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0f967fc
fix: bug
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- .gitattributes +0 -31
- .gitignore +0 -4
- README.md +0 -12
- app.py +0 -48
- best.pt +0 -3
- requirements.txt +0 -33
- ultralytics/yolov5/.dockerignore +0 -222
- ultralytics/yolov5/.gitattributes +0 -2
- ultralytics/yolov5/.gitignore +0 -256
- ultralytics/yolov5/.pre-commit-config.yaml +0 -66
- ultralytics/yolov5/CONTRIBUTING.md +0 -94
- ultralytics/yolov5/Dockerfile +0 -65
- ultralytics/yolov5/LICENSE +0 -674
- ultralytics/yolov5/README.md +0 -304
- ultralytics/yolov5/data/Argoverse.yaml +0 -67
- ultralytics/yolov5/data/GlobalWheat2020.yaml +0 -54
- ultralytics/yolov5/data/Objects365.yaml +0 -113
- ultralytics/yolov5/data/SKU-110K.yaml +0 -53
- ultralytics/yolov5/data/VOC.yaml +0 -80
- ultralytics/yolov5/data/VisDrone.yaml +0 -61
- ultralytics/yolov5/data/coco.yaml +0 -45
- ultralytics/yolov5/data/coco128.yaml +0 -30
- ultralytics/yolov5/data/hyps/hyp.Objects365.yaml +0 -34
- ultralytics/yolov5/data/hyps/hyp.VOC.yaml +0 -40
- ultralytics/yolov5/data/hyps/hyp.scratch-high.yaml +0 -34
- ultralytics/yolov5/data/hyps/hyp.scratch-low.yaml +0 -34
- ultralytics/yolov5/data/hyps/hyp.scratch-med.yaml +0 -34
- ultralytics/yolov5/data/images/bus.jpg +0 -0
- ultralytics/yolov5/data/images/zidane.jpg +0 -0
- ultralytics/yolov5/data/scripts/download_weights.sh +0 -20
- ultralytics/yolov5/data/scripts/get_coco.sh +0 -27
- ultralytics/yolov5/data/scripts/get_coco128.sh +0 -17
- ultralytics/yolov5/data/xView.yaml +0 -102
- ultralytics/yolov5/detect.py +0 -252
- ultralytics/yolov5/export.py +0 -559
- ultralytics/yolov5/hubconf.py +0 -143
- ultralytics/yolov5/models/__init__.py +0 -0
- ultralytics/yolov5/models/common.py +0 -684
- ultralytics/yolov5/models/experimental.py +0 -121
- ultralytics/yolov5/models/hub/anchors.yaml +0 -59
- ultralytics/yolov5/models/hub/yolov3-spp.yaml +0 -51
- ultralytics/yolov5/models/hub/yolov3-tiny.yaml +0 -41
- ultralytics/yolov5/models/hub/yolov3.yaml +0 -51
- ultralytics/yolov5/models/hub/yolov5-bifpn.yaml +0 -48
- ultralytics/yolov5/models/hub/yolov5-fpn.yaml +0 -42
- ultralytics/yolov5/models/hub/yolov5-p2.yaml +0 -54
- ultralytics/yolov5/models/hub/yolov5-p34.yaml +0 -41
- ultralytics/yolov5/models/hub/yolov5-p6.yaml +0 -56
- ultralytics/yolov5/models/hub/yolov5-p7.yaml +0 -67
- ultralytics/yolov5/models/hub/yolov5-panet.yaml +0 -48
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.DS_Store
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yolov5s.pt
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__pycache__
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gradio_queue.db
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README.md
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---
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title: Yolov5 Char
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emoji: 🌍
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colorFrom: yellow
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colorTo: purple
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sdk: gradio
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sdk_version: 3.1.4
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app_file: app.py
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pinned: false
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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app.py
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import gradio as gr
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import torch
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from PIL import Image
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import json
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# import gdown
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'''
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# a file
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url = "https://drive.google.com/uc?id=1-ZIa4KsSjhup4Pep70uBvI4BjnSUbocX"
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output = "best.pt"
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gdown.download(url, output, quiet=False)
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'''
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# Images
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torch.hub.download_url_to_file(
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'https://iiif.dl.itc.u-tokyo.ac.jp/iiif/genji/TIFF/A00_6587/01/01_0004.tif/full/1024,/0/default.jpg', '『源氏物語』(東京大学総合図書館所蔵).jpg')
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torch.hub.download_url_to_file(
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'https://rmda.kulib.kyoto-u.ac.jp/iiif/RB00007030/01/RB00007030_00003_0.ptif/full/1024,/0/default.jpg', '『源氏物語』(京都大学所蔵).jpg')
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torch.hub.download_url_to_file(
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'https://kotenseki.nijl.ac.jp/api/iiif/100312034/v4/HRSM/HRSM-00396/HRSM-00396-00012.tif/full/1024,/0/default.jpg', '『平家物語』(国文学研究資料館提供).jpg')
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# Model
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# model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # force_reload=True to update
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model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt', source="local")
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def yolo(im, size=1024):
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g = (size / max(im.size)) # gain
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im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize
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results = model(im) # inference
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results.render() # updates results.imgs with boxes and labels
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df = results.pandas().xyxy[0].to_json(orient="records")
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res = json.loads(df)
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return [Image.fromarray(results.imgs[0]), res]
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inputs = gr.inputs.Image(type='pil', label="Original Image")
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outputs = [gr.outputs.Image(type="pil", label="Output Image"),
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gr.outputs.JSON(label="Output JSON")]
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title = "YOLOv5 Character"
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description = "YOLOv5 Character Gradio demo for object detection. Upload an image or click an example image to use."
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article = "<p style='text-align: center'>YOLOv5 Character is an object detection model trained on the <a href=\"http://codh.rois.ac.jp/char-shape/\">日本古典籍くずし字データセット</a>.</p>"
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examples = [['『源氏物語』(東京大学総合図書館所蔵).jpg'], ['『源氏物語』(京都大学所蔵).jpg'], ['『平家物語』(国文学研究資料館提供).jpg']]
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gr.Interface(yolo, inputs, outputs, title=title, description=description, article=article, examples=examples, theme="huggingface").launch(enable_queue=True) # cache_examples=True,
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best.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:772f81467ecfbf27a5c9e2b5b1b8b783b94c1db6d56b41b4e7c83996365afc8e
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size 691395070
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requirements.txt
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# pip install -r requirements.txt
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# base ----------------------------------------
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matplotlib>=3.2.2
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numpy>=1.18.5
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opencv-python-headless
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Pillow
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PyYAML>=5.3.1
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scipy>=1.4.1
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torch>=1.7.0
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torchvision>=0.8.1
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tqdm>=4.41.0
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# logging -------------------------------------
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tensorboard>=2.4.1
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# wandb
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# plotting ------------------------------------
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seaborn>=0.11.0
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pandas
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# export --------------------------------------
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# coremltools>=4.1
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# onnx>=1.9.0
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# scikit-learn==0.19.2 # for coreml quantization
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# extras --------------------------------------
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# Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172
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# pycocotools>=2.0 # COCO mAP
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# albumentations>=1.0.3
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thop # FLOPs computation
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gdown
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# Repo-specific DockerIgnore -------------------------------------------------------------------------------------------
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#.git
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.idea
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runs
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output
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coco
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data/samples/*
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# Neural Network weights -----------------------------------------------------------------------------------------------
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# Below Copied From .gitignore -----------------------------------------------------------------------------------------
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# Below Copied From .gitignore -----------------------------------------------------------------------------------------
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# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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lib/
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parts/
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var/
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wheels/
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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coverage.xml
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*.cover
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# dotenv
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.env
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# virtualenv
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.venv*
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venv*/
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ENV*/
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# Spyder project settings
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# Rope project settings
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# mkdocs documentation
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/site
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|
| 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
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| 187 |
-
.idea/**/dynamic.xml
|
| 188 |
-
.idea/**/uiDesigner.xml
|
| 189 |
-
|
| 190 |
-
# Gradle:
|
| 191 |
-
.idea/**/gradle.xml
|
| 192 |
-
.idea/**/libraries
|
| 193 |
-
|
| 194 |
-
# CMake
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| 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 |
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fabric.properties
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|
ultralytics/yolov5/.gitattributes
DELETED
|
@@ -1,2 +0,0 @@
|
|
| 1 |
-
# this drop notebooks from GitHub language stats
|
| 2 |
-
*.ipynb linguist-vendored
|
|
|
|
|
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|
|
|
ultralytics/yolov5/.gitignore
DELETED
|
@@ -1,256 +0,0 @@
|
|
| 1 |
-
# Repo-specific GitIgnore ----------------------------------------------------------------------------------------------
|
| 2 |
-
*.jpg
|
| 3 |
-
*.jpeg
|
| 4 |
-
*.png
|
| 5 |
-
*.bmp
|
| 6 |
-
*.tif
|
| 7 |
-
*.tiff
|
| 8 |
-
*.heic
|
| 9 |
-
*.JPG
|
| 10 |
-
*.JPEG
|
| 11 |
-
*.PNG
|
| 12 |
-
*.BMP
|
| 13 |
-
*.TIF
|
| 14 |
-
*.TIFF
|
| 15 |
-
*.HEIC
|
| 16 |
-
*.mp4
|
| 17 |
-
*.mov
|
| 18 |
-
*.MOV
|
| 19 |
-
*.avi
|
| 20 |
-
*.data
|
| 21 |
-
*.json
|
| 22 |
-
*.cfg
|
| 23 |
-
!setup.cfg
|
| 24 |
-
!cfg/yolov3*.cfg
|
| 25 |
-
|
| 26 |
-
storage.googleapis.com
|
| 27 |
-
runs/*
|
| 28 |
-
data/*
|
| 29 |
-
data/images/*
|
| 30 |
-
!data/*.yaml
|
| 31 |
-
!data/hyps
|
| 32 |
-
!data/scripts
|
| 33 |
-
!data/images
|
| 34 |
-
!data/images/zidane.jpg
|
| 35 |
-
!data/images/bus.jpg
|
| 36 |
-
!data/*.sh
|
| 37 |
-
|
| 38 |
-
results*.csv
|
| 39 |
-
|
| 40 |
-
# Datasets -------------------------------------------------------------------------------------------------------------
|
| 41 |
-
coco/
|
| 42 |
-
coco128/
|
| 43 |
-
VOC/
|
| 44 |
-
|
| 45 |
-
# MATLAB GitIgnore -----------------------------------------------------------------------------------------------------
|
| 46 |
-
*.m~
|
| 47 |
-
*.mat
|
| 48 |
-
!targets*.mat
|
| 49 |
-
|
| 50 |
-
# Neural Network weights -----------------------------------------------------------------------------------------------
|
| 51 |
-
*.weights
|
| 52 |
-
*.pt
|
| 53 |
-
*.pb
|
| 54 |
-
*.onnx
|
| 55 |
-
*.engine
|
| 56 |
-
*.mlmodel
|
| 57 |
-
*.torchscript
|
| 58 |
-
*.tflite
|
| 59 |
-
*.h5
|
| 60 |
-
*_saved_model/
|
| 61 |
-
*_web_model/
|
| 62 |
-
*_openvino_model/
|
| 63 |
-
darknet53.conv.74
|
| 64 |
-
yolov3-tiny.conv.15
|
| 65 |
-
|
| 66 |
-
# GitHub Python GitIgnore ----------------------------------------------------------------------------------------------
|
| 67 |
-
# Byte-compiled / optimized / DLL files
|
| 68 |
-
__pycache__/
|
| 69 |
-
*.py[cod]
|
| 70 |
-
*$py.class
|
| 71 |
-
|
| 72 |
-
# C extensions
|
| 73 |
-
*.so
|
| 74 |
-
|
| 75 |
-
# Distribution / packaging
|
| 76 |
-
.Python
|
| 77 |
-
env/
|
| 78 |
-
build/
|
| 79 |
-
develop-eggs/
|
| 80 |
-
dist/
|
| 81 |
-
downloads/
|
| 82 |
-
eggs/
|
| 83 |
-
.eggs/
|
| 84 |
-
lib/
|
| 85 |
-
lib64/
|
| 86 |
-
parts/
|
| 87 |
-
sdist/
|
| 88 |
-
var/
|
| 89 |
-
wheels/
|
| 90 |
-
*.egg-info/
|
| 91 |
-
/wandb/
|
| 92 |
-
.installed.cfg
|
| 93 |
-
*.egg
|
| 94 |
-
|
| 95 |
-
|
| 96 |
-
# PyInstaller
|
| 97 |
-
# Usually these files are written by a python script from a template
|
| 98 |
-
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
| 99 |
-
*.manifest
|
| 100 |
-
*.spec
|
| 101 |
-
|
| 102 |
-
# Installer logs
|
| 103 |
-
pip-log.txt
|
| 104 |
-
pip-delete-this-directory.txt
|
| 105 |
-
|
| 106 |
-
# Unit test / coverage reports
|
| 107 |
-
htmlcov/
|
| 108 |
-
.tox/
|
| 109 |
-
.coverage
|
| 110 |
-
.coverage.*
|
| 111 |
-
.cache
|
| 112 |
-
nosetests.xml
|
| 113 |
-
coverage.xml
|
| 114 |
-
*.cover
|
| 115 |
-
.hypothesis/
|
| 116 |
-
|
| 117 |
-
# Translations
|
| 118 |
-
*.mo
|
| 119 |
-
*.pot
|
| 120 |
-
|
| 121 |
-
# Django stuff:
|
| 122 |
-
*.log
|
| 123 |
-
local_settings.py
|
| 124 |
-
|
| 125 |
-
# Flask stuff:
|
| 126 |
-
instance/
|
| 127 |
-
.webassets-cache
|
| 128 |
-
|
| 129 |
-
# Scrapy stuff:
|
| 130 |
-
.scrapy
|
| 131 |
-
|
| 132 |
-
# Sphinx documentation
|
| 133 |
-
docs/_build/
|
| 134 |
-
|
| 135 |
-
# PyBuilder
|
| 136 |
-
target/
|
| 137 |
-
|
| 138 |
-
# Jupyter Notebook
|
| 139 |
-
.ipynb_checkpoints
|
| 140 |
-
|
| 141 |
-
# pyenv
|
| 142 |
-
.python-version
|
| 143 |
-
|
| 144 |
-
# celery beat schedule file
|
| 145 |
-
celerybeat-schedule
|
| 146 |
-
|
| 147 |
-
# SageMath parsed files
|
| 148 |
-
*.sage.py
|
| 149 |
-
|
| 150 |
-
# dotenv
|
| 151 |
-
.env
|
| 152 |
-
|
| 153 |
-
# virtualenv
|
| 154 |
-
.venv*
|
| 155 |
-
venv*/
|
| 156 |
-
ENV*/
|
| 157 |
-
|
| 158 |
-
# Spyder project settings
|
| 159 |
-
.spyderproject
|
| 160 |
-
.spyproject
|
| 161 |
-
|
| 162 |
-
# Rope project settings
|
| 163 |
-
.ropeproject
|
| 164 |
-
|
| 165 |
-
# mkdocs documentation
|
| 166 |
-
/site
|
| 167 |
-
|
| 168 |
-
# mypy
|
| 169 |
-
.mypy_cache/
|
| 170 |
-
|
| 171 |
-
|
| 172 |
-
# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore -----------------------------------------------
|
| 173 |
-
|
| 174 |
-
# General
|
| 175 |
-
.DS_Store
|
| 176 |
-
.AppleDouble
|
| 177 |
-
.LSOverride
|
| 178 |
-
|
| 179 |
-
# Icon must end with two \r
|
| 180 |
-
Icon
|
| 181 |
-
Icon?
|
| 182 |
-
|
| 183 |
-
# Thumbnails
|
| 184 |
-
._*
|
| 185 |
-
|
| 186 |
-
# Files that might appear in the root of a volume
|
| 187 |
-
.DocumentRevisions-V100
|
| 188 |
-
.fseventsd
|
| 189 |
-
.Spotlight-V100
|
| 190 |
-
.TemporaryItems
|
| 191 |
-
.Trashes
|
| 192 |
-
.VolumeIcon.icns
|
| 193 |
-
.com.apple.timemachine.donotpresent
|
| 194 |
-
|
| 195 |
-
# Directories potentially created on remote AFP share
|
| 196 |
-
.AppleDB
|
| 197 |
-
.AppleDesktop
|
| 198 |
-
Network Trash Folder
|
| 199 |
-
Temporary Items
|
| 200 |
-
.apdisk
|
| 201 |
-
|
| 202 |
-
|
| 203 |
-
# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore
|
| 204 |
-
# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm
|
| 205 |
-
# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
|
| 206 |
-
|
| 207 |
-
# User-specific stuff:
|
| 208 |
-
.idea/*
|
| 209 |
-
.idea/**/workspace.xml
|
| 210 |
-
.idea/**/tasks.xml
|
| 211 |
-
.idea/dictionaries
|
| 212 |
-
.html # Bokeh Plots
|
| 213 |
-
.pg # TensorFlow Frozen Graphs
|
| 214 |
-
.avi # videos
|
| 215 |
-
|
| 216 |
-
# Sensitive or high-churn files:
|
| 217 |
-
.idea/**/dataSources/
|
| 218 |
-
.idea/**/dataSources.ids
|
| 219 |
-
.idea/**/dataSources.local.xml
|
| 220 |
-
.idea/**/sqlDataSources.xml
|
| 221 |
-
.idea/**/dynamic.xml
|
| 222 |
-
.idea/**/uiDesigner.xml
|
| 223 |
-
|
| 224 |
-
# Gradle:
|
| 225 |
-
.idea/**/gradle.xml
|
| 226 |
-
.idea/**/libraries
|
| 227 |
-
|
| 228 |
-
# CMake
|
| 229 |
-
cmake-build-debug/
|
| 230 |
-
cmake-build-release/
|
| 231 |
-
|
| 232 |
-
# Mongo Explorer plugin:
|
| 233 |
-
.idea/**/mongoSettings.xml
|
| 234 |
-
|
| 235 |
-
## File-based project format:
|
| 236 |
-
*.iws
|
| 237 |
-
|
| 238 |
-
## Plugin-specific files:
|
| 239 |
-
|
| 240 |
-
# IntelliJ
|
| 241 |
-
out/
|
| 242 |
-
|
| 243 |
-
# mpeltonen/sbt-idea plugin
|
| 244 |
-
.idea_modules/
|
| 245 |
-
|
| 246 |
-
# JIRA plugin
|
| 247 |
-
atlassian-ide-plugin.xml
|
| 248 |
-
|
| 249 |
-
# Cursive Clojure plugin
|
| 250 |
-
.idea/replstate.xml
|
| 251 |
-
|
| 252 |
-
# Crashlytics plugin (for Android Studio and IntelliJ)
|
| 253 |
-
com_crashlytics_export_strings.xml
|
| 254 |
-
crashlytics.properties
|
| 255 |
-
crashlytics-build.properties
|
| 256 |
-
fabric.properties
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
ultralytics/yolov5/.pre-commit-config.yaml
DELETED
|
@@ -1,66 +0,0 @@
|
|
| 1 |
-
# Define hooks for code formations
|
| 2 |
-
# Will be applied on any updated commit files if a user has installed and linked commit hook
|
| 3 |
-
|
| 4 |
-
default_language_version:
|
| 5 |
-
python: python3.8
|
| 6 |
-
|
| 7 |
-
# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
|
| 8 |
-
ci:
|
| 9 |
-
autofix_prs: true
|
| 10 |
-
autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
|
| 11 |
-
autoupdate_schedule: quarterly
|
| 12 |
-
# submodules: true
|
| 13 |
-
|
| 14 |
-
repos:
|
| 15 |
-
- repo: https://github.com/pre-commit/pre-commit-hooks
|
| 16 |
-
rev: v4.1.0
|
| 17 |
-
hooks:
|
| 18 |
-
- id: end-of-file-fixer
|
| 19 |
-
- id: trailing-whitespace
|
| 20 |
-
- id: check-case-conflict
|
| 21 |
-
- id: check-yaml
|
| 22 |
-
- id: check-toml
|
| 23 |
-
- id: pretty-format-json
|
| 24 |
-
- id: check-docstring-first
|
| 25 |
-
|
| 26 |
-
- repo: https://github.com/asottile/pyupgrade
|
| 27 |
-
rev: v2.31.0
|
| 28 |
-
hooks:
|
| 29 |
-
- id: pyupgrade
|
| 30 |
-
args: [--py36-plus]
|
| 31 |
-
name: Upgrade code
|
| 32 |
-
|
| 33 |
-
- repo: https://github.com/PyCQA/isort
|
| 34 |
-
rev: 5.10.1
|
| 35 |
-
hooks:
|
| 36 |
-
- id: isort
|
| 37 |
-
name: Sort imports
|
| 38 |
-
|
| 39 |
-
# TODO
|
| 40 |
-
#- repo: https://github.com/pre-commit/mirrors-yapf
|
| 41 |
-
# rev: v0.31.0
|
| 42 |
-
# hooks:
|
| 43 |
-
# - id: yapf
|
| 44 |
-
# name: formatting
|
| 45 |
-
|
| 46 |
-
# TODO
|
| 47 |
-
#- repo: https://github.com/executablebooks/mdformat
|
| 48 |
-
# rev: 0.7.7
|
| 49 |
-
# hooks:
|
| 50 |
-
# - id: mdformat
|
| 51 |
-
# additional_dependencies:
|
| 52 |
-
# - mdformat-gfm
|
| 53 |
-
# - mdformat-black
|
| 54 |
-
# - mdformat_frontmatter
|
| 55 |
-
|
| 56 |
-
# TODO
|
| 57 |
-
#- repo: https://github.com/asottile/yesqa
|
| 58 |
-
# rev: v1.2.3
|
| 59 |
-
# hooks:
|
| 60 |
-
# - id: yesqa
|
| 61 |
-
|
| 62 |
-
- repo: https://github.com/PyCQA/flake8
|
| 63 |
-
rev: 4.0.1
|
| 64 |
-
hooks:
|
| 65 |
-
- id: flake8
|
| 66 |
-
name: PEP8
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|
ultralytics/yolov5/CONTRIBUTING.md
DELETED
|
@@ -1,94 +0,0 @@
|
|
| 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 |
-
<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
|
| 22 |
-
|
| 23 |
-
### 2. Click 'Edit this file'
|
| 24 |
-
|
| 25 |
-
Button is in top-right corner.
|
| 26 |
-
<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
|
| 27 |
-
|
| 28 |
-
### 3. Make Changes
|
| 29 |
-
|
| 30 |
-
Change `matplotlib` version from `3.2.2` to `3.3`.
|
| 31 |
-
<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
|
| 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 |
-
<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
|
| 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/)
|
|
|
|
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|
|
ultralytics/yolov5/Dockerfile
DELETED
|
@@ -1,65 +0,0 @@
|
|
| 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 |
-
RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
|
| 23 |
-
# COPY . /usr/src/app
|
| 24 |
-
|
| 25 |
-
# Downloads to user config dir
|
| 26 |
-
ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/
|
| 27 |
-
|
| 28 |
-
# Set environment variables
|
| 29 |
-
# ENV HOME=/usr/src/app
|
| 30 |
-
|
| 31 |
-
|
| 32 |
-
# Usage Examples -------------------------------------------------------------------------------------------------------
|
| 33 |
-
|
| 34 |
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# 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
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ultralytics/yolov5/LICENSE
DELETED
|
@@ -1,674 +0,0 @@
|
|
| 1 |
-
GNU GENERAL PUBLIC LICENSE
|
| 2 |
-
Version 3, 29 June 2007
|
| 3 |
-
|
| 4 |
-
Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
|
| 5 |
-
Everyone is permitted to copy and distribute verbatim copies
|
| 6 |
-
of this license document, but changing it is not allowed.
|
| 7 |
-
|
| 8 |
-
Preamble
|
| 9 |
-
|
| 10 |
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The GNU General Public License is a free, copyleft license for
|
| 11 |
-
software and other kinds of works.
|
| 12 |
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| 13 |
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The licenses for most software and other practical works are designed
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| 14 |
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to take away your freedom to share and change the works. By contrast,
|
| 15 |
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the GNU General Public License is intended to guarantee your freedom to
|
| 16 |
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share and change all versions of a program--to make sure it remains free
|
| 17 |
-
software for all its users. We, the Free Software Foundation, use the
|
| 18 |
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GNU General Public License for most of our software; it applies also to
|
| 19 |
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any other work released this way by its authors. You can apply it to
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| 20 |
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your programs, too.
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| 21 |
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| 22 |
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When we speak of free software, we are referring to freedom, not
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| 23 |
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price. Our General Public Licenses are designed to make sure that you
|
| 24 |
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have the freedom to distribute copies of free software (and charge for
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them if you wish), that you receive source code or can get it if you
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| 26 |
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want it, that you can change the software or use pieces of it in new
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| 27 |
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free programs, and that you know you can do these things.
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| 28 |
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| 29 |
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To protect your rights, we need to prevent others from denying you
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these rights or asking you to surrender the rights. Therefore, you have
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certain responsibilities if you distribute copies of the software, or if
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you modify it: responsibilities to respect the freedom of others.
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For example, if you distribute copies of such a program, whether
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gratis or for a fee, you must pass on to the recipients the same
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know their rights.
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Developers that use the GNU GPL protect your rights with two steps:
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For the developers' and authors' protection, the GPL clearly explains
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Some devices are designed to deny users access to install or run
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stand ready to extend this provision to those domains in future versions
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Finally, every program is threatened constantly by software patents.
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patents cannot be used to render the program non-free.
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|
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The precise terms and conditions for copying, distribution and
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| 69 |
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modification follow.
|
| 70 |
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|
| 71 |
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TERMS AND CONDITIONS
|
| 72 |
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|
| 73 |
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0. Definitions.
|
| 74 |
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|
| 75 |
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"This License" refers to version 3 of the GNU General Public License.
|
| 76 |
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"Copyright" also means copyright-like laws that apply to other kinds of
|
| 78 |
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works, such as semiconductor masks.
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| 79 |
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| 80 |
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"The Program" refers to any copyrightable work licensed under this
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License. Each licensee is addressed as "you". "Licensees" and
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| 82 |
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"recipients" may be individuals or organizations.
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| 84 |
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To "modify" a work means to copy from or adapt all or part of the work
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in a fashion requiring copyright permission, other than the making of an
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exact copy. The resulting work is called a "modified version" of the
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earlier work or a work "based on" the earlier work.
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A "covered work" means either the unmodified Program or a work based
|
| 90 |
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on the Program.
|
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To "propagate" a work means to do anything with it that, without
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permission, would make you directly or secondarily liable for
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infringement under applicable copyright law, except executing it on a
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computer or modifying a private copy. Propagation includes copying,
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distribution (with or without modification), making available to the
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To "convey" a work means any kind of propagation that enables other
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An interactive user interface displays "Appropriate Legal Notices"
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tells the user that there is no warranty for the work (except to the
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|
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menu, a prominent item in the list meets this criterion.
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1. Source Code.
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The "source code" for a work means the preferred form of the work
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A "Standard Interface" means an interface that either is an official
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The "System Libraries" of an executable work include anything, other
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than the work as a whole, that (a) is included in the normal form of
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The "Corresponding Source" for a work in object code form means all
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the source code needed to generate, install, and (for an executable
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work) run the object code and to modify the work, including scripts to
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The Corresponding Source need not include anything that users
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The Corresponding Source for a work in source code form is that
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same work.
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2. Basic Permissions.
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All rights granted under this License are granted for the term of
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copyright on the Program, and are irrevocable provided the stated
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conditions are met. This License explicitly affirms your unlimited
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permission to run the unmodified Program. The output from running a
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covered work is covered by this License only if the output, given its
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content, constitutes a covered work. This License acknowledges your
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rights of fair use or other equivalent, as provided by copyright law.
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You may make, run and propagate covered works that you do not
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the terms of this License in conveying all material for which you do
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not control copyright. Those thus making or running the covered works
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for you must do so exclusively on your behalf, under your direction
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Conveying under any other circumstances is permitted solely under
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the conditions stated below. Sublicensing is not allowed; section 10
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makes it unnecessary.
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3. Protecting Users' Legal Rights From Anti-Circumvention Law.
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No covered work shall be deemed part of an effective technological
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When you convey a covered work, you waive any legal power to forbid
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You may convey verbatim copies of the Program's source code as you
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You may charge any price or no price for each copy that you convey,
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and you may offer support or warranty protection for a fee.
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5. Conveying Modified Source Versions.
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| 210 |
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You may convey a work based on the Program, or the modifications to
|
| 211 |
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produce it from the Program, in the form of source code under the
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terms of section 4, provided that you also meet all of these conditions:
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|
| 214 |
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a) The work must carry prominent notices stating that you modified
|
| 215 |
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it, and giving a relevant date.
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| 216 |
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|
| 217 |
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b) The work must carry prominent notices stating that it is
|
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released under this License and any conditions added under section
|
| 219 |
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7. This requirement modifies the requirement in section 4 to
|
| 220 |
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"keep intact all notices".
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|
| 222 |
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c) You must license the entire work, as a whole, under this
|
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License to anyone who comes into possession of a copy. This
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License will therefore apply, along with any applicable section 7
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regardless of how they are packaged. This License gives no
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permission to license the work in any other way, but it does not
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invalidate such permission if you have separately received it.
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d) If the work has interactive user interfaces, each must display
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in or on a volume of a storage or distribution medium, is called an
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used to limit the access or legal rights of the compilation's users
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beyond what the individual works permit. Inclusion of a covered work
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|
| 243 |
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parts of the aggregate.
|
| 244 |
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| 245 |
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6. Conveying Non-Source Forms.
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| 247 |
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You may convey a covered work in object code form under the terms
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machine-readable Corresponding Source under the terms of this License,
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a) Convey the object code in, or embodied in, a physical product
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customarily used for software interchange.
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b) Convey the object code in, or embodied in, a physical product
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written offer, valid for at least three years and valid for as
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long as you offer spare parts or customer support for that product
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copy of the Corresponding Source for all the software in the
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product that is covered by this License, on a durable physical
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medium customarily used for software interchange, for a price no
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more than your reasonable cost of physically performing this
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Corresponding Source from a network server at no charge.
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c) Convey individual copies of the object code with a copy of the
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alternative is allowed only occasionally and noncommercially, and
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| 272 |
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only if you received the object code with such an offer, in accord
|
| 273 |
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with subsection 6b.
|
| 274 |
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|
| 275 |
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d) Convey the object code by offering access from a designated
|
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place (gratis or for a charge), and offer equivalent access to the
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Corresponding Source in the same way through the same place at no
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further charge. You need not require recipients to copy the
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copy the object code is a network server, the Corresponding Source
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may be on a different server (operated by you or a third party)
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that supports equivalent copying facilities, provided you maintain
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clear directions next to the object code saying where to find the
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Corresponding Source, you remain obligated to ensure that it is
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available for as long as needed to satisfy these requirements.
|
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|
| 288 |
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e) Convey the object code using peer-to-peer transmission, provided
|
| 289 |
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you inform other peers where the object code and Corresponding
|
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Source of the work are being offered to the general public at no
|
| 291 |
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charge under subsection 6d.
|
| 292 |
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|
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A separable portion of the object code, whose source code is excluded
|
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from the Corresponding Source as a System Library, need not be
|
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included in conveying the object code work.
|
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A "User Product" is either (1) a "consumer product", which means any
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| 298 |
-
tangible personal property which is normally used for personal, family,
|
| 299 |
-
or household purposes, or (2) anything designed or sold for incorporation
|
| 300 |
-
into a dwelling. In determining whether a product is a consumer product,
|
| 301 |
-
doubtful cases shall be resolved in favor of coverage. For a particular
|
| 302 |
-
product received by a particular user, "normally used" refers to a
|
| 303 |
-
typical or common use of that class of product, regardless of the status
|
| 304 |
-
of the particular user or of the way in which the particular user
|
| 305 |
-
actually uses, or expects or is expected to use, the product. A product
|
| 306 |
-
is a consumer product regardless of whether the product has substantial
|
| 307 |
-
commercial, industrial or non-consumer uses, unless such uses represent
|
| 308 |
-
the only significant mode of use of the product.
|
| 309 |
-
|
| 310 |
-
"Installation Information" for a User Product means any methods,
|
| 311 |
-
procedures, authorization keys, or other information required to install
|
| 312 |
-
and execute modified versions of a covered work in that User Product from
|
| 313 |
-
a modified version of its Corresponding Source. The information must
|
| 314 |
-
suffice to ensure that the continued functioning of the modified object
|
| 315 |
-
code is in no case prevented or interfered with solely because
|
| 316 |
-
modification has been made.
|
| 317 |
-
|
| 318 |
-
If you convey an object code work under this section in, or with, or
|
| 319 |
-
specifically for use in, a User Product, and the conveying occurs as
|
| 320 |
-
part of a transaction in which the right of possession and use of the
|
| 321 |
-
User Product is transferred to the recipient in perpetuity or for a
|
| 322 |
-
fixed term (regardless of how the transaction is characterized), the
|
| 323 |
-
Corresponding Source conveyed under this section must be accompanied
|
| 324 |
-
by the Installation Information. But this requirement does not apply
|
| 325 |
-
if neither you nor any third party retains the ability to install
|
| 326 |
-
modified object code on the User Product (for example, the work has
|
| 327 |
-
been installed in ROM).
|
| 328 |
-
|
| 329 |
-
The requirement to provide Installation Information does not include a
|
| 330 |
-
requirement to continue to provide support service, warranty, or updates
|
| 331 |
-
for a work that has been modified or installed by the recipient, or for
|
| 332 |
-
the User Product in which it has been modified or installed. Access to a
|
| 333 |
-
network may be denied when the modification itself materially and
|
| 334 |
-
adversely affects the operation of the network or violates the rules and
|
| 335 |
-
protocols for communication across the network.
|
| 336 |
-
|
| 337 |
-
Corresponding Source conveyed, and Installation Information provided,
|
| 338 |
-
in accord with this section must be in a format that is publicly
|
| 339 |
-
documented (and with an implementation available to the public in
|
| 340 |
-
source code form), and must require no special password or key for
|
| 341 |
-
unpacking, reading or copying.
|
| 342 |
-
|
| 343 |
-
7. Additional Terms.
|
| 344 |
-
|
| 345 |
-
"Additional permissions" are terms that supplement the terms of this
|
| 346 |
-
License by making exceptions from one or more of its conditions.
|
| 347 |
-
Additional permissions that are applicable to the entire Program shall
|
| 348 |
-
be treated as though they were included in this License, to the extent
|
| 349 |
-
that they are valid under applicable law. If additional permissions
|
| 350 |
-
apply only to part of the Program, that part may be used separately
|
| 351 |
-
under those permissions, but the entire Program remains governed by
|
| 352 |
-
this License without regard to the additional permissions.
|
| 353 |
-
|
| 354 |
-
When you convey a copy of a covered work, you may at your option
|
| 355 |
-
remove any additional permissions from that copy, or from any part of
|
| 356 |
-
it. (Additional permissions may be written to require their own
|
| 357 |
-
removal in certain cases when you modify the work.) You may place
|
| 358 |
-
additional permissions on material, added by you to a covered work,
|
| 359 |
-
for which you have or can give appropriate copyright permission.
|
| 360 |
-
|
| 361 |
-
Notwithstanding any other provision of this License, for material you
|
| 362 |
-
add to a covered work, you may (if authorized by the copyright holders of
|
| 363 |
-
that material) supplement the terms of this License with terms:
|
| 364 |
-
|
| 365 |
-
a) Disclaiming warranty or limiting liability differently from the
|
| 366 |
-
terms of sections 15 and 16 of this License; or
|
| 367 |
-
|
| 368 |
-
b) Requiring preservation of specified reasonable legal notices or
|
| 369 |
-
author attributions in that material or in the Appropriate Legal
|
| 370 |
-
Notices displayed by works containing it; or
|
| 371 |
-
|
| 372 |
-
c) Prohibiting misrepresentation of the origin of that material, or
|
| 373 |
-
requiring that modified versions of such material be marked in
|
| 374 |
-
reasonable ways as different from the original version; or
|
| 375 |
-
|
| 376 |
-
d) Limiting the use for publicity purposes of names of licensors or
|
| 377 |
-
authors of the material; or
|
| 378 |
-
|
| 379 |
-
e) Declining to grant rights under trademark law for use of some
|
| 380 |
-
trade names, trademarks, or service marks; or
|
| 381 |
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|
| 382 |
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f) Requiring indemnification of licensors and authors of that
|
| 383 |
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material by anyone who conveys the material (or modified versions of
|
| 384 |
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it) with contractual assumptions of liability to the recipient, for
|
| 385 |
-
any liability that these contractual assumptions directly impose on
|
| 386 |
-
those licensors and authors.
|
| 387 |
-
|
| 388 |
-
All other non-permissive additional terms are considered "further
|
| 389 |
-
restrictions" within the meaning of section 10. If the Program as you
|
| 390 |
-
received it, or any part of it, contains a notice stating that it is
|
| 391 |
-
governed by this License along with a term that is a further
|
| 392 |
-
restriction, you may remove that term. If a license document contains
|
| 393 |
-
a further restriction but permits relicensing or conveying under this
|
| 394 |
-
License, you may add to a covered work material governed by the terms
|
| 395 |
-
of that license document, provided that the further restriction does
|
| 396 |
-
not survive such relicensing or conveying.
|
| 397 |
-
|
| 398 |
-
If you add terms to a covered work in accord with this section, you
|
| 399 |
-
must place, in the relevant source files, a statement of the
|
| 400 |
-
additional terms that apply to those files, or a notice indicating
|
| 401 |
-
where to find the applicable terms.
|
| 402 |
-
|
| 403 |
-
Additional terms, permissive or non-permissive, may be stated in the
|
| 404 |
-
form of a separately written license, or stated as exceptions;
|
| 405 |
-
the above requirements apply either way.
|
| 406 |
-
|
| 407 |
-
8. Termination.
|
| 408 |
-
|
| 409 |
-
You may not propagate or modify a covered work except as expressly
|
| 410 |
-
provided under this License. Any attempt otherwise to propagate or
|
| 411 |
-
modify it is void, and will automatically terminate your rights under
|
| 412 |
-
this License (including any patent licenses granted under the third
|
| 413 |
-
paragraph of section 11).
|
| 414 |
-
|
| 415 |
-
However, if you cease all violation of this License, then your
|
| 416 |
-
license from a particular copyright holder is reinstated (a)
|
| 417 |
-
provisionally, unless and until the copyright holder explicitly and
|
| 418 |
-
finally terminates your license, and (b) permanently, if the copyright
|
| 419 |
-
holder fails to notify you of the violation by some reasonable means
|
| 420 |
-
prior to 60 days after the cessation.
|
| 421 |
-
|
| 422 |
-
Moreover, your license from a particular copyright holder is
|
| 423 |
-
reinstated permanently if the copyright holder notifies you of the
|
| 424 |
-
violation by some reasonable means, this is the first time you have
|
| 425 |
-
received notice of violation of this License (for any work) from that
|
| 426 |
-
copyright holder, and you cure the violation prior to 30 days after
|
| 427 |
-
your receipt of the notice.
|
| 428 |
-
|
| 429 |
-
Termination of your rights under this section does not terminate the
|
| 430 |
-
licenses of parties who have received copies or rights from you under
|
| 431 |
-
this License. If your rights have been terminated and not permanently
|
| 432 |
-
reinstated, you do not qualify to receive new licenses for the same
|
| 433 |
-
material under section 10.
|
| 434 |
-
|
| 435 |
-
9. Acceptance Not Required for Having Copies.
|
| 436 |
-
|
| 437 |
-
You are not required to accept this License in order to receive or
|
| 438 |
-
run a copy of the Program. Ancillary propagation of a covered work
|
| 439 |
-
occurring solely as a consequence of using peer-to-peer transmission
|
| 440 |
-
to receive a copy likewise does not require acceptance. However,
|
| 441 |
-
nothing other than this License grants you permission to propagate or
|
| 442 |
-
modify any covered work. These actions infringe copyright if you do
|
| 443 |
-
not accept this License. Therefore, by modifying or propagating a
|
| 444 |
-
covered work, you indicate your acceptance of this License to do so.
|
| 445 |
-
|
| 446 |
-
10. Automatic Licensing of Downstream Recipients.
|
| 447 |
-
|
| 448 |
-
Each time you convey a covered work, the recipient automatically
|
| 449 |
-
receives a license from the original licensors, to run, modify and
|
| 450 |
-
propagate that work, subject to this License. You are not responsible
|
| 451 |
-
for enforcing compliance by third parties with this License.
|
| 452 |
-
|
| 453 |
-
An "entity transaction" is a transaction transferring control of an
|
| 454 |
-
organization, or substantially all assets of one, or subdividing an
|
| 455 |
-
organization, or merging organizations. If propagation of a covered
|
| 456 |
-
work results from an entity transaction, each party to that
|
| 457 |
-
transaction who receives a copy of the work also receives whatever
|
| 458 |
-
licenses to the work the party's predecessor in interest had or could
|
| 459 |
-
give under the previous paragraph, plus a right to possession of the
|
| 460 |
-
Corresponding Source of the work from the predecessor in interest, if
|
| 461 |
-
the predecessor has it or can get it with reasonable efforts.
|
| 462 |
-
|
| 463 |
-
You may not impose any further restrictions on the exercise of the
|
| 464 |
-
rights granted or affirmed under this License. For example, you may
|
| 465 |
-
not impose a license fee, royalty, or other charge for exercise of
|
| 466 |
-
rights granted under this License, and you may not initiate litigation
|
| 467 |
-
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
| 468 |
-
any patent claim is infringed by making, using, selling, offering for
|
| 469 |
-
sale, or importing the Program or any portion of it.
|
| 470 |
-
|
| 471 |
-
11. Patents.
|
| 472 |
-
|
| 473 |
-
A "contributor" is a copyright holder who authorizes use under this
|
| 474 |
-
License of the Program or a work on which the Program is based. The
|
| 475 |
-
work thus licensed is called the contributor's "contributor version".
|
| 476 |
-
|
| 477 |
-
A contributor's "essential patent claims" are all patent claims
|
| 478 |
-
owned or controlled by the contributor, whether already acquired or
|
| 479 |
-
hereafter acquired, that would be infringed by some manner, permitted
|
| 480 |
-
by this License, of making, using, or selling its contributor version,
|
| 481 |
-
but do not include claims that would be infringed only as a
|
| 482 |
-
consequence of further modification of the contributor version. For
|
| 483 |
-
purposes of this definition, "control" includes the right to grant
|
| 484 |
-
patent sublicenses in a manner consistent with the requirements of
|
| 485 |
-
this License.
|
| 486 |
-
|
| 487 |
-
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
| 488 |
-
patent license under the contributor's essential patent claims, to
|
| 489 |
-
make, use, sell, offer for sale, import and otherwise run, modify and
|
| 490 |
-
propagate the contents of its contributor version.
|
| 491 |
-
|
| 492 |
-
In the following three paragraphs, a "patent license" is any express
|
| 493 |
-
agreement or commitment, however denominated, not to enforce a patent
|
| 494 |
-
(such as an express permission to practice a patent or covenant not to
|
| 495 |
-
sue for patent infringement). To "grant" such a patent license to a
|
| 496 |
-
party means to make such an agreement or commitment not to enforce a
|
| 497 |
-
patent against the party.
|
| 498 |
-
|
| 499 |
-
If you convey a covered work, knowingly relying on a patent license,
|
| 500 |
-
and the Corresponding Source of the work is not available for anyone
|
| 501 |
-
to copy, free of charge and under the terms of this License, through a
|
| 502 |
-
publicly available network server or other readily accessible means,
|
| 503 |
-
then you must either (1) cause the Corresponding Source to be so
|
| 504 |
-
available, or (2) arrange to deprive yourself of the benefit of the
|
| 505 |
-
patent license for this particular work, or (3) arrange, in a manner
|
| 506 |
-
consistent with the requirements of this License, to extend the patent
|
| 507 |
-
license to downstream recipients. "Knowingly relying" means you have
|
| 508 |
-
actual knowledge that, but for the patent license, your conveying the
|
| 509 |
-
covered work in a country, or your recipient's use of the covered work
|
| 510 |
-
in a country, would infringe one or more identifiable patents in that
|
| 511 |
-
country that you have reason to believe are valid.
|
| 512 |
-
|
| 513 |
-
If, pursuant to or in connection with a single transaction or
|
| 514 |
-
arrangement, you convey, or propagate by procuring conveyance of, a
|
| 515 |
-
covered work, and grant a patent license to some of the parties
|
| 516 |
-
receiving the covered work authorizing them to use, propagate, modify
|
| 517 |
-
or convey a specific copy of the covered work, then the patent license
|
| 518 |
-
you grant is automatically extended to all recipients of the covered
|
| 519 |
-
work and works based on it.
|
| 520 |
-
|
| 521 |
-
A patent license is "discriminatory" if it does not include within
|
| 522 |
-
the scope of its coverage, prohibits the exercise of, or is
|
| 523 |
-
conditioned on the non-exercise of one or more of the rights that are
|
| 524 |
-
specifically granted under this License. You may not convey a covered
|
| 525 |
-
work if you are a party to an arrangement with a third party that is
|
| 526 |
-
in the business of distributing software, under which you make payment
|
| 527 |
-
to the third party based on the extent of your activity of conveying
|
| 528 |
-
the work, and under which the third party grants, to any of the
|
| 529 |
-
parties who would receive the covered work from you, a discriminatory
|
| 530 |
-
patent license (a) in connection with copies of the covered work
|
| 531 |
-
conveyed by you (or copies made from those copies), or (b) primarily
|
| 532 |
-
for and in connection with specific products or compilations that
|
| 533 |
-
contain the covered work, unless you entered into that arrangement,
|
| 534 |
-
or that patent license was granted, prior to 28 March 2007.
|
| 535 |
-
|
| 536 |
-
Nothing in this License shall be construed as excluding or limiting
|
| 537 |
-
any implied license or other defenses to infringement that may
|
| 538 |
-
otherwise be available to you under applicable patent law.
|
| 539 |
-
|
| 540 |
-
12. No Surrender of Others' Freedom.
|
| 541 |
-
|
| 542 |
-
If conditions are imposed on you (whether by court order, agreement or
|
| 543 |
-
otherwise) that contradict the conditions of this License, they do not
|
| 544 |
-
excuse you from the conditions of this License. If you cannot convey a
|
| 545 |
-
covered work so as to satisfy simultaneously your obligations under this
|
| 546 |
-
License and any other pertinent obligations, then as a consequence you may
|
| 547 |
-
not convey it at all. For example, if you agree to terms that obligate you
|
| 548 |
-
to collect a royalty for further conveying from those to whom you convey
|
| 549 |
-
the Program, the only way you could satisfy both those terms and this
|
| 550 |
-
License would be to refrain entirely from conveying the Program.
|
| 551 |
-
|
| 552 |
-
13. Use with the GNU Affero General Public License.
|
| 553 |
-
|
| 554 |
-
Notwithstanding any other provision of this License, you have
|
| 555 |
-
permission to link or combine any covered work with a work licensed
|
| 556 |
-
under version 3 of the GNU Affero General Public License into a single
|
| 557 |
-
combined work, and to convey the resulting work. The terms of this
|
| 558 |
-
License will continue to apply to the part which is the covered work,
|
| 559 |
-
but the special requirements of the GNU Affero General Public License,
|
| 560 |
-
section 13, concerning interaction through a network will apply to the
|
| 561 |
-
combination as such.
|
| 562 |
-
|
| 563 |
-
14. Revised Versions of this License.
|
| 564 |
-
|
| 565 |
-
The Free Software Foundation may publish revised and/or new versions of
|
| 566 |
-
the GNU General Public License from time to time. Such new versions will
|
| 567 |
-
be similar in spirit to the present version, but may differ in detail to
|
| 568 |
-
address new problems or concerns.
|
| 569 |
-
|
| 570 |
-
Each version is given a distinguishing version number. If the
|
| 571 |
-
Program specifies that a certain numbered version of the GNU General
|
| 572 |
-
Public License "or any later version" applies to it, you have the
|
| 573 |
-
option of following the terms and conditions either of that numbered
|
| 574 |
-
version or of any later version published by the Free Software
|
| 575 |
-
Foundation. If the Program does not specify a version number of the
|
| 576 |
-
GNU General Public License, you may choose any version ever published
|
| 577 |
-
by the Free Software Foundation.
|
| 578 |
-
|
| 579 |
-
If the Program specifies that a proxy can decide which future
|
| 580 |
-
versions of the GNU General Public License can be used, that proxy's
|
| 581 |
-
public statement of acceptance of a version permanently authorizes you
|
| 582 |
-
to choose that version for the Program.
|
| 583 |
-
|
| 584 |
-
Later license versions may give you additional or different
|
| 585 |
-
permissions. However, no additional obligations are imposed on any
|
| 586 |
-
author or copyright holder as a result of your choosing to follow a
|
| 587 |
-
later version.
|
| 588 |
-
|
| 589 |
-
15. Disclaimer of Warranty.
|
| 590 |
-
|
| 591 |
-
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
| 592 |
-
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
| 593 |
-
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
| 594 |
-
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
| 595 |
-
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
| 596 |
-
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
| 597 |
-
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
| 598 |
-
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
| 599 |
-
|
| 600 |
-
16. Limitation of Liability.
|
| 601 |
-
|
| 602 |
-
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
| 603 |
-
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
| 604 |
-
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
| 605 |
-
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
| 606 |
-
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
| 607 |
-
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
| 608 |
-
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
| 609 |
-
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
| 610 |
-
SUCH DAMAGES.
|
| 611 |
-
|
| 612 |
-
17. Interpretation of Sections 15 and 16.
|
| 613 |
-
|
| 614 |
-
If the disclaimer of warranty and limitation of liability provided
|
| 615 |
-
above cannot be given local legal effect according to their terms,
|
| 616 |
-
reviewing courts shall apply local law that most closely approximates
|
| 617 |
-
an absolute waiver of all civil liability in connection with the
|
| 618 |
-
Program, unless a warranty or assumption of liability accompanies a
|
| 619 |
-
copy of the Program in return for a fee.
|
| 620 |
-
|
| 621 |
-
END OF TERMS AND CONDITIONS
|
| 622 |
-
|
| 623 |
-
How to Apply These Terms to Your New Programs
|
| 624 |
-
|
| 625 |
-
If you develop a new program, and you want it to be of the greatest
|
| 626 |
-
possible use to the public, the best way to achieve this is to make it
|
| 627 |
-
free software which everyone can redistribute and change under these terms.
|
| 628 |
-
|
| 629 |
-
To do so, attach the following notices to the program. It is safest
|
| 630 |
-
to attach them to the start of each source file to most effectively
|
| 631 |
-
state the exclusion of warranty; and each file should have at least
|
| 632 |
-
the "copyright" line and a pointer to where the full notice is found.
|
| 633 |
-
|
| 634 |
-
<one line to give the program's name and a brief idea of what it does.>
|
| 635 |
-
Copyright (C) <year> <name of author>
|
| 636 |
-
|
| 637 |
-
This program is free software: you can redistribute it and/or modify
|
| 638 |
-
it under the terms of the GNU General Public License as published by
|
| 639 |
-
the Free Software Foundation, either version 3 of the License, or
|
| 640 |
-
(at your option) any later version.
|
| 641 |
-
|
| 642 |
-
This program is distributed in the hope that it will be useful,
|
| 643 |
-
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
| 644 |
-
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
| 645 |
-
GNU General Public License for more details.
|
| 646 |
-
|
| 647 |
-
You should have received a copy of the GNU General Public License
|
| 648 |
-
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
| 649 |
-
|
| 650 |
-
Also add information on how to contact you by electronic and paper mail.
|
| 651 |
-
|
| 652 |
-
If the program does terminal interaction, make it output a short
|
| 653 |
-
notice like this when it starts in an interactive mode:
|
| 654 |
-
|
| 655 |
-
<program> Copyright (C) <year> <name of author>
|
| 656 |
-
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
| 657 |
-
This is free software, and you are welcome to redistribute it
|
| 658 |
-
under certain conditions; type `show c' for details.
|
| 659 |
-
|
| 660 |
-
The hypothetical commands `show w' and `show c' should show the appropriate
|
| 661 |
-
parts of the General Public License. Of course, your program's commands
|
| 662 |
-
might be different; for a GUI interface, you would use an "about box".
|
| 663 |
-
|
| 664 |
-
You should also get your employer (if you work as a programmer) or school,
|
| 665 |
-
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
| 666 |
-
For more information on this, and how to apply and follow the GNU GPL, see
|
| 667 |
-
<http://www.gnu.org/licenses/>.
|
| 668 |
-
|
| 669 |
-
The GNU General Public License does not permit incorporating your program
|
| 670 |
-
into proprietary programs. If your program is a subroutine library, you
|
| 671 |
-
may consider it more useful to permit linking proprietary applications with
|
| 672 |
-
the library. If this is what you want to do, use the GNU Lesser General
|
| 673 |
-
Public License instead of this License. But first, please read
|
| 674 |
-
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
|
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|
|
ultralytics/yolov5/README.md
DELETED
|
@@ -1,304 +0,0 @@
|
|
| 1 |
-
<div align="center">
|
| 2 |
-
<p>
|
| 3 |
-
<a align="left" href="https://ultralytics.com/yolov5" target="_blank">
|
| 4 |
-
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
|
| 5 |
-
</p>
|
| 6 |
-
<br>
|
| 7 |
-
<div>
|
| 8 |
-
<a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
|
| 9 |
-
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
|
| 10 |
-
<a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
|
| 11 |
-
<br>
|
| 12 |
-
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
|
| 13 |
-
<a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
| 14 |
-
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
|
| 15 |
-
</div>
|
| 16 |
-
|
| 17 |
-
<br>
|
| 18 |
-
<p>
|
| 19 |
-
YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
|
| 20 |
-
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
|
| 21 |
-
</p>
|
| 22 |
-
|
| 23 |
-
<div align="center">
|
| 24 |
-
<a href="https://github.com/ultralytics">
|
| 25 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
|
| 26 |
-
</a>
|
| 27 |
-
<img width="2%" />
|
| 28 |
-
<a href="https://www.linkedin.com/company/ultralytics">
|
| 29 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
|
| 30 |
-
</a>
|
| 31 |
-
<img width="2%" />
|
| 32 |
-
<a href="https://twitter.com/ultralytics">
|
| 33 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
|
| 34 |
-
</a>
|
| 35 |
-
<img width="2%" />
|
| 36 |
-
<a href="https://www.producthunt.com/@glenn_jocher">
|
| 37 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="2%"/>
|
| 38 |
-
</a>
|
| 39 |
-
<img width="2%" />
|
| 40 |
-
<a href="https://youtube.com/ultralytics">
|
| 41 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
|
| 42 |
-
</a>
|
| 43 |
-
<img width="2%" />
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<a href="https://www.facebook.com/ultralytics">
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
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| 46 |
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</a>
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| 47 |
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<img width="2%" />
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| 48 |
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<a href="https://www.instagram.com/ultralytics/">
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| 49 |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
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| 50 |
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</a>
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| 51 |
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</div>
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| 52 |
-
|
| 53 |
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<!--
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| 54 |
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<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
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| 55 |
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<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
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| 56 |
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-->
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| 57 |
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| 58 |
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</div>
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| 59 |
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| 60 |
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## <div align="center">Documentation</div>
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See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
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| 64 |
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## <div align="center">Quick Start Examples</div>
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| 65 |
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<details open>
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| 67 |
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<summary>Install</summary>
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| 68 |
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| 69 |
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Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a
|
| 70 |
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[**Python>=3.7.0**](https://www.python.org/) environment, including
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[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/).
|
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```bash
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| 74 |
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git clone https://github.com/ultralytics/yolov5 # clone
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| 75 |
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cd yolov5
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| 76 |
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pip install -r requirements.txt # install
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| 77 |
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```
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| 78 |
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| 79 |
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</details>
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| 80 |
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| 81 |
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<details open>
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| 82 |
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<summary>Inference</summary>
|
| 83 |
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|
| 84 |
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Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)
|
| 85 |
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. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
|
| 86 |
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YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
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```python
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import torch
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# Model
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model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
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| 93 |
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# Images
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
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# Inference
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results = model(img)
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# Results
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results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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```
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| 103 |
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</details>
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<details>
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| 109 |
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<summary>Inference with detect.py</summary>
|
| 110 |
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`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
|
| 112 |
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the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
|
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```bash
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python detect.py --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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path/ # directory
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path/*.jpg # glob
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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| 122 |
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```
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| 123 |
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| 124 |
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</details>
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| 125 |
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<details>
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| 127 |
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<summary>Training</summary>
|
| 128 |
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| 129 |
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The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
|
| 130 |
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results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
|
| 131 |
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and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
|
| 132 |
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YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
|
| 133 |
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1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
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| 134 |
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largest `--batch-size` possible, or pass `--batch-size -1` for
|
| 135 |
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YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
|
| 136 |
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|
| 137 |
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```bash
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| 138 |
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python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128
|
| 139 |
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yolov5s 64
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| 140 |
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yolov5m 40
|
| 141 |
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yolov5l 24
|
| 142 |
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yolov5x 16
|
| 143 |
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```
|
| 144 |
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|
| 145 |
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<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
|
| 146 |
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|
| 147 |
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</details>
|
| 148 |
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|
| 149 |
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<details open>
|
| 150 |
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<summary>Tutorials</summary>
|
| 151 |
-
|
| 152 |
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* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED
|
| 153 |
-
* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
|
| 154 |
-
RECOMMENDED
|
| 155 |
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* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
|
| 156 |
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* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
|
| 157 |
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* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
|
| 158 |
-
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
|
| 159 |
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* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
|
| 160 |
-
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
|
| 161 |
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* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
|
| 162 |
-
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
|
| 163 |
-
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
|
| 164 |
-
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
|
| 165 |
-
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
|
| 166 |
-
|
| 167 |
-
</details>
|
| 168 |
-
|
| 169 |
-
## <div align="center">Environments</div>
|
| 170 |
-
|
| 171 |
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Get started in seconds with our verified environments. Click each icon below for details.
|
| 172 |
-
|
| 173 |
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<div align="center">
|
| 174 |
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<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
|
| 175 |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
|
| 176 |
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</a>
|
| 177 |
-
<a href="https://www.kaggle.com/ultralytics/yolov5">
|
| 178 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
|
| 179 |
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</a>
|
| 180 |
-
<a href="https://hub.docker.com/r/ultralytics/yolov5">
|
| 181 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
|
| 182 |
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</a>
|
| 183 |
-
<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
|
| 184 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
|
| 185 |
-
</a>
|
| 186 |
-
<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
|
| 187 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
|
| 188 |
-
</a>
|
| 189 |
-
</div>
|
| 190 |
-
|
| 191 |
-
## <div align="center">Integrations</div>
|
| 192 |
-
|
| 193 |
-
<div align="center">
|
| 194 |
-
<a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
|
| 195 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
|
| 196 |
-
</a>
|
| 197 |
-
<a href="https://roboflow.com/?ref=ultralytics">
|
| 198 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
|
| 199 |
-
</a>
|
| 200 |
-
</div>
|
| 201 |
-
|
| 202 |
-
|Weights and Biases|Roboflow ⭐ NEW|
|
| 203 |
-
|:-:|:-:|
|
| 204 |
-
|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
|
| 205 |
-
|
| 206 |
-
|
| 207 |
-
<!-- ## <div align="center">Compete and Win</div>
|
| 208 |
-
|
| 209 |
-
We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
|
| 210 |
-
|
| 211 |
-
<p align="center">
|
| 212 |
-
<a href="https://github.com/ultralytics/yolov5/discussions/3213">
|
| 213 |
-
<img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
|
| 214 |
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</p> -->
|
| 215 |
-
|
| 216 |
-
## <div align="center">Why YOLOv5</div>
|
| 217 |
-
|
| 218 |
-
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
|
| 219 |
-
<details>
|
| 220 |
-
<summary>YOLOv5-P5 640 Figure (click to expand)</summary>
|
| 221 |
-
|
| 222 |
-
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
|
| 223 |
-
</details>
|
| 224 |
-
<details>
|
| 225 |
-
<summary>Figure Notes (click to expand)</summary>
|
| 226 |
-
|
| 227 |
-
* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
|
| 228 |
-
* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
|
| 229 |
-
* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
|
| 230 |
-
* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
| 231 |
-
</details>
|
| 232 |
-
|
| 233 |
-
### Pretrained Checkpoints
|
| 234 |
-
|
| 235 |
-
[assets]: https://github.com/ultralytics/yolov5/releases
|
| 236 |
-
|
| 237 |
-
[TTA]: https://github.com/ultralytics/yolov5/issues/303
|
| 238 |
-
|
| 239 |
-
|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
|
| 240 |
-
|--- |--- |--- |--- |--- |--- |--- |--- |---
|
| 241 |
-
|[YOLOv5n][assets] |640 |28.0 |45.7 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
|
| 242 |
-
|[YOLOv5s][assets] |640 |37.4 |56.8 |98 |6.4 |0.9 |7.2 |16.5
|
| 243 |
-
|[YOLOv5m][assets] |640 |45.4 |64.1 |224 |8.2 |1.7 |21.2 |49.0
|
| 244 |
-
|[YOLOv5l][assets] |640 |49.0 |67.3 |430 |10.1 |2.7 |46.5 |109.1
|
| 245 |
-
|[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7
|
| 246 |
-
| | | | | | | | |
|
| 247 |
-
|[YOLOv5n6][assets] |1280 |36.0 |54.4 |153 |8.1 |2.1 |3.2 |4.6
|
| 248 |
-
|[YOLOv5s6][assets] |1280 |44.8 |63.7 |385 |8.2 |3.6 |12.6 |16.8
|
| 249 |
-
|[YOLOv5m6][assets] |1280 |51.3 |69.3 |887 |11.1 |6.8 |35.7 |50.0
|
| 250 |
-
|[YOLOv5l6][assets] |1280 |53.7 |71.3 |1784 |15.8 |10.5 |76.8 |111.4
|
| 251 |
-
|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |55.0<br>**55.8** |72.7<br>**72.7** |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
|
| 252 |
-
|
| 253 |
-
<details>
|
| 254 |
-
<summary>Table Notes (click to expand)</summary>
|
| 255 |
-
|
| 256 |
-
* All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
|
| 257 |
-
* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
| 258 |
-
* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
|
| 259 |
-
* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
| 260 |
-
|
| 261 |
-
</details>
|
| 262 |
-
|
| 263 |
-
## <div align="center">Contribute</div>
|
| 264 |
-
|
| 265 |
-
We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
|
| 266 |
-
|
| 267 |
-
<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
|
| 268 |
-
|
| 269 |
-
## <div align="center">Contact</div>
|
| 270 |
-
|
| 271 |
-
For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
|
| 272 |
-
professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
|
| 273 |
-
|
| 274 |
-
<br>
|
| 275 |
-
|
| 276 |
-
<div align="center">
|
| 277 |
-
<a href="https://github.com/ultralytics">
|
| 278 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
|
| 279 |
-
</a>
|
| 280 |
-
<img width="3%" />
|
| 281 |
-
<a href="https://www.linkedin.com/company/ultralytics">
|
| 282 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
|
| 283 |
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</a>
|
| 284 |
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<img width="3%" />
|
| 285 |
-
<a href="https://twitter.com/ultralytics">
|
| 286 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
|
| 287 |
-
</a>
|
| 288 |
-
<img width="3%" />
|
| 289 |
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<a href="https://www.producthunt.com/@glenn_jocher">
|
| 290 |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-producthunt.png" width="3%"/>
|
| 291 |
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</a>
|
| 292 |
-
<img width="3%" />
|
| 293 |
-
<a href="https://youtube.com/ultralytics">
|
| 294 |
-
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
|
| 295 |
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</a>
|
| 296 |
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<img width="3%" />
|
| 297 |
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<a href="https://www.facebook.com/ultralytics">
|
| 298 |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
|
| 299 |
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</a>
|
| 300 |
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<img width="3%" />
|
| 301 |
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<a href="https://www.instagram.com/ultralytics/">
|
| 302 |
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<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
|
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</a>
|
| 304 |
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</div>
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ultralytics/yolov5/data/Argoverse.yaml
DELETED
|
@@ -1,67 +0,0 @@
|
|
| 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
|
| 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 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
|
|
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|
ultralytics/yolov5/data/GlobalWheat2020.yaml
DELETED
|
@@ -1,54 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
|
| 3 |
-
# Example usage: python train.py --data GlobalWheat2020.yaml
|
| 4 |
-
# parent
|
| 5 |
-
# ├── yolov5
|
| 6 |
-
# └── datasets
|
| 7 |
-
# └── GlobalWheat2020 ← downloads here
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
| 11 |
-
path: ../datasets/GlobalWheat2020 # dataset root dir
|
| 12 |
-
train: # train images (relative to 'path') 3422 images
|
| 13 |
-
- images/arvalis_1
|
| 14 |
-
- images/arvalis_2
|
| 15 |
-
- images/arvalis_3
|
| 16 |
-
- images/ethz_1
|
| 17 |
-
- images/rres_1
|
| 18 |
-
- images/inrae_1
|
| 19 |
-
- images/usask_1
|
| 20 |
-
val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
|
| 21 |
-
- images/ethz_1
|
| 22 |
-
test: # test images (optional) 1276 images
|
| 23 |
-
- images/utokyo_1
|
| 24 |
-
- images/utokyo_2
|
| 25 |
-
- images/nau_1
|
| 26 |
-
- images/uq_1
|
| 27 |
-
|
| 28 |
-
# Classes
|
| 29 |
-
nc: 1 # number of classes
|
| 30 |
-
names: ['wheat_head'] # class names
|
| 31 |
-
|
| 32 |
-
|
| 33 |
-
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
| 34 |
-
download: |
|
| 35 |
-
from utils.general import download, Path
|
| 36 |
-
|
| 37 |
-
|
| 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
|
|
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|
ultralytics/yolov5/data/Objects365.yaml
DELETED
|
@@ -1,113 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
# Objects365 dataset https://www.objects365.org/ by Megvii
|
| 3 |
-
# Example usage: python train.py --data Objects365.yaml
|
| 4 |
-
# parent
|
| 5 |
-
# ├── yolov5
|
| 6 |
-
# └── datasets
|
| 7 |
-
# └── Objects365 ← downloads here
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
| 11 |
-
path: ../datasets/Objects365 # dataset root dir
|
| 12 |
-
train: images/train # train images (relative to 'path') 1742289 images
|
| 13 |
-
val: images/val # val images (relative to 'path') 80000 images
|
| 14 |
-
test: # test images (optional)
|
| 15 |
-
|
| 16 |
-
# Classes
|
| 17 |
-
nc: 365 # number of classes
|
| 18 |
-
names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
|
| 19 |
-
'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
|
| 20 |
-
'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
|
| 21 |
-
'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
|
| 22 |
-
'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
|
| 23 |
-
'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
|
| 24 |
-
'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
|
| 25 |
-
'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
|
| 26 |
-
'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
|
| 27 |
-
'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
|
| 28 |
-
'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
|
| 29 |
-
'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
|
| 30 |
-
'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
|
| 31 |
-
'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
|
| 32 |
-
'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
|
| 33 |
-
'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
|
| 34 |
-
'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
|
| 35 |
-
'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
|
| 36 |
-
'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
|
| 37 |
-
'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
|
| 38 |
-
'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
|
| 39 |
-
'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
|
| 40 |
-
'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
|
| 41 |
-
'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
|
| 42 |
-
'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
|
| 43 |
-
'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
|
| 44 |
-
'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
|
| 45 |
-
'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
|
| 46 |
-
'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
|
| 47 |
-
'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
|
| 48 |
-
'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
|
| 49 |
-
'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
|
| 50 |
-
'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
|
| 51 |
-
'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
|
| 52 |
-
'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
|
| 53 |
-
'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
|
| 54 |
-
'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
|
| 55 |
-
'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
|
| 56 |
-
'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
|
| 57 |
-
'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
|
| 58 |
-
'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
|
| 59 |
-
|
| 60 |
-
|
| 61 |
-
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
| 62 |
-
download: |
|
| 63 |
-
from pycocotools.coco import COCO
|
| 64 |
-
from tqdm import tqdm
|
| 65 |
-
|
| 66 |
-
from utils.general import Path, download, np, xyxy2xywhn
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
# Make Directories
|
| 70 |
-
dir = Path(yaml['path']) # dataset root dir
|
| 71 |
-
for p in 'images', 'labels':
|
| 72 |
-
(dir / p).mkdir(parents=True, exist_ok=True)
|
| 73 |
-
for q in 'train', 'val':
|
| 74 |
-
(dir / p / q).mkdir(parents=True, exist_ok=True)
|
| 75 |
-
|
| 76 |
-
# Train, Val Splits
|
| 77 |
-
for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
|
| 78 |
-
print(f"Processing {split} in {patches} patches ...")
|
| 79 |
-
images, labels = dir / 'images' / split, dir / 'labels' / split
|
| 80 |
-
|
| 81 |
-
# Download
|
| 82 |
-
url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
|
| 83 |
-
if split == 'train':
|
| 84 |
-
download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
|
| 85 |
-
download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
|
| 86 |
-
elif split == 'val':
|
| 87 |
-
download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
|
| 88 |
-
download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
|
| 89 |
-
download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
|
| 90 |
-
|
| 91 |
-
# Move
|
| 92 |
-
for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
|
| 93 |
-
f.rename(images / f.name) # move to /images/{split}
|
| 94 |
-
|
| 95 |
-
# Labels
|
| 96 |
-
coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
|
| 97 |
-
names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
|
| 98 |
-
for cid, cat in enumerate(names):
|
| 99 |
-
catIds = coco.getCatIds(catNms=[cat])
|
| 100 |
-
imgIds = coco.getImgIds(catIds=catIds)
|
| 101 |
-
for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
|
| 102 |
-
width, height = im["width"], im["height"]
|
| 103 |
-
path = Path(im["file_name"]) # image filename
|
| 104 |
-
try:
|
| 105 |
-
with open(labels / path.with_suffix('.txt').name, 'a') as file:
|
| 106 |
-
annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
|
| 107 |
-
for a in coco.loadAnns(annIds):
|
| 108 |
-
x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
|
| 109 |
-
xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
|
| 110 |
-
x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
|
| 111 |
-
file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
|
| 112 |
-
except Exception as e:
|
| 113 |
-
print(e)
|
|
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|
ultralytics/yolov5/data/SKU-110K.yaml
DELETED
|
@@ -1,53 +0,0 @@
|
|
| 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
|
| 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 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
|
|
|
|
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|
|
ultralytics/yolov5/data/VOC.yaml
DELETED
|
@@ -1,80 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
| 3 |
-
# Example usage: python train.py --data VOC.yaml
|
| 4 |
-
# parent
|
| 5 |
-
# ├── yolov5
|
| 6 |
-
# └── datasets
|
| 7 |
-
# └── VOC ← downloads here
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
| 11 |
-
path: ../datasets/VOC
|
| 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 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, 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 |
-
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
|
| 76 |
-
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
| 77 |
-
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
| 78 |
-
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
| 79 |
-
f.rename(imgs_path / f.name) # move image
|
| 80 |
-
convert_label(path, lb_path, year, id) # convert labels to YOLO format
|
|
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ultralytics/yolov5/data/VisDrone.yaml
DELETED
|
@@ -1,61 +0,0 @@
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|
| 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
|
| 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 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, 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
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ultralytics/yolov5/data/coco.yaml
DELETED
|
@@ -1,45 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
# COCO 2017 dataset http://cocodataset.org by Microsoft
|
| 3 |
-
# Example usage: python train.py --data coco.yaml
|
| 4 |
-
# parent
|
| 5 |
-
# ├── yolov5
|
| 6 |
-
# └── datasets
|
| 7 |
-
# └── coco ← downloads here
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
| 11 |
-
path: ../datasets/coco # dataset root dir
|
| 12 |
-
train: train2017.txt # train images (relative to 'path') 118287 images
|
| 13 |
-
val: val2017.txt # val images (relative to 'path') 5000 images
|
| 14 |
-
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
| 15 |
-
|
| 16 |
-
# Classes
|
| 17 |
-
nc: 80 # number of classes
|
| 18 |
-
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
| 19 |
-
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
| 20 |
-
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
| 21 |
-
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
| 22 |
-
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
| 23 |
-
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
| 24 |
-
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
| 25 |
-
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
| 26 |
-
'hair drier', 'toothbrush'] # class names
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
# Download script/URL (optional)
|
| 30 |
-
download: |
|
| 31 |
-
from utils.general import download, Path
|
| 32 |
-
|
| 33 |
-
|
| 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)
|
|
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ultralytics/yolov5/data/coco128.yaml
DELETED
|
@@ -1,30 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
| 3 |
-
# Example usage: python train.py --data coco128.yaml
|
| 4 |
-
# parent
|
| 5 |
-
# ├── yolov5
|
| 6 |
-
# └── datasets
|
| 7 |
-
# └── coco128 ← downloads here
|
| 8 |
-
|
| 9 |
-
|
| 10 |
-
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
| 11 |
-
path: ../datasets/coco128 # dataset root dir
|
| 12 |
-
train: images/train2017 # train images (relative to 'path') 128 images
|
| 13 |
-
val: images/train2017 # val images (relative to 'path') 128 images
|
| 14 |
-
test: # test images (optional)
|
| 15 |
-
|
| 16 |
-
# Classes
|
| 17 |
-
nc: 80 # number of classes
|
| 18 |
-
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
| 19 |
-
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
| 20 |
-
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
| 21 |
-
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
| 22 |
-
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
| 23 |
-
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
| 24 |
-
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
| 25 |
-
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
| 26 |
-
'hair drier', 'toothbrush'] # class names
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
# Download script/URL (optional)
|
| 30 |
-
download: https://ultralytics.com/assets/coco128.zip
|
|
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ultralytics/yolov5/data/hyps/hyp.Objects365.yaml
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 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
|
|
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|
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|
ultralytics/yolov5/data/hyps/hyp.VOC.yaml
DELETED
|
@@ -1,40 +0,0 @@
|
|
| 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
|
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|
ultralytics/yolov5/data/hyps/hyp.scratch-high.yaml
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 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)
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ultralytics/yolov5/data/hyps/hyp.scratch-low.yaml
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 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)
|
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ultralytics/yolov5/data/hyps/hyp.scratch-med.yaml
DELETED
|
@@ -1,34 +0,0 @@
|
|
| 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)
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ultralytics/yolov5/data/images/bus.jpg
DELETED
|
Binary file (487 kB)
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|
ultralytics/yolov5/data/images/zidane.jpg
DELETED
|
Binary file (169 kB)
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ultralytics/yolov5/data/scripts/download_weights.sh
DELETED
|
@@ -1,20 +0,0 @@
|
|
| 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 - <<EOF
|
| 12 |
-
from utils.downloads import attempt_download
|
| 13 |
-
|
| 14 |
-
models = ['n', 's', 'm', 'l', 'x']
|
| 15 |
-
models.extend([x + '6' for x in models]) # add P6 models
|
| 16 |
-
|
| 17 |
-
for x in models:
|
| 18 |
-
attempt_download(f'yolov5{x}.pt')
|
| 19 |
-
|
| 20 |
-
EOF
|
|
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ultralytics/yolov5/data/scripts/get_coco.sh
DELETED
|
@@ -1,27 +0,0 @@
|
|
| 1 |
-
#!/bin/bash
|
| 2 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 3 |
-
# Download COCO 2017 dataset http://cocodataset.org
|
| 4 |
-
# Example usage: bash data/scripts/get_coco.sh
|
| 5 |
-
# parent
|
| 6 |
-
# ├── yolov5
|
| 7 |
-
# └── datasets
|
| 8 |
-
# └── coco ← downloads here
|
| 9 |
-
|
| 10 |
-
# Download/unzip labels
|
| 11 |
-
d='../datasets' # unzip directory
|
| 12 |
-
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
| 13 |
-
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
|
| 14 |
-
echo 'Downloading' $url$f ' ...'
|
| 15 |
-
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
| 16 |
-
|
| 17 |
-
# Download/unzip images
|
| 18 |
-
d='../datasets/coco/images' # unzip directory
|
| 19 |
-
url=http://images.cocodataset.org/zips/
|
| 20 |
-
f1='train2017.zip' # 19G, 118k images
|
| 21 |
-
f2='val2017.zip' # 1G, 5k images
|
| 22 |
-
f3='test2017.zip' # 7G, 41k images (optional)
|
| 23 |
-
for f in $f1 $f2; do
|
| 24 |
-
echo 'Downloading' $url$f '...'
|
| 25 |
-
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
| 26 |
-
done
|
| 27 |
-
wait # finish background tasks
|
|
|
|
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ultralytics/yolov5/data/scripts/get_coco128.sh
DELETED
|
@@ -1,17 +0,0 @@
|
|
| 1 |
-
#!/bin/bash
|
| 2 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 3 |
-
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
| 4 |
-
# Example usage: bash data/scripts/get_coco128.sh
|
| 5 |
-
# parent
|
| 6 |
-
# ├── yolov5
|
| 7 |
-
# └── datasets
|
| 8 |
-
# └── coco128 ← downloads here
|
| 9 |
-
|
| 10 |
-
# Download/unzip images and labels
|
| 11 |
-
d='../datasets' # unzip directory
|
| 12 |
-
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
| 13 |
-
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
|
| 14 |
-
echo 'Downloading' $url$f ' ...'
|
| 15 |
-
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
| 16 |
-
|
| 17 |
-
wait # finish background tasks
|
|
|
|
|
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|
ultralytics/yolov5/data/xView.yaml
DELETED
|
@@ -1,102 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
|
| 3 |
-
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
|
| 4 |
-
# Example usage: python train.py --data xView.yaml
|
| 5 |
-
# parent
|
| 6 |
-
# ├── yolov5
|
| 7 |
-
# └── datasets
|
| 8 |
-
# └── xView ← downloads here
|
| 9 |
-
|
| 10 |
-
|
| 11 |
-
# 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, ..]
|
| 12 |
-
path: ../datasets/xView # dataset root dir
|
| 13 |
-
train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
|
| 14 |
-
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
| 15 |
-
|
| 16 |
-
# Classes
|
| 17 |
-
nc: 60 # number of classes
|
| 18 |
-
names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
|
| 19 |
-
'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
|
| 20 |
-
'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
|
| 21 |
-
'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
|
| 22 |
-
'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
|
| 23 |
-
'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
|
| 24 |
-
'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
|
| 25 |
-
'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
|
| 26 |
-
'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower'] # class names
|
| 27 |
-
|
| 28 |
-
|
| 29 |
-
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
| 30 |
-
download: |
|
| 31 |
-
import json
|
| 32 |
-
import os
|
| 33 |
-
from pathlib import Path
|
| 34 |
-
|
| 35 |
-
import numpy as np
|
| 36 |
-
from PIL import Image
|
| 37 |
-
from tqdm import tqdm
|
| 38 |
-
|
| 39 |
-
from utils.datasets import autosplit
|
| 40 |
-
from utils.general import download, xyxy2xywhn
|
| 41 |
-
|
| 42 |
-
|
| 43 |
-
def convert_labels(fname=Path('xView/xView_train.geojson')):
|
| 44 |
-
# Convert xView geoJSON labels to YOLO format
|
| 45 |
-
path = fname.parent
|
| 46 |
-
with open(fname) as f:
|
| 47 |
-
print(f'Loading {fname}...')
|
| 48 |
-
data = json.load(f)
|
| 49 |
-
|
| 50 |
-
# Make dirs
|
| 51 |
-
labels = Path(path / 'labels' / 'train')
|
| 52 |
-
os.system(f'rm -rf {labels}')
|
| 53 |
-
labels.mkdir(parents=True, exist_ok=True)
|
| 54 |
-
|
| 55 |
-
# xView classes 11-94 to 0-59
|
| 56 |
-
xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
|
| 57 |
-
12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
|
| 58 |
-
29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
|
| 59 |
-
47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
|
| 60 |
-
|
| 61 |
-
shapes = {}
|
| 62 |
-
for feature in tqdm(data['features'], desc=f'Converting {fname}'):
|
| 63 |
-
p = feature['properties']
|
| 64 |
-
if p['bounds_imcoords']:
|
| 65 |
-
id = p['image_id']
|
| 66 |
-
file = path / 'train_images' / id
|
| 67 |
-
if file.exists(): # 1395.tif missing
|
| 68 |
-
try:
|
| 69 |
-
box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
|
| 70 |
-
assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
|
| 71 |
-
cls = p['type_id']
|
| 72 |
-
cls = xview_class2index[int(cls)] # xView class to 0-60
|
| 73 |
-
assert 59 >= 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')
|
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|
|
ultralytics/yolov5/detect.py
DELETED
|
@@ -1,252 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
"""
|
| 3 |
-
Run inference on images, videos, directories, streams, etc.
|
| 4 |
-
|
| 5 |
-
Usage - sources:
|
| 6 |
-
$ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam
|
| 7 |
-
img.jpg # image
|
| 8 |
-
vid.mp4 # video
|
| 9 |
-
path/ # directory
|
| 10 |
-
path/*.jpg # glob
|
| 11 |
-
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
| 12 |
-
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
| 13 |
-
|
| 14 |
-
Usage - formats:
|
| 15 |
-
$ python path/to/detect.py --weights yolov5s.pt # PyTorch
|
| 16 |
-
yolov5s.torchscript # TorchScript
|
| 17 |
-
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
| 18 |
-
yolov5s.xml # OpenVINO
|
| 19 |
-
yolov5s.engine # TensorRT
|
| 20 |
-
yolov5s.mlmodel # CoreML (MacOS-only)
|
| 21 |
-
yolov5s_saved_model # TensorFlow SavedModel
|
| 22 |
-
yolov5s.pb # TensorFlow GraphDef
|
| 23 |
-
yolov5s.tflite # TensorFlow Lite
|
| 24 |
-
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
| 25 |
-
"""
|
| 26 |
-
|
| 27 |
-
import argparse
|
| 28 |
-
import os
|
| 29 |
-
import sys
|
| 30 |
-
from pathlib import Path
|
| 31 |
-
|
| 32 |
-
import cv2
|
| 33 |
-
import torch
|
| 34 |
-
import torch.backends.cudnn as cudnn
|
| 35 |
-
|
| 36 |
-
FILE = Path(__file__).resolve()
|
| 37 |
-
ROOT = FILE.parents[0] # YOLOv5 root directory
|
| 38 |
-
if str(ROOT) not in sys.path:
|
| 39 |
-
sys.path.append(str(ROOT)) # add ROOT to PATH
|
| 40 |
-
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
| 41 |
-
|
| 42 |
-
from models.common import DetectMultiBackend
|
| 43 |
-
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
|
| 44 |
-
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
|
| 45 |
-
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
|
| 46 |
-
from utils.plots import Annotator, colors, save_one_box
|
| 47 |
-
from utils.torch_utils import select_device, time_sync
|
| 48 |
-
|
| 49 |
-
|
| 50 |
-
@torch.no_grad()
|
| 51 |
-
def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s)
|
| 52 |
-
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
|
| 53 |
-
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
|
| 54 |
-
imgsz=(640, 640), # inference size (height, width)
|
| 55 |
-
conf_thres=0.25, # confidence threshold
|
| 56 |
-
iou_thres=0.45, # NMS IOU threshold
|
| 57 |
-
max_det=1000, # maximum detections per image
|
| 58 |
-
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
| 59 |
-
view_img=False, # show results
|
| 60 |
-
save_txt=False, # save results to *.txt
|
| 61 |
-
save_conf=False, # save confidences in --save-txt labels
|
| 62 |
-
save_crop=False, # save cropped prediction boxes
|
| 63 |
-
nosave=False, # do not save images/videos
|
| 64 |
-
classes=None, # filter by class: --class 0, or --class 0 2 3
|
| 65 |
-
agnostic_nms=False, # class-agnostic NMS
|
| 66 |
-
augment=False, # augmented inference
|
| 67 |
-
visualize=False, # visualize features
|
| 68 |
-
update=False, # update all models
|
| 69 |
-
project=ROOT / 'runs/detect', # save results to project/name
|
| 70 |
-
name='exp', # save results to project/name
|
| 71 |
-
exist_ok=False, # existing project/name ok, do not increment
|
| 72 |
-
line_thickness=3, # bounding box thickness (pixels)
|
| 73 |
-
hide_labels=False, # hide labels
|
| 74 |
-
hide_conf=False, # hide confidences
|
| 75 |
-
half=False, # use FP16 half-precision inference
|
| 76 |
-
dnn=False, # use OpenCV DNN for ONNX inference
|
| 77 |
-
):
|
| 78 |
-
source = str(source)
|
| 79 |
-
save_img = not nosave and not source.endswith('.txt') # save inference images
|
| 80 |
-
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
| 81 |
-
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
| 82 |
-
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
|
| 83 |
-
if is_url and is_file:
|
| 84 |
-
source = check_file(source) # download
|
| 85 |
-
|
| 86 |
-
# Directories
|
| 87 |
-
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
| 88 |
-
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
| 89 |
-
|
| 90 |
-
# Load model
|
| 91 |
-
device = select_device(device)
|
| 92 |
-
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
| 93 |
-
stride, names, pt = model.stride, model.names, model.pt
|
| 94 |
-
imgsz = check_img_size(imgsz, s=stride) # check image size
|
| 95 |
-
|
| 96 |
-
# Dataloader
|
| 97 |
-
if webcam:
|
| 98 |
-
view_img = check_imshow()
|
| 99 |
-
cudnn.benchmark = True # set True to speed up constant image size inference
|
| 100 |
-
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
|
| 101 |
-
bs = len(dataset) # batch_size
|
| 102 |
-
else:
|
| 103 |
-
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
|
| 104 |
-
bs = 1 # batch_size
|
| 105 |
-
vid_path, vid_writer = [None] * bs, [None] * bs
|
| 106 |
-
|
| 107 |
-
# Run inference
|
| 108 |
-
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
|
| 109 |
-
dt, seen = [0.0, 0.0, 0.0], 0
|
| 110 |
-
for path, im, im0s, vid_cap, s in dataset:
|
| 111 |
-
t1 = time_sync()
|
| 112 |
-
im = torch.from_numpy(im).to(device)
|
| 113 |
-
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
| 114 |
-
im /= 255 # 0 - 255 to 0.0 - 1.0
|
| 115 |
-
if len(im.shape) == 3:
|
| 116 |
-
im = im[None] # expand for batch dim
|
| 117 |
-
t2 = time_sync()
|
| 118 |
-
dt[0] += t2 - t1
|
| 119 |
-
|
| 120 |
-
# Inference
|
| 121 |
-
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
| 122 |
-
pred = model(im, augment=augment, visualize=visualize)
|
| 123 |
-
t3 = time_sync()
|
| 124 |
-
dt[1] += t3 - t2
|
| 125 |
-
|
| 126 |
-
# NMS
|
| 127 |
-
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
| 128 |
-
dt[2] += time_sync() - t3
|
| 129 |
-
|
| 130 |
-
# Second-stage classifier (optional)
|
| 131 |
-
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
| 132 |
-
|
| 133 |
-
# Process predictions
|
| 134 |
-
for i, det in enumerate(pred): # per image
|
| 135 |
-
seen += 1
|
| 136 |
-
if webcam: # batch_size >= 1
|
| 137 |
-
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
| 138 |
-
s += f'{i}: '
|
| 139 |
-
else:
|
| 140 |
-
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
|
| 141 |
-
|
| 142 |
-
p = Path(p) # to Path
|
| 143 |
-
save_path = str(save_dir / p.name) # im.jpg
|
| 144 |
-
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
|
| 145 |
-
s += '%gx%g ' % im.shape[2:] # print string
|
| 146 |
-
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
|
| 147 |
-
imc = im0.copy() if save_crop else im0 # for save_crop
|
| 148 |
-
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
| 149 |
-
if len(det):
|
| 150 |
-
# Rescale boxes from img_size to im0 size
|
| 151 |
-
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
|
| 152 |
-
|
| 153 |
-
# Print results
|
| 154 |
-
for c in det[:, -1].unique():
|
| 155 |
-
n = (det[:, -1] == c).sum() # detections per class
|
| 156 |
-
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
| 157 |
-
|
| 158 |
-
# Write results
|
| 159 |
-
for *xyxy, conf, cls in reversed(det):
|
| 160 |
-
if save_txt: # Write to file
|
| 161 |
-
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
| 162 |
-
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
| 163 |
-
with open(txt_path + '.txt', 'a') as f:
|
| 164 |
-
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
| 165 |
-
|
| 166 |
-
if save_img or save_crop or view_img: # Add bbox to image
|
| 167 |
-
c = int(cls) # integer class
|
| 168 |
-
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
|
| 169 |
-
annotator.box_label(xyxy, label, color=colors(c, True))
|
| 170 |
-
if save_crop:
|
| 171 |
-
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
|
| 172 |
-
|
| 173 |
-
# Stream results
|
| 174 |
-
im0 = annotator.result()
|
| 175 |
-
if view_img:
|
| 176 |
-
cv2.imshow(str(p), im0)
|
| 177 |
-
cv2.waitKey(1) # 1 millisecond
|
| 178 |
-
|
| 179 |
-
# Save results (image with detections)
|
| 180 |
-
if save_img:
|
| 181 |
-
if dataset.mode == 'image':
|
| 182 |
-
cv2.imwrite(save_path, im0)
|
| 183 |
-
else: # 'video' or 'stream'
|
| 184 |
-
if vid_path[i] != save_path: # new video
|
| 185 |
-
vid_path[i] = save_path
|
| 186 |
-
if isinstance(vid_writer[i], cv2.VideoWriter):
|
| 187 |
-
vid_writer[i].release() # release previous video writer
|
| 188 |
-
if vid_cap: # video
|
| 189 |
-
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
| 190 |
-
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
| 191 |
-
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
| 192 |
-
else: # stream
|
| 193 |
-
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
| 194 |
-
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
| 195 |
-
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
| 196 |
-
vid_writer[i].write(im0)
|
| 197 |
-
|
| 198 |
-
# Print time (inference-only)
|
| 199 |
-
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
|
| 200 |
-
|
| 201 |
-
# Print results
|
| 202 |
-
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
|
| 203 |
-
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
|
| 204 |
-
if save_txt or save_img:
|
| 205 |
-
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
| 206 |
-
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
| 207 |
-
if update:
|
| 208 |
-
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
|
| 209 |
-
|
| 210 |
-
|
| 211 |
-
def parse_opt():
|
| 212 |
-
parser = argparse.ArgumentParser()
|
| 213 |
-
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
|
| 214 |
-
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
|
| 215 |
-
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
|
| 216 |
-
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
| 217 |
-
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
|
| 218 |
-
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
|
| 219 |
-
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
|
| 220 |
-
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
| 221 |
-
parser.add_argument('--view-img', action='store_true', help='show results')
|
| 222 |
-
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
| 223 |
-
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
| 224 |
-
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
|
| 225 |
-
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
|
| 226 |
-
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
|
| 227 |
-
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
| 228 |
-
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
| 229 |
-
parser.add_argument('--visualize', action='store_true', help='visualize features')
|
| 230 |
-
parser.add_argument('--update', action='store_true', help='update all models')
|
| 231 |
-
parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
|
| 232 |
-
parser.add_argument('--name', default='exp', help='save results to project/name')
|
| 233 |
-
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
| 234 |
-
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
|
| 235 |
-
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
|
| 236 |
-
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
| 237 |
-
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
| 238 |
-
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
| 239 |
-
opt = parser.parse_args()
|
| 240 |
-
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
| 241 |
-
print_args(FILE.stem, opt)
|
| 242 |
-
return opt
|
| 243 |
-
|
| 244 |
-
|
| 245 |
-
def main(opt):
|
| 246 |
-
check_requirements(exclude=('tensorboard', 'thop'))
|
| 247 |
-
run(**vars(opt))
|
| 248 |
-
|
| 249 |
-
|
| 250 |
-
if __name__ == "__main__":
|
| 251 |
-
opt = parse_opt()
|
| 252 |
-
main(opt)
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|
ultralytics/yolov5/export.py
DELETED
|
@@ -1,559 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
"""
|
| 3 |
-
Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
|
| 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 |
-
|
| 23 |
-
Usage:
|
| 24 |
-
$ python path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
|
| 25 |
-
|
| 26 |
-
Inference:
|
| 27 |
-
$ python path/to/detect.py --weights yolov5s.pt # PyTorch
|
| 28 |
-
yolov5s.torchscript # TorchScript
|
| 29 |
-
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
| 30 |
-
yolov5s.xml # OpenVINO
|
| 31 |
-
yolov5s.engine # TensorRT
|
| 32 |
-
yolov5s.mlmodel # CoreML (MacOS-only)
|
| 33 |
-
yolov5s_saved_model # TensorFlow SavedModel
|
| 34 |
-
yolov5s.pb # TensorFlow GraphDef
|
| 35 |
-
yolov5s.tflite # TensorFlow Lite
|
| 36 |
-
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
| 37 |
-
|
| 38 |
-
TensorFlow.js:
|
| 39 |
-
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
|
| 40 |
-
$ npm install
|
| 41 |
-
$ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
|
| 42 |
-
$ npm start
|
| 43 |
-
"""
|
| 44 |
-
|
| 45 |
-
import argparse
|
| 46 |
-
import json
|
| 47 |
-
import os
|
| 48 |
-
import platform
|
| 49 |
-
import subprocess
|
| 50 |
-
import sys
|
| 51 |
-
import time
|
| 52 |
-
import warnings
|
| 53 |
-
from pathlib import Path
|
| 54 |
-
|
| 55 |
-
import pandas as pd
|
| 56 |
-
import torch
|
| 57 |
-
import torch.nn as nn
|
| 58 |
-
from torch.utils.mobile_optimizer import optimize_for_mobile
|
| 59 |
-
|
| 60 |
-
FILE = Path(__file__).resolve()
|
| 61 |
-
ROOT = FILE.parents[0] # YOLOv5 root directory
|
| 62 |
-
if str(ROOT) not in sys.path:
|
| 63 |
-
sys.path.append(str(ROOT)) # add ROOT to PATH
|
| 64 |
-
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
| 65 |
-
|
| 66 |
-
from models.common import Conv
|
| 67 |
-
from models.experimental import attempt_load
|
| 68 |
-
from models.yolo import Detect
|
| 69 |
-
from utils.activations import SiLU
|
| 70 |
-
from utils.datasets import LoadImages
|
| 71 |
-
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, colorstr,
|
| 72 |
-
file_size, print_args, url2file)
|
| 73 |
-
from utils.torch_utils import select_device
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
def export_formats():
|
| 77 |
-
# YOLOv5 export formats
|
| 78 |
-
x = [['PyTorch', '-', '.pt', True],
|
| 79 |
-
['TorchScript', 'torchscript', '.torchscript', True],
|
| 80 |
-
['ONNX', 'onnx', '.onnx', True],
|
| 81 |
-
['OpenVINO', 'openvino', '_openvino_model', False],
|
| 82 |
-
['TensorRT', 'engine', '.engine', True],
|
| 83 |
-
['CoreML', 'coreml', '.mlmodel', False],
|
| 84 |
-
['TensorFlow SavedModel', 'saved_model', '_saved_model', True],
|
| 85 |
-
['TensorFlow GraphDef', 'pb', '.pb', True],
|
| 86 |
-
['TensorFlow Lite', 'tflite', '.tflite', False],
|
| 87 |
-
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False],
|
| 88 |
-
['TensorFlow.js', 'tfjs', '_web_model', False]]
|
| 89 |
-
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'GPU'])
|
| 90 |
-
|
| 91 |
-
|
| 92 |
-
def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
|
| 93 |
-
# YOLOv5 TorchScript model export
|
| 94 |
-
try:
|
| 95 |
-
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
|
| 96 |
-
f = file.with_suffix('.torchscript')
|
| 97 |
-
|
| 98 |
-
ts = torch.jit.trace(model, im, strict=False)
|
| 99 |
-
d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
|
| 100 |
-
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
|
| 101 |
-
if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
|
| 102 |
-
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
|
| 103 |
-
else:
|
| 104 |
-
ts.save(str(f), _extra_files=extra_files)
|
| 105 |
-
|
| 106 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 107 |
-
return f
|
| 108 |
-
except Exception as e:
|
| 109 |
-
LOGGER.info(f'{prefix} export failure: {e}')
|
| 110 |
-
|
| 111 |
-
|
| 112 |
-
def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
|
| 113 |
-
# YOLOv5 ONNX export
|
| 114 |
-
try:
|
| 115 |
-
check_requirements(('onnx',))
|
| 116 |
-
import onnx
|
| 117 |
-
|
| 118 |
-
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
|
| 119 |
-
f = file.with_suffix('.onnx')
|
| 120 |
-
|
| 121 |
-
torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
|
| 122 |
-
training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
|
| 123 |
-
do_constant_folding=not train,
|
| 124 |
-
input_names=['images'],
|
| 125 |
-
output_names=['output'],
|
| 126 |
-
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
|
| 127 |
-
'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
| 128 |
-
} if dynamic else None)
|
| 129 |
-
|
| 130 |
-
# Checks
|
| 131 |
-
model_onnx = onnx.load(f) # load onnx model
|
| 132 |
-
onnx.checker.check_model(model_onnx) # check onnx model
|
| 133 |
-
# LOGGER.info(onnx.helper.printable_graph(model_onnx.graph)) # print
|
| 134 |
-
|
| 135 |
-
# Simplify
|
| 136 |
-
if simplify:
|
| 137 |
-
try:
|
| 138 |
-
check_requirements(('onnx-simplifier',))
|
| 139 |
-
import onnxsim
|
| 140 |
-
|
| 141 |
-
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
|
| 142 |
-
model_onnx, check = onnxsim.simplify(
|
| 143 |
-
model_onnx,
|
| 144 |
-
dynamic_input_shape=dynamic,
|
| 145 |
-
input_shapes={'images': list(im.shape)} if dynamic else None)
|
| 146 |
-
assert check, 'assert check failed'
|
| 147 |
-
onnx.save(model_onnx, f)
|
| 148 |
-
except Exception as e:
|
| 149 |
-
LOGGER.info(f'{prefix} simplifier failure: {e}')
|
| 150 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 151 |
-
return f
|
| 152 |
-
except Exception as e:
|
| 153 |
-
LOGGER.info(f'{prefix} export failure: {e}')
|
| 154 |
-
|
| 155 |
-
|
| 156 |
-
def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')):
|
| 157 |
-
# YOLOv5 OpenVINO export
|
| 158 |
-
try:
|
| 159 |
-
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
| 160 |
-
import openvino.inference_engine as ie
|
| 161 |
-
|
| 162 |
-
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
|
| 163 |
-
f = str(file).replace('.pt', '_openvino_model' + os.sep)
|
| 164 |
-
|
| 165 |
-
cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}"
|
| 166 |
-
subprocess.check_output(cmd, shell=True)
|
| 167 |
-
|
| 168 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 169 |
-
return f
|
| 170 |
-
except Exception as e:
|
| 171 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 172 |
-
|
| 173 |
-
|
| 174 |
-
def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
|
| 175 |
-
# YOLOv5 CoreML export
|
| 176 |
-
try:
|
| 177 |
-
check_requirements(('coremltools',))
|
| 178 |
-
import coremltools as ct
|
| 179 |
-
|
| 180 |
-
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
|
| 181 |
-
f = file.with_suffix('.mlmodel')
|
| 182 |
-
|
| 183 |
-
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
|
| 184 |
-
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
|
| 185 |
-
ct_model.save(f)
|
| 186 |
-
|
| 187 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 188 |
-
return ct_model, f
|
| 189 |
-
except Exception as e:
|
| 190 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 191 |
-
return None, None
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
|
| 195 |
-
# YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt
|
| 196 |
-
try:
|
| 197 |
-
check_requirements(('tensorrt',))
|
| 198 |
-
import tensorrt as trt
|
| 199 |
-
|
| 200 |
-
if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
|
| 201 |
-
grid = model.model[-1].anchor_grid
|
| 202 |
-
model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
|
| 203 |
-
export_onnx(model, im, file, 12, train, False, simplify) # opset 12
|
| 204 |
-
model.model[-1].anchor_grid = grid
|
| 205 |
-
else: # TensorRT >= 8
|
| 206 |
-
check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
|
| 207 |
-
export_onnx(model, im, file, 13, train, False, simplify) # opset 13
|
| 208 |
-
onnx = file.with_suffix('.onnx')
|
| 209 |
-
|
| 210 |
-
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
|
| 211 |
-
assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
|
| 212 |
-
assert onnx.exists(), f'failed to export ONNX file: {onnx}'
|
| 213 |
-
f = file.with_suffix('.engine') # TensorRT engine file
|
| 214 |
-
logger = trt.Logger(trt.Logger.INFO)
|
| 215 |
-
if verbose:
|
| 216 |
-
logger.min_severity = trt.Logger.Severity.VERBOSE
|
| 217 |
-
|
| 218 |
-
builder = trt.Builder(logger)
|
| 219 |
-
config = builder.create_builder_config()
|
| 220 |
-
config.max_workspace_size = workspace * 1 << 30
|
| 221 |
-
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
|
| 222 |
-
|
| 223 |
-
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
|
| 224 |
-
network = builder.create_network(flag)
|
| 225 |
-
parser = trt.OnnxParser(network, logger)
|
| 226 |
-
if not parser.parse_from_file(str(onnx)):
|
| 227 |
-
raise RuntimeError(f'failed to load ONNX file: {onnx}')
|
| 228 |
-
|
| 229 |
-
inputs = [network.get_input(i) for i in range(network.num_inputs)]
|
| 230 |
-
outputs = [network.get_output(i) for i in range(network.num_outputs)]
|
| 231 |
-
LOGGER.info(f'{prefix} Network Description:')
|
| 232 |
-
for inp in inputs:
|
| 233 |
-
LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}')
|
| 234 |
-
for out in outputs:
|
| 235 |
-
LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}')
|
| 236 |
-
|
| 237 |
-
LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 else 32} engine in {f}')
|
| 238 |
-
if builder.platform_has_fast_fp16:
|
| 239 |
-
config.set_flag(trt.BuilderFlag.FP16)
|
| 240 |
-
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
|
| 241 |
-
t.write(engine.serialize())
|
| 242 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 243 |
-
return f
|
| 244 |
-
except Exception as e:
|
| 245 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 246 |
-
|
| 247 |
-
|
| 248 |
-
def export_saved_model(model, im, file, dynamic,
|
| 249 |
-
tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
|
| 250 |
-
conf_thres=0.25, keras=False, prefix=colorstr('TensorFlow SavedModel:')):
|
| 251 |
-
# YOLOv5 TensorFlow SavedModel export
|
| 252 |
-
try:
|
| 253 |
-
import tensorflow as tf
|
| 254 |
-
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
| 255 |
-
|
| 256 |
-
from models.tf import TFDetect, TFModel
|
| 257 |
-
|
| 258 |
-
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
| 259 |
-
f = str(file).replace('.pt', '_saved_model')
|
| 260 |
-
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
| 261 |
-
|
| 262 |
-
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
| 263 |
-
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
|
| 264 |
-
_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
| 265 |
-
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
|
| 266 |
-
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
| 267 |
-
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
|
| 268 |
-
keras_model.trainable = False
|
| 269 |
-
keras_model.summary()
|
| 270 |
-
if keras:
|
| 271 |
-
keras_model.save(f, save_format='tf')
|
| 272 |
-
else:
|
| 273 |
-
m = tf.function(lambda x: keras_model(x)) # full model
|
| 274 |
-
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
|
| 275 |
-
m = m.get_concrete_function(spec)
|
| 276 |
-
frozen_func = convert_variables_to_constants_v2(m)
|
| 277 |
-
tfm = tf.Module()
|
| 278 |
-
tfm.__call__ = tf.function(lambda x: frozen_func(x)[0], [spec])
|
| 279 |
-
tfm.__call__(im)
|
| 280 |
-
tf.saved_model.save(
|
| 281 |
-
tfm,
|
| 282 |
-
f,
|
| 283 |
-
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if
|
| 284 |
-
check_version(tf.__version__, '2.6') else tf.saved_model.SaveOptions())
|
| 285 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 286 |
-
return keras_model, f
|
| 287 |
-
except Exception as e:
|
| 288 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 289 |
-
return None, None
|
| 290 |
-
|
| 291 |
-
|
| 292 |
-
def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
|
| 293 |
-
# YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
|
| 294 |
-
try:
|
| 295 |
-
import tensorflow as tf
|
| 296 |
-
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
| 297 |
-
|
| 298 |
-
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
| 299 |
-
f = file.with_suffix('.pb')
|
| 300 |
-
|
| 301 |
-
m = tf.function(lambda x: keras_model(x)) # full model
|
| 302 |
-
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
| 303 |
-
frozen_func = convert_variables_to_constants_v2(m)
|
| 304 |
-
frozen_func.graph.as_graph_def()
|
| 305 |
-
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
| 306 |
-
|
| 307 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 308 |
-
return f
|
| 309 |
-
except Exception as e:
|
| 310 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 311 |
-
|
| 312 |
-
|
| 313 |
-
def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
|
| 314 |
-
# YOLOv5 TensorFlow Lite export
|
| 315 |
-
try:
|
| 316 |
-
import tensorflow as tf
|
| 317 |
-
|
| 318 |
-
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
| 319 |
-
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
| 320 |
-
f = str(file).replace('.pt', '-fp16.tflite')
|
| 321 |
-
|
| 322 |
-
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
| 323 |
-
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
| 324 |
-
converter.target_spec.supported_types = [tf.float16]
|
| 325 |
-
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
| 326 |
-
if int8:
|
| 327 |
-
from models.tf import representative_dataset_gen
|
| 328 |
-
dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
|
| 329 |
-
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
|
| 330 |
-
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
| 331 |
-
converter.target_spec.supported_types = []
|
| 332 |
-
converter.inference_input_type = tf.uint8 # or tf.int8
|
| 333 |
-
converter.inference_output_type = tf.uint8 # or tf.int8
|
| 334 |
-
converter.experimental_new_quantizer = True
|
| 335 |
-
f = str(file).replace('.pt', '-int8.tflite')
|
| 336 |
-
|
| 337 |
-
tflite_model = converter.convert()
|
| 338 |
-
open(f, "wb").write(tflite_model)
|
| 339 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 340 |
-
return f
|
| 341 |
-
except Exception as e:
|
| 342 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 343 |
-
|
| 344 |
-
|
| 345 |
-
def export_edgetpu(keras_model, im, file, prefix=colorstr('Edge TPU:')):
|
| 346 |
-
# YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
|
| 347 |
-
try:
|
| 348 |
-
cmd = 'edgetpu_compiler --version'
|
| 349 |
-
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
|
| 350 |
-
assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
|
| 351 |
-
if subprocess.run(cmd + ' >/dev/null', shell=True).returncode != 0:
|
| 352 |
-
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
|
| 353 |
-
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
|
| 354 |
-
for c in ['curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
|
| 355 |
-
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
|
| 356 |
-
'sudo apt-get update',
|
| 357 |
-
'sudo apt-get install edgetpu-compiler']:
|
| 358 |
-
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
|
| 359 |
-
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
|
| 360 |
-
|
| 361 |
-
LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
|
| 362 |
-
f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
|
| 363 |
-
f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
|
| 364 |
-
|
| 365 |
-
cmd = f"edgetpu_compiler -s {f_tfl}"
|
| 366 |
-
subprocess.run(cmd, shell=True, check=True)
|
| 367 |
-
|
| 368 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 369 |
-
return f
|
| 370 |
-
except Exception as e:
|
| 371 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 372 |
-
|
| 373 |
-
|
| 374 |
-
def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
|
| 375 |
-
# YOLOv5 TensorFlow.js export
|
| 376 |
-
try:
|
| 377 |
-
check_requirements(('tensorflowjs',))
|
| 378 |
-
import re
|
| 379 |
-
|
| 380 |
-
import tensorflowjs as tfjs
|
| 381 |
-
|
| 382 |
-
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
| 383 |
-
f = str(file).replace('.pt', '_web_model') # js dir
|
| 384 |
-
f_pb = file.with_suffix('.pb') # *.pb path
|
| 385 |
-
f_json = f + '/model.json' # *.json path
|
| 386 |
-
|
| 387 |
-
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \
|
| 388 |
-
f'--output_node_names="Identity,Identity_1,Identity_2,Identity_3" {f_pb} {f}'
|
| 389 |
-
subprocess.run(cmd, shell=True)
|
| 390 |
-
|
| 391 |
-
json = open(f_json).read()
|
| 392 |
-
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
| 393 |
-
subst = re.sub(
|
| 394 |
-
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
| 395 |
-
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
| 396 |
-
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
| 397 |
-
r'"Identity.?.?": {"name": "Identity.?.?"}}}',
|
| 398 |
-
r'{"outputs": {"Identity": {"name": "Identity"}, '
|
| 399 |
-
r'"Identity_1": {"name": "Identity_1"}, '
|
| 400 |
-
r'"Identity_2": {"name": "Identity_2"}, '
|
| 401 |
-
r'"Identity_3": {"name": "Identity_3"}}}',
|
| 402 |
-
json)
|
| 403 |
-
j.write(subst)
|
| 404 |
-
|
| 405 |
-
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
| 406 |
-
return f
|
| 407 |
-
except Exception as e:
|
| 408 |
-
LOGGER.info(f'\n{prefix} export failure: {e}')
|
| 409 |
-
|
| 410 |
-
|
| 411 |
-
@torch.no_grad()
|
| 412 |
-
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
|
| 413 |
-
weights=ROOT / 'yolov5s.pt', # weights path
|
| 414 |
-
imgsz=(640, 640), # image (height, width)
|
| 415 |
-
batch_size=1, # batch size
|
| 416 |
-
device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
| 417 |
-
include=('torchscript', 'onnx'), # include formats
|
| 418 |
-
half=False, # FP16 half-precision export
|
| 419 |
-
inplace=False, # set YOLOv5 Detect() inplace=True
|
| 420 |
-
train=False, # model.train() mode
|
| 421 |
-
optimize=False, # TorchScript: optimize for mobile
|
| 422 |
-
int8=False, # CoreML/TF INT8 quantization
|
| 423 |
-
dynamic=False, # ONNX/TF: dynamic axes
|
| 424 |
-
simplify=False, # ONNX: simplify model
|
| 425 |
-
opset=12, # ONNX: opset version
|
| 426 |
-
verbose=False, # TensorRT: verbose log
|
| 427 |
-
workspace=4, # TensorRT: workspace size (GB)
|
| 428 |
-
nms=False, # TF: add NMS to model
|
| 429 |
-
agnostic_nms=False, # TF: add agnostic NMS to model
|
| 430 |
-
topk_per_class=100, # TF.js NMS: topk per class to keep
|
| 431 |
-
topk_all=100, # TF.js NMS: topk for all classes to keep
|
| 432 |
-
iou_thres=0.45, # TF.js NMS: IoU threshold
|
| 433 |
-
conf_thres=0.25 # TF.js NMS: confidence threshold
|
| 434 |
-
):
|
| 435 |
-
t = time.time()
|
| 436 |
-
include = [x.lower() for x in include] # to lowercase
|
| 437 |
-
formats = tuple(export_formats()['Argument'][1:]) # --include arguments
|
| 438 |
-
flags = [x in include for x in formats]
|
| 439 |
-
assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {formats}'
|
| 440 |
-
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = flags # export booleans
|
| 441 |
-
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights
|
| 442 |
-
|
| 443 |
-
# Load PyTorch model
|
| 444 |
-
device = select_device(device)
|
| 445 |
-
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
|
| 446 |
-
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
|
| 447 |
-
nc, names = model.nc, model.names # number of classes, class names
|
| 448 |
-
|
| 449 |
-
# Checks
|
| 450 |
-
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
|
| 451 |
-
opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12
|
| 452 |
-
assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'
|
| 453 |
-
|
| 454 |
-
# Input
|
| 455 |
-
gs = int(max(model.stride)) # grid size (max stride)
|
| 456 |
-
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
|
| 457 |
-
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
|
| 458 |
-
|
| 459 |
-
# Update model
|
| 460 |
-
if half:
|
| 461 |
-
im, model = im.half(), model.half() # to FP16
|
| 462 |
-
model.train() if train else model.eval() # training mode = no Detect() layer grid construction
|
| 463 |
-
for k, m in model.named_modules():
|
| 464 |
-
if isinstance(m, Conv): # assign export-friendly activations
|
| 465 |
-
if isinstance(m.act, nn.SiLU):
|
| 466 |
-
m.act = SiLU()
|
| 467 |
-
elif isinstance(m, Detect):
|
| 468 |
-
m.inplace = inplace
|
| 469 |
-
m.onnx_dynamic = dynamic
|
| 470 |
-
if hasattr(m, 'forward_export'):
|
| 471 |
-
m.forward = m.forward_export # assign custom forward (optional)
|
| 472 |
-
|
| 473 |
-
for _ in range(2):
|
| 474 |
-
y = model(im) # dry runs
|
| 475 |
-
shape = tuple(y[0].shape) # model output shape
|
| 476 |
-
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
|
| 477 |
-
|
| 478 |
-
# Exports
|
| 479 |
-
f = [''] * 10 # exported filenames
|
| 480 |
-
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
|
| 481 |
-
if jit:
|
| 482 |
-
f[0] = export_torchscript(model, im, file, optimize)
|
| 483 |
-
if engine: # TensorRT required before ONNX
|
| 484 |
-
f[1] = export_engine(model, im, file, train, half, simplify, workspace, verbose)
|
| 485 |
-
if onnx or xml: # OpenVINO requires ONNX
|
| 486 |
-
f[2] = export_onnx(model, im, file, opset, train, dynamic, simplify)
|
| 487 |
-
if xml: # OpenVINO
|
| 488 |
-
f[3] = export_openvino(model, im, file)
|
| 489 |
-
if coreml:
|
| 490 |
-
_, f[4] = export_coreml(model, im, file)
|
| 491 |
-
|
| 492 |
-
# TensorFlow Exports
|
| 493 |
-
if any((saved_model, pb, tflite, edgetpu, tfjs)):
|
| 494 |
-
if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707
|
| 495 |
-
check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow`
|
| 496 |
-
assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
| 497 |
-
model, f[5] = export_saved_model(model.cpu(), im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs,
|
| 498 |
-
agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class,
|
| 499 |
-
topk_all=topk_all, conf_thres=conf_thres, iou_thres=iou_thres) # keras model
|
| 500 |
-
if pb or tfjs: # pb prerequisite to tfjs
|
| 501 |
-
f[6] = export_pb(model, im, file)
|
| 502 |
-
if tflite or edgetpu:
|
| 503 |
-
f[7] = export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100)
|
| 504 |
-
if edgetpu:
|
| 505 |
-
f[8] = export_edgetpu(model, im, file)
|
| 506 |
-
if tfjs:
|
| 507 |
-
f[9] = export_tfjs(model, im, file)
|
| 508 |
-
|
| 509 |
-
# Finish
|
| 510 |
-
f = [str(x) for x in f if x] # filter out '' and None
|
| 511 |
-
if any(f):
|
| 512 |
-
LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
|
| 513 |
-
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
| 514 |
-
f"\nDetect: python detect.py --weights {f[-1]}"
|
| 515 |
-
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}')"
|
| 516 |
-
f"\nValidate: python val.py --weights {f[-1]}"
|
| 517 |
-
f"\nVisualize: https://netron.app")
|
| 518 |
-
return f # return list of exported files/dirs
|
| 519 |
-
|
| 520 |
-
|
| 521 |
-
def parse_opt():
|
| 522 |
-
parser = argparse.ArgumentParser()
|
| 523 |
-
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
| 524 |
-
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)')
|
| 525 |
-
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
|
| 526 |
-
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
| 527 |
-
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
| 528 |
-
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
|
| 529 |
-
parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
|
| 530 |
-
parser.add_argument('--train', action='store_true', help='model.train() mode')
|
| 531 |
-
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
|
| 532 |
-
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
|
| 533 |
-
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
|
| 534 |
-
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
|
| 535 |
-
parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version')
|
| 536 |
-
parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
|
| 537 |
-
parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
|
| 538 |
-
parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
|
| 539 |
-
parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
|
| 540 |
-
parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
|
| 541 |
-
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
|
| 542 |
-
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
|
| 543 |
-
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
|
| 544 |
-
parser.add_argument('--include', nargs='+',
|
| 545 |
-
default=['torchscript', 'onnx'],
|
| 546 |
-
help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs')
|
| 547 |
-
opt = parser.parse_args()
|
| 548 |
-
print_args(FILE.stem, opt)
|
| 549 |
-
return opt
|
| 550 |
-
|
| 551 |
-
|
| 552 |
-
def main(opt):
|
| 553 |
-
for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
|
| 554 |
-
run(**vars(opt))
|
| 555 |
-
|
| 556 |
-
|
| 557 |
-
if __name__ == "__main__":
|
| 558 |
-
opt = parse_opt()
|
| 559 |
-
main(opt)
|
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|
|
ultralytics/yolov5/hubconf.py
DELETED
|
@@ -1,143 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
"""
|
| 3 |
-
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
|
| 4 |
-
|
| 5 |
-
Usage:
|
| 6 |
-
import torch
|
| 7 |
-
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
|
| 8 |
-
model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch
|
| 9 |
-
"""
|
| 10 |
-
|
| 11 |
-
import torch
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 15 |
-
"""Creates or loads a YOLOv5 model
|
| 16 |
-
|
| 17 |
-
Arguments:
|
| 18 |
-
name (str): model name 'yolov5s' or path 'path/to/best.pt'
|
| 19 |
-
pretrained (bool): load pretrained weights into the model
|
| 20 |
-
channels (int): number of input channels
|
| 21 |
-
classes (int): number of model classes
|
| 22 |
-
autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
|
| 23 |
-
verbose (bool): print all information to screen
|
| 24 |
-
device (str, torch.device, None): device to use for model parameters
|
| 25 |
-
|
| 26 |
-
Returns:
|
| 27 |
-
YOLOv5 model
|
| 28 |
-
"""
|
| 29 |
-
from pathlib import Path
|
| 30 |
-
|
| 31 |
-
from models.common import AutoShape, DetectMultiBackend
|
| 32 |
-
from models.yolo import Model
|
| 33 |
-
from utils.downloads import attempt_download
|
| 34 |
-
from utils.general import LOGGER, check_requirements, intersect_dicts, logging
|
| 35 |
-
from utils.torch_utils import select_device
|
| 36 |
-
|
| 37 |
-
if not verbose:
|
| 38 |
-
LOGGER.setLevel(logging.WARNING)
|
| 39 |
-
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
|
| 40 |
-
name = Path(name)
|
| 41 |
-
path = name.with_suffix('.pt') if name.suffix == '' else name # checkpoint path
|
| 42 |
-
try:
|
| 43 |
-
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
|
| 44 |
-
|
| 45 |
-
if pretrained and channels == 3 and classes == 80:
|
| 46 |
-
model = DetectMultiBackend(path, device=device) # download/load FP32 model
|
| 47 |
-
# model = models.experimental.attempt_load(path, map_location=device) # download/load FP32 model
|
| 48 |
-
else:
|
| 49 |
-
cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
|
| 50 |
-
model = Model(cfg, channels, classes) # create model
|
| 51 |
-
if pretrained:
|
| 52 |
-
ckpt = torch.load(attempt_download(path), map_location=device) # load
|
| 53 |
-
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
|
| 54 |
-
csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect
|
| 55 |
-
model.load_state_dict(csd, strict=False) # load
|
| 56 |
-
if len(ckpt['model'].names) == classes:
|
| 57 |
-
model.names = ckpt['model'].names # set class names attribute
|
| 58 |
-
if autoshape:
|
| 59 |
-
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
|
| 60 |
-
return model.to(device)
|
| 61 |
-
|
| 62 |
-
except Exception as e:
|
| 63 |
-
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
|
| 64 |
-
s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
|
| 65 |
-
raise Exception(s) from e
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
|
| 69 |
-
# YOLOv5 custom or local model
|
| 70 |
-
return _create(path, autoshape=autoshape, verbose=verbose, device=device)
|
| 71 |
-
|
| 72 |
-
|
| 73 |
-
def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 74 |
-
# YOLOv5-nano model https://github.com/ultralytics/yolov5
|
| 75 |
-
return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
|
| 76 |
-
|
| 77 |
-
|
| 78 |
-
def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 79 |
-
# YOLOv5-small model https://github.com/ultralytics/yolov5
|
| 80 |
-
return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
|
| 81 |
-
|
| 82 |
-
|
| 83 |
-
def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 84 |
-
# YOLOv5-medium model https://github.com/ultralytics/yolov5
|
| 85 |
-
return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)
|
| 86 |
-
|
| 87 |
-
|
| 88 |
-
def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 89 |
-
# YOLOv5-large model https://github.com/ultralytics/yolov5
|
| 90 |
-
return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)
|
| 91 |
-
|
| 92 |
-
|
| 93 |
-
def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 94 |
-
# YOLOv5-xlarge model https://github.com/ultralytics/yolov5
|
| 95 |
-
return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
|
| 96 |
-
|
| 97 |
-
|
| 98 |
-
def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 99 |
-
# YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
|
| 100 |
-
return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
|
| 101 |
-
|
| 102 |
-
|
| 103 |
-
def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 104 |
-
# YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
|
| 105 |
-
return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 109 |
-
# YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
|
| 110 |
-
return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)
|
| 111 |
-
|
| 112 |
-
|
| 113 |
-
def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 114 |
-
# YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
|
| 115 |
-
return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)
|
| 116 |
-
|
| 117 |
-
|
| 118 |
-
def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
| 119 |
-
# YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
|
| 120 |
-
return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)
|
| 121 |
-
|
| 122 |
-
|
| 123 |
-
if __name__ == '__main__':
|
| 124 |
-
model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
|
| 125 |
-
# model = custom(path='path/to/model.pt') # custom
|
| 126 |
-
|
| 127 |
-
# Verify inference
|
| 128 |
-
from pathlib import Path
|
| 129 |
-
|
| 130 |
-
import cv2
|
| 131 |
-
import numpy as np
|
| 132 |
-
from PIL import Image
|
| 133 |
-
|
| 134 |
-
imgs = ['data/images/zidane.jpg', # filename
|
| 135 |
-
Path('data/images/zidane.jpg'), # Path
|
| 136 |
-
'https://ultralytics.com/images/zidane.jpg', # URI
|
| 137 |
-
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
|
| 138 |
-
Image.open('data/images/bus.jpg'), # PIL
|
| 139 |
-
np.zeros((320, 640, 3))] # numpy
|
| 140 |
-
|
| 141 |
-
results = model(imgs, size=320) # batched inference
|
| 142 |
-
results.print()
|
| 143 |
-
results.save()
|
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|
ultralytics/yolov5/models/__init__.py
DELETED
|
File without changes
|
ultralytics/yolov5/models/common.py
DELETED
|
@@ -1,684 +0,0 @@
|
|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
| 2 |
-
"""
|
| 3 |
-
Common modules
|
| 4 |
-
"""
|
| 5 |
-
|
| 6 |
-
import json
|
| 7 |
-
import math
|
| 8 |
-
import platform
|
| 9 |
-
import warnings
|
| 10 |
-
from collections import OrderedDict, namedtuple
|
| 11 |
-
from copy import copy
|
| 12 |
-
from pathlib import Path
|
| 13 |
-
|
| 14 |
-
import cv2
|
| 15 |
-
import numpy as np
|
| 16 |
-
import pandas as pd
|
| 17 |
-
import requests
|
| 18 |
-
import torch
|
| 19 |
-
import torch.nn as nn
|
| 20 |
-
import yaml
|
| 21 |
-
from PIL import Image
|
| 22 |
-
from torch.cuda import amp
|
| 23 |
-
|
| 24 |
-
from utils.datasets import exif_transpose, letterbox
|
| 25 |
-
from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path,
|
| 26 |
-
make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
|
| 27 |
-
from utils.plots import Annotator, colors, save_one_box
|
| 28 |
-
from utils.torch_utils import copy_attr, time_sync
|
| 29 |
-
|
| 30 |
-
|
| 31 |
-
def autopad(k, p=None): # kernel, padding
|
| 32 |
-
# Pad to 'same'
|
| 33 |
-
if p is None:
|
| 34 |
-
p = k // 2 if isinstance(k, int) else (x // 2 for x in k) # auto-pad
|
| 35 |
-
return p
|
| 36 |
-
|
| 37 |
-
|
| 38 |
-
class Conv(nn.Module):
|
| 39 |
-
# Standard convolution
|
| 40 |
-
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
| 41 |
-
super().__init__()
|
| 42 |
-
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
|
| 43 |
-
self.bn = nn.BatchNorm2d(c2)
|
| 44 |
-
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
|
| 45 |
-
|
| 46 |
-
def forward(self, x):
|
| 47 |
-
return self.act(self.bn(self.conv(x)))
|
| 48 |
-
|
| 49 |
-
def forward_fuse(self, x):
|
| 50 |
-
return self.act(self.conv(x))
|
| 51 |
-
|
| 52 |
-
|
| 53 |
-
class DWConv(Conv):
|
| 54 |
-
# Depth-wise convolution class
|
| 55 |
-
def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
| 56 |
-
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
|
| 57 |
-
|
| 58 |
-
|
| 59 |
-
class TransformerLayer(nn.Module):
|
| 60 |
-
# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
|
| 61 |
-
def __init__(self, c, num_heads):
|
| 62 |
-
super().__init__()
|
| 63 |
-
self.q = nn.Linear(c, c, bias=False)
|
| 64 |
-
self.k = nn.Linear(c, c, bias=False)
|
| 65 |
-
self.v = nn.Linear(c, c, bias=False)
|
| 66 |
-
self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
|
| 67 |
-
self.fc1 = nn.Linear(c, c, bias=False)
|
| 68 |
-
self.fc2 = nn.Linear(c, c, bias=False)
|
| 69 |
-
|
| 70 |
-
def forward(self, x):
|
| 71 |
-
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
|
| 72 |
-
x = self.fc2(self.fc1(x)) + x
|
| 73 |
-
return x
|
| 74 |
-
|
| 75 |
-
|
| 76 |
-
class TransformerBlock(nn.Module):
|
| 77 |
-
# Vision Transformer https://arxiv.org/abs/2010.11929
|
| 78 |
-
def __init__(self, c1, c2, num_heads, num_layers):
|
| 79 |
-
super().__init__()
|
| 80 |
-
self.conv = None
|
| 81 |
-
if c1 != c2:
|
| 82 |
-
self.conv = Conv(c1, c2)
|
| 83 |
-
self.linear = nn.Linear(c2, c2) # learnable position embedding
|
| 84 |
-
self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers)))
|
| 85 |
-
self.c2 = c2
|
| 86 |
-
|
| 87 |
-
def forward(self, x):
|
| 88 |
-
if self.conv is not None:
|
| 89 |
-
x = self.conv(x)
|
| 90 |
-
b, _, w, h = x.shape
|
| 91 |
-
p = x.flatten(2).permute(2, 0, 1)
|
| 92 |
-
return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
|
| 93 |
-
|
| 94 |
-
|
| 95 |
-
class Bottleneck(nn.Module):
|
| 96 |
-
# Standard bottleneck
|
| 97 |
-
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
|
| 98 |
-
super().__init__()
|
| 99 |
-
c_ = int(c2 * e) # hidden channels
|
| 100 |
-
self.cv1 = Conv(c1, c_, 1, 1)
|
| 101 |
-
self.cv2 = Conv(c_, c2, 3, 1, g=g)
|
| 102 |
-
self.add = shortcut and c1 == c2
|
| 103 |
-
|
| 104 |
-
def forward(self, x):
|
| 105 |
-
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
| 106 |
-
|
| 107 |
-
|
| 108 |
-
class BottleneckCSP(nn.Module):
|
| 109 |
-
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
| 110 |
-
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
| 111 |
-
super().__init__()
|
| 112 |
-
c_ = int(c2 * e) # hidden channels
|
| 113 |
-
self.cv1 = Conv(c1, c_, 1, 1)
|
| 114 |
-
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
|
| 115 |
-
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
|
| 116 |
-
self.cv4 = Conv(2 * c_, c2, 1, 1)
|
| 117 |
-
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
|
| 118 |
-
self.act = nn.SiLU()
|
| 119 |
-
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
|
| 120 |
-
|
| 121 |
-
def forward(self, x):
|
| 122 |
-
y1 = self.cv3(self.m(self.cv1(x)))
|
| 123 |
-
y2 = self.cv2(x)
|
| 124 |
-
return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
|
| 125 |
-
|
| 126 |
-
|
| 127 |
-
class C3(nn.Module):
|
| 128 |
-
# CSP Bottleneck with 3 convolutions
|
| 129 |
-
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
| 130 |
-
super().__init__()
|
| 131 |
-
c_ = int(c2 * e) # hidden channels
|
| 132 |
-
self.cv1 = Conv(c1, c_, 1, 1)
|
| 133 |
-
self.cv2 = Conv(c1, c_, 1, 1)
|
| 134 |
-
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
|
| 135 |
-
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
|
| 136 |
-
# self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
|
| 137 |
-
|
| 138 |
-
def forward(self, x):
|
| 139 |
-
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
class C3TR(C3):
|
| 143 |
-
# C3 module with TransformerBlock()
|
| 144 |
-
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
|
| 145 |
-
super().__init__(c1, c2, n, shortcut, g, e)
|
| 146 |
-
c_ = int(c2 * e)
|
| 147 |
-
self.m = TransformerBlock(c_, c_, 4, n)
|
| 148 |
-
|
| 149 |
-
|
| 150 |
-
class C3SPP(C3):
|
| 151 |
-
# C3 module with SPP()
|
| 152 |
-
def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
|
| 153 |
-
super().__init__(c1, c2, n, shortcut, g, e)
|
| 154 |
-
c_ = int(c2 * e)
|
| 155 |
-
self.m = SPP(c_, c_, k)
|
| 156 |
-
|
| 157 |
-
|
| 158 |
-
class C3Ghost(C3):
|
| 159 |
-
# C3 module with GhostBottleneck()
|
| 160 |
-
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
|
| 161 |
-
super().__init__(c1, c2, n, shortcut, g, e)
|
| 162 |
-
c_ = int(c2 * e) # hidden channels
|
| 163 |
-
self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n)))
|
| 164 |
-
|
| 165 |
-
|
| 166 |
-
class SPP(nn.Module):
|
| 167 |
-
# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
|
| 168 |
-
def __init__(self, c1, c2, k=(5, 9, 13)):
|
| 169 |
-
super().__init__()
|
| 170 |
-
c_ = c1 // 2 # hidden channels
|
| 171 |
-
self.cv1 = Conv(c1, c_, 1, 1)
|
| 172 |
-
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
|
| 173 |
-
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
|
| 174 |
-
|
| 175 |
-
def forward(self, x):
|
| 176 |
-
x = self.cv1(x)
|
| 177 |
-
with warnings.catch_warnings():
|
| 178 |
-
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
|
| 179 |
-
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
|
| 180 |
-
|
| 181 |
-
|
| 182 |
-
class SPPF(nn.Module):
|
| 183 |
-
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
|
| 184 |
-
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
|
| 185 |
-
super().__init__()
|
| 186 |
-
c_ = c1 // 2 # hidden channels
|
| 187 |
-
self.cv1 = Conv(c1, c_, 1, 1)
|
| 188 |
-
self.cv2 = Conv(c_ * 4, c2, 1, 1)
|
| 189 |
-
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
|
| 190 |
-
|
| 191 |
-
def forward(self, x):
|
| 192 |
-
x = self.cv1(x)
|
| 193 |
-
with warnings.catch_warnings():
|
| 194 |
-
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
|
| 195 |
-
y1 = self.m(x)
|
| 196 |
-
y2 = self.m(y1)
|
| 197 |
-
return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
|
| 198 |
-
|
| 199 |
-
|
| 200 |
-
class Focus(nn.Module):
|
| 201 |
-
# Focus wh information into c-space
|
| 202 |
-
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
| 203 |
-
super().__init__()
|
| 204 |
-
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
|
| 205 |
-
# self.contract = Contract(gain=2)
|
| 206 |
-
|
| 207 |
-
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
|
| 208 |
-
return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
|
| 209 |
-
# return self.conv(self.contract(x))
|
| 210 |
-
|
| 211 |
-
|
| 212 |
-
class GhostConv(nn.Module):
|
| 213 |
-
# Ghost Convolution https://github.com/huawei-noah/ghostnet
|
| 214 |
-
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
|
| 215 |
-
super().__init__()
|
| 216 |
-
c_ = c2 // 2 # hidden channels
|
| 217 |
-
self.cv1 = Conv(c1, c_, k, s, None, g, act)
|
| 218 |
-
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
|
| 219 |
-
|
| 220 |
-
def forward(self, x):
|
| 221 |
-
y = self.cv1(x)
|
| 222 |
-
return torch.cat((y, self.cv2(y)), 1)
|
| 223 |
-
|
| 224 |
-
|
| 225 |
-
class GhostBottleneck(nn.Module):
|
| 226 |
-
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
|
| 227 |
-
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
|
| 228 |
-
super().__init__()
|
| 229 |
-
c_ = c2 // 2
|
| 230 |
-
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
|
| 231 |
-
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
|
| 232 |
-
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
|
| 233 |
-
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
|
| 234 |
-
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
|
| 235 |
-
|
| 236 |
-
def forward(self, x):
|
| 237 |
-
return self.conv(x) + self.shortcut(x)
|
| 238 |
-
|
| 239 |
-
|
| 240 |
-
class Contract(nn.Module):
|
| 241 |
-
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
|
| 242 |
-
def __init__(self, gain=2):
|
| 243 |
-
super().__init__()
|
| 244 |
-
self.gain = gain
|
| 245 |
-
|
| 246 |
-
def forward(self, x):
|
| 247 |
-
b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain'
|
| 248 |
-
s = self.gain
|
| 249 |
-
x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2)
|
| 250 |
-
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
|
| 251 |
-
return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
|
| 252 |
-
|
| 253 |
-
|
| 254 |
-
class Expand(nn.Module):
|
| 255 |
-
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
|
| 256 |
-
def __init__(self, gain=2):
|
| 257 |
-
super().__init__()
|
| 258 |
-
self.gain = gain
|
| 259 |
-
|
| 260 |
-
def forward(self, x):
|
| 261 |
-
b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
|
| 262 |
-
s = self.gain
|
| 263 |
-
x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80)
|
| 264 |
-
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
|
| 265 |
-
return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
|
| 266 |
-
|
| 267 |
-
|
| 268 |
-
class Concat(nn.Module):
|
| 269 |
-
# Concatenate a list of tensors along dimension
|
| 270 |
-
def __init__(self, dimension=1):
|
| 271 |
-
super().__init__()
|
| 272 |
-
self.d = dimension
|
| 273 |
-
|
| 274 |
-
def forward(self, x):
|
| 275 |
-
return torch.cat(x, self.d)
|
| 276 |
-
|
| 277 |
-
|
| 278 |
-
class DetectMultiBackend(nn.Module):
|
| 279 |
-
# YOLOv5 MultiBackend class for python inference on various backends
|
| 280 |
-
def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False):
|
| 281 |
-
# Usage:
|
| 282 |
-
# PyTorch: weights = *.pt
|
| 283 |
-
# TorchScript: *.torchscript
|
| 284 |
-
# ONNX Runtime: *.onnx
|
| 285 |
-
# ONNX OpenCV DNN: *.onnx with --dnn
|
| 286 |
-
# OpenVINO: *.xml
|
| 287 |
-
# CoreML: *.mlmodel
|
| 288 |
-
# TensorRT: *.engine
|
| 289 |
-
# TensorFlow SavedModel: *_saved_model
|
| 290 |
-
# TensorFlow GraphDef: *.pb
|
| 291 |
-
# TensorFlow Lite: *.tflite
|
| 292 |
-
# TensorFlow Edge TPU: *_edgetpu.tflite
|
| 293 |
-
from models.experimental import attempt_download, attempt_load # scoped to avoid circular import
|
| 294 |
-
|
| 295 |
-
super().__init__()
|
| 296 |
-
w = str(weights[0] if isinstance(weights, list) else weights)
|
| 297 |
-
pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs = self.model_type(w) # get backend
|
| 298 |
-
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
|
| 299 |
-
w = attempt_download(w) # download if not local
|
| 300 |
-
fp16 &= (pt or jit or onnx or engine) and device.type != 'cpu' # FP16
|
| 301 |
-
if data: # data.yaml path (optional)
|
| 302 |
-
with open(data, errors='ignore') as f:
|
| 303 |
-
names = yaml.safe_load(f)['names'] # class names
|
| 304 |
-
|
| 305 |
-
if pt: # PyTorch
|
| 306 |
-
model = attempt_load(weights if isinstance(weights, list) else w, map_location=device)
|
| 307 |
-
stride = max(int(model.stride.max()), 32) # model stride
|
| 308 |
-
names = model.module.names if hasattr(model, 'module') else model.names # get class names
|
| 309 |
-
model.half() if fp16 else model.float()
|
| 310 |
-
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
|
| 311 |
-
elif jit: # TorchScript
|
| 312 |
-
LOGGER.info(f'Loading {w} for TorchScript inference...')
|
| 313 |
-
extra_files = {'config.txt': ''} # model metadata
|
| 314 |
-
model = torch.jit.load(w, _extra_files=extra_files)
|
| 315 |
-
model.half() if fp16 else model.float()
|
| 316 |
-
if extra_files['config.txt']:
|
| 317 |
-
d = json.loads(extra_files['config.txt']) # extra_files dict
|
| 318 |
-
stride, names = int(d['stride']), d['names']
|
| 319 |
-
elif dnn: # ONNX OpenCV DNN
|
| 320 |
-
LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
|
| 321 |
-
check_requirements(('opencv-python>=4.5.4',))
|
| 322 |
-
net = cv2.dnn.readNetFromONNX(w)
|
| 323 |
-
elif onnx: # ONNX Runtime
|
| 324 |
-
LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
|
| 325 |
-
cuda = torch.cuda.is_available()
|
| 326 |
-
check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
|
| 327 |
-
import onnxruntime
|
| 328 |
-
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
|
| 329 |
-
session = onnxruntime.InferenceSession(w, providers=providers)
|
| 330 |
-
elif xml: # OpenVINO
|
| 331 |
-
LOGGER.info(f'Loading {w} for OpenVINO inference...')
|
| 332 |
-
check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
| 333 |
-
import openvino.inference_engine as ie
|
| 334 |
-
core = ie.IECore()
|
| 335 |
-
if not Path(w).is_file(): # if not *.xml
|
| 336 |
-
w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir
|
| 337 |
-
network = core.read_network(model=w, weights=Path(w).with_suffix('.bin')) # *.xml, *.bin paths
|
| 338 |
-
executable_network = core.load_network(network, device_name='CPU', num_requests=1)
|
| 339 |
-
elif engine: # TensorRT
|
| 340 |
-
LOGGER.info(f'Loading {w} for TensorRT inference...')
|
| 341 |
-
import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
|
| 342 |
-
check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
|
| 343 |
-
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
|
| 344 |
-
logger = trt.Logger(trt.Logger.INFO)
|
| 345 |
-
with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
|
| 346 |
-
model = runtime.deserialize_cuda_engine(f.read())
|
| 347 |
-
bindings = OrderedDict()
|
| 348 |
-
fp16 = False # default updated below
|
| 349 |
-
for index in range(model.num_bindings):
|
| 350 |
-
name = model.get_binding_name(index)
|
| 351 |
-
dtype = trt.nptype(model.get_binding_dtype(index))
|
| 352 |
-
shape = tuple(model.get_binding_shape(index))
|
| 353 |
-
data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device)
|
| 354 |
-
bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
|
| 355 |
-
if model.binding_is_input(index) and dtype == np.float16:
|
| 356 |
-
fp16 = True
|
| 357 |
-
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
|
| 358 |
-
context = model.create_execution_context()
|
| 359 |
-
batch_size = bindings['images'].shape[0]
|
| 360 |
-
elif coreml: # CoreML
|
| 361 |
-
LOGGER.info(f'Loading {w} for CoreML inference...')
|
| 362 |
-
import coremltools as ct
|
| 363 |
-
model = ct.models.MLModel(w)
|
| 364 |
-
else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
|
| 365 |
-
if saved_model: # SavedModel
|
| 366 |
-
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
|
| 367 |
-
import tensorflow as tf
|
| 368 |
-
keras = False # assume TF1 saved_model
|
| 369 |
-
model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
|
| 370 |
-
elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
|
| 371 |
-
LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
|
| 372 |
-
import tensorflow as tf
|
| 373 |
-
|
| 374 |
-
def wrap_frozen_graph(gd, inputs, outputs):
|
| 375 |
-
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
|
| 376 |
-
ge = x.graph.as_graph_element
|
| 377 |
-
return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
|
| 378 |
-
|
| 379 |
-
gd = tf.Graph().as_graph_def() # graph_def
|
| 380 |
-
gd.ParseFromString(open(w, 'rb').read())
|
| 381 |
-
frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs="Identity:0")
|
| 382 |
-
elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
|
| 383 |
-
try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
|
| 384 |
-
from tflite_runtime.interpreter import Interpreter, load_delegate
|
| 385 |
-
except ImportError:
|
| 386 |
-
import tensorflow as tf
|
| 387 |
-
Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
|
| 388 |
-
if edgetpu: # Edge TPU https://coral.ai/software/#edgetpu-runtime
|
| 389 |
-
LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
|
| 390 |
-
delegate = {'Linux': 'libedgetpu.so.1',
|
| 391 |
-
'Darwin': 'libedgetpu.1.dylib',
|
| 392 |
-
'Windows': 'edgetpu.dll'}[platform.system()]
|
| 393 |
-
interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
|
| 394 |
-
else: # Lite
|
| 395 |
-
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
|
| 396 |
-
interpreter = Interpreter(model_path=w) # load TFLite model
|
| 397 |
-
interpreter.allocate_tensors() # allocate
|
| 398 |
-
input_details = interpreter.get_input_details() # inputs
|
| 399 |
-
output_details = interpreter.get_output_details() # outputs
|
| 400 |
-
elif tfjs:
|
| 401 |
-
raise Exception('ERROR: YOLOv5 TF.js inference is not supported')
|
| 402 |
-
self.__dict__.update(locals()) # assign all variables to self
|
| 403 |
-
|
| 404 |
-
def forward(self, im, augment=False, visualize=False, val=False):
|
| 405 |
-
# YOLOv5 MultiBackend inference
|
| 406 |
-
b, ch, h, w = im.shape # batch, channel, height, width
|
| 407 |
-
if self.pt or self.jit: # PyTorch
|
| 408 |
-
y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize)
|
| 409 |
-
return y if val else y[0]
|
| 410 |
-
elif self.dnn: # ONNX OpenCV DNN
|
| 411 |
-
im = im.cpu().numpy() # torch to numpy
|
| 412 |
-
self.net.setInput(im)
|
| 413 |
-
y = self.net.forward()
|
| 414 |
-
elif self.onnx: # ONNX Runtime
|
| 415 |
-
im = im.cpu().numpy() # torch to numpy
|
| 416 |
-
y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
|
| 417 |
-
elif self.xml: # OpenVINO
|
| 418 |
-
im = im.cpu().numpy() # FP32
|
| 419 |
-
desc = self.ie.TensorDesc(precision='FP32', dims=im.shape, layout='NCHW') # Tensor Description
|
| 420 |
-
request = self.executable_network.requests[0] # inference request
|
| 421 |
-
request.set_blob(blob_name='images', blob=self.ie.Blob(desc, im)) # name=next(iter(request.input_blobs))
|
| 422 |
-
request.infer()
|
| 423 |
-
y = request.output_blobs['output'].buffer # name=next(iter(request.output_blobs))
|
| 424 |
-
elif self.engine: # TensorRT
|
| 425 |
-
assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape)
|
| 426 |
-
self.binding_addrs['images'] = int(im.data_ptr())
|
| 427 |
-
self.context.execute_v2(list(self.binding_addrs.values()))
|
| 428 |
-
y = self.bindings['output'].data
|
| 429 |
-
elif self.coreml: # CoreML
|
| 430 |
-
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
| 431 |
-
im = Image.fromarray((im[0] * 255).astype('uint8'))
|
| 432 |
-
# im = im.resize((192, 320), Image.ANTIALIAS)
|
| 433 |
-
y = self.model.predict({'image': im}) # coordinates are xywh normalized
|
| 434 |
-
if 'confidence' in y:
|
| 435 |
-
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
|
| 436 |
-
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
|
| 437 |
-
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
|
| 438 |
-
else:
|
| 439 |
-
k = 'var_' + str(sorted(int(k.replace('var_', '')) for k in y)[-1]) # output key
|
| 440 |
-
y = y[k] # output
|
| 441 |
-
else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
|
| 442 |
-
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
| 443 |
-
if self.saved_model: # SavedModel
|
| 444 |
-
y = (self.model(im, training=False) if self.keras else self.model(im)).numpy()
|
| 445 |
-
elif self.pb: # GraphDef
|
| 446 |
-
y = self.frozen_func(x=self.tf.constant(im)).numpy()
|
| 447 |
-
else: # Lite or Edge TPU
|
| 448 |
-
input, output = self.input_details[0], self.output_details[0]
|
| 449 |
-
int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model
|
| 450 |
-
if int8:
|
| 451 |
-
scale, zero_point = input['quantization']
|
| 452 |
-
im = (im / scale + zero_point).astype(np.uint8) # de-scale
|
| 453 |
-
self.interpreter.set_tensor(input['index'], im)
|
| 454 |
-
self.interpreter.invoke()
|
| 455 |
-
y = self.interpreter.get_tensor(output['index'])
|
| 456 |
-
if int8:
|
| 457 |
-
scale, zero_point = output['quantization']
|
| 458 |
-
y = (y.astype(np.float32) - zero_point) * scale # re-scale
|
| 459 |
-
y[..., :4] *= [w, h, w, h] # xywh normalized to pixels
|
| 460 |
-
|
| 461 |
-
if isinstance(y, np.ndarray):
|
| 462 |
-
y = torch.tensor(y, device=self.device)
|
| 463 |
-
return (y, []) if val else y
|
| 464 |
-
|
| 465 |
-
def warmup(self, imgsz=(1, 3, 640, 640)):
|
| 466 |
-
# Warmup model by running inference once
|
| 467 |
-
if any((self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb)): # warmup types
|
| 468 |
-
if self.device.type != 'cpu': # only warmup GPU models
|
| 469 |
-
im = torch.zeros(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input
|
| 470 |
-
for _ in range(2 if self.jit else 1): #
|
| 471 |
-
self.forward(im) # warmup
|
| 472 |
-
|
| 473 |
-
@staticmethod
|
| 474 |
-
def model_type(p='path/to/model.pt'):
|
| 475 |
-
# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
|
| 476 |
-
from export import export_formats
|
| 477 |
-
suffixes = list(export_formats().Suffix) + ['.xml'] # export suffixes
|
| 478 |
-
check_suffix(p, suffixes) # checks
|
| 479 |
-
p = Path(p).name # eliminate trailing separators
|
| 480 |
-
pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, xml2 = (s in p for s in suffixes)
|
| 481 |
-
xml |= xml2 # *_openvino_model or *.xml
|
| 482 |
-
tflite &= not edgetpu # *.tflite
|
| 483 |
-
return pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs
|
| 484 |
-
|
| 485 |
-
|
| 486 |
-
class AutoShape(nn.Module):
|
| 487 |
-
# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
| 488 |
-
conf = 0.25 # NMS confidence threshold
|
| 489 |
-
iou = 0.45 # NMS IoU threshold
|
| 490 |
-
agnostic = False # NMS class-agnostic
|
| 491 |
-
multi_label = False # NMS multiple labels per box
|
| 492 |
-
classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
|
| 493 |
-
max_det = 1000 # maximum number of detections per image
|
| 494 |
-
amp = False # Automatic Mixed Precision (AMP) inference
|
| 495 |
-
|
| 496 |
-
def __init__(self, model):
|
| 497 |
-
super().__init__()
|
| 498 |
-
LOGGER.info('Adding AutoShape... ')
|
| 499 |
-
copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
|
| 500 |
-
self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance
|
| 501 |
-
self.pt = not self.dmb or model.pt # PyTorch model
|
| 502 |
-
self.model = model.eval()
|
| 503 |
-
|
| 504 |
-
def _apply(self, fn):
|
| 505 |
-
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
| 506 |
-
self = super()._apply(fn)
|
| 507 |
-
if self.pt:
|
| 508 |
-
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
|
| 509 |
-
m.stride = fn(m.stride)
|
| 510 |
-
m.grid = list(map(fn, m.grid))
|
| 511 |
-
if isinstance(m.anchor_grid, list):
|
| 512 |
-
m.anchor_grid = list(map(fn, m.anchor_grid))
|
| 513 |
-
return self
|
| 514 |
-
|
| 515 |
-
@torch.no_grad()
|
| 516 |
-
def forward(self, imgs, size=640, augment=False, profile=False):
|
| 517 |
-
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
|
| 518 |
-
# file: imgs = 'data/images/zidane.jpg' # str or PosixPath
|
| 519 |
-
# URI: = 'https://ultralytics.com/images/zidane.jpg'
|
| 520 |
-
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
|
| 521 |
-
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
|
| 522 |
-
# numpy: = np.zeros((640,1280,3)) # HWC
|
| 523 |
-
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
|
| 524 |
-
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
| 525 |
-
|
| 526 |
-
t = [time_sync()]
|
| 527 |
-
p = next(self.model.parameters()) if self.pt else torch.zeros(1) # for device and type
|
| 528 |
-
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
|
| 529 |
-
if isinstance(imgs, torch.Tensor): # torch
|
| 530 |
-
with amp.autocast(autocast):
|
| 531 |
-
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
|
| 532 |
-
|
| 533 |
-
# Pre-process
|
| 534 |
-
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
|
| 535 |
-
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
| 536 |
-
for i, im in enumerate(imgs):
|
| 537 |
-
f = f'image{i}' # filename
|
| 538 |
-
if isinstance(im, (str, Path)): # filename or uri
|
| 539 |
-
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
|
| 540 |
-
im = np.asarray(exif_transpose(im))
|
| 541 |
-
elif isinstance(im, Image.Image): # PIL Image
|
| 542 |
-
im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
|
| 543 |
-
files.append(Path(f).with_suffix('.jpg').name)
|
| 544 |
-
if im.shape[0] < 5: # image in CHW
|
| 545 |
-
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
| 546 |
-
im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input
|
| 547 |
-
s = im.shape[:2] # HWC
|
| 548 |
-
shape0.append(s) # image shape
|
| 549 |
-
g = (size / max(s)) # gain
|
| 550 |
-
shape1.append([y * g for y in s])
|
| 551 |
-
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
| 552 |
-
shape1 = [make_divisible(x, self.stride) if self.pt else size for x in np.array(shape1).max(0)] # inf shape
|
| 553 |
-
x = [letterbox(im, shape1, auto=False)[0] for im in imgs] # pad
|
| 554 |
-
x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
|
| 555 |
-
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
|
| 556 |
-
t.append(time_sync())
|
| 557 |
-
|
| 558 |
-
with amp.autocast(autocast):
|
| 559 |
-
# Inference
|
| 560 |
-
y = self.model(x, augment, profile) # forward
|
| 561 |
-
t.append(time_sync())
|
| 562 |
-
|
| 563 |
-
# Post-process
|
| 564 |
-
y = non_max_suppression(y if self.dmb else y[0], self.conf, self.iou, self.classes, self.agnostic,
|
| 565 |
-
self.multi_label, max_det=self.max_det) # NMS
|
| 566 |
-
for i in range(n):
|
| 567 |
-
scale_coords(shape1, y[i][:, :4], shape0[i])
|
| 568 |
-
|
| 569 |
-
t.append(time_sync())
|
| 570 |
-
return Detections(imgs, y, files, t, self.names, x.shape)
|
| 571 |
-
|
| 572 |
-
|
| 573 |
-
class Detections:
|
| 574 |
-
# YOLOv5 detections class for inference results
|
| 575 |
-
def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None):
|
| 576 |
-
super().__init__()
|
| 577 |
-
d = pred[0].device # device
|
| 578 |
-
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations
|
| 579 |
-
self.imgs = imgs # list of images as numpy arrays
|
| 580 |
-
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
|
| 581 |
-
self.names = names # class names
|
| 582 |
-
self.files = files # image filenames
|
| 583 |
-
self.times = times # profiling times
|
| 584 |
-
self.xyxy = pred # xyxy pixels
|
| 585 |
-
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
|
| 586 |
-
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
|
| 587 |
-
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
|
| 588 |
-
self.n = len(self.pred) # number of images (batch size)
|
| 589 |
-
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
|
| 590 |
-
self.s = shape # inference BCHW shape
|
| 591 |
-
|
| 592 |
-
def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
|
| 593 |
-
crops = []
|
| 594 |
-
for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
|
| 595 |
-
s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
|
| 596 |
-
if pred.shape[0]:
|
| 597 |
-
for c in pred[:, -1].unique():
|
| 598 |
-
n = (pred[:, -1] == c).sum() # detections per class
|
| 599 |
-
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
|
| 600 |
-
if show or save or render or crop:
|
| 601 |
-
annotator = Annotator(im, example=str(self.names))
|
| 602 |
-
for *box, conf, cls in reversed(pred): # xyxy, confidence, class
|
| 603 |
-
label = f'{self.names[int(cls)]} {conf:.2f}'
|
| 604 |
-
if crop:
|
| 605 |
-
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
|
| 606 |
-
crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
|
| 607 |
-
'im': save_one_box(box, im, file=file, save=save)})
|
| 608 |
-
else: # all others
|
| 609 |
-
annotator.box_label(box, label, color=colors(cls))
|
| 610 |
-
im = annotator.im
|
| 611 |
-
else:
|
| 612 |
-
s += '(no detections)'
|
| 613 |
-
|
| 614 |
-
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
|
| 615 |
-
if pprint:
|
| 616 |
-
LOGGER.info(s.rstrip(', '))
|
| 617 |
-
if show:
|
| 618 |
-
im.show(self.files[i]) # show
|
| 619 |
-
if save:
|
| 620 |
-
f = self.files[i]
|
| 621 |
-
im.save(save_dir / f) # save
|
| 622 |
-
if i == self.n - 1:
|
| 623 |
-
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
|
| 624 |
-
if render:
|
| 625 |
-
self.imgs[i] = np.asarray(im)
|
| 626 |
-
if crop:
|
| 627 |
-
if save:
|
| 628 |
-
LOGGER.info(f'Saved results to {save_dir}\n')
|
| 629 |
-
return crops
|
| 630 |
-
|
| 631 |
-
def print(self):
|
| 632 |
-
self.display(pprint=True) # print results
|
| 633 |
-
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
|
| 634 |
-
self.t)
|
| 635 |
-
|
| 636 |
-
def show(self):
|
| 637 |
-
self.display(show=True) # show results
|
| 638 |
-
|
| 639 |
-
def save(self, save_dir='runs/detect/exp'):
|
| 640 |
-
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
|
| 641 |
-
self.display(save=True, save_dir=save_dir) # save results
|
| 642 |
-
|
| 643 |
-
def crop(self, save=True, save_dir='runs/detect/exp'):
|
| 644 |
-
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
|
| 645 |
-
return self.display(crop=True, save=save, save_dir=save_dir) # crop results
|
| 646 |
-
|
| 647 |
-
def render(self):
|
| 648 |
-
self.display(render=True) # render results
|
| 649 |
-
return self.imgs
|
| 650 |
-
|
| 651 |
-
def pandas(self):
|
| 652 |
-
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
|
| 653 |
-
new = copy(self) # return copy
|
| 654 |
-
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
|
| 655 |
-
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
|
| 656 |
-
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
|
| 657 |
-
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
|
| 658 |
-
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
|
| 659 |
-
return new
|
| 660 |
-
|
| 661 |
-
def tolist(self):
|
| 662 |
-
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
| 663 |
-
r = range(self.n) # iterable
|
| 664 |
-
x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
|
| 665 |
-
# for d in x:
|
| 666 |
-
# for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
| 667 |
-
# setattr(d, k, getattr(d, k)[0]) # pop out of list
|
| 668 |
-
return x
|
| 669 |
-
|
| 670 |
-
def __len__(self):
|
| 671 |
-
return self.n
|
| 672 |
-
|
| 673 |
-
|
| 674 |
-
class Classify(nn.Module):
|
| 675 |
-
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
| 676 |
-
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
| 677 |
-
super().__init__()
|
| 678 |
-
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
| 679 |
-
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
| 680 |
-
self.flat = nn.Flatten()
|
| 681 |
-
|
| 682 |
-
def forward(self, x):
|
| 683 |
-
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
| 684 |
-
return self.flat(self.conv(z)) # flatten to x(b,c2)
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|
ultralytics/yolov5/models/experimental.py
DELETED
|
@@ -1,121 +0,0 @@
|
|
| 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']:
|
| 119 |
-
setattr(model, k, getattr(model[-1], k))
|
| 120 |
-
model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
|
| 121 |
-
return model # return ensemble
|
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|
ultralytics/yolov5/models/hub/anchors.yaml
DELETED
|
@@ -1,59 +0,0 @@
|
|
| 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
|
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ultralytics/yolov5/models/hub/yolov3-spp.yaml
DELETED
|
@@ -1,51 +0,0 @@
|
|
| 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 |
-
]
|
|
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ultralytics/yolov5/models/hub/yolov3-tiny.yaml
DELETED
|
@@ -1,41 +0,0 @@
|
|
| 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 |
-
]
|
|
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|
ultralytics/yolov5/models/hub/yolov3.yaml
DELETED
|
@@ -1,51 +0,0 @@
|
|
| 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 |
-
]
|
|
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|
ultralytics/yolov5/models/hub/yolov5-bifpn.yaml
DELETED
|
@@ -1,48 +0,0 @@
|
|
| 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 |
-
]
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ultralytics/yolov5/models/hub/yolov5-fpn.yaml
DELETED
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@@ -1,42 +0,0 @@
|
|
| 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 |
-
]
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ultralytics/yolov5/models/hub/yolov5-p2.yaml
DELETED
|
@@ -1,54 +0,0 @@
|
|
| 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 |
-
]
|
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ultralytics/yolov5/models/hub/yolov5-p34.yaml
DELETED
|
@@ -1,41 +0,0 @@
|
|
| 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 |
-
]
|
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ultralytics/yolov5/models/hub/yolov5-p6.yaml
DELETED
|
@@ -1,56 +0,0 @@
|
|
| 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 |
-
]
|
|
|
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ultralytics/yolov5/models/hub/yolov5-p7.yaml
DELETED
|
@@ -1,67 +0,0 @@
|
|
| 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
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| 6 |
-
width_multiple: 1.0 # layer channel multiple
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| 7 |
-
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
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| 8 |
-
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| 9 |
-
# YOLOv5 v6.0 backbone
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| 10 |
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backbone:
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| 11 |
-
# [from, number, module, args]
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| 12 |
-
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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| 13 |
-
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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| 14 |
-
[-1, 3, C3, [128]],
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| 15 |
-
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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| 16 |
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[-1, 6, C3, [256]],
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| 17 |
-
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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| 18 |
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[-1, 9, C3, [512]],
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| 19 |
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[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
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| 20 |
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[-1, 3, C3, [768]],
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| 21 |
-
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
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| 22 |
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[-1, 3, C3, [1024]],
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| 23 |
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[-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
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| 24 |
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[-1, 3, C3, [1280]],
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| 25 |
-
[-1, 1, SPPF, [1280, 5]], # 13
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| 26 |
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]
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| 27 |
-
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| 28 |
-
# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs
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| 29 |
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head:
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| 30 |
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[[-1, 1, Conv, [1024, 1, 1]],
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| 31 |
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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| 32 |
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[[-1, 10], 1, Concat, [1]], # cat backbone P6
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| 33 |
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[-1, 3, C3, [1024, False]], # 17
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| 34 |
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| 35 |
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[-1, 1, Conv, [768, 1, 1]],
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| 36 |
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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| 37 |
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[[-1, 8], 1, Concat, [1]], # cat backbone P5
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| 38 |
-
[-1, 3, C3, [768, False]], # 21
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| 39 |
-
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| 40 |
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[-1, 1, Conv, [512, 1, 1]],
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| 41 |
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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| 42 |
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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| 43 |
-
[-1, 3, C3, [512, False]], # 25
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| 44 |
-
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| 45 |
-
[-1, 1, Conv, [256, 1, 1]],
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| 46 |
-
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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| 47 |
-
[[-1, 4], 1, Concat, [1]], # cat backbone P3
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| 48 |
-
[-1, 3, C3, [256, False]], # 29 (P3/8-small)
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| 49 |
-
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| 50 |
-
[-1, 1, Conv, [256, 3, 2]],
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| 51 |
-
[[-1, 26], 1, Concat, [1]], # cat head P4
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| 52 |
-
[-1, 3, C3, [512, False]], # 32 (P4/16-medium)
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| 53 |
-
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| 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)
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| 57 |
-
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| 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 |
-
]
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ultralytics/yolov5/models/hub/yolov5-panet.yaml
DELETED
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@@ -1,48 +0,0 @@
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|
| 1 |
-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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| 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 |
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]
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