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Update docs building code (#7601)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com> Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
GitHub
parent
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commit
d021524e85
3
.github/workflows/publish.yml
vendored
3
.github/workflows/publish.yml
vendored
@ -28,7 +28,7 @@ jobs:
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- name: Set up Python environment
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uses: actions/setup-python@v5
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with:
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python-version: '3.10'
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python-version: '3.11'
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cache: 'pip' # caching pip dependencies
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- name: Install dependencies
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run: |
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@ -66,6 +66,7 @@ jobs:
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env:
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PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
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INDEXNOW_KEY: ${{ secrets.INDEXNOW_KEY_DOCS }}
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WEGLOT_KEY: ${{ secrets.WEGLOT_KEY_DOCS }}
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run: |
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python docs/build_docs.py
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git config --global user.name "Glenn Jocher"
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@ -23,14 +23,17 @@ Usage:
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Note:
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- This script is built to be run in an environment where Python and MkDocs are installed and properly configured.
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"""
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import os
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import re
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import shutil
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import subprocess
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from pathlib import Path
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from tqdm import tqdm
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DOCS = Path(__file__).parent.resolve()
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SITE = DOCS.parent / "site"
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LANGUAGES = True
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def build_docs():
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@ -44,9 +47,10 @@ def build_docs():
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subprocess.run(f"mkdocs build -f {DOCS}/mkdocs.yml", check=True, shell=True)
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# Build other localized documentations
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for file in DOCS.glob("mkdocs_*.yml"):
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print(f"Building MkDocs site with configuration file: {file}")
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subprocess.run(f"mkdocs build -f {file}", check=True, shell=True)
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if LANGUAGES:
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for file in DOCS.glob("mkdocs_*.yml"):
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print(f"Building MkDocs site with configuration file: {file}")
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subprocess.run(f"mkdocs build -f {file}", check=True, shell=True)
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print(f"Site built at {SITE}")
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@ -100,19 +104,51 @@ def update_page_title(file_path: Path, new_title: str):
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file.write(updated_content)
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def update_html_head(key=""):
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"""Update the HTML head section of each file."""
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html_files = Path(SITE).rglob("*.html")
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for html_file in tqdm(html_files, desc="Processing HTML files"):
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with html_file.open("r", encoding="utf-8") as file:
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html_content = file.read()
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script = f"""
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<script type="text/javascript" src="https://cdn.weglot.com/weglot.min.js"></script>
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<script>
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Weglot.initialize({{
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api_key: '{key}'
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}});
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</script>
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"""
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if script in html_content: # script already in HTML file
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return
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head_end_index = html_content.lower().rfind("</head>")
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if head_end_index != -1:
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# Add the specified JavaScript to the HTML file just before the end of the head tag.
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new_html_content = html_content[:head_end_index] + script + html_content[head_end_index:]
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with html_file.open("w", encoding="utf-8") as file:
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file.write(new_html_content)
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def main():
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# Build the docs
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build_docs()
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# Update titles
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update_page_title(SITE / "404.html", new_title="Ultralytics Docs - Not Found")
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# Update .md in href links
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update_html_links()
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if LANGUAGES:
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update_html_links()
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# Update HTML file head section
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key = os.environ.get("WEGLOT_KEY")
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if not LANGUAGES and key:
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update_html_head(key)
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# Show command to serve built website
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print('Serve site at http://localhost:8000 with "python -m http.server --directory site"')
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# Update titles
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update_page_title(SITE / "404.html", new_title="Ultralytics Docs - Not Found")
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if __name__ == "__main__":
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main()
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@ -8,6 +8,15 @@ keywords: Open Images V7, object detection, segmentation masks, visual relations
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[Open Images V7](https://storage.googleapis.com/openimages/web/index.html) is a versatile and expansive dataset championed by Google. Aimed at propelling research in the realm of computer vision, it boasts a vast collection of images annotated with a plethora of data, including image-level labels, object bounding boxes, object segmentation masks, visual relationships, and localized narratives.
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## Open Images V7 Pretrained Models
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| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) |
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|-------------------------------------------------------------------------------------------|-----------------------|----------------------|--------------------------------|-------------------------------------|--------------------|-------------------|
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| [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n-oiv7.pt) | 640 | 18.4 | 142.4 | 1.21 | 3.5 | 10.5 |
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| [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8s-oiv7.pt) | 640 | 27.7 | 183.1 | 1.40 | 11.4 | 29.7 |
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| [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8m-oiv7.pt) | 640 | 33.6 | 408.5 | 2.26 | 26.2 | 80.6 |
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| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-oiv7.pt) | 640 | 34.9 | 596.9 | 2.43 | 44.1 | 167.4 |
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| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-oiv7.pt) | 640 | 36.3 | 860.6 | 3.56 | 68.7 | 260.6 |
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## Key Features
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@ -174,21 +174,21 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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| Name | Type | Default | Description |
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|---------------------|-------------|----------------------------|-----------------------------------------------|
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| view_img | `bool` | `False` | Display frames with counts |
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| view_in_counts | `bool` | `True` | Display incounts only on video frame |
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| view_out_counts | `bool` | `True` | Display outcounts only on video frame |
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| line_thickness | `int` | `2` | Increase bounding boxes thickness |
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| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| classes_names | `dict` | `model.model.names` | Dictionary of Class Names |
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| region_color | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
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| track_thickness | `int` | `2` | Thickness of Tracking Lines |
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| draw_tracks | `bool` | `False` | Enable drawing Track lines |
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| track_color | `RGB Color` | `(0, 255, 0)` | Color for each track line |
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| line_dist_thresh | `int` | `15` | Euclidean Distance threshold for line counter |
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| count_txt_thickness | `int` | `2` | Thickness of Object counts text |
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| count_txt_color | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text |
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| count_color | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text |
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| region_thickness | `int` | `5` | Thickness for object counter region or line |
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| `view_img` | `bool` | `False` | Display frames with counts |
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| `view_in_counts` | `bool` | `True` | Display incounts only on video frame |
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| `view_out_counts` | `bool` | `True` | Display outcounts only on video frame |
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| `line_thickness` | `int` | `2` | Increase bounding boxes thickness |
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| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
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| `region_color` | `RGB Color` | `(255, 0, 255)` | Color of the Object counting Region or Line |
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| `track_thickness` | `int` | `2` | Thickness of Tracking Lines |
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| `draw_tracks` | `bool` | `False` | Enable drawing Track lines |
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| `track_color` | `RGB Color` | `(0, 255, 0)` | Color for each track line |
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| `line_dist_thresh` | `int` | `15` | Euclidean Distance threshold for line counter |
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| `count_txt_thickness` | `int` | `2` | Thickness of Object counts text |
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| `count_txt_color` | `RGB Color` | `(0, 0, 0)` | Foreground color for Object counts text |
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| `count_color` | `RGB Color` | `(255, 255, 255)` | Background color for Object counts text |
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| `region_thickness` | `int` | `5` | Thickness for object counter region or line |
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### Arguments `model.track`
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@ -34,10 +34,10 @@ FastSAM is designed to address the limitations of the [Segment Anything Model (S
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This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
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| FastSAM-s | `FastSAM-s.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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| FastSAM-x | `FastSAM-x.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
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| FastSAM-s | [FastSAM-s.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-s.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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| FastSAM-x | [FastSAM-x.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/FastSAM-x.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ✅ |
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## Usage Examples
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@ -20,9 +20,9 @@ MobileSAM is trained on a single GPU with a 100k dataset (1% of the original ima
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This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
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| MobileSAM | `mobile_sam.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|-----------------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
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| MobileSAM | [mobile_sam.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/mobile_sam.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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## Adapting from SAM to MobileSAM
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@ -63,10 +63,10 @@ This example provides simple RT-DETRR training and inference examples. For full
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This table presents the model types, the specific pre-trained weights, the tasks supported by each model, and the various modes ([Train](../modes/train.md) , [Val](../modes/val.md), [Predict](../modes/predict.md), [Export](../modes/export.md)) that are supported, indicated by ✅ emojis.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|---------------------|---------------------|----------------------------------------|-----------|------------|----------|--------|
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| RT-DETR Large | `rtdetr-l.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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| RT-DETR Extra-Large | `rtdetr-x.pt` | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|---------------------|-------------------------------------------------------------------------------------------|----------------------------------------|-----------|------------|----------|--------|
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| RT-DETR Large | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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| RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅ | ✅ | ✅ | ✅ |
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## Citations and Acknowledgements
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@ -29,10 +29,10 @@ For an in-depth look at the Segment Anything Model and the SA-1B dataset, please
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This table presents the available models with their specific pre-trained weights, the tasks they support, and their compatibility with different operating modes like [Inference](../modes/predict.md), [Validation](../modes/val.md), [Training](../modes/train.md), and [Export](../modes/export.md), indicated by ✅ emojis for supported modes and ❌ emojis for unsupported modes.
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
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| SAM base | `sam_b.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| SAM large | `sam_l.pt` | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| Model Type | Pre-trained Weights | Tasks Supported | Inference | Validation | Training | Export |
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|------------|-------------------------------------------------------------------------------------|----------------------------------------------|-----------|------------|----------|--------|
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| SAM base | [sam_b.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/sam_b.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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| SAM large | [sam_l.pt](https://github.com/ultralytics/assets/releases/download/v8.1.0/sam_l.pt) | [Instance Segmentation](../tasks/segment.md) | ✅ | ❌ | ❌ | ❌ |
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## How to Use SAM: Versatility and Power in Image Segmentation
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@ -547,7 +547,7 @@ def xywhr2xyxyxyxy(rboxes):
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be in degrees from 0 to 90.
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Args:
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center (numpy.ndarray | torch.Tensor): Input data in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5).
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rboxes (numpy.ndarray | torch.Tensor): Input data in [cx, cy, w, h, rotation] format of shape (n, 5) or (b, n, 5).
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Returns:
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(numpy.ndarray | torch.Tensor): Converted corner points of shape (n, 4, 2) or (b, n, 4, 2).
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