├── .dockerignore ├── .gitattributes ├── .gitignore ├── .pre-commit-config.yaml ├── CONTRIBUTING.md ├── Dockerfile ├── LICENSE ├── OFFICAL_README.md ├── README.md ├── data ├── Argoverse.yaml ├── Fruit.yaml ├── GlobalWheat2020.yaml ├── Objects365.yaml ├── SKU-110K.yaml ├── VOC.yaml ├── VOC2007.yaml ├── VOC_val_500.yaml ├── VisDrone.yaml ├── abnormal_driving.yaml ├── coco.yaml ├── coco128.yaml ├── hyps │ ├── hyp.Objects365.yaml │ ├── hyp.VOC.yaml │ ├── hyp.scratch-high.yaml │ ├── hyp.scratch-low.yaml │ └── hyp.scratch-med.yaml ├── images │ ├── bus.jpg │ └── zidane.jpg ├── scripts │ ├── download_weights.sh │ ├── get_coco.sh │ ├── get_coco128.sh │ └── get_voc.sh └── xView.yaml ├── demo_test.py ├── detect.py ├── export.py ├── hubconf.py ├── maketxt.py ├── models ├── __init__.py ├── abnormal_driving │ ├── yolov5l_bifpn_dd.yaml │ ├── yolov5l_ca_dd.yaml │ ├── yolov5l_dd.yaml │ ├── yolov5l_ghost_bifpn_ca_dd.yaml │ ├── yolov5l_ghost_dd.yaml │ ├── yolov5m_bifpn_dd.yaml │ ├── yolov5m_ca_dd.yaml │ ├── yolov5m_dd.yaml │ ├── yolov5m_ghost_bifpn_ca_dd.yaml │ ├── yolov5m_ghost_dd.yaml │ ├── yolov5n_dd.yaml │ ├── yolov5n_ghost_bifpn_ca_dd.yaml │ ├── yolov5n_ghost_dd.yaml │ ├── yolov5s_bifpn_dd.yaml │ ├── yolov5s_ca_dd.yaml │ ├── yolov5s_dd.yaml │ ├── yolov5s_ghost_bifpn2_ca_dd.yaml │ ├── yolov5s_ghost_bifpn_ca_dd.yaml │ ├── yolov5s_ghost_ca_dd.yaml │ ├── yolov5s_ghost_dd.yaml │ ├── yolov5x_bifpn_dd.yaml │ ├── yolov5x_ca_dd.yaml │ ├── yolov5x_dd.yaml │ ├── yolov5x_ghost_bifpn_ca_dd.yaml │ ├── yolov5x_ghost_ca_dd.yaml │ └── yolov5x_ghost_dd.yaml ├── bishe_voc │ ├── yolov5l_ghost_bifpn_ca_voc.yaml │ ├── yolov5l_voc.yaml │ ├── yolov5m_ghost_bifpn_ca_voc.yaml │ ├── yolov5m_voc.yaml │ ├── yolov5n_ghost_bifpn_ca_voc.yaml │ ├── yolov5n_voc.yaml │ ├── yolov5s_ca_voc.yaml │ ├── yolov5s_ghost_bifpn_ca_voc.yaml │ ├── yolov5s_ghost_bifpn_voc.yaml │ ├── yolov5s_ghost_voc.yaml │ ├── yolov5s_voc.yaml │ ├── yolov5x_ghost_bifpn_ca_voc.yaml │ └── yolov5x_voc.yaml ├── common.py ├── experimental.py ├── hub │ ├── anchors.yaml │ ├── yolov3-spp.yaml │ ├── yolov3-tiny.yaml │ ├── yolov3.yaml │ ├── yolov5-bifpn.yaml │ ├── yolov5-fpn.yaml │ ├── yolov5-p2.yaml │ ├── yolov5-p34.yaml │ ├── yolov5-p6.yaml │ ├── yolov5-p7.yaml │ ├── yolov5-panet.yaml │ ├── yolov5l6.yaml │ ├── yolov5m6.yaml │ ├── yolov5n6.yaml │ ├── yolov5s-ghost.yaml │ └── yolov5s-transformer.yaml ├── tf.py ├── yolo.py ├── yolov5l.yaml ├── yolov5m.yaml ├── yolov5n.yaml ├── yolov5s-bifpn-new.yaml ├── yolov5s-voc │ ├── yolov5s-bifpn-ca-voc.yaml │ ├── yolov5s-bifpn-voc.yaml │ ├── yolov5s-bifpn_new-voc.yaml │ ├── yolov5s-ca-voc.yaml │ ├── yolov5s-cbam-voc.yaml │ ├── yolov5s-ghostbottleneck-ca-voc.yaml │ ├── yolov5s-ghostconv-bifpn-ca-voc.yaml │ ├── yolov5s-ghostconv-bifpn1-ca-voc.yaml │ ├── yolov5s-ghostconv-bifpn4-ca-voc.yaml │ ├── yolov5s-ghostconv-bifpn_new-ca-voc.yaml │ ├── yolov5s-ghostconv-bifpn_new-voc.yaml │ ├── yolov5s-ghostconv-ca-voc.yaml │ ├── yolov5s-ghostconv-voc.yaml │ ├── yolov5s-ghostnet-bifpn-ca-voc.yaml │ ├── yolov5s-ghostnet-bifpn-voc.yaml │ ├── yolov5s-mobilenet-voc.yaml │ ├── yolov5s-p2-p5-voc.yaml │ ├── yolov5s-shufflenet-voc.yaml │ ├── yolov5s-voc.yaml │ ├── yolov5x-ghostconv-bifpn_new-ca-voc.yaml │ └── yolov5x-voc.yaml ├── yolov5s.yaml ├── yolov5s6.yaml ├── yolov5x.yaml └── yolov5x6.yaml ├── readme.md ├── requirements.txt ├── setup.cfg ├── test ├── activation_test.py ├── data_augment_test.py ├── demo.py ├── focus_vs_conv.py ├── gpu_memory.py ├── images │ ├── 1.jpg │ ├── 2.jpg │ ├── 3.jpg │ └── 4.jpg ├── labels │ ├── 1.txt │ ├── 2.txt │ ├── 3.txt │ └── 4.txt ├── model_test.py ├── outputs │ ├── cutmix.jpg │ ├── cutout.jpg │ ├── fliplr.jpg │ ├── flipud.jpg │ ├── hsv.jpg │ ├── mixup.jpg │ ├── mixup_origin.jpg │ ├── mosaic_with_label.jpg │ ├── mosaic_without_label.jpg │ ├── perspective.jpg │ ├── rotation.jpg │ ├── scale.jpg │ ├── shear.jpg │ └── translation.jpg ├── param_metric_test.py ├── spp_vs_sppf.py └── train_test.py ├── train.py ├── tutorial.ipynb ├── utils ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-38.pyc │ ├── activations.cpython-38.pyc │ ├── augmentations.cpython-38.pyc │ ├── autoanchor.cpython-38.pyc │ ├── autobatch.cpython-38.pyc │ ├── callbacks.cpython-38.pyc │ ├── datasets.cpython-38.pyc │ ├── downloads.cpython-38.pyc │ ├── general.cpython-38.pyc │ ├── loss.cpython-38.pyc │ ├── metrics.cpython-38.pyc │ ├── plots.cpython-38.pyc │ └── torch_utils.cpython-38.pyc ├── activations.py ├── augmentations.py ├── autoanchor.py ├── autobatch.py ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── benchmarks.py ├── callbacks.py ├── datasets.py ├── downloads.py ├── flask_rest_api │ ├── README.md │ ├── example_request.py │ └── restapi.py ├── general.py ├── google_app_engine │ ├── Dockerfile │ ├── additional_requirements.txt │ └── app.yaml ├── loggers │ ├── __init__.py │ ├── __pycache__ │ │ └── __init__.cpython-38.pyc │ └── wandb │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-38.pyc │ │ └── wandb_utils.cpython-38.pyc │ │ ├── log_dataset.py │ │ ├── sweep.py │ │ ├── sweep.yaml │ │ └── wandb_utils.py ├── loss.py ├── metrics.py ├── plots.py └── torch_utils.py ├── val.py ├── xml2txt.py └── 改进算法整体框架.png /.gitattributes: -------------------------------------------------------------------------------- 1 | # this drop notebooks from GitHub language stats 2 | *.ipynb linguist-vendored 3 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | # Define hooks for code formations 2 | # Will be applied on any updated commit files if a user has installed and linked commit hook 3 | 4 | default_language_version: 5 | python: python3.8 6 | 7 | # Define bot property if installed via https://github.com/marketplace/pre-commit-ci 8 | ci: 9 | autofix_prs: true 10 | autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions' 11 | autoupdate_schedule: quarterly 12 | # submodules: true 13 | 14 | repos: 15 | - repo: https://github.com/pre-commit/pre-commit-hooks 16 | rev: v4.1.0 17 | hooks: 18 | - id: end-of-file-fixer 19 | - id: trailing-whitespace 20 | - id: check-case-conflict 21 | - id: check-yaml 22 | - id: check-toml 23 | - id: pretty-format-json 24 | - id: check-docstring-first 25 | 26 | - repo: https://github.com/asottile/pyupgrade 27 | rev: v2.31.0 28 | hooks: 29 | - id: pyupgrade 30 | args: [--py36-plus] 31 | name: Upgrade code 32 | 33 | - repo: https://github.com/PyCQA/isort 34 | rev: 5.10.1 35 | hooks: 36 | - id: isort 37 | name: Sort imports 38 | 39 | # TODO 40 | #- repo: https://github.com/pre-commit/mirrors-yapf 41 | # rev: v0.31.0 42 | # hooks: 43 | # - id: yapf 44 | # name: formatting 45 | 46 | # TODO 47 | #- repo: https://github.com/executablebooks/mdformat 48 | # rev: 0.7.7 49 | # hooks: 50 | # - id: mdformat 51 | # additional_dependencies: 52 | # - mdformat-gfm 53 | # - mdformat-black 54 | # - mdformat_frontmatter 55 | 56 | # TODO 57 | #- repo: https://github.com/asottile/yesqa 58 | # rev: v1.2.3 59 | # hooks: 60 | # - id: yesqa 61 | 62 | - repo: https://github.com/PyCQA/flake8 63 | rev: 4.0.1 64 | hooks: 65 | - id: flake8 66 | name: PEP8 67 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch 4 | FROM nvcr.io/nvidia/pytorch:21.10-py3 5 | 6 | # Install linux packages 7 | RUN apt update && apt install -y zip htop screen libgl1-mesa-glx 8 | 9 | # Install python dependencies 10 | COPY requirements.txt . 11 | RUN python -m pip install --upgrade pip 12 | RUN pip uninstall -y torch torchvision torchtext 13 | RUN pip install --no-cache -r requirements.txt albumentations wandb gsutil notebook \ 14 | torch==1.10.2+cu113 torchvision==0.11.3+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html 15 | # RUN pip install --no-cache -U torch torchvision 16 | 17 | # Create working directory 18 | RUN mkdir -p /usr/src/app 19 | WORKDIR /usr/src/app 20 | 21 | # Copy contents 22 | COPY . /usr/src/app 23 | 24 | # Downloads to user config dir 25 | ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/ 26 | 27 | # Set environment variables 28 | # ENV HOME=/usr/src/app 29 | 30 | 31 | # Usage Examples ------------------------------------------------------------------------------------------------------- 32 | 33 | # Build and Push 34 | # t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t 35 | 36 | # Pull and Run 37 | # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t 38 | 39 | # Pull and Run with local directory access 40 | # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t 41 | 42 | # Kill all 43 | # sudo docker kill $(sudo docker ps -q) 44 | 45 | # Kill all image-based 46 | # sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) 47 | 48 | # Bash into running container 49 | # sudo docker exec -it 5a9b5863d93d bash 50 | 51 | # Bash into stopped container 52 | # id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash 53 | 54 | # Clean up 55 | # docker system prune -a --volumes 56 | 57 | # Update Ubuntu drivers 58 | # https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ 59 | 60 | # DDP test 61 | # python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 62 | 63 | # GCP VM from Image 64 | # docker.io/ultralytics/yolov5:latest 65 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # YOLOv5-Abnormal-Driving 2 | 3 | > 山东大学-本科毕设-基于深度学习的异常行为检测算法研究 4 | > 5 | > Author: 嗜睡的篠龙 6 | > 7 | > Email: 442082944@qq.com 8 | 9 | 对YOLOv5算法的4种改进方案： 10 | 11 | 12 | 13 | 14 | - 引入轻量化卷积Ghostconv，大大降低了模型参数量； 15 | - 引入BiFPN，并充分利用中小型目标检测层； 16 | - 引入CA注意力机制，使得模型对目标框的定位更精确； 17 | - 将CIoU替换为Alpha-EIoU，使得模型在不引入额外参数的情况下，检测精度得到大幅提升。 18 | 19 | 更多细节见：https://blog.csdn.net/weixin_43799388?spm=1000.2115.3001.5343 20 | -------------------------------------------------------------------------------- /data/Fruit.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO 2017 dataset http://cocodataset.org by Microsoft 3 | # Example usage: python train.py --data coco.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | # path: ../datasets/coco # dataset root dir 12 | train: ../datasets/Fruit/train.txt 13 | val: ../datasets/Fruit/val.txt 14 | test: ../datasets/Fruit/test.txt 15 | 16 | # Classes 17 | nc: 3 # number of classes 18 | names: ['apple', 'banana', 'grape'] # class names 19 | -------------------------------------------------------------------------------- /data/GlobalWheat2020.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan 3 | # Example usage: python train.py --data GlobalWheat2020.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── GlobalWheat2020 ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/GlobalWheat2020 # dataset root dir 12 | train: # train images (relative to 'path') 3422 images 13 | - images/arvalis_1 14 | - images/arvalis_2 15 | - images/arvalis_3 16 | - images/ethz_1 17 | - images/rres_1 18 | - images/inrae_1 19 | - images/usask_1 20 | val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) 21 | - images/ethz_1 22 | test: # test images (optional) 1276 images 23 | - images/utokyo_1 24 | - images/utokyo_2 25 | - images/nau_1 26 | - images/uq_1 27 | 28 | # Classes 29 | nc: 1 # number of classes 30 | names: ['wheat_head'] # class names 31 | 32 | 33 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 34 | download: | 35 | from utils.general import download, Path 36 | 37 | # Download 38 | dir = Path(yaml['path']) # dataset root dir 39 | urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', 40 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] 41 | download(urls, dir=dir) 42 | 43 | # Make Directories 44 | for p in 'annotations', 'images', 'labels': 45 | (dir / p).mkdir(parents=True, exist_ok=True) 46 | 47 | # Move 48 | for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ 49 | 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': 50 | (dir / p).rename(dir / 'images' / p) # move to /images 51 | f = (dir / p).with_suffix('.json') # json file 52 | if f.exists(): 53 | f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations 54 | -------------------------------------------------------------------------------- /data/VOC.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 ? by Ultralytics, GPL-3.0 license 2 | # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford 3 | # Example usage: python train.py --data VOC.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── VOC ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/VOC # dataset root dir 12 | train: train.txt # train images (relative to 'path') 128 images 13 | val: val.txt # val images (relative to 'path') 128 images 14 | 15 | # Classes 16 | nc: 20 # number of classes 17 | names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 18 | 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names 19 | -------------------------------------------------------------------------------- /data/VOC2007.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford 3 | # Example usage: python train.py --data VOC2007.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── VOC2007 ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/VOC2007 # dataset root dir 12 | train: train.txt # train images (relative to 'path') 128 images 13 | val: val.txt # val images (relative to 'path') 128 images 14 | test: test.txt # test images (optional) 15 | 16 | # Classes 17 | nc: 20 # number of classes 18 | names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 19 | 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names 20 | -------------------------------------------------------------------------------- /data/VOC_val_500.yaml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/data/VOC_val_500.yaml -------------------------------------------------------------------------------- /data/abnormal_driving.yaml: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/data/abnormal_driving.yaml -------------------------------------------------------------------------------- /data/coco.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO 2017 dataset http://cocodataset.org by Microsoft 3 | # Example usage: python train.py --data coco.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco # dataset root dir 12 | train: train2017.txt # train images (relative to 'path') 118287 images 13 | val: val2017.txt # val images (relative to 'path') 5000 images 14 | test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 15 | 16 | # Classes 17 | nc: 80 # number of classes 18 | names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 19 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 20 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 21 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 22 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 23 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 24 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 25 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 26 | 'hair drier', 'toothbrush'] # class names 27 | 28 | 29 | # Download script/URL (optional) 30 | download: | 31 | from utils.general import download, Path 32 | 33 | # Download labels 34 | segments = False # segment or box labels 35 | dir = Path(yaml['path']) # dataset root dir 36 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 37 | urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels 38 | download(urls, dir=dir.parent) 39 | 40 | # Download data 41 | urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 42 | 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 43 | 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) 44 | download(urls, dir=dir / 'images', threads=3) 45 | -------------------------------------------------------------------------------- /data/coco128.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics 3 | # Example usage: python train.py --data coco128.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── coco128 ← downloads here 8 | 9 | 10 | # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] 11 | path: ../datasets/coco128 # dataset root dir 12 | train: images/train2017 # train images (relative to 'path') 128 images 13 | val: images/train2017 # val images (relative to 'path') 128 images 14 | test: # test images (optional) 15 | 16 | # Classes 17 | nc: 80 # number of classes 18 | names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 19 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 20 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 21 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 22 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 23 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 24 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 25 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 26 | 'hair drier', 'toothbrush'] # class names 27 | 28 | 29 | # Download script/URL (optional) 30 | download: https://ultralytics.com/assets/coco128.zip 31 | -------------------------------------------------------------------------------- /data/hyps/hyp.Objects365.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for Objects365 training 3 | # python train.py --weights yolov5m.pt --data Objects365.yaml --evolve 4 | # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.00258 7 | lrf: 0.17 8 | momentum: 0.779 9 | weight_decay: 0.00058 10 | warmup_epochs: 1.33 11 | warmup_momentum: 0.86 12 | warmup_bias_lr: 0.0711 13 | box: 0.0539 14 | cls: 0.299 15 | cls_pw: 0.825 16 | obj: 0.632 17 | obj_pw: 1.0 18 | iou_t: 0.2 19 | anchor_t: 3.44 20 | anchors: 3.2 21 | fl_gamma: 0.0 22 | hsv_h: 0.0188 23 | hsv_s: 0.704 24 | hsv_v: 0.36 25 | degrees: 0.0 26 | translate: 0.0902 27 | scale: 0.491 28 | shear: 0.0 29 | perspective: 0.0 30 | flipud: 0.0 31 | fliplr: 0.5 32 | mosaic: 1.0 33 | mixup: 0.0 34 | copy_paste: 0.0 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.VOC.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for VOC training 3 | # python train.py --batch 128 --weights yolov5m6.pt --data VOC2007.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve 4 | # See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials 5 | 6 | # YOLOv5 Hyperparameter Evolution Results 7 | # Best generation: 319 8 | # Last generation: 434 9 | # metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss 10 | # 0.86236, 0.86184, 0.91274, 0.72647, 0.0077056, 0.0042449, 0.0013846 11 | 12 | lr0: 0.00334 13 | lrf: 0.15135 14 | momentum: 0.74832 15 | weight_decay: 0.00025 16 | warmup_epochs: 3.3835 17 | warmup_momentum: 0.59462 18 | warmup_bias_lr: 0.18657 19 | box: 0.02 20 | cls: 0.21638 21 | cls_pw: 0.5 22 | obj: 0.51728 23 | obj_pw: 0.67198 24 | iou_t: 0.2 25 | anchor_t: 3.3744 26 | fl_gamma: 0.0 27 | hsv_h: 0.01041 28 | hsv_s: 0.54703 29 | hsv_v: 0.27739 30 | degrees: 0.0 31 | translate: 0.04591 32 | scale: 0.75544 33 | shear: 0.0 34 | perspective: 0.0 35 | flipud: 0.0 36 | fliplr: 0.5 37 | mosaic: 0.85834 38 | mixup: 0.04266 39 | copy_paste: 0.0 40 | anchors: 3.412 41 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-high.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for high-augmentation COCO training from scratch 3 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | # 1. train 7 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 8 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 9 | momentum: 0.937 # SGD momentum/Adam beta1 10 | weight_decay: 0.0005 # optimizer weight decay 5e-4 11 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 12 | warmup_momentum: 0.8 # warmup initial momentum 13 | warmup_bias_lr: 0.1 # warmup initial bias lr 14 | 15 | # 2. loss function 16 | box: 0.05 # box loss gain 17 | cls: 0.3 # cls loss gain 18 | cls_pw: 1.0 # cls BCELoss positive_weight 19 | obj: 0.7 # obj loss gain (scale with pixels) 20 | obj_pw: 1.0 # obj BCELoss positive_weight 21 | 22 | # 3. others 23 | iou_t: 0.20 # IoU training threshold 24 | anchor_t: 4.0 # anchor-multiple threshold 25 | # anchors: 3 # anchors per output layer (0 to ignore) 26 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 27 | 28 | # 4. data augmentation 29 | # 1. hsv增强系数 色调 饱和度 亮度 30 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 31 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 32 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 33 | 34 | # 2. random_perspective增强系数 旋转角度 平移 缩放 剪切 透明度 35 | degrees: 0.0 # image rotation (+/- deg) 36 | translate: 0.1 # image translation (+/- fraction) 37 | scale: 0.9 # image scale (+/- gain) 38 | shear: 0.0 # image shear (+/- deg) 39 | perspective: 0.0 # image perspective (+/- fraction) 40 | 41 | # 3. 图像翻转 上下 左右 42 | flipud: 0.0 # image flip up-down (probability) 43 | fliplr: 0.5 # image flip left-right (probability) 44 | 45 | # 4. 图片级数据增强 46 | mosaic: 1.0 # image mosaic (probability) 47 | mixup: 0.1 # image mixup (probability) 48 | cutout: 0.0 # image cutout (probability) 49 | copy_paste: 0.1 # segment copy-paste (probability) -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-low.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for low-augmentation COCO training from scratch 3 | # python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.5 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 1.0 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.5 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.0 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/hyps/hyp.scratch-med.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Hyperparameters for medium-augmentation COCO training from scratch 3 | # python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 4 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 5 | 6 | lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) 8 | momentum: 0.937 # SGD momentum/Adam beta1 9 | weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | warmup_momentum: 0.8 # warmup initial momentum 12 | warmup_bias_lr: 0.1 # warmup initial bias lr 13 | box: 0.05 # box loss gain 14 | cls: 0.3 # cls loss gain 15 | cls_pw: 1.0 # cls BCELoss positive_weight 16 | obj: 0.7 # obj loss gain (scale with pixels) 17 | obj_pw: 1.0 # obj BCELoss positive_weight 18 | iou_t: 0.20 # IoU training threshold 19 | anchor_t: 4.0 # anchor-multiple threshold 20 | # anchors: 3 # anchors per output layer (0 to ignore) 21 | fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | degrees: 0.0 # image rotation (+/- deg) 26 | translate: 0.1 # image translation (+/- fraction) 27 | scale: 0.9 # image scale (+/- gain) 28 | shear: 0.0 # image shear (+/- deg) 29 | perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | flipud: 0.0 # image flip up-down (probability) 31 | fliplr: 0.5 # image flip left-right (probability) 32 | mosaic: 1.0 # image mosaic (probability) 33 | mixup: 0.1 # image mixup (probability) 34 | copy_paste: 0.0 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /data/images/bus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/data/images/bus.jpg -------------------------------------------------------------------------------- /data/images/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/data/images/zidane.jpg -------------------------------------------------------------------------------- /data/scripts/download_weights.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 3 | # Download latest models from https://github.com/ultralytics/yolov5/releases 4 | # Example usage: bash path/to/download_weights.sh 5 | # parent 6 | # └── yolov5 7 | # ├── yolov5s.pt ← downloads here 8 | # ├── yolov5m.pt 9 | # └── ... 10 | 11 | python - < 山东大学-本科毕设-基于深度学习的异常行为检测算法研究 4 | > 5 | > Author: 任瑞龙 6 | > 7 | > Email: 442082944@qq.com 8 | 9 | 10 | 11 | 对YOLOv5算法的4种改进方案： 12 | 13 | 14 | 15 | - 引入轻量化卷积Ghostconv，大大降低了模型参数量； 16 | - 引入BiFPN，并充分利用中小型目标检测层； 17 | - 引入CA注意力机制，使得模型对目标框的定位更精确； 18 | - 将CIoU替换为Alpha-EIoU，使得模型在不引入额外参数的情况下，检测精度得到大幅提升。 19 | 20 | 21 | 22 | 更多细节见：https://blog.csdn.net/weixin_43799388?spm=1000.2115.3001.5343 23 | 24 | 25 | 26 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | # pip install -r requirements.txt 2 | 3 | # Base ---------------------------------------- 4 | matplotlib>=3.2.2 5 | numpy>=1.18.5 6 | opencv-python>=4.1.2 7 | Pillow>=7.1.2 8 | PyYAML>=5.3.1 9 | requests>=2.23.0 10 | scipy>=1.4.1 11 | torch>=1.7.0 12 | torchvision>=0.8.1 13 | tqdm>=4.41.0 14 | 15 | # Logging ------------------------------------- 16 | tensorboard>=2.4.1 17 | # wandb 18 | 19 | # Plotting ------------------------------------ 20 | pandas>=1.1.4 21 | seaborn>=0.11.0 22 | 23 | # Export -------------------------------------- 24 | # coremltools>=4.1 # CoreML export 25 | # onnx>=1.9.0 # ONNX export 26 | # onnx-simplifier>=0.3.6 # ONNX simplifier 27 | # scikit-learn==0.19.2 # CoreML quantization 28 | # tensorflow>=2.4.1 # TFLite export 29 | # tensorflowjs>=3.9.0 # TF.js export 30 | # openvino-dev # OpenVINO export 31 | 32 | # Extras -------------------------------------- 33 | # albumentations>=1.0.3 34 | # Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172 35 | # pycocotools>=2.0 # COCO mAP 36 | # roboflow 37 | thop # FLOPs computation 38 | -------------------------------------------------------------------------------- /setup.cfg: -------------------------------------------------------------------------------- 1 | # Project-wide configuration file, can be used for package metadata and other toll configurations 2 | # Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments 3 | 4 | [metadata] 5 | license_file = LICENSE 6 | description-file = README.md 7 | 8 | 9 | [tool:pytest] 10 | norecursedirs = 11 | .git 12 | dist 13 | build 14 | addopts = 15 | --doctest-modules 16 | --durations=25 17 | --color=yes 18 | 19 | 20 | [flake8] 21 | max-line-length = 120 22 | exclude = .tox,*.egg,build,temp 23 | select = E,W,F 24 | doctests = True 25 | verbose = 2 26 | # https://pep8.readthedocs.io/en/latest/intro.html#error-codes 27 | format = pylint 28 | # see: https://www.flake8rules.com/ 29 | ignore = 30 | E731 # Do not assign a lambda expression, use a def 31 | F405 # name may be undefined, or defined from star imports: module 32 | E402 # module level import not at top of file 33 | F401 # module imported but unused 34 | W504 # line break after binary operator 35 | E127 # continuation line over-indented for visual indent 36 | W504 # line break after binary operator 37 | E231 # missing whitespace after ‘,’, ‘;’, or ‘:’ 38 | E501 # line too long 39 | F403 # ‘from module import *’ used; unable to detect undefined names 40 | 41 | 42 | [isort] 43 | # https://pycqa.github.io/isort/docs/configuration/options.html 44 | line_length = 120 45 | multi_line_output = 0 46 | -------------------------------------------------------------------------------- /test/activation_test.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from matplotlib import pyplot as plt 4 | 5 | relu = nn.ReLU() 6 | leaky_relu = nn.LeakyReLU() 7 | mish = nn.Mish() 8 | silu = nn.SiLU() 9 | 10 | x = torch.linspace(-10, 10, 1000) 11 | y_relu = relu(x) 12 | y_leaky_relu = leaky_relu(x) 13 | y_mish = mish(x) 14 | y_silu = silu(x) 15 | 16 | plt.plot(x, y_relu, 'g-') 17 | plt.plot(x, y_leaky_relu, 'y-') 18 | plt.plot(x, y_mish, 'b-') 19 | plt.plot(x, y_silu, 'r-') 20 | plt.grid() 21 | plt.show() 22 | -------------------------------------------------------------------------------- /test/demo.py: -------------------------------------------------------------------------------- 1 | import matplotlib 2 | print(matplotlib.matplotlib_fname()) -------------------------------------------------------------------------------- /test/focus_vs_conv.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import torch 4 | import torch.nn as nn 5 | 6 | 7 | class Conv(nn.Module): 8 | def __init__(self): 9 | super().__init__() 10 | self.conv = nn.Conv2d(in_channels=3, 11 | out_channels=32, 12 | kernel_size=6, 13 | stride=2, 14 | padding=2) 15 | self.bn = nn.BatchNorm2d(32) 16 | self.act = nn.SiLU() 17 | 18 | def forward(self, x): 19 | return self.act(self.bn(self.conv(x))) 20 | 21 | 22 | class Focus(nn.Module): 23 | def __init__(self): 24 | super().__init__() 25 | self.conv = nn.Sequential( 26 | nn.Conv2d(in_channels=3 * 4, 27 | out_channels=32, 28 | kernel_size=3, 29 | stride=1, 30 | padding=3 // 2), 31 | nn.BatchNorm2d(32), 32 | nn.SiLU() 33 | ) 34 | 35 | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) 36 | # 假设x = [1,2,3,4,5,6,7,8,9] x[::2] = [1,3,5,7,9] 间隔2个取样 37 | # x[1::2] = [2, 4, 6, 8] 从第二个数据开始，间隔2个取样 38 | return self.conv(torch.cat([x[..., ::2, ::2], 39 | x[..., 1::2, ::2], 40 | x[..., ::2, 1::2], 41 | x[..., 1::2, 1::2]], 1)) 42 | 43 | 44 | if __name__ == '__main__': 45 | input_data = torch.rand(1, 3, 640, 640) 46 | 47 | conv = Conv() 48 | focus = Focus() 49 | 50 | output1 = conv(input_data) 51 | output2 = focus(input_data) 52 | 53 | print(f"output1 size: {output1.size()}") 54 | print(f"output2 size: {output2.size()}") 55 | print(torch.equal(output1, output2)) 56 | 57 | # 速度对比 58 | t_start = time.time() 59 | for _ in range(300): 60 | conv(input_data) 61 | print(f"6x6 conv time: {time.time() - t_start}") 62 | 63 | t_start = time.time() 64 | for _ in range(300): 65 | focus(input_data) 66 | print(f"focus time: {time.time() - t_start}") 67 | 68 | ''' 69 | output1 size: torch.Size([1, 32, 320, 320]) 70 | output2 size: torch.Size([1, 32, 320, 320]) 71 | False 72 | 6x6 conv time: 2.4613001346588135 73 | focus time: 2.8948004245758057 74 | ''' 75 | -------------------------------------------------------------------------------- /test/gpu_memory.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | print("torch版本号: ", end="") 4 | print(torch.__version__) 5 | 6 | print("判断torch是否可用: ", end="") 7 | print(torch.cuda.is_available()) 8 | 9 | print("gpu数量: ", end="") 10 | print(torch.cuda.device_count()) 11 | 12 | print("gpu名字，设备索引默认从0开始: ", end="") 13 | print(torch.cuda.get_device_name(0)) 14 | print("现在正在使用的GPU编号: ", end="") 15 | print(torch.cuda.current_device()) -------------------------------------------------------------------------------- /test/images/1.jpg: -------------------------------------------------------------------------------- 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https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/test/images/4.jpg -------------------------------------------------------------------------------- /test/labels/1.txt: -------------------------------------------------------------------------------- 1 | 16 0.182 0.534343 0.18 0.288889 2 | 36 0.70079 0.530333 0.18242 0.507859 3 | -------------------------------------------------------------------------------- /test/labels/2.txt: -------------------------------------------------------------------------------- 1 | 4 0.516492 0.469388 0.912516 0.748282 2 | -------------------------------------------------------------------------------- /test/labels/3.txt: -------------------------------------------------------------------------------- 1 | 32 0.680102 0.631552 0.012953 0.01867 2 | 0 0.36418 0.679347 0.163484 0.322734 3 | 0 0.274594 0.74064 0.198437 0.272808 4 | 34 0.385367 0.612722 0.099141 0.113128 5 | 35 0.361008 0.702106 0.037734 0.050468 6 | 35 0.887125 0.633781 0.016406 0.01968 7 | 0 0.346883 0.574544 0.039703 0.174261 8 | 0 0.888102 0.593793 0.068953 0.12133 9 | 0 0.125453 0.7 0.142562 0.375271 10 | 0 0.354016 0.457796 0.006906 0.019729 11 | -------------------------------------------------------------------------------- /test/labels/4.txt: -------------------------------------------------------------------------------- 1 | 14 0.820783 0.56129 0.218633 0.3527 2 | 14 0.293458 0.37634 0.159617 0.33484 3 | 14 0.525983 0.41653 0.166667 0.32554 4 | 14 0.486708 0.66271 0.16165 0.24138 5 | 14 0.267033 0.79969 0.1538 0.3225 6 | 14 0.139517 0.17415 0.159167 0.26742 7 | 14 0.2955 0.60918 0.1904 0.26864 8 | 14 0.859717 0.79617 0.157067 0.30918 9 | -------------------------------------------------------------------------------- /test/model_test.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import os 3 | import torch 4 | import yaml 5 | 6 | from models.yolo import Model 7 | from torchstat import stat 8 | 9 | os.environ['CUDA_VISIBLE_DEVICES'] = '2' 10 | 11 | 12 | def parse_opt(known=False): 13 | parser = argparse.ArgumentParser() 14 | # yolov5s-voc/yolov5s-ghostconv-bifpn1-ca-voc 15 | parser.add_argument('--cfg', type=str, 16 | default='../models/abnormal_driving/yolov5s_dd.yaml', 17 | help='model.yaml path') 18 | parser.add_argument('--hyp', type=str, default='../data/hyps/hyp.scratch-high.yaml', 19 | help='hyperparameters path') 20 | 21 | opt = parser.parse_known_args()[0] if known else parser.parse_args() 22 | return opt 23 | 24 | 25 | def main(opt): 26 | with open(opt.hyp, encoding='utf-8', errors='ignore') as f: 27 | hyp = yaml.safe_load(f) # load hyps dict 字典形式 28 | 29 | # 如果配置文件中有中文，打开时要加encoding = 'utf-8'参数 30 | with open(opt.cfg, encoding='ascii', errors='ignore') as f: 31 | cfg = yaml.safe_load(f) # model dict 取到配置文件中每条的信息 32 | 33 | nc = cfg['nc'] # 获取数据集的类别数 34 | device = torch.device('cuda:0') if torch.cuda.is_available() else 'cpu' 35 | print(f'device: {device}') 36 | # input_img = torch.zeros(size=(1, 3, 1280, 1280)) 37 | input_img = torch.zeros(size=(1, 3, 640, 640)) 38 | input_img = input_img.to(device, non_blocking=True).float() 39 | print(f'the model of \'{opt.cfg}\' is :') 40 | model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create 41 | output = model(input_img) 42 | 43 | print(f'number of detect layers: {len(output)}') 44 | print('Detect head output: ') 45 | # print(f'P2/4: {output[len(output) - 4].shape}') 46 | print(f'P3/8: {output[len(output) - 3].shape}') 47 | print(f'P4/16: {output[len(output) - 2].shape}') 48 | print(f'P5/32: {output[len(output) - 1].shape}') 49 | 50 | ''' 51 | 3 52 | torch.Size([1, 3, 80, 80, 85]) 53 | torch.Size([1, 3, 40, 40, 85]) 54 | torch.Size([1, 3, 20, 20, 85]) 55 | ''' 56 | 57 | 58 | if __name__ == '__main__': 59 | opt = parse_opt() 60 | main(opt) 61 | 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| 可以用来计算pytorch构建的网络的参数，空间大小，MAdd，FLOPs等指标，简单好用。 4 | 比如：我想知道alexnet的网络的一些参数 5 | ''' 6 | from torchstat import stat 7 | import torchvision.models as models 8 | 9 | model = models.alexnet() 10 | stat(model, (3, 224, 224)) 11 | -------------------------------------------------------------------------------- /test/spp_vs_sppf.py: -------------------------------------------------------------------------------- 1 | import torch.nn as nn 2 | import torch 3 | import time 4 | 5 | 6 | class SPP(nn.Module): 7 | def __init__(self): 8 | super().__init__() 9 | self.maxpool1 = nn.MaxPool2d(kernel_size=5, stride=1, padding=5 // 2) 10 | self.maxpool2 = nn.MaxPool2d(kernel_size=9, stride=1, padding=9 // 2) 11 | self.maxpool3 = nn.MaxPool2d(kernel_size=13, stride=1, padding=13 // 2) 12 | 13 | def forward(self, x): 14 | p1 = self.maxpool1(x) 15 | p2 = self.maxpool2(x) 16 | p3 = self.maxpool3(x) 17 | return torch.cat([x, p1, p2, p3], dim=1) 18 | 19 | 20 | class SPPF(nn.Module): 21 | def __init__(self): 22 | super().__init__() 23 | self.maxpool = nn.MaxPool2d(kernel_size=5, stride=1, padding=5 // 2) 24 | 25 | def forward(self, x): 26 | p1 = self.maxpool(x) 27 | p2 = self.maxpool(p1) 28 | p3 = self.maxpool(p2) 29 | return torch.cat([x, p1, p2, p3], dim=1) 30 | 31 | 32 | if __name__ == '__main__': 33 | input_data = torch.rand(8, 32, 64, 64) 34 | spp = SPP() 35 | sppf = SPPF() 36 | output1 = spp(input_data) 37 | output2 = sppf(input_data) 38 | 39 | print(torch.equal(output1, output2)) 40 | 41 | t_start = time.time() 42 | for _ in range(100): 43 | spp(input_data) 44 | print(f"spp time: {time.time() - t_start}") 45 | 46 | t_start = time.time() 47 | for _ in range(100): 48 | sppf(input_data) 49 | print(f"sppf time: {time.time() - t_start}") 50 | 51 | ''' 52 | True 53 | spp time: 4.364669561386108 54 | sppf time: 1.5898349285125732 55 | ''' 56 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | utils/initialization 4 | """ 5 | 6 | 7 | def notebook_init(verbose=True): 8 | # Check system software and hardware 9 | print('Checking setup...') 10 | 11 | import os 12 | import shutil 13 | 14 | from utils.general import check_requirements, emojis, is_colab 15 | from utils.torch_utils import select_device # imports 16 | 17 | check_requirements(('psutil', 'IPython')) 18 | import psutil 19 | from IPython import display # to display images and clear console output 20 | 21 | if is_colab(): 22 | shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory 23 | 24 | if verbose: 25 | # System info 26 | # gb = 1 / 1000 ** 3 # bytes to GB 27 | gib = 1 / 1024 ** 3 # bytes to GiB 28 | ram = psutil.virtual_memory().total 29 | total, used, free = shutil.disk_usage("/") 30 | display.clear_output() 31 | s = f'({os.cpu_count()} CPUs, {ram * gib:.1f} GB RAM, {(total - free) * gib:.1f}/{total * gib:.1f} GB disk)' 32 | else: 33 | s = '' 34 | 35 | select_device(newline=False) 36 | print(emojis(f'Setup complete ✅ {s}')) 37 | return display 38 | -------------------------------------------------------------------------------- /utils/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /utils/__pycache__/activations.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/__pycache__/activations.cpython-38.pyc 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memory 24 | # Usage: 25 | # import torch 26 | # from utils.autobatch import autobatch 27 | # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) 28 | # print(autobatch(model)) 29 | 30 | prefix = colorstr('AutoBatch: ') 31 | LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') 32 | device = next(model.parameters()).device # get model device 33 | if device.type == 'cpu': 34 | LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') 35 | return batch_size 36 | 37 | d = str(device).upper() # 'CUDA:0' 38 | properties = torch.cuda.get_device_properties(device) # device properties 39 | t = properties.total_memory / 1024 ** 3 # (GiB) 40 | r = torch.cuda.memory_reserved(device) / 1024 ** 3 # (GiB) 41 | a = torch.cuda.memory_allocated(device) / 1024 ** 3 # (GiB) 42 | f = t - (r + a) # free inside reserved 43 | LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') 44 | 45 | batch_sizes = [1, 2, 4, 8, 16] 46 | try: 47 | img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes] 48 | y = profile(img, model, n=3, device=device) 49 | except Exception as e: 50 | LOGGER.warning(f'{prefix}{e}') 51 | 52 | y = [x[2] for x in y if x] # memory [2] 53 | batch_sizes = batch_sizes[:len(y)] 54 | p = np.polyfit(batch_sizes, y, deg=1) # first degree polynomial fit 55 | b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) 56 | LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)') 57 | return b 58 | -------------------------------------------------------------------------------- /utils/aws/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/aws/__init__.py -------------------------------------------------------------------------------- /utils/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /utils/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolov5/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | FILE = Path(__file__).resolve() 12 | ROOT = FILE.parents[2] # YOLOv5 root directory 13 | if str(ROOT) not in sys.path: 14 | sys.path.append(str(ROOT)) # add ROOT to PATH 15 | 16 | port = 0 # --master_port 17 | path = Path('').resolve() 18 | for last in path.rglob('*/**/last.pt'): 19 | ckpt = torch.load(last) 20 | if ckpt['optimizer'] is None: 21 | continue 22 | 23 | # Load opt.yaml 24 | with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: 25 | opt = yaml.safe_load(f) 26 | 27 | # Get device count 28 | d = opt['device'].split(',') # devices 29 | nd = len(d) # number of devices 30 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 31 | 32 | if ddp: # multi-GPU 33 | port += 1 34 | cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 35 | else: # single-GPU 36 | cmd = f'python train.py --resume {last}' 37 | 38 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 39 | print(cmd) 40 | os.system(cmd) 41 | -------------------------------------------------------------------------------- /utils/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolov5 ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 11 | cd yolov5 12 | bash data/scripts/get_coco.sh && echo "COCO done." & 13 | sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /utils/flask_rest_api/README.md: -------------------------------------------------------------------------------- 1 | # Flask REST API 2 | 3 | [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are 4 | commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API 5 | created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). 6 | 7 | ## Requirements 8 | 9 | [Flask](https://palletsprojects.com/p/flask/) is required. Install with: 10 | 11 | ```shell 12 | $ pip install Flask 13 | ``` 14 | 15 | ## Run 16 | 17 | After Flask installation run: 18 | 19 | ```shell 20 | $ python3 restapi.py --port 5000 21 | ``` 22 | 23 | Then use [curl](https://curl.se/) to perform a request: 24 | 25 | ```shell 26 | $ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' 27 | ``` 28 | 29 | The model inference results are returned as a JSON response: 30 | 31 | ```json 32 | [ 33 | { 34 | "class": 0, 35 | "confidence": 0.8900438547, 36 | "height": 0.9318675399, 37 | "name": "person", 38 | "width": 0.3264600933, 39 | "xcenter": 0.7438579798, 40 | "ycenter": 0.5207948685 41 | }, 42 | { 43 | "class": 0, 44 | "confidence": 0.8440024257, 45 | "height": 0.7155083418, 46 | "name": "person", 47 | "width": 0.6546785235, 48 | "xcenter": 0.427829951, 49 | "ycenter": 0.6334488392 50 | }, 51 | { 52 | "class": 27, 53 | "confidence": 0.3771208823, 54 | "height": 0.3902671337, 55 | "name": "tie", 56 | "width": 0.0696444362, 57 | "xcenter": 0.3675483763, 58 | "ycenter": 0.7991207838 59 | }, 60 | { 61 | "class": 27, 62 | "confidence": 0.3527112305, 63 | "height": 0.1540903747, 64 | "name": "tie", 65 | "width": 0.0336618312, 66 | "xcenter": 0.7814827561, 67 | "ycenter": 0.5065554976 68 | } 69 | ] 70 | ``` 71 | 72 | An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given 73 | in `example_request.py` 74 | -------------------------------------------------------------------------------- /utils/flask_rest_api/example_request.py: -------------------------------------------------------------------------------- 1 | """Perform test request""" 2 | import pprint 3 | 4 | import requests 5 | 6 | DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" 7 | TEST_IMAGE = "zidane.jpg" 8 | 9 | image_data = open(TEST_IMAGE, "rb").read() 10 | 11 | response = requests.post(DETECTION_URL, files={"image": image_data}).json() 12 | 13 | pprint.pprint(response) 14 | -------------------------------------------------------------------------------- /utils/flask_rest_api/restapi.py: -------------------------------------------------------------------------------- 1 | """ 2 | Run a rest API exposing the yolov5s object detection model 3 | """ 4 | import argparse 5 | import io 6 | 7 | import torch 8 | from flask import Flask, request 9 | from PIL import Image 10 | 11 | app = Flask(__name__) 12 | 13 | DETECTION_URL = "/v1/object-detection/yolov5s" 14 | 15 | 16 | @app.route(DETECTION_URL, methods=["POST"]) 17 | def predict(): 18 | if not request.method == "POST": 19 | return 20 | 21 | if request.files.get("image"): 22 | image_file = request.files["image"] 23 | image_bytes = image_file.read() 24 | 25 | img = Image.open(io.BytesIO(image_bytes)) 26 | 27 | results = model(img, size=640) # reduce size=320 for faster inference 28 | return results.pandas().xyxy[0].to_json(orient="records") 29 | 30 | 31 | if __name__ == "__main__": 32 | parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") 33 | parser.add_argument("--port", default=5000, type=int, help="port number") 34 | args = parser.parse_args() 35 | 36 | model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache 37 | app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat 38 | -------------------------------------------------------------------------------- /utils/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /utils/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==21.1 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /utils/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolov5app 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 15 | -------------------------------------------------------------------------------- /utils/loggers/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/loggers/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /utils/loggers/wandb/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/loggers/wandb/__init__.py -------------------------------------------------------------------------------- /utils/loggers/wandb/__pycache__/__init__.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/loggers/wandb/__pycache__/__init__.cpython-38.pyc -------------------------------------------------------------------------------- /utils/loggers/wandb/__pycache__/wandb_utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/utils/loggers/wandb/__pycache__/wandb_utils.cpython-38.pyc -------------------------------------------------------------------------------- /utils/loggers/wandb/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | from wandb_utils import WandbLogger 4 | 5 | from utils.general import LOGGER 6 | 7 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 8 | 9 | 10 | def create_dataset_artifact(opt): 11 | logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused 12 | if not logger.wandb: 13 | LOGGER.info("install wandb using `pip install wandb` to log the dataset") 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') 19 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 20 | parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') 21 | parser.add_argument('--entity', default=None, help='W&B entity') 22 | parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run') 23 | 24 | opt = parser.parse_args() 25 | opt.resume = False # Explicitly disallow resume check for dataset upload job 26 | 27 | create_dataset_artifact(opt) 28 | -------------------------------------------------------------------------------- /utils/loggers/wandb/sweep.py: -------------------------------------------------------------------------------- 1 | import sys 2 | from pathlib import Path 3 | 4 | import wandb 5 | 6 | FILE = Path(__file__).resolve() 7 | ROOT = FILE.parents[3] # YOLOv5 root directory 8 | if str(ROOT) not in sys.path: 9 | sys.path.append(str(ROOT)) # add ROOT to PATH 10 | 11 | from train import parse_opt, train 12 | from utils.callbacks import Callbacks 13 | from utils.general import increment_path 14 | from utils.torch_utils import select_device 15 | 16 | 17 | def sweep(): 18 | wandb.init() 19 | # Get hyp dict from sweep agent 20 | hyp_dict = vars(wandb.config).get("_items") 21 | 22 | # Workaround: get necessary opt args 23 | opt = parse_opt(known=True) 24 | opt.batch_size = hyp_dict.get("batch_size") 25 | opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) 26 | opt.epochs = hyp_dict.get("epochs") 27 | opt.nosave = True 28 | opt.data = hyp_dict.get("data") 29 | opt.weights = str(opt.weights) 30 | opt.cfg = str(opt.cfg) 31 | opt.data = str(opt.data) 32 | opt.hyp = str(opt.hyp) 33 | opt.project = str(opt.project) 34 | device = select_device(opt.device, batch_size=opt.batch_size) 35 | 36 | # train 37 | train(hyp_dict, opt, device, callbacks=Callbacks()) 38 | 39 | 40 | if __name__ == "__main__": 41 | sweep() 42 | -------------------------------------------------------------------------------- /改进算法整体框架.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Xiaolong-RRL/YOLOv5-Abnormal-Driving/276a53a032c937da3457981605880a75c377a659/改进算法整体框架.png --------------------------------------------------------------------------------