoint path, resume from it, while if not specify, try to auto resume from the latest checkpoint in the work directory.
   --cfg-options CFG_OPTIONS [CFG_OPTIONS ...]
                           override some settings in the used config, the key-value pair in xxx=yyy format will be merged into config file. If the value to be overwritten is a list, it should be like key="[a,b]" or key=a,b It also allows nested
                           list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation marks are necessary and that no white space is allowed.
   --launcher {none,pytorch,slurm,mpi}
                           job launcher
   --local_rank LOCAL_RANK
   

2. Evaluation

   1.1 Single GPU

   python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

   1.2 Multi GPU

   CUDA_VISIBLE_DEVICES=x python tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [optional arguments]

   You could run python tools/test.py --help to get detailed information of this scripts.

   Detailed arguments

   positional arguments:
   config                test config file path
   checkpoint            checkpoint file
   
   optional arguments:
   -h, --help            show this help message and exit
   --work-dir WORK_DIR   the directory to save the file containing evaluation metrics
   --out OUT             output result file (must be a .pkl file) in pickle format
   --json-prefix JSON_PREFIX
                           the prefix of the output json file without perform evaluation, which is useful when you want to format the result to a specific format and submit it to the test server
   --tta                 Whether to use test time augmentation
   --show                show prediction results
   --deploy              Switch model to deployment mode
   --show-dir SHOW_DIR   directory where painted images will be saved. If specified, it will be automatically saved to the work_dir/timestamp/show_dir
   --wait-time WAIT_TIME
                           the interval of show (s)
   --cfg-options CFG_OPTIONS [CFG_OPTIONS ...]
                           override some settings in the used config, the key-value pair in xxx=yyy format will be merged into config file. If the value to be overwritten is a list, it should be like key="[a,b]" or key=a,b It also allows nested
                           list/tuple values, e.g. key="[(a,b),(c,d)]" Note that the quotation marks are necessary and that no white space is allowed.
   --launcher {none,pytorch,slurm,mpi}
                           job launcher
   --local_rank LOCAL_RANK
   

3. Deployment

# Build docker images
docker build docker/mmdeploy/ -t mmdeploy:inside --build-arg USE_SRC_INSIDE=true
# Run docker container
docker run --gpus all --name mmdeploy_yoloms -dit mmdeploy:inside
# Convert ${CONFIG_FILE}
python tools/misc/print_config.py ${O_CONFIG_FILE} --save-path ${CONFIG_FILE}
# Copy local file into docker container
docker cp deploy.sh mmdeploy_yoloms:/root/workspace
docker cp ${DEPLOY_CONFIG_FILE}  mmdeploy_yoloms:/root/workspace/${DEPLOY_CONFIG_FILE}
docker cp ${CONFIG_FILE} mmdeploy_yoloms:/root/workspace/${CONFIG_FILE}
docker cp ${CHECKPOINT_FILE} mmdeploy_yoloms:/root/workspace/${CHECKPOINT_FILE}
# Attach docker container
docker attach mmdeploy_yoloms
# Run the deployment shell
sh deploy.sh ${DEPLOY_CONFIG_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${SAVE_DIR}
# Copy the results to local
docker cp mmdeploy_yoloms:/root/workspace/${SAVE_DIR} ${SAVE_DIR}

• DEPLOY_CONFIG_FILE: Config file for deployment.
• O_CONFIG_FILE: Original config file of model.
• CONFIG_FILE: Converted config file of model.
• CHECKPOINT_FILE: Checkpoint of model.
• SAVE_DIR: Save dir.

1. Test FPS

   4.1 Deployed Model

   # Attach docker container
   docker attach mmdeploy_yoloms
   # In docker container
   # Copy local file into docker container
   docker cp deploy.sh mmdeploy_yoloms:/root/worksapce
   # Run the FPS shell
   sh fps.sh ${DEPLOY_CONFIG_FILE} ${CONFIG_FILE} ${SAVE_DIR}

   4.2 Undeployed Model

   python tools/analysis_tools/benchmark.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} [optional arguments]

2. Test FLOPs and Params

python tools/analysis_tools/get_flops.py ${CONFIG_FILE} --shape 640 640 [optional arguments]

🏡 Model Zoo 🔝

• YOLOv5-MS
• YOLOX-MS
• YOLOv6-MS
• YOLOv7-MS
• PPYOLOE-MS
• YOLOv8-MS
• YOLO-MS (Based on RTMDet)

1. YOLO-MS

Model	Resolution	Epoch	Params(M)	FLOPs(G)	$AP$	$AP_s$	$AP_m$	$AP_l$	Config	🔗
XS	640	300	4.5	8.7	43.1	24.0	47.8	59.1	[config]	[model]
XS*	640	300	4.5	8.7	43.4	23.7	48.3	60.3	[config]	[model]
S	640	300	8.1	15.6	46.2	27.5	50.6	62.9	[config]	[model]
S*	640	300	8.1	15.6	46.2	26.9	50.5	63.0	[config]	[model]
-	640	300	22.0	40.1	50.8	33.2	54.8	66.4	[config]	[model]
-*	640	300	22.2	40.1	50.8	33.2	54.8	66.4	[config]	[model]

* refers to with SE attention

2. YOLOv6

Model	Resolution	Epoch	Params(M)	FLOPs(G)	$AP$	$AP_s$	$AP_m$	$AP_l$	Config	🔗
t	640	400	9.7	12.4	41.0	21.2	45.7	57.7	[config]	[model]
t-MS	640	400	8.1	9.6	43.5 (+2.5)	26.0	48.3	57.8	[config]	[model]

3. YOLOv8

Model	Resolution	Epoch	Params(M)	FLOPs(G)	$AP$	$AP_s$	$AP_m$	$AP_l$	Config	🔗
n	640	500	2.9	4.4	37.2	18.9	40.5	52.5	[config]	[model]
n-MS	640	500	2.9	4.4	40.3 (+3.1)	22.0	44.6	53.7	[config]	[model]

🏗️ Supported Tasks 🔝

• Object Detection
• Instance Segmentation (TBD)
• Rotated Object Detection (TBD)
• Object Tracking (TBD)
• Detection in Crowded Scene (TBD)
• Small Object Detection (TBD)

📖 Citation 🔝

If you find our repo useful for your research, please cite us:

@misc{chen2023yoloms,
      title={YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection},
      author={Yuming Chen and Xinbin Yuan and Ruiqi Wu and Jiabao Wang and Qibin Hou and Ming-Ming Cheng},
      year={2023},
      eprint={2308.05480},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

This project is based on the open source codebase MMYOLO.

@misc{mmyolo2022,
    title={{MMYOLO: OpenMMLab YOLO} series toolbox and benchmark},
    author={MMYOLO Contributors},
    howpublished = {\url{https://github.com/open-mmlab/mmyolo}},
    year={2022}
}

📜 License 🔝

Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first.

📮 Contact 🔝

For technical questions, please contact chenyuming[AT]mail.nankai.edu.cn. For commercial licensing, please contact cmm[AT]nankai.edu.cn and andrewhoux[AT]gmail.com.

🤝 Acknowledgement 🔝

This repo is modified from open source real-time object detection codebase MMYOLO. The README file is referred to LED and CrossKD