├── .pre-commit-config.yaml ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── data ├── Argoverse.yaml ├── GlobalWheat2020.yaml ├── Objects365.yaml ├── SKU-110K.yaml ├── VOC.yaml ├── VisDrone.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 ├── my_person.yaml ├── scripts │ ├── download_weights.sh │ ├── get_coco.sh │ └── get_coco128.sh └── xView.yaml ├── datasets └── mydata │ ├── Annotations │ ├── 1066405,1b8000ef60354f.xml │ ├── 1066405,2a72f000f214d26a.xml │ ├── 1066405,2ac2400079a6d80f.xml │ ├── 1066405,2b6000ffe20c07.xml │ ├── 1066405,2bf6e00075455d3c.xml │ ├── 1066405,2bfbf000c47880b7.xml │ ├── 1066405,2c8c7000530eb0e7.xml │ ├── 1066405,2cdca0006185e7eb.xml │ ├── 1066405,2d2c6000adf6f6f4.xml │ └── 1066405,2d6f2000fec9dcab.xml │ ├── images │ ├── 1066405,1b8000ef60354f.jpg │ ├── 1066405,2a72f000f214d26a.jpg │ ├── 1066405,2ac2400079a6d80f.jpg │ ├── 1066405,2b6000ffe20c07.jpg │ ├── 1066405,2bf6e00075455d3c.jpg │ ├── 1066405,2bfbf000c47880b7.jpg │ ├── 1066405,2c8c7000530eb0e7.jpg │ ├── 1066405,2cdca0006185e7eb.jpg │ ├── 1066405,2d2c6000adf6f6f4.jpg │ └── 1066405,2d6f2000fec9dcab.jpg │ ├── labels │ ├── 1066405,1b8000ef60354f.txt │ ├── 1066405,2a72f000f214d26a.txt │ ├── 1066405,2ac2400079a6d80f.txt │ ├── 1066405,2b6000ffe20c07.txt │ ├── 1066405,2bf6e00075455d3c.txt │ ├── 1066405,2bfbf000c47880b7.txt │ ├── 1066405,2c8c7000530eb0e7.txt │ ├── 1066405,2cdca0006185e7eb.txt │ ├── 1066405,2d2c6000adf6f6f4.txt │ └── 1066405,2d6f2000fec9dcab.txt │ ├── test.txt │ ├── train.cache │ ├── train.txt │ ├── val.cache │ └── val.txt ├── detect.py ├── export.py ├── hubconf.py ├── models ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── common.cpython-37.pyc │ ├── experimental.cpython-37.pyc │ └── yolo.cpython-37.pyc ├── 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 │ ├── yolov5s6.yaml │ └── yolov5x6.yaml ├── tf.py ├── yolo.py ├── yolov5l.yaml ├── yolov5m.yaml ├── yolov5m_C3CA.yaml ├── yolov5m_C3CBAM.yaml ├── yolov5m_C3ECA.yaml ├── yolov5m_C3SE.yaml ├── yolov5m_CBAM.yaml ├── yolov5m_CoordAtt.yaml ├── yolov5m_ECA.yaml ├── yolov5m_SE.yaml ├── yolov5n.yaml ├── yolov5s.yaml └── yolov5x.yaml ├── requirements.txt ├── setup.cfg ├── split.py ├── train.py ├── tutorial.ipynb ├── utils ├── __init__.py ├── __pycache__ │ ├── __init__.cpython-37.pyc │ ├── augmentations.cpython-37.pyc │ ├── autoanchor.cpython-37.pyc │ ├── autobatch.cpython-37.pyc │ ├── callbacks.cpython-37.pyc │ ├── datasets.cpython-37.pyc │ ├── downloads.cpython-37.pyc │ ├── general.cpython-37.pyc │ ├── loss.cpython-37.pyc │ ├── metrics.cpython-37.pyc │ ├── plots.cpython-37.pyc │ └── torch_utils.cpython-37.pyc ├── activations.py ├── augmentations.py ├── autoanchor.py ├── autobatch.py ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── benchmarks.py ├── callbacks.py ├── datasets.py ├── docker │ ├── .dockerignore │ ├── Dockerfile │ └── Dockerfile-cpu ├── 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-37.pyc │ └── wandb │ │ ├── README.md │ │ ├── __init__.py │ │ ├── __pycache__ │ │ ├── __init__.cpython-37.pyc │ │ └── wandb_utils.cpython-37.pyc │ │ ├── log_dataset.py │ │ ├── sweep.py │ │ ├── sweep.yaml │ │ └── wandb_utils.py ├── loss.py ├── metrics.py ├── plots.py └── torch_utils.py ├── val.py └── voc_label.py /.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: v2.31.1 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 | - repo: https://github.com/pre-commit/mirrors-yapf 40 | rev: v0.32.0 41 | hooks: 42 | - id: yapf 43 | name: YAPF formatting 44 | 45 | # TODO 46 | #- repo: https://github.com/executablebooks/mdformat 47 | # rev: 0.7.7 48 | # hooks: 49 | # - id: mdformat 50 | # additional_dependencies: 51 | # - mdformat-gfm 52 | # - mdformat-black 53 | # - mdformat_frontmatter 54 | 55 | - repo: https://github.com/asottile/yesqa 56 | rev: v1.3.0 57 | hooks: 58 | - id: yesqa 59 | 60 | - repo: https://github.com/PyCQA/flake8 61 | rev: 4.0.1 62 | hooks: 63 | - id: flake8 64 | name: PEP8 65 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | ## Contributing to YOLOv5 🚀 2 | 3 | We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's: 4 | 5 | - Reporting a bug 6 | - Discussing the current state of the code 7 | - Submitting a fix 8 | - Proposing a new feature 9 | - Becoming a maintainer 10 | 11 | YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be 12 | helping push the frontiers of what's possible in AI 😃! 13 | 14 | ## Submitting a Pull Request (PR) 🛠️ 15 | 16 | Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps: 17 | 18 | ### 1. Select File to Update 19 | 20 | Select `requirements.txt` to update by clicking on it in GitHub. 21 |

PR_step1

22 | 23 | ### 2. Click 'Edit this file' 24 | 25 | Button is in top-right corner. 26 |

PR_step2

27 | 28 | ### 3. Make Changes 29 | 30 | Change `matplotlib` version from `3.2.2` to `3.3`. 31 |

PR_step3

32 | 33 | ### 4. Preview Changes and Submit PR 34 | 35 | Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** 36 | for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose 37 | changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃! 38 |

PR_step4

39 | 40 | ### PR recommendations 41 | 42 | To allow your work to be integrated as seamlessly as possible, we advise you to: 43 | 44 | - ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an 45 | automatic [GitHub Actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) merge may 46 | be attempted by writing /rebase in a new comment, or by running the following code, replacing 'feature' with the name 47 | of your local branch: 48 | 49 | ```bash 50 | git remote add upstream https://github.com/ultralytics/yolov5.git 51 | git fetch upstream 52 | # git checkout feature # <--- replace 'feature' with local branch name 53 | git merge upstream/master 54 | git push -u origin -f 55 | ``` 56 | 57 | - ✅ Verify all Continuous Integration (CI) **checks are passing**. 58 | - ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase 59 | but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee 60 | 61 | ## Submitting a Bug Report 🐛 62 | 63 | If you spot a problem with YOLOv5 please submit a Bug Report! 64 | 65 | For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few 66 | short guidelines below to help users provide what we need in order to get started. 67 | 68 | When asking a question, people will be better able to provide help if you provide **code** that they can easily 69 | understand and use to **reproduce** the problem. This is referred to by community members as creating 70 | a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces 71 | the problem should be: 72 | 73 | * ✅ **Minimal** – Use as little code as possible that still produces the same problem 74 | * ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself 75 | * ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem 76 | 77 | In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code 78 | should be: 79 | 80 | * ✅ **Current** – Verify that your code is up-to-date with current 81 | GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new 82 | copy to ensure your problem has not already been resolved by previous commits. 83 | * ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this 84 | repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. 85 | 86 | If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 ** 87 | Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing 88 | a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better 89 | understand and diagnose your problem. 90 | 91 | ## License 92 | 93 | By contributing, you agree that your contributions will be licensed under 94 | the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/) 95 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # yolov5_attention 2 | YOLOV5 v6.1添加 ECA CA SE CBAM C3SE C3ECA C3CBAM C3CA注意力机制 3 | 哔哩哔哩 4 | https://www.bilibili.com/video/BV1kS4y1c7Bm?spm_id_from=333.999.0.0 5 | -------------------------------------------------------------------------------- /data/Argoverse.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI 3 | # Example usage: python train.py --data Argoverse.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── Argoverse ← downloads here (31.3 GB) 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/Argoverse # dataset root dir 12 | train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images 13 | val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images 14 | test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview 15 | 16 | # Classes 17 | nc: 8 # number of classes 18 | names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names 19 | 20 | 21 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 22 | download: | 23 | import json 24 | 25 | from tqdm.auto import tqdm 26 | from utils.general import download, Path 27 | 28 | 29 | def argoverse2yolo(set): 30 | labels = {} 31 | a = json.load(open(set, "rb")) 32 | for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): 33 | img_id = annot['image_id'] 34 | img_name = a['images'][img_id]['name'] 35 | img_label_name = img_name[:-3] + "txt" 36 | 37 | cls = annot['category_id'] # instance class id 38 | x_center, y_center, width, height = annot['bbox'] 39 | x_center = (x_center + width / 2) / 1920.0 # offset and scale 40 | y_center = (y_center + height / 2) / 1200.0 # offset and scale 41 | width /= 1920.0 # scale 42 | height /= 1200.0 # scale 43 | 44 | img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] 45 | if not img_dir.exists(): 46 | img_dir.mkdir(parents=True, exist_ok=True) 47 | 48 | k = str(img_dir / img_label_name) 49 | if k not in labels: 50 | labels[k] = [] 51 | labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") 52 | 53 | for k in labels: 54 | with open(k, "w") as f: 55 | f.writelines(labels[k]) 56 | 57 | 58 | # Download 59 | dir = Path('../datasets/Argoverse') # dataset root dir 60 | urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] 61 | download(urls, dir=dir, delete=False) 62 | 63 | # Convert 64 | annotations_dir = 'Argoverse-HD/annotations/' 65 | (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' 66 | for d in "train.json", "val.json": 67 | argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels 68 | -------------------------------------------------------------------------------- /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 (7.0 GB) 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 | 38 | # Download 39 | dir = Path(yaml['path']) # dataset root dir 40 | urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', 41 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] 42 | download(urls, dir=dir) 43 | 44 | # Make Directories 45 | for p in 'annotations', 'images', 'labels': 46 | (dir / p).mkdir(parents=True, exist_ok=True) 47 | 48 | # Move 49 | for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ 50 | 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': 51 | (dir / p).rename(dir / 'images' / p) # move to /images 52 | f = (dir / p).with_suffix('.json') # json file 53 | if f.exists(): 54 | f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations 55 | -------------------------------------------------------------------------------- /data/SKU-110K.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail 3 | # Example usage: python train.py --data SKU-110K.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── SKU-110K ← downloads here (13.6 GB) 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/SKU-110K # dataset root dir 12 | train: train.txt # train images (relative to 'path') 8219 images 13 | val: val.txt # val images (relative to 'path') 588 images 14 | test: test.txt # test images (optional) 2936 images 15 | 16 | # Classes 17 | nc: 1 # number of classes 18 | names: ['object'] # class names 19 | 20 | 21 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 22 | download: | 23 | import shutil 24 | from tqdm.auto import tqdm 25 | from utils.general import np, pd, Path, download, xyxy2xywh 26 | 27 | 28 | # Download 29 | dir = Path(yaml['path']) # dataset root dir 30 | parent = Path(dir.parent) # download dir 31 | urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] 32 | download(urls, dir=parent, delete=False) 33 | 34 | # Rename directories 35 | if dir.exists(): 36 | shutil.rmtree(dir) 37 | (parent / 'SKU110K_fixed').rename(dir) # rename dir 38 | (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir 39 | 40 | # Convert labels 41 | names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names 42 | for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': 43 | x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations 44 | images, unique_images = x[:, 0], np.unique(x[:, 0]) 45 | with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: 46 | f.writelines(f'./images/{s}\n' for s in unique_images) 47 | for im in tqdm(unique_images, desc=f'Converting {dir / d}'): 48 | cls = 0 # single-class dataset 49 | with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: 50 | for r in x[images == im]: 51 | w, h = r[6], r[7] # image width, height 52 | xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance 53 | f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label 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 (2.8 GB) 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 12 | train: # train images (relative to 'path') 16551 images 13 | - images/train2012 14 | - images/train2007 15 | - images/val2012 16 | - images/val2007 17 | val: # val images (relative to 'path') 4952 images 18 | - images/test2007 19 | test: # test images (optional) 20 | - images/test2007 21 | 22 | # Classes 23 | nc: 20 # number of classes 24 | names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 25 | 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names 26 | 27 | 28 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 29 | download: | 30 | import xml.etree.ElementTree as ET 31 | 32 | from tqdm.auto import tqdm 33 | from utils.general import download, Path 34 | 35 | 36 | def convert_label(path, lb_path, year, image_id): 37 | def convert_box(size, box): 38 | dw, dh = 1. / size[0], 1. / size[1] 39 | x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] 40 | return x * dw, y * dh, w * dw, h * dh 41 | 42 | in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') 43 | out_file = open(lb_path, 'w') 44 | tree = ET.parse(in_file) 45 | root = tree.getroot() 46 | size = root.find('size') 47 | w = int(size.find('width').text) 48 | h = int(size.find('height').text) 49 | 50 | for obj in root.iter('object'): 51 | cls = obj.find('name').text 52 | if cls in yaml['names'] and not int(obj.find('difficult').text) == 1: 53 | xmlbox = obj.find('bndbox') 54 | bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) 55 | cls_id = yaml['names'].index(cls) # class id 56 | out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') 57 | 58 | 59 | # Download 60 | dir = Path(yaml['path']) # dataset root dir 61 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 62 | urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images 63 | url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images 64 | url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images 65 | download(urls, dir=dir / 'images', delete=False, curl=True, threads=3) 66 | 67 | # Convert 68 | path = dir / f'images/VOCdevkit' 69 | for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): 70 | imgs_path = dir / 'images' / f'{image_set}{year}' 71 | lbs_path = dir / 'labels' / f'{image_set}{year}' 72 | imgs_path.mkdir(exist_ok=True, parents=True) 73 | lbs_path.mkdir(exist_ok=True, parents=True) 74 | 75 | with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f: 76 | image_ids = f.read().strip().split() 77 | for id in tqdm(image_ids, desc=f'{image_set}{year}'): 78 | f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path 79 | lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path 80 | f.rename(imgs_path / f.name) # move image 81 | convert_label(path, lb_path, year, id) # convert labels to YOLO format 82 | -------------------------------------------------------------------------------- /data/VisDrone.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University 3 | # Example usage: python train.py --data VisDrone.yaml 4 | # parent 5 | # ├── yolov5 6 | # └── datasets 7 | # └── VisDrone ← downloads here (2.3 GB) 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/VisDrone # dataset root dir 12 | train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images 13 | val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images 14 | test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images 15 | 16 | # Classes 17 | nc: 10 # number of classes 18 | names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] 19 | 20 | 21 | # Download script/URL (optional) --------------------------------------------------------------------------------------- 22 | download: | 23 | from utils.general import download, os, Path 24 | 25 | def visdrone2yolo(dir): 26 | from PIL import Image 27 | from tqdm.auto import tqdm 28 | 29 | def convert_box(size, box): 30 | # Convert VisDrone box to YOLO xywh box 31 | dw = 1. / size[0] 32 | dh = 1. / size[1] 33 | return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh 34 | 35 | (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory 36 | pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') 37 | for f in pbar: 38 | img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size 39 | lines = [] 40 | with open(f, 'r') as file: # read annotation.txt 41 | for row in [x.split(',') for x in file.read().strip().splitlines()]: 42 | if row[4] == '0': # VisDrone 'ignored regions' class 0 43 | continue 44 | cls = int(row[5]) - 1 45 | box = convert_box(img_size, tuple(map(int, row[:4]))) 46 | lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") 47 | with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: 48 | fl.writelines(lines) # write label.txt 49 | 50 | 51 | # Download 52 | dir = Path(yaml['path']) # dataset root dir 53 | urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', 54 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', 55 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', 56 | 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] 57 | download(urls, dir=dir, curl=True, threads=4) 58 | 59 | # Convert 60 | for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': 61 | visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels 62 | -------------------------------------------------------------------------------- /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 (20.1 GB) 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 | 34 | # Download labels 35 | segments = False # segment or box labels 36 | dir = Path(yaml['path']) # dataset root dir 37 | url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' 38 | urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels 39 | download(urls, dir=dir.parent) 40 | 41 | # Download data 42 | urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images 43 | 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images 44 | 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) 45 | download(urls, dir=dir / 'images', threads=3) 46 | -------------------------------------------------------------------------------- /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 (7 MB) 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 VOC.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: 467 8 | # Last generation: 996 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 | # 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865 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 | 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: 0.1 # segment copy-paste (probability) 35 | -------------------------------------------------------------------------------- /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/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/data/images/bus.jpg -------------------------------------------------------------------------------- /data/images/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/data/images/zidane.jpg -------------------------------------------------------------------------------- /data/my_person.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: ../yolov5/datasets/mydata # dataset root dir 12 | train: train.txt # train images (relative to 'path') 118287 images 13 | val: val.txt # val images (relative to 'path') 5000 images 14 | test: test.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 15 | 16 | # Classes 17 | nc: 1 # number of classes 18 | names: ['person'] # class names 19 | 20 | -------------------------------------------------------------------------------- /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 - <= cls >= 0, f'incorrect class index {cls}' 74 | 75 | # Write YOLO label 76 | if id not in shapes: 77 | shapes[id] = Image.open(file).size 78 | box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) 79 | with open((labels / id).with_suffix('.txt'), 'a') as f: 80 | f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt 81 | except Exception as e: 82 | print(f'WARNING: skipping one label for {file}: {e}') 83 | 84 | 85 | # Download manually from https://challenge.xviewdataset.org 86 | dir = Path(yaml['path']) # dataset root dir 87 | # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels 88 | # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images 89 | # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) 90 | # download(urls, dir=dir, delete=False) 91 | 92 | # Convert labels 93 | convert_labels(dir / 'xView_train.geojson') 94 | 95 | # Move images 96 | images = Path(dir / 'images') 97 | images.mkdir(parents=True, exist_ok=True) 98 | Path(dir / 'train_images').rename(dir / 'images' / 'train') 99 | Path(dir / 'val_images').rename(dir / 'images' / 'val') 100 | 101 | # Split 102 | autosplit(dir / 'images' / 'train') 103 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,1b8000ef60354f.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,1b8000ef60354f.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,1b8000ef60354f.jpg 5 | 6 | Unknown 7 | 8 | 9 | 1024 10 | 683 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 698 21 | 419 22 | 723 23 | 490 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 724 33 | 418 34 | 746 35 | 469 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 528 45 | 448 46 | 580 47 | 514 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 639 57 | 422 58 | 654 59 | 462 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 596 69 | 426 70 | 614 71 | 462 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 96 81 | 442 82 | 116 83 | 468 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 729 93 | 208 94 | 750 95 | 266 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 658 105 | 427 106 | 678 107 | 479 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 675 117 | 426 118 | 693 119 | 482 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 687 129 | 422 130 | 713 131 | 495 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 400 141 | 478 142 | 442 143 | 584 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 124 153 | 460 154 | 199 155 | 561 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 0 165 | 468 166 | 43 167 | 557 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 321 177 | 466 178 | 365 179 | 556 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 755 189 | 420 190 | 810 191 | 532 192 | 193 | 194 | 195 | person 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 967 201 | 221 202 | 1022 203 | 369 204 | 205 | 206 | 207 | person 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 85 213 | 456 214 | 152 215 | 565 216 | 217 | 218 | 219 | person 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 198 225 | 460 226 | 258 227 | 560 228 | 229 | 230 | 231 | person 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 428 237 | 417 238 | 486 239 | 596 240 | 241 | 242 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2a72f000f214d26a.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2a72f000f214d26a.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2a72f000f214d26a.jpg 5 | 6 | Unknown 7 | 8 | 9 | 980 10 | 653 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 738 21 | 441 22 | 753 23 | 522 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 117 33 | 472 34 | 127 35 | 493 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 949 45 | 447 46 | 959 47 | 469 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 854 57 | 447 58 | 867 59 | 472 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 505 69 | 458 70 | 518 71 | 484 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 193 81 | 469 82 | 203 83 | 491 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 595 93 | 451 94 | 611 95 | 480 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 69 105 | 476 106 | 102 107 | 527 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 403 117 | 454 118 | 415 119 | 490 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 870 129 | 442 130 | 887 131 | 472 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 490 141 | 451 142 | 500 143 | 485 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 756 153 | 427 154 | 785 155 | 518 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 292 165 | 461 166 | 324 167 | 511 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 675 177 | 437 178 | 703 179 | 517 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 346 189 | 436 190 | 373 191 | 505 192 | 193 | 194 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2ac2400079a6d80f.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2ac2400079a6d80f.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2ac2400079a6d80f.jpg 5 | 6 | Unknown 7 | 8 | 9 | 974 10 | 493 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 324 21 | 248 22 | 379 23 | 432 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 853 33 | 212 34 | 912 35 | 343 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 255 45 | 205 46 | 376 47 | 434 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 812 57 | 212 58 | 850 59 | 313 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 668 69 | 331 70 | 730 71 | 419 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 867 81 | 312 82 | 959 83 | 418 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 246 93 | 184 94 | 314 95 | 440 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 94 105 | 290 106 | 146 107 | 433 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 161 117 | 263 118 | 209 119 | 440 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 855 129 | 210 130 | 912 131 | 274 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 134 141 | 270 142 | 174 143 | 435 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 197 153 | 268 154 | 246 155 | 433 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 541 165 | 45 166 | 673 167 | 491 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 829 177 | 342 178 | 880 179 | 418 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 0 189 | 223 190 | 45 191 | 444 192 | 193 | 194 | 195 | person 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 38 201 | 212 202 | 99 203 | 445 204 | 205 | 206 | 207 | person 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 900 213 | 225 214 | 969 215 | 347 216 | 217 | 218 | 219 | person 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 380 225 | 66 226 | 519 227 | 492 228 | 229 | 230 | 231 | person 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 638 237 | 24 238 | 824 239 | 493 240 | 241 | 242 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2bf6e00075455d3c.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2bf6e00075455d3c.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2bf6e00075455d3c.jpg 5 | 6 | Unknown 7 | 8 | 9 | 800 10 | 533 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 474 21 | 232 22 | 506 23 | 308 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 444 33 | 231 34 | 478 35 | 326 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 409 45 | 259 46 | 437 47 | 340 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 686 57 | 243 58 | 742 59 | 350 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 268 69 | 218 70 | 303 71 | 297 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 17 81 | 212 82 | 57 83 | 269 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 231 93 | 223 94 | 270 95 | 295 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 352 105 | 218 106 | 395 107 | 292 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 424 117 | 316 118 | 507 119 | 481 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 336 129 | 306 130 | 441 131 | 494 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 482 141 | 306 142 | 560 143 | 486 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 48 153 | 332 154 | 111 155 | 446 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 65 165 | 209 166 | 115 167 | 269 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 529 177 | 310 178 | 645 179 | 494 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 0 189 | 319 190 | 54 191 | 445 192 | 193 | 194 | 195 | person 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 249 201 | 299 202 | 396 203 | 501 204 | 205 | 206 | 207 | person 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 146 213 | 287 214 | 307 215 | 506 216 | 217 | 218 | 219 | person 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 615 225 | 304 226 | 767 227 | 514 228 | 229 | 230 | 231 | person 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 97 237 | 287 238 | 205 239 | 495 240 | 241 | 242 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2bfbf000c47880b7.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2bfbf000c47880b7.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2bfbf000c47880b7.jpg 5 | 6 | Unknown 7 | 8 | 9 | 1300 10 | 956 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 905 21 | 36 22 | 964 23 | 225 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 368 33 | 226 34 | 425 35 | 377 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 1001 45 | 67 46 | 1076 47 | 226 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 722 57 | 42 58 | 772 59 | 161 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 227 69 | 151 70 | 303 71 | 412 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 1067 81 | 51 82 | 1114 83 | 128 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 545 93 | 98 94 | 631 95 | 348 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 0 105 | 148 106 | 59 107 | 459 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 736 117 | 66 118 | 839 119 | 412 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 939 129 | 103 130 | 1020 131 | 376 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 278 141 | 150 142 | 347 143 | 405 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 311 153 | 146 154 | 373 155 | 398 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 102 165 | 174 166 | 193 167 | 437 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 26 177 | 138 178 | 118 179 | 454 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 658 189 | 97 190 | 787 191 | 465 192 | 193 | 194 | 195 | person 196 | Unspecified 197 | 0 198 | 0 199 | 200 | 459 201 | 112 202 | 546 203 | 360 204 | 205 | 206 | 207 | person 208 | Unspecified 209 | 0 210 | 0 211 | 212 | 459 213 | 395 214 | 825 215 | 774 216 | 217 | 218 | 219 | person 220 | Unspecified 221 | 0 222 | 0 223 | 224 | 156 225 | 140 226 | 264 227 | 447 228 | 229 | 230 | 231 | person 232 | Unspecified 233 | 0 234 | 0 235 | 236 | 832 237 | 30 238 | 945 239 | 363 240 | 241 | 242 | 243 | person 244 | Unspecified 245 | 0 246 | 0 247 | 248 | 1169 249 | 0 250 | 1299 251 | 580 252 | 253 | 254 | 255 | person 256 | Unspecified 257 | 0 258 | 0 259 | 260 | 156 261 | 352 262 | 471 263 | 707 264 | 265 | 266 | 267 | person 268 | Unspecified 269 | 0 270 | 0 271 | 272 | 741 273 | 111 274 | 1250 275 | 865 276 | 277 | 278 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2c8c7000530eb0e7.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2c8c7000530eb0e7.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2c8c7000530eb0e7.jpg 5 | 6 | Unknown 7 | 8 | 9 | 640 10 | 480 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 549 21 | 318 22 | 619 23 | 414 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 586 33 | 221 34 | 612 35 | 283 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 580 45 | 270 46 | 636 47 | 348 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 510 57 | 213 58 | 535 59 | 287 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 596 69 | 304 70 | 640 71 | 480 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 437 81 | 318 82 | 538 83 | 454 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 520 93 | 257 94 | 566 95 | 321 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 437 105 | 285 106 | 490 107 | 383 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 90 117 | 248 118 | 115 119 | 370 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 151 129 | 239 130 | 182 131 | 349 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 107 141 | 257 142 | 155 143 | 357 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 1 153 | 229 154 | 103 155 | 478 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 190 165 | 202 166 | 274 167 | 458 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 266 177 | 204 178 | 444 179 | 480 180 | 181 | 182 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2cdca0006185e7eb.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2cdca0006185e7eb.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2cdca0006185e7eb.jpg 5 | 6 | Unknown 7 | 8 | 9 | 1632 10 | 385 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 1295 21 | 298 22 | 1344 23 | 383 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 834 33 | 291 34 | 895 35 | 383 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 1247 45 | 263 46 | 1306 47 | 385 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 601 57 | 246 58 | 674 59 | 381 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 1395 69 | 281 70 | 1479 71 | 385 72 | 73 | 74 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2d2c6000adf6f6f4.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2d2c6000adf6f6f4.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2d2c6000adf6f6f4.jpg 5 | 6 | Unknown 7 | 8 | 9 | 1600 10 | 1063 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 422 21 | 552 22 | 479 23 | 616 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 1 33 | 475 34 | 21 35 | 569 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 1180 45 | 524 46 | 1246 47 | 652 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 38 57 | 533 58 | 92 59 | 642 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 1127 69 | 544 70 | 1177 71 | 629 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 592 81 | 498 82 | 640 83 | 605 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 637 93 | 496 94 | 695 95 | 622 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 1478 105 | 531 106 | 1540 107 | 606 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 471 117 | 528 118 | 595 119 | 659 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 927 129 | 552 130 | 1026 131 | 671 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 1205 141 | 420 142 | 1595 143 | 873 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 2 153 | 451 154 | 509 155 | 912 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 24 165 | 605 166 | 720 167 | 1001 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 989 177 | 507 178 | 1542 179 | 1025 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 558 189 | 280 190 | 1030 191 | 924 192 | 193 | 194 | -------------------------------------------------------------------------------- /datasets/mydata/Annotations/1066405,2d6f2000fec9dcab.xml: -------------------------------------------------------------------------------- 1 | 2 | D:/youtube_video/data/need/images 3 | 1066405,2d6f2000fec9dcab.jpg 4 | D:\youtube_video\data\yolov5D:/youtube_video/data/need/images\1066405,2d6f2000fec9dcab.jpg 5 | 6 | Unknown 7 | 8 | 9 | 847 10 | 450 11 | 3 12 | 13 | 0 14 | 15 | person 16 | Unspecified 17 | 0 18 | 0 19 | 20 | 499 21 | 152 22 | 542 23 | 203 24 | 25 | 26 | 27 | person 28 | Unspecified 29 | 0 30 | 0 31 | 32 | 567 33 | 146 34 | 605 35 | 209 36 | 37 | 38 | 39 | person 40 | Unspecified 41 | 0 42 | 0 43 | 44 | 83 45 | 138 46 | 134 47 | 292 48 | 49 | 50 | 51 | person 52 | Unspecified 53 | 0 54 | 0 55 | 56 | 673 57 | 161 58 | 723 59 | 204 60 | 61 | 62 | 63 | person 64 | Unspecified 65 | 0 66 | 0 67 | 68 | 368 69 | 156 70 | 405 71 | 288 72 | 73 | 74 | 75 | person 76 | Unspecified 77 | 0 78 | 0 79 | 80 | 810 81 | 163 82 | 847 83 | 359 84 | 85 | 86 | 87 | person 88 | Unspecified 89 | 0 90 | 0 91 | 92 | 0 93 | 270 94 | 42 95 | 321 96 | 97 | 98 | 99 | person 100 | Unspecified 101 | 0 102 | 0 103 | 104 | 611 105 | 165 106 | 662 107 | 212 108 | 109 | 110 | 111 | person 112 | Unspecified 113 | 0 114 | 0 115 | 116 | 90 117 | 148 118 | 230 119 | 350 120 | 121 | 122 | 123 | person 124 | Unspecified 125 | 0 126 | 0 127 | 128 | 0 129 | 319 130 | 101 131 | 447 132 | 133 | 134 | 135 | person 136 | Unspecified 137 | 0 138 | 0 139 | 140 | 103 141 | 331 142 | 300 143 | 448 144 | 145 | 146 | 147 | person 148 | Unspecified 149 | 0 150 | 0 151 | 152 | 365 153 | 161 154 | 502 155 | 390 156 | 157 | 158 | 159 | person 160 | Unspecified 161 | 0 162 | 0 163 | 164 | 235 165 | 158 166 | 340 167 | 361 168 | 169 | 170 | 171 | person 172 | Unspecified 173 | 0 174 | 0 175 | 176 | 32 177 | 141 178 | 92 179 | 325 180 | 181 | 182 | 183 | person 184 | Unspecified 185 | 0 186 | 0 187 | 188 | 746 189 | 157 190 | 843 191 | 439 192 | 193 | 194 | -------------------------------------------------------------------------------- /datasets/mydata/images/1066405,1b8000ef60354f.jpg: 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https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/models/__pycache__/experimental.cpython-37.pyc -------------------------------------------------------------------------------- /models/__pycache__/yolo.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/models/__pycache__/yolo.cpython-37.pyc -------------------------------------------------------------------------------- /models/experimental.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Experimental modules 4 | """ 5 | import math 6 | 7 | import numpy as np 8 | import torch 9 | import torch.nn as nn 10 | 11 | from models.common import Conv 12 | from utils.downloads import attempt_download 13 | 14 | 15 | class CrossConv(nn.Module): 16 | # Cross Convolution Downsample 17 | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): 18 | # ch_in, ch_out, kernel, stride, groups, expansion, shortcut 19 | super().__init__() 20 | c_ = int(c2 * e) # hidden channels 21 | self.cv1 = Conv(c1, c_, (1, k), (1, s)) 22 | self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) 23 | self.add = shortcut and c1 == c2 24 | 25 | def forward(self, x): 26 | return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) 27 | 28 | 29 | class Sum(nn.Module): 30 | # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 31 | def __init__(self, n, weight=False): # n: number of inputs 32 | super().__init__() 33 | self.weight = weight # apply weights boolean 34 | self.iter = range(n - 1) # iter object 35 | if weight: 36 | self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights 37 | 38 | def forward(self, x): 39 | y = x[0] # no weight 40 | if self.weight: 41 | w = torch.sigmoid(self.w) * 2 42 | for i in self.iter: 43 | y = y + x[i + 1] * w[i] 44 | else: 45 | for i in self.iter: 46 | y = y + x[i + 1] 47 | return y 48 | 49 | 50 | class MixConv2d(nn.Module): 51 | # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 52 | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy 53 | super().__init__() 54 | n = len(k) # number of convolutions 55 | if equal_ch: # equal c_ per group 56 | i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices 57 | c_ = [(i == g).sum() for g in range(n)] # intermediate channels 58 | else: # equal weight.numel() per group 59 | b = [c2] + [0] * n 60 | a = np.eye(n + 1, n, k=-1) 61 | a -= np.roll(a, 1, axis=1) 62 | a *= np.array(k) ** 2 63 | a[0] = 1 64 | c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b 65 | 66 | self.m = nn.ModuleList([ 67 | nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) 68 | self.bn = nn.BatchNorm2d(c2) 69 | self.act = nn.SiLU() 70 | 71 | def forward(self, x): 72 | return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) 73 | 74 | 75 | class Ensemble(nn.ModuleList): 76 | # Ensemble of models 77 | def __init__(self): 78 | super().__init__() 79 | 80 | def forward(self, x, augment=False, profile=False, visualize=False): 81 | y = [] 82 | for module in self: 83 | y.append(module(x, augment, profile, visualize)[0]) 84 | # y = torch.stack(y).max(0)[0] # max ensemble 85 | # y = torch.stack(y).mean(0) # mean ensemble 86 | y = torch.cat(y, 1) # nms ensemble 87 | return y, None # inference, train output 88 | 89 | 90 | def attempt_load(weights, map_location=None, inplace=True, fuse=True): 91 | from models.yolo import Detect, Model 92 | 93 | # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a 94 | model = Ensemble() 95 | for w in weights if isinstance(weights, list) else [weights]: 96 | ckpt = torch.load(attempt_download(w), map_location=map_location) # load 97 | ckpt = (ckpt.get('ema') or ckpt['model']).float() # FP32 model 98 | model.append(ckpt.fuse().eval() if fuse else ckpt.eval()) # fused or un-fused model in eval mode 99 | 100 | # Compatibility updates 101 | for m in model.modules(): 102 | t = type(m) 103 | if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): 104 | m.inplace = inplace # torch 1.7.0 compatibility 105 | if t is Detect: 106 | if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility 107 | delattr(m, 'anchor_grid') 108 | setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) 109 | elif t is Conv: 110 | m._non_persistent_buffers_set = set() # torch 1.6.0 compatibility 111 | elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'): 112 | m.recompute_scale_factor = None # torch 1.11.0 compatibility 113 | 114 | if len(model) == 1: 115 | return model[-1] # return model 116 | else: 117 | print(f'Ensemble created with {weights}\n') 118 | for k in 'names', 'nc', 'yaml': 119 | setattr(model, k, getattr(model[0], k)) 120 | model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride 121 | assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}' 122 | return model # return ensemble 123 | -------------------------------------------------------------------------------- /models/hub/anchors.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | # Default anchors for COCO data 3 | 4 | 5 | # P5 ------------------------------------------------------------------------------------------------------------------- 6 | # P5-640: 7 | anchors_p5_640: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | 13 | # P6 ------------------------------------------------------------------------------------------------------------------- 14 | # P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 15 | anchors_p6_640: 16 | - [9,11, 21,19, 17,41] # P3/8 17 | - [43,32, 39,70, 86,64] # P4/16 18 | - [65,131, 134,130, 120,265] # P5/32 19 | - [282,180, 247,354, 512,387] # P6/64 20 | 21 | # P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 22 | anchors_p6_1280: 23 | - [19,27, 44,40, 38,94] # P3/8 24 | - [96,68, 86,152, 180,137] # P4/16 25 | - [140,301, 303,264, 238,542] # P5/32 26 | - [436,615, 739,380, 925,792] # P6/64 27 | 28 | # P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 29 | anchors_p6_1920: 30 | - [28,41, 67,59, 57,141] # P3/8 31 | - [144,103, 129,227, 270,205] # P4/16 32 | - [209,452, 455,396, 358,812] # P5/32 33 | - [653,922, 1109,570, 1387,1187] # P6/64 34 | 35 | 36 | # P7 ------------------------------------------------------------------------------------------------------------------- 37 | # P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 38 | anchors_p7_640: 39 | - [11,11, 13,30, 29,20] # P3/8 40 | - [30,46, 61,38, 39,92] # P4/16 41 | - [78,80, 146,66, 79,163] # P5/32 42 | - [149,150, 321,143, 157,303] # P6/64 43 | - [257,402, 359,290, 524,372] # P7/128 44 | 45 | # P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 46 | anchors_p7_1280: 47 | - [19,22, 54,36, 32,77] # P3/8 48 | - [70,83, 138,71, 75,173] # P4/16 49 | - [165,159, 148,334, 375,151] # P5/32 50 | - [334,317, 251,626, 499,474] # P6/64 51 | - [750,326, 534,814, 1079,818] # P7/128 52 | 53 | # P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 54 | anchors_p7_1920: 55 | - [29,34, 81,55, 47,115] # P3/8 56 | - [105,124, 207,107, 113,259] # P4/16 57 | - [247,238, 222,500, 563,227] # P5/32 58 | - [501,476, 376,939, 749,711] # P6/64 59 | - [1126,489, 801,1222, 1618,1227] # P7/128 60 | -------------------------------------------------------------------------------- /models/hub/yolov3-spp.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3-SPP head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, SPP, [512, [5, 9, 13]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /models/hub/yolov3-tiny.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,14, 23,27, 37,58] # P4/16 9 | - [81,82, 135,169, 344,319] # P5/32 10 | 11 | # YOLOv3-tiny backbone 12 | backbone: 13 | # [from, number, module, args] 14 | [[-1, 1, Conv, [16, 3, 1]], # 0 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 16 | [-1, 1, Conv, [32, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 18 | [-1, 1, Conv, [64, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 20 | [-1, 1, Conv, [128, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 22 | [-1, 1, Conv, [256, 3, 1]], 23 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 24 | [-1, 1, Conv, [512, 3, 1]], 25 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 26 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 27 | ] 28 | 29 | # YOLOv3-tiny head 30 | head: 31 | [[-1, 1, Conv, [1024, 3, 1]], 32 | [-1, 1, Conv, [256, 1, 1]], 33 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 34 | 35 | [-2, 1, Conv, [128, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 38 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 39 | 40 | [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) 41 | ] 42 | -------------------------------------------------------------------------------- /models/hub/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3 head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, Conv, [512, 1, 1]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /models/hub/yolov5-bifpn.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 BiFPN head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5-fpn.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 FPN head 28 | head: 29 | [[-1, 3, C3, [1024, False]], # 10 (P5/32-large) 30 | 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 3, C3, [512, False]], # 14 (P4/16-medium) 35 | 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 3, C3, [256, False]], # 18 (P3/8-small) 40 | 41 | [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 42 | ] 43 | -------------------------------------------------------------------------------- /models/hub/yolov5-p2.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 13 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 14 | [-1, 3, C3, [128]], 15 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 16 | [-1, 6, C3, [256]], 17 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 18 | [-1, 9, C3, [512]], 19 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 20 | [-1, 3, C3, [1024]], 21 | [-1, 1, SPPF, [1024, 5]], # 9 22 | ] 23 | 24 | # YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs 25 | head: 26 | [[-1, 1, Conv, [512, 1, 1]], 27 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 28 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 29 | [-1, 3, C3, [512, False]], # 13 30 | 31 | [-1, 1, Conv, [256, 1, 1]], 32 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 33 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 34 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 35 | 36 | [-1, 1, Conv, [128, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 2], 1, Concat, [1]], # cat backbone P2 39 | [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) 40 | 41 | [-1, 1, Conv, [128, 3, 2]], 42 | [[-1, 18], 1, Concat, [1]], # cat head P3 43 | [-1, 3, C3, [256, False]], # 24 (P3/8-small) 44 | 45 | [-1, 1, Conv, [256, 3, 2]], 46 | [[-1, 14], 1, Concat, [1]], # cat head P4 47 | [-1, 3, C3, [512, False]], # 27 (P4/16-medium) 48 | 49 | [-1, 1, Conv, [512, 3, 2]], 50 | [[-1, 10], 1, Concat, [1]], # cat head P5 51 | [-1, 3, C3, [1024, False]], # 30 (P5/32-large) 52 | 53 | [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) 54 | ] 55 | -------------------------------------------------------------------------------- /models/hub/yolov5-p34.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2 13 | [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 14 | [ -1, 3, C3, [ 128 ] ], 15 | [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 16 | [ -1, 6, C3, [ 256 ] ], 17 | [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 18 | [ -1, 9, C3, [ 512 ] ], 19 | [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 20 | [ -1, 3, C3, [ 1024 ] ], 21 | [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 22 | ] 23 | 24 | # YOLOv5 v6.0 head with (P3, P4) outputs 25 | head: 26 | [ [ -1, 1, Conv, [ 512, 1, 1 ] ], 27 | [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], 28 | [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 29 | [ -1, 3, C3, [ 512, False ] ], # 13 30 | 31 | [ -1, 1, Conv, [ 256, 1, 1 ] ], 32 | [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], 33 | [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 34 | [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) 35 | 36 | [ -1, 1, Conv, [ 256, 3, 2 ] ], 37 | [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 38 | [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) 39 | 40 | [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4) 41 | ] 42 | -------------------------------------------------------------------------------- /models/hub/yolov5-p6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 13 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 14 | [-1, 3, C3, [128]], 15 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 16 | [-1, 6, C3, [256]], 17 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 18 | [-1, 9, C3, [512]], 19 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 20 | [-1, 3, C3, [768]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 22 | [-1, 3, C3, [1024]], 23 | [-1, 1, SPPF, [1024, 5]], # 11 24 | ] 25 | 26 | # YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs 27 | head: 28 | [[-1, 1, Conv, [768, 1, 1]], 29 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 30 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 31 | [-1, 3, C3, [768, False]], # 15 32 | 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 35 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 36 | [-1, 3, C3, [512, False]], # 19 37 | 38 | [-1, 1, Conv, [256, 1, 1]], 39 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 40 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 41 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 42 | 43 | [-1, 1, Conv, [256, 3, 2]], 44 | [[-1, 20], 1, Concat, [1]], # cat head P4 45 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 46 | 47 | [-1, 1, Conv, [512, 3, 2]], 48 | [[-1, 16], 1, Concat, [1]], # cat head P5 49 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 50 | 51 | [-1, 1, Conv, [768, 3, 2]], 52 | [[-1, 12], 1, Concat, [1]], # cat head P6 53 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 54 | 55 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 56 | ] 57 | -------------------------------------------------------------------------------- /models/hub/yolov5-p7.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 3 # AutoAnchor evolves 3 anchors per P output layer 8 | 9 | # YOLOv5 v6.0 backbone 10 | backbone: 11 | # [from, number, module, args] 12 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 13 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 14 | [-1, 3, C3, [128]], 15 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 16 | [-1, 6, C3, [256]], 17 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 18 | [-1, 9, C3, [512]], 19 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 20 | [-1, 3, C3, [768]], 21 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 22 | [-1, 3, C3, [1024]], 23 | [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 24 | [-1, 3, C3, [1280]], 25 | [-1, 1, SPPF, [1280, 5]], # 13 26 | ] 27 | 28 | # YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs 29 | head: 30 | [[-1, 1, Conv, [1024, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 10], 1, Concat, [1]], # cat backbone P6 33 | [-1, 3, C3, [1024, False]], # 17 34 | 35 | [-1, 1, Conv, [768, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 38 | [-1, 3, C3, [768, False]], # 21 39 | 40 | [-1, 1, Conv, [512, 1, 1]], 41 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 42 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 43 | [-1, 3, C3, [512, False]], # 25 44 | 45 | [-1, 1, Conv, [256, 1, 1]], 46 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 47 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 48 | [-1, 3, C3, [256, False]], # 29 (P3/8-small) 49 | 50 | [-1, 1, Conv, [256, 3, 2]], 51 | [[-1, 26], 1, Concat, [1]], # cat head P4 52 | [-1, 3, C3, [512, False]], # 32 (P4/16-medium) 53 | 54 | [-1, 1, Conv, [512, 3, 2]], 55 | [[-1, 22], 1, Concat, [1]], # cat head P5 56 | [-1, 3, C3, [768, False]], # 35 (P5/32-large) 57 | 58 | [-1, 1, Conv, [768, 3, 2]], 59 | [[-1, 18], 1, Concat, [1]], # cat head P6 60 | [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) 61 | 62 | [-1, 1, Conv, [1024, 3, 2]], 63 | [[-1, 14], 1, Concat, [1]], # cat head P7 64 | [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) 65 | 66 | [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) 67 | ] 68 | -------------------------------------------------------------------------------- /models/hub/yolov5-panet.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 PANet head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5l6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5m6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5n6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.25 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5s-ghost.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3Ghost, [128]], 18 | [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3Ghost, [256]], 20 | [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3Ghost, [512]], 22 | [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3Ghost, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, GhostConv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3Ghost, [512, False]], # 13 33 | 34 | [-1, 1, GhostConv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, GhostConv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, GhostConv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5s-transformer.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/hub/yolov5s6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/hub/yolov5x6.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.33 # model depth multiple 6 | width_multiple: 1.25 # layer channel multiple 7 | anchors: 8 | - [19,27, 44,40, 38,94] # P3/8 9 | - [96,68, 86,152, 180,137] # P4/16 10 | - [140,301, 303,264, 238,542] # P5/32 11 | - [436,615, 739,380, 925,792] # P6/64 12 | 13 | # YOLOv5 v6.0 backbone 14 | backbone: 15 | # [from, number, module, args] 16 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 17 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 18 | [-1, 3, C3, [128]], 19 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 20 | [-1, 6, C3, [256]], 21 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 22 | [-1, 9, C3, [512]], 23 | [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 24 | [-1, 3, C3, [768]], 25 | [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 26 | [-1, 3, C3, [1024]], 27 | [-1, 1, SPPF, [1024, 5]], # 11 28 | ] 29 | 30 | # YOLOv5 v6.0 head 31 | head: 32 | [[-1, 1, Conv, [768, 1, 1]], 33 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 34 | [[-1, 8], 1, Concat, [1]], # cat backbone P5 35 | [-1, 3, C3, [768, False]], # 15 36 | 37 | [-1, 1, Conv, [512, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 40 | [-1, 3, C3, [512, False]], # 19 41 | 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 44 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 45 | [-1, 3, C3, [256, False]], # 23 (P3/8-small) 46 | 47 | [-1, 1, Conv, [256, 3, 2]], 48 | [[-1, 20], 1, Concat, [1]], # cat head P4 49 | [-1, 3, C3, [512, False]], # 26 (P4/16-medium) 50 | 51 | [-1, 1, Conv, [512, 3, 2]], 52 | [[-1, 16], 1, Concat, [1]], # cat head P5 53 | [-1, 3, C3, [768, False]], # 29 (P5/32-large) 54 | 55 | [-1, 1, Conv, [768, 3, 2]], 56 | [[-1, 12], 1, Concat, [1]], # cat head P6 57 | [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) 58 | 59 | [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) 60 | ] 61 | -------------------------------------------------------------------------------- /models/yolov5l.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.0 # model depth multiple 6 | width_multiple: 1.0 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m_C3CA.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3CA, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3CA, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3CA, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3CA, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m_C3CBAM.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3CBAM, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3CBAM, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3CBAM, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3CBAM, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m_C3ECA.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3ECA, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3ECA, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3ECA, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3ECA, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m_C3SE.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3SE, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3SE, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3SE, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3SE, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5m_CBAM.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, CBAM, [1024]], 25 | [-1, 1, SPPF, [1024, 5]], # 9 26 | ] 27 | 28 | # YOLOv5 v6.0 head 29 | head: 30 | [[-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 3, C3, [512, False]], # 13 34 | 35 | [-1, 1, Conv, [256, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 39 | 40 | [-1, 1, Conv, [256, 3, 2]], 41 | [[-1, 14], 1, Concat, [1]], # cat head P4 42 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 43 | 44 | [-1, 1, Conv, [512, 3, 2]], 45 | [[-1, 10], 1, Concat, [1]], # cat head P5 46 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 47 | 48 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /models/yolov5m_CoordAtt.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, CoordAtt, [1024]], 25 | [-1, 1, SPPF, [1024, 5]], # 9 26 | ] 27 | 28 | # YOLOv5 v6.0 head 29 | head: 30 | [[-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 3, C3, [512, False]], # 13 34 | 35 | [-1, 1, Conv, [256, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 39 | 40 | [-1, 1, Conv, [256, 3, 2]], 41 | [[-1, 14], 1, Concat, [1]], # cat head P4 42 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 43 | 44 | [-1, 1, Conv, [512, 3, 2]], 45 | [[-1, 10], 1, Concat, [1]], # cat head P5 46 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 47 | 48 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /models/yolov5m_ECA.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, ECA, [1024]], 25 | [-1, 1, SPPF, [1024, 5]], # 9 26 | ] 27 | 28 | # YOLOv5 v6.0 head 29 | head: 30 | [[-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 3, C3, [512, False]], # 13 34 | 35 | [-1, 1, Conv, [256, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 39 | 40 | [-1, 1, Conv, [256, 3, 2]], 41 | [[-1, 14], 1, Concat, [1]], # cat head P4 42 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 43 | 44 | [-1, 1, Conv, [512, 3, 2]], 45 | [[-1, 10], 1, Concat, [1]], # cat head P5 46 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 47 | 48 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /models/yolov5m_SE.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.67 # model depth multiple 6 | width_multiple: 0.75 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SE, [1024]], 25 | [-1, 1, SPPF, [1024, 5]], # 9 26 | ] 27 | 28 | # YOLOv5 v6.0 head 29 | head: 30 | [[-1, 1, Conv, [512, 1, 1]], 31 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 32 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 33 | [-1, 3, C3, [512, False]], # 13 34 | 35 | [-1, 1, Conv, [256, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 38 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 39 | 40 | [-1, 1, Conv, [256, 3, 2]], 41 | [[-1, 14], 1, Concat, [1]], # cat head P4 42 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 43 | 44 | [-1, 1, Conv, [512, 3, 2]], 45 | [[-1, 10], 1, Concat, [1]], # cat head P5 46 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 47 | 48 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 49 | ] 50 | -------------------------------------------------------------------------------- /models/yolov5n.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.25 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5s.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 0.33 # model depth multiple 6 | width_multiple: 0.50 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /models/yolov5x.yaml: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Parameters 4 | nc: 80 # number of classes 5 | depth_multiple: 1.33 # model depth multiple 6 | width_multiple: 1.25 # layer channel multiple 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # YOLOv5 v6.0 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 16 | [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 17 | [-1, 3, C3, [128]], 18 | [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 19 | [-1, 6, C3, [256]], 20 | [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 21 | [-1, 9, C3, [512]], 22 | [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 23 | [-1, 3, C3, [1024]], 24 | [-1, 1, SPPF, [1024, 5]], # 9 25 | ] 26 | 27 | # YOLOv5 v6.0 head 28 | head: 29 | [[-1, 1, Conv, [512, 1, 1]], 30 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 31 | [[-1, 6], 1, Concat, [1]], # cat backbone P4 32 | [-1, 3, C3, [512, False]], # 13 33 | 34 | [-1, 1, Conv, [256, 1, 1]], 35 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 36 | [[-1, 4], 1, Concat, [1]], # cat backbone P3 37 | [-1, 3, C3, [256, False]], # 17 (P3/8-small) 38 | 39 | [-1, 1, Conv, [256, 3, 2]], 40 | [[-1, 14], 1, Concat, [1]], # cat head P4 41 | [-1, 3, C3, [512, False]], # 20 (P4/16-medium) 42 | 43 | [-1, 1, Conv, [512, 3, 2]], 44 | [[-1, 10], 1, Concat, [1]], # cat head P5 45 | [-1, 3, C3, [1024, False]], # 23 (P5/32-large) 46 | 47 | [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 48 | ] 49 | -------------------------------------------------------------------------------- /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 | # Local usage: pip install pre-commit, pre-commit run --all-files 4 | 5 | [metadata] 6 | license_file = LICENSE 7 | description_file = README.md 8 | 9 | 10 | [tool:pytest] 11 | norecursedirs = 12 | .git 13 | dist 14 | build 15 | addopts = 16 | --doctest-modules 17 | --durations=25 18 | --color=yes 19 | 20 | 21 | [flake8] 22 | max-line-length = 120 23 | exclude = .tox,*.egg,build,temp 24 | select = E,W,F 25 | doctests = True 26 | verbose = 2 27 | # https://pep8.readthedocs.io/en/latest/intro.html#error-codes 28 | format = pylint 29 | # see: https://www.flake8rules.com/ 30 | ignore = 31 | E731 # Do not assign a lambda expression, use a def 32 | F405 # name may be undefined, or defined from star imports: module 33 | E402 # module level import not at top of file 34 | F401 # module imported but unused 35 | W504 # line break after binary operator 36 | E127 # continuation line over-indented for visual indent 37 | W504 # line break after binary operator 38 | E231 # missing whitespace after ‘,’, ‘;’, or ‘:’ 39 | E501 # line too long 40 | F403 # ‘from module import *’ used; unable to detect undefined names 41 | 42 | 43 | [isort] 44 | # https://pycqa.github.io/isort/docs/configuration/options.html 45 | line_length = 120 46 | # see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html 47 | multi_line_output = 0 48 | 49 | 50 | [yapf] 51 | based_on_style = pep8 52 | spaces_before_comment = 2 53 | COLUMN_LIMIT = 120 54 | COALESCE_BRACKETS = True 55 | SPACES_AROUND_POWER_OPERATOR = True 56 | SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False 57 | SPLIT_BEFORE_CLOSING_BRACKET = False 58 | SPLIT_BEFORE_FIRST_ARGUMENT = False 59 | # EACH_DICT_ENTRY_ON_SEPARATE_LINE = False 60 | -------------------------------------------------------------------------------- /split.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | 3 | import os 4 | import random 5 | import argparse 6 | 7 | parser = argparse.ArgumentParser() 8 | path='' 9 | #xml文件的地址，根据自己的数据进行修改 xml一般存放在Annotations下 10 | parser.add_argument('--xml_path', default='datasets/mydata/images/', type=str, help='input xml label path') 11 | #数据集的划分，地址选择自己数据下的ImageSets/Main 12 | parser.add_argument('--txt_path', default='datasets/mydata/', type=str, help='output txt label path') 13 | opt = parser.parse_args() 14 | 15 | trainval_percent = 1.0 16 | train_percent = 0.9 17 | xmlfilepath = opt.xml_path 18 | txtsavepath = opt.txt_path 19 | total_xml = os.listdir(xmlfilepath) 20 | if not os.path.exists(txtsavepath): 21 | os.makedirs(txtsavepath) 22 | 23 | num = len(total_xml) 24 | list_index = range(num) 25 | tv = int(num * trainval_percent) 26 | tr = int(tv * train_percent) 27 | trainval = random.sample(list_index, tv) 28 | train = random.sample(trainval, tr) 29 | 30 | #file_trainval = open(txtsavepath + '/trainval.txt', 'w') 31 | file_test = open(txtsavepath + '/test.txt', 'w') 32 | file_train = open(txtsavepath + '/train.txt', 'w') 33 | file_val = open(txtsavepath + '/val.txt', 'w') 34 | 35 | 36 | for i in list_index: 37 | name = total_xml[i][:-4] + '\n' 38 | if i in train: 39 | file_train.write(path+name) 40 | else: 41 | file_val.write(path+name) 42 | 43 | 44 | file_train.close() 45 | file_val.close() 46 | file_test.close() -------------------------------------------------------------------------------- /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 | # System info 25 | if verbose: 26 | gb = 1 << 30 # bytes to GiB (1024 ** 3) 27 | ram = psutil.virtual_memory().total 28 | total, used, free = shutil.disk_usage("/") 29 | display.clear_output() 30 | s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)' 31 | else: 32 | s = '' 33 | 34 | select_device(newline=False) 35 | print(emojis(f'Setup complete ✅ {s}')) 36 | return display 37 | -------------------------------------------------------------------------------- /utils/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- 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CoreML and ONNX 24 | 25 | 26 | class Mish(nn.Module): 27 | # Mish activation https://github.com/digantamisra98/Mish 28 | @staticmethod 29 | def forward(x): 30 | return x * F.softplus(x).tanh() 31 | 32 | 33 | class MemoryEfficientMish(nn.Module): 34 | # Mish activation memory-efficient 35 | class F(torch.autograd.Function): 36 | 37 | @staticmethod 38 | def forward(ctx, x): 39 | ctx.save_for_backward(x) 40 | return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) 41 | 42 | @staticmethod 43 | def backward(ctx, grad_output): 44 | x = ctx.saved_tensors[0] 45 | sx = torch.sigmoid(x) 46 | fx = F.softplus(x).tanh() 47 | return grad_output * (fx + x * sx * (1 - fx * fx)) 48 | 49 | def forward(self, x): 50 | return self.F.apply(x) 51 | 52 | 53 | class FReLU(nn.Module): 54 | # FReLU activation https://arxiv.org/abs/2007.11824 55 | def __init__(self, c1, k=3): # ch_in, kernel 56 | super().__init__() 57 | self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) 58 | self.bn = nn.BatchNorm2d(c1) 59 | 60 | def forward(self, x): 61 | return torch.max(x, self.bn(self.conv(x))) 62 | 63 | 64 | class AconC(nn.Module): 65 | r""" ACON activation (activate or not) 66 | AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter 67 | according to "Activate or Not: Learning Customized Activation" . 68 | """ 69 | 70 | def __init__(self, c1): 71 | super().__init__() 72 | self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) 73 | self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) 74 | self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) 75 | 76 | def forward(self, x): 77 | dpx = (self.p1 - self.p2) * x 78 | return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x 79 | 80 | 81 | class MetaAconC(nn.Module): 82 | r""" ACON activation (activate or not) 83 | MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network 84 | according to "Activate or Not: Learning Customized Activation" . 85 | """ 86 | 87 | def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r 88 | super().__init__() 89 | c2 = max(r, c1 // r) 90 | self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) 91 | self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) 92 | self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) 93 | self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) 94 | # self.bn1 = nn.BatchNorm2d(c2) 95 | # self.bn2 = nn.BatchNorm2d(c1) 96 | 97 | def forward(self, x): 98 | y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) 99 | # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 100 | # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable 101 | beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed 102 | dpx = (self.p1 - self.p2) * x 103 | return dpx * torch.sigmoid(beta * dpx) + self.p2 * x 104 | -------------------------------------------------------------------------------- /utils/autobatch.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Auto-batch utils 4 | """ 5 | 6 | from copy import deepcopy 7 | 8 | import numpy as np 9 | import torch 10 | from torch.cuda import amp 11 | 12 | from utils.general import LOGGER, colorstr 13 | from utils.torch_utils import profile 14 | 15 | 16 | def check_train_batch_size(model, imgsz=640): 17 | # Check YOLOv5 training batch size 18 | with amp.autocast(): 19 | return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size 20 | 21 | 22 | def autobatch(model, imgsz=640, fraction=0.9, batch_size=16): 23 | # Automatically estimate best batch size to use `fraction` of available CUDA 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 | gb = 1 << 30 # bytes to GiB (1024 ** 3) 38 | d = str(device).upper() # 'CUDA:0' 39 | properties = torch.cuda.get_device_properties(device) # device properties 40 | t = properties.total_memory / gb # (GiB) 41 | r = torch.cuda.memory_reserved(device) / gb # (GiB) 42 | a = torch.cuda.memory_allocated(device) / gb # (GiB) 43 | f = t - (r + a) # free inside reserved 44 | LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') 45 | 46 | batch_sizes = [1, 2, 4, 8, 16] 47 | try: 48 | img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes] 49 | y = profile(img, model, n=3, device=device) 50 | except Exception as e: 51 | LOGGER.warning(f'{prefix}{e}') 52 | 53 | y = [x[2] for x in y if x] # memory [2] 54 | batch_sizes = batch_sizes[:len(y)] 55 | p = np.polyfit(batch_sizes, y, deg=1) # first degree polynomial fit 56 | b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) 57 | LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)') 58 | return b 59 | -------------------------------------------------------------------------------- /utils/aws/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/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/benchmarks.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Run YOLOv5 benchmarks on all supported export formats 4 | 5 | Format | `export.py --include` | Model 6 | --- | --- | --- 7 | PyTorch | - | yolov5s.pt 8 | TorchScript | `torchscript` | yolov5s.torchscript 9 | ONNX | `onnx` | yolov5s.onnx 10 | OpenVINO | `openvino` | yolov5s_openvino_model/ 11 | TensorRT | `engine` | yolov5s.engine 12 | CoreML | `coreml` | yolov5s.mlmodel 13 | TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ 14 | TensorFlow GraphDef | `pb` | yolov5s.pb 15 | TensorFlow Lite | `tflite` | yolov5s.tflite 16 | TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite 17 | TensorFlow.js | `tfjs` | yolov5s_web_model/ 18 | 19 | Requirements: 20 | $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU 21 | $ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU 22 | $ pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # TensorRT 23 | 24 | Usage: 25 | $ python utils/benchmarks.py --weights yolov5s.pt --img 640 26 | """ 27 | 28 | import argparse 29 | import sys 30 | import time 31 | from pathlib import Path 32 | 33 | import pandas as pd 34 | 35 | FILE = Path(__file__).resolve() 36 | ROOT = FILE.parents[1] # YOLOv5 root directory 37 | if str(ROOT) not in sys.path: 38 | sys.path.append(str(ROOT)) # add ROOT to PATH 39 | # ROOT = ROOT.relative_to(Path.cwd()) # relative 40 | 41 | import export 42 | import val 43 | from utils import notebook_init 44 | from utils.general import LOGGER, print_args 45 | from utils.torch_utils import select_device 46 | 47 | 48 | def run( 49 | weights=ROOT / 'yolov5s.pt', # weights path 50 | imgsz=640, # inference size (pixels) 51 | batch_size=1, # batch size 52 | data=ROOT / 'data/coco128.yaml', # dataset.yaml path 53 | device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu 54 | half=False, # use FP16 half-precision inference 55 | test=False, # test exports only 56 | ): 57 | y, t = [], time.time() 58 | formats = export.export_formats() 59 | device = select_device(device) 60 | for i, (name, f, suffix, gpu) in formats.iterrows(): # index, (name, file, suffix, gpu-capable) 61 | try: 62 | assert i != 9, 'Edge TPU not supported' 63 | assert i != 10, 'TF.js not supported' 64 | if device.type != 'cpu': 65 | assert gpu, f'{name} inference not supported on GPU' 66 | 67 | # Export 68 | if f == '-': 69 | w = weights # PyTorch format 70 | else: 71 | w = export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # all others 72 | assert suffix in str(w), 'export failed' 73 | 74 | # Validate 75 | result = val.run(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half) 76 | metrics = result[0] # metrics (mp, mr, map50, map, *losses(box, obj, cls)) 77 | speeds = result[2] # times (preprocess, inference, postprocess) 78 | y.append([name, round(metrics[3], 4), round(speeds[1], 2)]) # mAP, t_inference 79 | except Exception as e: 80 | LOGGER.warning(f'WARNING: Benchmark failure for {name}: {e}') 81 | y.append([name, None, None]) # mAP, t_inference 82 | 83 | # Print results 84 | LOGGER.info('\n') 85 | parse_opt() 86 | notebook_init() # print system info 87 | py = pd.DataFrame(y, columns=['Format', 'mAP@0.5:0.95', 'Inference time (ms)'] if map else ['Format', 'Export', '']) 88 | LOGGER.info(f'\nBenchmarks complete ({time.time() - t:.2f}s)') 89 | LOGGER.info(str(py if map else py.iloc[:, :2])) 90 | return py 91 | 92 | 93 | def test( 94 | weights=ROOT / 'yolov5s.pt', # weights path 95 | imgsz=640, # inference size (pixels) 96 | batch_size=1, # batch size 97 | data=ROOT / 'data/coco128.yaml', # dataset.yaml path 98 | device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu 99 | half=False, # use FP16 half-precision inference 100 | test=False, # test exports only 101 | ): 102 | y, t = [], time.time() 103 | formats = export.export_formats() 104 | device = select_device(device) 105 | for i, (name, f, suffix, gpu) in formats.iterrows(): # index, (name, file, suffix, gpu-capable) 106 | try: 107 | w = weights if f == '-' else \ 108 | export.run(weights=weights, imgsz=[imgsz], include=[f], device=device, half=half)[-1] # weights 109 | assert suffix in str(w), 'export failed' 110 | y.append([name, True]) 111 | except Exception: 112 | y.append([name, False]) # mAP, t_inference 113 | 114 | # Print results 115 | LOGGER.info('\n') 116 | parse_opt() 117 | notebook_init() # print system info 118 | py = pd.DataFrame(y, columns=['Format', 'Export']) 119 | LOGGER.info(f'\nExports complete ({time.time() - t:.2f}s)') 120 | LOGGER.info(str(py)) 121 | return py 122 | 123 | 124 | def parse_opt(): 125 | parser = argparse.ArgumentParser() 126 | parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') 127 | parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') 128 | parser.add_argument('--batch-size', type=int, default=1, help='batch size') 129 | parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') 130 | parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 131 | parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') 132 | parser.add_argument('--test', action='store_true', help='test exports only') 133 | opt = parser.parse_args() 134 | print_args(vars(opt)) 135 | return opt 136 | 137 | 138 | def main(opt): 139 | test(**vars(opt)) if opt.test else run(**vars(opt)) 140 | 141 | 142 | if __name__ == "__main__": 143 | opt = parse_opt() 144 | main(opt) 145 | -------------------------------------------------------------------------------- /utils/callbacks.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Callback utils 4 | """ 5 | 6 | 7 | class Callbacks: 8 | """" 9 | Handles all registered callbacks for YOLOv5 Hooks 10 | """ 11 | 12 | def __init__(self): 13 | # Define the available callbacks 14 | self._callbacks = { 15 | 'on_pretrain_routine_start': [], 16 | 'on_pretrain_routine_end': [], 17 | 'on_train_start': [], 18 | 'on_train_epoch_start': [], 19 | 'on_train_batch_start': [], 20 | 'optimizer_step': [], 21 | 'on_before_zero_grad': [], 22 | 'on_train_batch_end': [], 23 | 'on_train_epoch_end': [], 24 | 'on_val_start': [], 25 | 'on_val_batch_start': [], 26 | 'on_val_image_end': [], 27 | 'on_val_batch_end': [], 28 | 'on_val_end': [], 29 | 'on_fit_epoch_end': [], # fit = train + val 30 | 'on_model_save': [], 31 | 'on_train_end': [], 32 | 'on_params_update': [], 33 | 'teardown': [],} 34 | self.stop_training = False # set True to interrupt training 35 | 36 | def register_action(self, hook, name='', callback=None): 37 | """ 38 | Register a new action to a callback hook 39 | 40 | Args: 41 | hook: The callback hook name to register the action to 42 | name: The name of the action for later reference 43 | callback: The callback to fire 44 | """ 45 | assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" 46 | assert callable(callback), f"callback '{callback}' is not callable" 47 | self._callbacks[hook].append({'name': name, 'callback': callback}) 48 | 49 | def get_registered_actions(self, hook=None): 50 | """" 51 | Returns all the registered actions by callback hook 52 | 53 | Args: 54 | hook: The name of the hook to check, defaults to all 55 | """ 56 | return self._callbacks[hook] if hook else self._callbacks 57 | 58 | def run(self, hook, *args, **kwargs): 59 | """ 60 | Loop through the registered actions and fire all callbacks 61 | 62 | Args: 63 | hook: The name of the hook to check, defaults to all 64 | args: Arguments to receive from YOLOv5 65 | kwargs: Keyword Arguments to receive from YOLOv5 66 | """ 67 | 68 | assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" 69 | 70 | for logger in self._callbacks[hook]: 71 | logger['callback'](*args, **kwargs) 72 | -------------------------------------------------------------------------------- /utils/docker/.dockerignore: -------------------------------------------------------------------------------- 1 | # Repo-specific DockerIgnore ------------------------------------------------------------------------------------------- 2 | #.git 3 | .cache 4 | .idea 5 | runs 6 | output 7 | coco 8 | storage.googleapis.com 9 | 10 | data/samples/* 11 | **/results*.csv 12 | *.jpg 13 | 14 | # Neural Network weights ----------------------------------------------------------------------------------------------- 15 | **/*.pt 16 | **/*.pth 17 | **/*.onnx 18 | **/*.engine 19 | **/*.mlmodel 20 | **/*.torchscript 21 | **/*.torchscript.pt 22 | **/*.tflite 23 | **/*.h5 24 | **/*.pb 25 | *_saved_model/ 26 | *_web_model/ 27 | *_openvino_model/ 28 | 29 | # Below Copied From .gitignore ----------------------------------------------------------------------------------------- 30 | # Below Copied From .gitignore ----------------------------------------------------------------------------------------- 31 | 32 | 33 | # GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- 34 | # Byte-compiled / optimized / DLL files 35 | __pycache__/ 36 | *.py[cod] 37 | *$py.class 38 | 39 | # C extensions 40 | *.so 41 | 42 | # Distribution / packaging 43 | .Python 44 | env/ 45 | build/ 46 | develop-eggs/ 47 | dist/ 48 | downloads/ 49 | eggs/ 50 | .eggs/ 51 | lib/ 52 | lib64/ 53 | parts/ 54 | sdist/ 55 | var/ 56 | wheels/ 57 | *.egg-info/ 58 | wandb/ 59 | .installed.cfg 60 | *.egg 61 | 62 | # PyInstaller 63 | # Usually these files are written by a python script from a template 64 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 65 | *.manifest 66 | *.spec 67 | 68 | # Installer logs 69 | pip-log.txt 70 | pip-delete-this-directory.txt 71 | 72 | # Unit test / coverage reports 73 | htmlcov/ 74 | .tox/ 75 | .coverage 76 | .coverage.* 77 | .cache 78 | nosetests.xml 79 | coverage.xml 80 | *.cover 81 | .hypothesis/ 82 | 83 | # Translations 84 | *.mo 85 | *.pot 86 | 87 | # Django stuff: 88 | *.log 89 | local_settings.py 90 | 91 | # Flask stuff: 92 | instance/ 93 | .webassets-cache 94 | 95 | # Scrapy stuff: 96 | .scrapy 97 | 98 | # Sphinx documentation 99 | docs/_build/ 100 | 101 | # PyBuilder 102 | target/ 103 | 104 | # Jupyter Notebook 105 | .ipynb_checkpoints 106 | 107 | # pyenv 108 | .python-version 109 | 110 | # celery beat schedule file 111 | celerybeat-schedule 112 | 113 | # SageMath parsed files 114 | *.sage.py 115 | 116 | # dotenv 117 | .env 118 | 119 | # virtualenv 120 | .venv* 121 | venv*/ 122 | ENV*/ 123 | 124 | # Spyder project settings 125 | .spyderproject 126 | .spyproject 127 | 128 | # Rope project settings 129 | .ropeproject 130 | 131 | # mkdocs documentation 132 | /site 133 | 134 | # mypy 135 | .mypy_cache/ 136 | 137 | 138 | # https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- 139 | 140 | # General 141 | .DS_Store 142 | .AppleDouble 143 | .LSOverride 144 | 145 | # Icon must end with two \r 146 | Icon 147 | Icon? 148 | 149 | # Thumbnails 150 | ._* 151 | 152 | # Files that might appear in the root of a volume 153 | .DocumentRevisions-V100 154 | .fseventsd 155 | .Spotlight-V100 156 | .TemporaryItems 157 | .Trashes 158 | .VolumeIcon.icns 159 | .com.apple.timemachine.donotpresent 160 | 161 | # Directories potentially created on remote AFP share 162 | .AppleDB 163 | .AppleDesktop 164 | Network Trash Folder 165 | Temporary Items 166 | .apdisk 167 | 168 | 169 | # https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore 170 | # Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm 171 | # Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 172 | 173 | # User-specific stuff: 174 | .idea/* 175 | .idea/**/workspace.xml 176 | .idea/**/tasks.xml 177 | .idea/dictionaries 178 | .html # Bokeh Plots 179 | .pg # TensorFlow Frozen Graphs 180 | .avi # videos 181 | 182 | # Sensitive or high-churn files: 183 | .idea/**/dataSources/ 184 | .idea/**/dataSources.ids 185 | .idea/**/dataSources.local.xml 186 | .idea/**/sqlDataSources.xml 187 | .idea/**/dynamic.xml 188 | .idea/**/uiDesigner.xml 189 | 190 | # Gradle: 191 | .idea/**/gradle.xml 192 | .idea/**/libraries 193 | 194 | # CMake 195 | cmake-build-debug/ 196 | cmake-build-release/ 197 | 198 | # Mongo Explorer plugin: 199 | .idea/**/mongoSettings.xml 200 | 201 | ## File-based project format: 202 | *.iws 203 | 204 | ## Plugin-specific files: 205 | 206 | # IntelliJ 207 | out/ 208 | 209 | # mpeltonen/sbt-idea plugin 210 | .idea_modules/ 211 | 212 | # JIRA plugin 213 | atlassian-ide-plugin.xml 214 | 215 | # Cursive Clojure plugin 216 | .idea/replstate.xml 217 | 218 | # Crashlytics plugin (for Android Studio and IntelliJ) 219 | com_crashlytics_export_strings.xml 220 | crashlytics.properties 221 | crashlytics-build.properties 222 | fabric.properties 223 | -------------------------------------------------------------------------------- /utils/docker/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==1.11.0+cu113 torchvision==0.12.0+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 | RUN git clone https://github.com/ultralytics/yolov5 /usr/src/yolov5 24 | 25 | # Downloads to user config dir 26 | ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ 27 | 28 | # Set environment variables 29 | ENV OMP_NUM_THREADS=8 30 | 31 | 32 | # Usage Examples ------------------------------------------------------------------------------------------------------- 33 | 34 | # Build and Push 35 | # t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t 36 | 37 | # Pull and Run 38 | # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t 39 | 40 | # Pull and Run with local directory access 41 | # t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t 42 | 43 | # Kill all 44 | # sudo docker kill $(sudo docker ps -q) 45 | 46 | # Kill all image-based 47 | # sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) 48 | 49 | # Bash into running container 50 | # sudo docker exec -it 5a9b5863d93d bash 51 | 52 | # Bash into stopped container 53 | # id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash 54 | 55 | # Clean up 56 | # docker system prune -a --volumes 57 | 58 | # Update Ubuntu drivers 59 | # https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ 60 | 61 | # DDP test 62 | # python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 63 | 64 | # GCP VM from Image 65 | # docker.io/ultralytics/yolov5:latest 66 | -------------------------------------------------------------------------------- /utils/docker/Dockerfile-cpu: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | 3 | # Start FROM Ubuntu image https://hub.docker.com/_/ubuntu 4 | FROM ubuntu:latest 5 | 6 | # Install linux packages 7 | RUN apt update 8 | RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata 9 | RUN apt install -y python3-pip git zip curl htop screen libgl1-mesa-glx libglib2.0-0 10 | RUN alias python=python3 11 | 12 | # Install python dependencies 13 | COPY requirements.txt . 14 | RUN python3 -m pip install --upgrade pip 15 | RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ 16 | coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu tensorflowjs \ 17 | torch==1.11.0+cpu torchvision==0.12.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html 18 | 19 | # Create working directory 20 | RUN mkdir -p /usr/src/app 21 | WORKDIR /usr/src/app 22 | 23 | # Copy contents 24 | COPY . /usr/src/app 25 | RUN git clone https://github.com/ultralytics/yolov5 /usr/src/yolov5 26 | 27 | # Downloads to user config dir 28 | ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ 29 | 30 | 31 | # Usage Examples ------------------------------------------------------------------------------------------------------- 32 | 33 | # Build and Push 34 | # t=ultralytics/yolov5:latest-cpu && sudo docker build -t $t . && sudo docker push $t 35 | 36 | # Pull and Run 37 | # t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t 38 | -------------------------------------------------------------------------------- /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 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Perform test request 4 | """ 5 | 6 | import pprint 7 | 8 | import requests 9 | 10 | DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" 11 | IMAGE = "zidane.jpg" 12 | 13 | # Read image 14 | with open(IMAGE, "rb") as f: 15 | image_data = f.read() 16 | 17 | response = requests.post(DETECTION_URL, files={"image": image_data}).json() 18 | 19 | pprint.pprint(response) 20 | -------------------------------------------------------------------------------- /utils/flask_rest_api/restapi.py: -------------------------------------------------------------------------------- 1 | # YOLOv5 🚀 by Ultralytics, GPL-3.0 license 2 | """ 3 | Run a Flask REST API exposing a YOLOv5s model 4 | """ 5 | 6 | import argparse 7 | import io 8 | 9 | import torch 10 | from flask import Flask, request 11 | from PIL import Image 12 | 13 | app = Flask(__name__) 14 | 15 | DETECTION_URL = "/v1/object-detection/yolov5s" 16 | 17 | 18 | @app.route(DETECTION_URL, methods=["POST"]) 19 | def predict(): 20 | if not request.method == "POST": 21 | return 22 | 23 | if request.files.get("image"): 24 | image_file = request.files["image"] 25 | image_bytes = image_file.read() 26 | 27 | img = Image.open(io.BytesIO(image_bytes)) 28 | 29 | results = model(img, size=640) # reduce size=320 for faster inference 30 | return results.pandas().xyxy[0].to_json(orient="records") 31 | 32 | 33 | if __name__ == "__main__": 34 | parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") 35 | parser.add_argument("--port", default=5000, type=int, help="port number") 36 | opt = parser.parse_args() 37 | 38 | # Fix known issue urllib.error.HTTPError 403: rate limit exceeded https://github.com/ultralytics/yolov5/pull/7210 39 | torch.hub._validate_not_a_forked_repo = lambda a, b, c: True 40 | 41 | model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache 42 | app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat 43 | -------------------------------------------------------------------------------- /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-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/utils/loggers/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /utils/loggers/wandb/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/utils/loggers/wandb/__init__.py -------------------------------------------------------------------------------- /utils/loggers/wandb/__pycache__/__init__.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/utils/loggers/wandb/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /utils/loggers/wandb/__pycache__/wandb_utils.cpython-37.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mohenghui/yolov5_attention/b3b22930ab6e839a49d2c3a244826375130b93cb/utils/loggers/wandb/__pycache__/wandb_utils.cpython-37.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. Copy because train() modifies parameters which confused wandb. 20 | hyp_dict = vars(wandb.config).get("_items").copy() 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 | -------------------------------------------------------------------------------- /utils/loggers/wandb/sweep.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for training 2 | # To set range- 3 | # Provide min and max values as: 4 | # parameter: 5 | # 6 | # min: scalar 7 | # max: scalar 8 | # OR 9 | # 10 | # Set a specific list of search space- 11 | # parameter: 12 | # values: [scalar1, scalar2, scalar3...] 13 | # 14 | # You can use grid, bayesian and hyperopt search strategy 15 | # For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration 16 | 17 | program: utils/loggers/wandb/sweep.py 18 | method: random 19 | metric: 20 | name: metrics/mAP_0.5 21 | goal: maximize 22 | 23 | parameters: 24 | # hyperparameters: set either min, max range or values list 25 | data: 26 | value: "data/coco128.yaml" 27 | batch_size: 28 | values: [64] 29 | epochs: 30 | values: [10] 31 | 32 | lr0: 33 | distribution: uniform 34 | min: 1e-5 35 | max: 1e-1 36 | lrf: 37 | distribution: uniform 38 | min: 0.01 39 | max: 1.0 40 | momentum: 41 | distribution: uniform 42 | min: 0.6 43 | max: 0.98 44 | weight_decay: 45 | distribution: uniform 46 | min: 0.0 47 | max: 0.001 48 | warmup_epochs: 49 | distribution: uniform 50 | min: 0.0 51 | max: 5.0 52 | warmup_momentum: 53 | distribution: uniform 54 | min: 0.0 55 | max: 0.95 56 | warmup_bias_lr: 57 | distribution: uniform 58 | min: 0.0 59 | max: 0.2 60 | box: 61 | distribution: uniform 62 | min: 0.02 63 | max: 0.2 64 | cls: 65 | distribution: uniform 66 | min: 0.2 67 | max: 4.0 68 | cls_pw: 69 | distribution: uniform 70 | min: 0.5 71 | max: 2.0 72 | obj: 73 | distribution: uniform 74 | min: 0.2 75 | max: 4.0 76 | obj_pw: 77 | distribution: uniform 78 | min: 0.5 79 | max: 2.0 80 | iou_t: 81 | distribution: uniform 82 | min: 0.1 83 | max: 0.7 84 | anchor_t: 85 | distribution: uniform 86 | min: 2.0 87 | max: 8.0 88 | fl_gamma: 89 | distribution: uniform 90 | min: 0.0 91 | max: 4.0 92 | hsv_h: 93 | distribution: uniform 94 | min: 0.0 95 | max: 0.1 96 | hsv_s: 97 | distribution: uniform 98 | min: 0.0 99 | max: 0.9 100 | hsv_v: 101 | distribution: uniform 102 | min: 0.0 103 | max: 0.9 104 | degrees: 105 | distribution: uniform 106 | min: 0.0 107 | max: 45.0 108 | translate: 109 | distribution: uniform 110 | min: 0.0 111 | max: 0.9 112 | scale: 113 | distribution: uniform 114 | min: 0.0 115 | max: 0.9 116 | shear: 117 | distribution: uniform 118 | min: 0.0 119 | max: 10.0 120 | perspective: 121 | distribution: uniform 122 | min: 0.0 123 | max: 0.001 124 | flipud: 125 | distribution: uniform 126 | min: 0.0 127 | max: 1.0 128 | fliplr: 129 | distribution: uniform 130 | min: 0.0 131 | max: 1.0 132 | mosaic: 133 | distribution: uniform 134 | min: 0.0 135 | max: 1.0 136 | mixup: 137 | distribution: uniform 138 | min: 0.0 139 | max: 1.0 140 | copy_paste: 141 | distribution: uniform 142 | min: 0.0 143 | max: 1.0 144 | -------------------------------------------------------------------------------- /voc_label.py: -------------------------------------------------------------------------------- 1 | import xml.etree.ElementTree as ET 2 | import pickle 3 | import os 4 | import os.path 5 | from os import listdir, getcwd 6 | from os.path import join 7 | sets = ['train', 'test','val'] 8 | classes = ['person'] 9 | def convert(size, box): 10 | dw = 1. / size[0] 11 | dh = 1. / size[1] 12 | x = (box[0] + box[1]) / 2.0 13 | y = (box[2] + box[3]) / 2.0 14 | w = box[1] - box[0] 15 | h = box[3] - box[2] 16 | x = x * dw 17 | w = w * dw 18 | y = y * dh 19 | h = h * dh 20 | return (x, y, w, h) 21 | def convert_annotation(image_id): 22 | if os.path.isfile('datasets/mydata/Annotations/%s.xml' % (image_id)): 23 | in_file = open('datasets/mydata/Annotations/%s.xml' % (image_id)) 24 | out_file = open('datasets/mydata/labels/%s.txt' % (image_id), 'w') 25 | tree = ET.parse(in_file) 26 | root = tree.getroot() 27 | size = root.find('size') 28 | w = int(size.find('width').text) 29 | h = int(size.find('height').text) 30 | for obj in root.iter('object'): 31 | difficult = obj.find('difficult').text 32 | cls = obj.find('name').text 33 | if cls not in classes or int(difficult) == 1: 34 | continue 35 | cls_id = classes.index(cls) 36 | xmlbox = obj.find('bndbox') 37 | b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), 38 | float(xmlbox.find('ymax').text)) 39 | bb = convert((w, h), b) 40 | out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n') 41 | wd = getcwd() 42 | print(wd) 43 | for image_set in sets: 44 | if not os.path.exists('datasets/mydata/labels/'): 45 | os.makedirs('datasets/mydata/labels/') 46 | image_ids = open('datasets/mydata/%s.txt' % (image_set)).read().strip().split() 47 | list_file = open('datasets/mydata/%s.txt' % (image_set), 'w') 48 | for image_id in image_ids: 49 | list_file.write('datasets/mydata/images/%s.jpg\n' % (image_id)) 50 | convert_annotation(image_id) 51 | list_file.close() --------------------------------------------------------------------------------