From 9e58c32c15835e54e57f7b8c925367a64cb94951 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 13 Mar 2023 00:40:11 +0100 Subject: [PATCH] Docs URL fixes and YAML updates (#1383) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Huijae Lee <46982469+ZeroAct@users.noreply.github.com> --- .github/workflows/publish.yml | 6 +-- README.md | 2 +- README.zh-CN.md | 4 +- docs/modes/predict.md | 54 +++++++++---------- docs/modes/track.md | 2 +- docs/stylesheets/style.css | 35 ++++-------- docs/tasks/classify.md | 2 +- docs/tasks/detect.md | 2 +- docs/tasks/keypoints.md | 2 +- docs/tasks/segment.md | 2 +- examples/tutorial.ipynb | 2 +- mkdocs.yml | 14 +++++ ultralytics/datasets/Argoverse.yaml | 2 +- ultralytics/datasets/GlobalWheat2020.yaml | 2 +- ultralytics/datasets/ImageNet.yaml | 2 +- ultralytics/datasets/Objects365.yaml | 2 +- ultralytics/datasets/SKU-110K.yaml | 2 +- ultralytics/datasets/VOC.yaml | 2 +- ultralytics/datasets/VisDrone.yaml | 2 +- ultralytics/datasets/coco.yaml | 2 +- ultralytics/datasets/coco128-seg.yaml | 2 +- ultralytics/datasets/coco128.yaml | 2 +- ultralytics/datasets/coco8-seg.yaml | 2 +- ultralytics/datasets/coco8.yaml | 2 +- ultralytics/datasets/xView.yaml | 2 +- ultralytics/models/README.md | 2 +- ultralytics/yolo/data/scripts/get_coco.sh | 2 +- ultralytics/yolo/data/scripts/get_coco128.sh | 2 +- ultralytics/yolo/data/scripts/get_imagenet.sh | 2 +- ultralytics/yolo/engine/results.py | 2 +- ultralytics/yolo/v8/detect/predict.py | 7 ++- ultralytics/yolo/v8/segment/predict.py | 10 ++-- 32 files changed, 87 insertions(+), 93 deletions(-) diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index c533e33..15c13bf 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -64,6 +64,6 @@ jobs: env: PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }} run: | - mkdocs gh-deploy || true - git checkout gh-pages - git push https://$PERSONAL_ACCESS_TOKEN@github.com/ultralytics/docs gh-pages --force + mkdocs gh-deploy --force || true + # git checkout gh-pages + # git push https://$PERSONAL_ACCESS_TOKEN@github.com/ultralytics/docs gh-pages --force diff --git a/README.md b/README.md index 88e0cd8..cf6d9b0 100644 --- a/README.md +++ b/README.md @@ -88,7 +88,7 @@ yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" #### Python YOLOv8 may also be used directly in a Python environment, and accepts the -same [arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above: +same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above: ```python from ultralytics import YOLO diff --git a/README.zh-CN.md b/README.zh-CN.md index 42822e4..7911d71 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -75,7 +75,7 @@ yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg" ``` `yolo`可以用于各种任务和模式，并接受额外的参数，例如 `imgsz=640`。参见 YOLOv8 [文档](https://docs.ultralytics.com) -中可用`yolo`[参数](https://docs.ultralytics.com/cfg/)的完整列表。 +中可用`yolo`[参数](https://docs.ultralytics.com/usage/cfg/)的完整列表。 ```bash yolo task=detect mode=train model=yolov8n.pt args... @@ -84,7 +84,7 @@ yolo task=detect mode=train model=yolov8n.pt args... export yolov8n.pt format=onnx args... ``` -YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/cfg/)： +YOLOv8 也可以在 Python 环境中直接使用，并接受与上面 CLI 例子中相同的[参数](https://docs.ultralytics.com/usage/cfg/)： ```python from ultralytics import YOLO diff --git a/docs/modes/predict.md b/docs/modes/predict.md index ed60327..ffc8722 100644 --- a/docs/modes/predict.md +++ b/docs/modes/predict.md @@ -59,18 +59,18 @@ For images, YOLOv8 supports a variety of image formats defined in [yolo/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/data/utils.py). The following suffixes are valid for images: -| Image Suffixes | Example Predict Command | Reference | -|----------------|----------------------------------|--------------------------------------------------------------------------------------| -| bmp | `yolo predict source=image.bmp` | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/gdi/bitmap-file-format) | -| dng | `yolo predict source=image.dng` | [Adobe](https://helpx.adobe.com/photoshop/using/digital-negative.html) | -| jpeg | `yolo predict source=image.jpeg` | [Joint Photographic Experts Group](https://jpeg.org/jpeg/) | -| jpg | `yolo predict source=image.jpg` | [Joint Photographic Experts Group](https://jpeg.org/jpeg/) | -| mpo | `yolo predict source=image.mpo` | [CIPA](https://www.cipa.jp/std/documents/e/DC-007-Translation-2018-E.pdf) | -| png | `yolo predict source=image.png` | [Portable Network Graphics](https://www.w3.org/TR/PNG/) | -| tif | `yolo predict source=image.tif` | [Adobe](https://www.adobe.com/content/dam/acom/en/products/photoshop/pdfs/tiff6.pdf) | -| tiff | `yolo predict source=image.tiff` | [Adobe](https://www.adobe.com/content/dam/acom/en/products/photoshop/pdfs/tiff6.pdf) | -| webp | `yolo predict source=image.webp` | [Google Developers](https://developers.google.com/speed/webp) | -| pfm | `yolo predict source=image.pfm` | [HDR Labs](http://hdrlabs.com/tools/pfrenchy/) | +| Image Suffixes | Example Predict Command | Reference | +|----------------|----------------------------------|-------------------------------------------------------------------------------| +| .bmp | `yolo predict source=image.bmp` | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format) | +| .dng | `yolo predict source=image.dng` | [Adobe DNG](https://www.adobe.com/products/photoshop/extend.displayTab2.html) | +| .jpeg | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) | +| .jpg | `yolo predict source=image.jpg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) | +| .mpo | `yolo predict source=image.mpo` | [Multi Picture Object](https://fileinfo.com/extension/mpo) | +| .png | `yolo predict source=image.png` | [Portable Network Graphics](https://en.wikipedia.org/wiki/PNG) | +| .tif | `yolo predict source=image.tif` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF) | +| .tiff | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF) | +| .webp | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP) | +| .pfm | `yolo predict source=image.pfm` | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats) | ## Video Formats @@ -78,20 +78,20 @@ For videos, YOLOv8 also supports a variety of video formats defined in [yolo/data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/yolo/data/utils.py). The following suffixes are valid for videos: -| Video Suffixes | Example Predict Command | Reference | -|----------------|----------------------------------|----------------------------------------------------------------------------------------------------------------| -| asf | `yolo predict source=video.asf` | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/wmformat/asf-file-structure) | -| avi | `yolo predict source=video.avi` | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/directshow/avi-riff-file-reference) | -| gif | `yolo predict source=video.gif` | [CompuServe](https://www.w3.org/Graphics/GIF/spec-gif89a.txt) | -| m4v | `yolo predict source=video.m4v` | [Apple](https://developer.apple.com/library/archive/documentation/QuickTime/QTFF/QTFFChap2/qtff2.html) | -| mkv | `yolo predict source=video.mkv` | [Matroska](https://matroska.org/technical/specs/index.html) | -| mov | `yolo predict source=video.mov` | [Apple](https://developer.apple.com/library/archive/documentation/QuickTime/QTFF/QTFFPreface/qtffPreface.html) | -| mp4 | `yolo predict source=video.mp4` | [ISO 68939](https://www.iso.org/standard/68939.html) | -| mpeg | `yolo predict source=video.mpeg` | [ISO 56021](https://www.iso.org/standard/56021.html) | -| mpg | `yolo predict source=video.mpg` | [ISO 56021](https://www.iso.org/standard/56021.html) | -| ts | `yolo predict source=video.ts` | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream) | -| wmv | `yolo predict source=video.wmv` | [Microsoft](https://docs.microsoft.com/en-us/windows/win32/wmformat/wmv-file-structure) | -| webm | `yolo predict source=video.webm` | [Google Developers](https://developers.google.com/media/vp9/getting-started/webm-file-format) | +| Video Suffixes | Example Predict Command | Reference | +|----------------|----------------------------------|----------------------------------------------------------------------------------| +| .asf | `yolo predict source=video.asf` | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) | +| .avi | `yolo predict source=video.avi` | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave) | +| .gif | `yolo predict source=video.gif` | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF) | +| .m4v | `yolo predict source=video.m4v` | [MPEG-4 Part 14](https://en.wikipedia.org/wiki/M4V) | +| .mkv | `yolo predict source=video.mkv` | [Matroska](https://en.wikipedia.org/wiki/Matroska) | +| .mov | `yolo predict source=video.mov` | [QuickTime File Format](https://en.wikipedia.org/wiki/QuickTime_File_Format) | +| .mp4 | `yolo predict source=video.mp4` | [MPEG-4 Part 14 - Wikipedia](https://en.wikipedia.org/wiki/MPEG-4_Part_14) | +| .mpeg | `yolo predict source=video.mpeg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1) | +| .mpg | `yolo predict source=video.mpg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1) | +| .ts | `yolo predict source=video.ts` | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream) | +| .wmv | `yolo predict source=video.wmv` | [Windows Media Video](https://en.wikipedia.org/wiki/Windows_Media_Video) | +| .webm | `yolo predict source=video.webm` | [WebM Project](https://en.wikipedia.org/wiki/WebM) | ## Working with Results @@ -163,7 +163,7 @@ Class reference documentation for `Results` module and its components can be fou ## Plotting results You can use `plot()` function of `Result` object to plot results on in image object. It plots all components(boxes, -masks, classification logits, etc) found in the results object +masks, classification logits, etc.) found in the results object ```python res = model(img) diff --git a/docs/modes/track.md b/docs/modes/track.md index 551c502..c1dd5c3 100644 --- a/docs/modes/track.md +++ b/docs/modes/track.md @@ -52,7 +52,7 @@ do is loading the corresponding (detection or segmentation) model. ### Tracking Tracking shares the configuration with predict, i.e `conf`, `iou`, `show`. More configurations please refer -to [predict page](https://docs.ultralytics.com/cfg/#prediction). +to [predict page](https://docs.ultralytics.com/usage/cfg/#prediction). !!! example "" === "Python" diff --git a/docs/stylesheets/style.css b/docs/stylesheets/style.css index fc10c4f..4bed4e1 100644 --- a/docs/stylesheets/style.css +++ b/docs/stylesheets/style.css @@ -1,31 +1,14 @@ th, td { - border: 1px solid var(--md-typeset-table-color); - border-spacing: 0px; - border-bottom: none; - border-left: none; - border-top: none; + border: 0.5px solid var(--md-typeset-table-color); + border-spacing: 0px; + border-bottom: none; + border-left: none; + border-top: none; } - .md-typeset__table { - line-height: 1; + min-width: 100%; + line-height: 1; } - -.md-typeset__table table:not([class]) { - font-size: .74rem; - border-right: none; +.md-typeset table:not([class]) { + display: table; } - -.md-typeset__table table:not([class]) td, -.md-typeset__table table:not([class]) th { - padding: 15px; -} - -/* light mode alternating table bg colors */ -.md-typeset__table tr:nth-child(2n) { - background-color: #f8f8f8; -} - -/* dark mode alternating table bg colors */ -[data-md-color-scheme="slate"] .md-typeset__table tr:nth-child(2n) { - background-color: hsla(var(--md-hue),25%,25%,1) -} \ No newline at end of file diff --git a/docs/tasks/classify.md b/docs/tasks/classify.md index b2da5b8..f0d518c 100644 --- a/docs/tasks/classify.md +++ b/docs/tasks/classify.md @@ -91,7 +91,7 @@ Use a trained YOLOv8n-cls model to run predictions on images. yolo classify predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model ``` -Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page. +Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page. ## Export diff --git a/docs/tasks/detect.md b/docs/tasks/detect.md index 5b5e545..442c2f3 100644 --- a/docs/tasks/detect.md +++ b/docs/tasks/detect.md @@ -93,7 +93,7 @@ Use a trained YOLOv8n model to run predictions on images. yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model ``` -Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page. +Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page. ## Export diff --git a/docs/tasks/keypoints.md b/docs/tasks/keypoints.md index 6c4c74d..14cd9f5 100644 --- a/docs/tasks/keypoints.md +++ b/docs/tasks/keypoints.md @@ -95,7 +95,7 @@ Use a trained YOLOv8n model to run predictions on images. yolo detect predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model ``` -Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page. +Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page. ## Export TODO diff --git a/docs/tasks/segment.md b/docs/tasks/segment.md index d38f317..47a03d5 100644 --- a/docs/tasks/segment.md +++ b/docs/tasks/segment.md @@ -97,7 +97,7 @@ Use a trained YOLOv8n-seg model to run predictions on images. yolo segment predict model=path/to/best.pt source="https://ultralytics.com/images/bus.jpg" # predict with custom model ``` -Read more details of `predict` in our [Predict](https://docs.ultralytics.com/predict/) page. +Read more details of `predict` in our [Predict](https://docs.ultralytics.com/modes/predict/) page. ## Export diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index 2a22bad..91f69ac 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -86,7 +86,7 @@ "source": [ "# 1. Predict\n", "\n", - "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n" + "YOLOv8 may be used directly in the Command Line Interface (CLI) with a `yolo` command for a variety of tasks and modes and accepts additional arguments, i.e. `imgsz=640`. See a full list of available `yolo` [arguments](https://docs.ultralytics.com/usage/cfg/) in the YOLOv8 [Docs](https://docs.ultralytics.com).\n" ] }, { diff --git a/mkdocs.yml b/mkdocs.yml index 8d07614..296822d 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -51,6 +51,20 @@ extra: analytics: provider: google property: G-2M5EHKC0BH + feedback: + title: Was this page helpful? + ratings: + - icon: material/heart + name: This page was helpful + data: 1 + note: Thanks for your feedback! + - icon: material/heart-broken + name: This page could be improved + data: 0 + note: >- + Thanks for your feedback!
+ Tell us what we can improve. + social: - icon: fontawesome/brands/github link: https://github.com/ultralytics diff --git a/ultralytics/datasets/Argoverse.yaml b/ultralytics/datasets/Argoverse.yaml index 0be17c8..98cafc6 100644 --- a/ultralytics/datasets/Argoverse.yaml +++ b/ultralytics/datasets/Argoverse.yaml @@ -2,7 +2,7 @@ # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI # Example usage: yolo train data=Argoverse.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── Argoverse ← downloads here (31.3 GB) diff --git a/ultralytics/datasets/GlobalWheat2020.yaml b/ultralytics/datasets/GlobalWheat2020.yaml index c41cb4f..10df6c4 100644 --- a/ultralytics/datasets/GlobalWheat2020.yaml +++ b/ultralytics/datasets/GlobalWheat2020.yaml @@ -2,7 +2,7 @@ # Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan # Example usage: yolo train data=GlobalWheat2020.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── GlobalWheat2020 ← downloads here (7.0 GB) diff --git a/ultralytics/datasets/ImageNet.yaml b/ultralytics/datasets/ImageNet.yaml index 87cf6f1..2775809 100644 --- a/ultralytics/datasets/ImageNet.yaml +++ b/ultralytics/datasets/ImageNet.yaml @@ -3,7 +3,7 @@ # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels # Example usage: yolo train task=classify data=imagenet # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── imagenet ← downloads here (144 GB) diff --git a/ultralytics/datasets/Objects365.yaml b/ultralytics/datasets/Objects365.yaml index ad9e925..db4a892 100644 --- a/ultralytics/datasets/Objects365.yaml +++ b/ultralytics/datasets/Objects365.yaml @@ -2,7 +2,7 @@ # Objects365 dataset https://www.objects365.org/ by Megvii # Example usage: yolo train data=Objects365.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── Objects365 ← downloads here (712 GB = 367G data + 345G zips) diff --git a/ultralytics/datasets/SKU-110K.yaml b/ultralytics/datasets/SKU-110K.yaml index 6052177..da9595f 100644 --- a/ultralytics/datasets/SKU-110K.yaml +++ b/ultralytics/datasets/SKU-110K.yaml @@ -2,7 +2,7 @@ # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail # Example usage: yolo train data=SKU-110K.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── SKU-110K ← downloads here (13.6 GB) diff --git a/ultralytics/datasets/VOC.yaml b/ultralytics/datasets/VOC.yaml index 8e5d68c..6c6b3d5 100644 --- a/ultralytics/datasets/VOC.yaml +++ b/ultralytics/datasets/VOC.yaml @@ -2,7 +2,7 @@ # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford # Example usage: yolo train data=VOC.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── VOC ← downloads here (2.8 GB) diff --git a/ultralytics/datasets/VisDrone.yaml b/ultralytics/datasets/VisDrone.yaml index 141b568..a481066 100644 --- a/ultralytics/datasets/VisDrone.yaml +++ b/ultralytics/datasets/VisDrone.yaml @@ -2,7 +2,7 @@ # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University # Example usage: yolo train data=VisDrone.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── VisDrone ← downloads here (2.3 GB) diff --git a/ultralytics/datasets/coco.yaml b/ultralytics/datasets/coco.yaml index 6c4bc20..4734643 100644 --- a/ultralytics/datasets/coco.yaml +++ b/ultralytics/datasets/coco.yaml @@ -2,7 +2,7 @@ # COCO 2017 dataset http://cocodataset.org by Microsoft # Example usage: yolo train data=coco.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco ← downloads here (20.1 GB) diff --git a/ultralytics/datasets/coco128-seg.yaml b/ultralytics/datasets/coco128-seg.yaml index 4240387..7c2145f 100644 --- a/ultralytics/datasets/coco128-seg.yaml +++ b/ultralytics/datasets/coco128-seg.yaml @@ -2,7 +2,7 @@ # COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Example usage: yolo train data=coco128.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco128-seg ← downloads here (7 MB) diff --git a/ultralytics/datasets/coco128.yaml b/ultralytics/datasets/coco128.yaml index 0e02812..fe093d5 100644 --- a/ultralytics/datasets/coco128.yaml +++ b/ultralytics/datasets/coco128.yaml @@ -2,7 +2,7 @@ # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Example usage: yolo train data=coco128.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco128 ← downloads here (7 MB) diff --git a/ultralytics/datasets/coco8-seg.yaml b/ultralytics/datasets/coco8-seg.yaml index 0dfa376..e05951a 100644 --- a/ultralytics/datasets/coco8-seg.yaml +++ b/ultralytics/datasets/coco8-seg.yaml @@ -2,7 +2,7 @@ # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics # Example usage: yolo train data=coco8-seg.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco8-seg ← downloads here (1 MB) diff --git a/ultralytics/datasets/coco8.yaml b/ultralytics/datasets/coco8.yaml index cb80516..56e8151 100644 --- a/ultralytics/datasets/coco8.yaml +++ b/ultralytics/datasets/coco8.yaml @@ -2,7 +2,7 @@ # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics # Example usage: yolo train data=coco8.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco8 ← downloads here (1 MB) diff --git a/ultralytics/datasets/xView.yaml b/ultralytics/datasets/xView.yaml index e2ffca6..1448cff 100644 --- a/ultralytics/datasets/xView.yaml +++ b/ultralytics/datasets/xView.yaml @@ -3,7 +3,7 @@ # -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! -------- # Example usage: yolo train data=xView.yaml # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── xView ← downloads here (20.7 GB) diff --git a/ultralytics/models/README.md b/ultralytics/models/README.md index 074c418..5c88b88 100644 --- a/ultralytics/models/README.md +++ b/ultralytics/models/README.md @@ -24,7 +24,7 @@ yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100 ``` They may also be used directly in a Python environment, and accepts the same -[arguments](https://docs.ultralytics.com/cfg/) as in the CLI example above: +[arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above: ```python from ultralytics import YOLO diff --git a/ultralytics/yolo/data/scripts/get_coco.sh b/ultralytics/yolo/data/scripts/get_coco.sh index 8648f7f..0cae5ad 100755 --- a/ultralytics/yolo/data/scripts/get_coco.sh +++ b/ultralytics/yolo/data/scripts/get_coco.sh @@ -3,7 +3,7 @@ # Download COCO 2017 dataset http://cocodataset.org # Example usage: bash data/scripts/get_coco.sh # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco ← downloads here diff --git a/ultralytics/yolo/data/scripts/get_coco128.sh b/ultralytics/yolo/data/scripts/get_coco128.sh index be3ccaf..6002a63 100755 --- a/ultralytics/yolo/data/scripts/get_coco128.sh +++ b/ultralytics/yolo/data/scripts/get_coco128.sh @@ -3,7 +3,7 @@ # Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) # Example usage: bash data/scripts/get_coco128.sh # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── coco128 ← downloads here diff --git a/ultralytics/yolo/data/scripts/get_imagenet.sh b/ultralytics/yolo/data/scripts/get_imagenet.sh index b0e4a6d..4428b23 100755 --- a/ultralytics/yolo/data/scripts/get_imagenet.sh +++ b/ultralytics/yolo/data/scripts/get_imagenet.sh @@ -3,7 +3,7 @@ # Download ILSVRC2012 ImageNet dataset https://image-net.org # Example usage: bash data/scripts/get_imagenet.sh # parent -# ├── yolov5 +# ├── ultralytics # └── datasets # └── imagenet ← downloads here diff --git a/ultralytics/yolo/engine/results.py b/ultralytics/yolo/engine/results.py index bd23fcc..8f80a36 100644 --- a/ultralytics/yolo/engine/results.py +++ b/ultralytics/yolo/engine/results.py @@ -2,7 +2,7 @@ """ Ultralytics Results, Boxes and Masks classes for handling inference results -Usage: See https://docs.ultralytics.com/predict/ +Usage: See https://docs.ultralytics.com/modes/predict/ """ import pprint diff --git a/ultralytics/yolo/v8/detect/predict.py b/ultralytics/yolo/v8/detect/predict.py index 6443585..98210f6 100644 --- a/ultralytics/yolo/v8/detect/predict.py +++ b/ultralytics/yolo/v8/detect/predict.py @@ -63,15 +63,14 @@ class DetectionPredictor(BasePredictor): # write for d in reversed(det): - cls, conf = d.cls.squeeze(), d.conf.squeeze() + cls, conf, id = d.cls.squeeze(), d.conf.squeeze(), None if d.id is None else int(d.id.item()) if self.args.save_txt: # Write to file - line = (cls, *(d.xywhn.view(-1).tolist()), conf) \ - if self.args.save_conf else (cls, *(d.xywhn.view(-1).tolist())) # label format + line = (cls, *d.xywhn.view(-1)) + (conf, ) * self.args.save_conf + (() if id is None else (id, )) with open(f'{self.txt_path}.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if self.args.save or self.args.save_crop or self.args.show: # Add bbox to image c = int(cls) # integer class - name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c] + name = ('' if id is None else f'id:{id} ') + self.model.names[c] label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}') self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) if self.args.save_crop: diff --git a/ultralytics/yolo/v8/segment/predict.py b/ultralytics/yolo/v8/segment/predict.py index 41b436d..ef5a8d8 100644 --- a/ultralytics/yolo/v8/segment/predict.py +++ b/ultralytics/yolo/v8/segment/predict.py @@ -76,17 +76,15 @@ class SegmentationPredictor(DetectionPredictor): # Write results for j, d in enumerate(reversed(det)): - cls, conf = d.cls.squeeze(), d.conf.squeeze() + cls, conf, id = d.cls.squeeze(), d.conf.squeeze(), None if d.id is None else int(d.id.item()) if self.args.save_txt: # Write to file - seg = mask.segments[len(det) - j - 1].copy() # reversed mask.segments - seg = seg.reshape(-1) # (n,2) to (n*2) - line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg) # label format + seg = mask.segments[len(det) - j - 1].copy().reshape(-1) # reversed mask.segments, (n,2) to (n*2) + line = (cls, *seg) + (conf, ) * self.args.save_conf + (() if id is None else (id, )) with open(f'{self.txt_path}.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') - if self.args.save or self.args.save_crop or self.args.show: # Add bbox to image c = int(cls) # integer class - name = f'id:{int(d.id.item())} {self.model.names[c]}' if d.id is not None else self.model.names[c] + name = ('' if id is None else f'id:{id} ') + self.model.names[c] label = None if self.args.hide_labels else (name if self.args.hide_conf else f'{name} {conf:.2f}') self.annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) if self.args.boxes else None if self.args.save_crop: