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Docs URL fixes and YAML updates (#1383)

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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>
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6
.github/workflows/publish.yml vendored
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@ -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

2
README.md
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@ -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

4
README.zh-CN.md
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@ -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

54
docs/modes/predict.md
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@ -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)

2
docs/modes/track.md
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@ -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"

35
docs/stylesheets/style.css
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@ -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)
}

2
docs/tasks/classify.md
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@ -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

2
docs/tasks/detect.md
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@ -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

2
docs/tasks/keypoints.md
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@ -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

2
docs/tasks/segment.md
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@ -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

2
examples/tutorial.ipynb
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@ -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"
]
},
{

14
mkdocs.yml
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@ -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!<br>
<a href="https://github.com/ultralytics/ultralytics/issues/new?title=Docs+Feedback+for+{title}+page+at+{url}&labels=enhancement&template=feature-request.yml" target="_blank" rel="noopener">Tell us what we can improve.</a>
social:
- icon: fontawesome/brands/github
link: https://github.com/ultralytics

2
ultralytics/datasets/Argoverse.yaml
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@ -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)

2
ultralytics/datasets/GlobalWheat2020.yaml
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@ -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)

2
ultralytics/datasets/ImageNet.yaml
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@ -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)

2
ultralytics/datasets/Objects365.yaml
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@ -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)

2
ultralytics/datasets/SKU-110K.yaml
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@ -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)

2
ultralytics/datasets/VOC.yaml
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@ -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)

2
ultralytics/datasets/VisDrone.yaml
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@ -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)

2
ultralytics/datasets/coco.yaml
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@ -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)

2
ultralytics/datasets/coco128-seg.yaml
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@ -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)

2
ultralytics/datasets/coco128.yaml
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@ -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)

2
ultralytics/datasets/coco8-seg.yaml
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@ -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)

2
ultralytics/datasets/coco8.yaml
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@ -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)

2
ultralytics/datasets/xView.yaml
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@ -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)

2
ultralytics/models/README.md
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@ -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

2
ultralytics/yolo/data/scripts/get_coco.sh
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@ -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

2
ultralytics/yolo/data/scripts/get_coco128.sh
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@ -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

2
ultralytics/yolo/data/scripts/get_imagenet.sh
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@ -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

2
ultralytics/yolo/engine/results.py
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@ -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

7
ultralytics/yolo/v8/detect/predict.py
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@ -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:

10
ultralytics/yolo/v8/segment/predict.py
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@ -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:

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