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ultralytics 8.0.212 add Windows UTF-8 support (#6407)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
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Glenn Jocher 2023-11-18 04:40:34 +01:00 • committed by GitHub
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2
docs/en/guides/model-deployment-options.md
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@ -1,6 +1,6 @@
---
comments: true
Description: A guide to help determine which deployment option to choose for your YOLOv8 model, including essential considerations.
description: A guide to help determine which deployment option to choose for your YOLOv8 model, including essential considerations.
keywords: YOLOv8, Deployment, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow, Export
---

2
docs/en/guides/yolo-performance-metrics.md
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@ -1,6 +1,6 @@
---
comments: true
Description: A comprehensive guide on various performance metrics related to YOLOv8, their significance, and how to interpret them.
description: A comprehensive guide on various performance metrics related to YOLOv8, their significance, and how to interpret them.
keywords: YOLOv8, Performance metrics, Object detection, Intersection over Union (IoU), Average Precision (AP), Mean Average Precision (mAP), Precision, Recall, Validation mode, Ultralytics
---

2
docs/en/integrations/comet.md
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@ -133,7 +133,7 @@ You can control the number of image predictions that Comet ML logs during your e
```python
import os
os.environ["COMET_MAX_IMAGE_PREDICTIONS"] = "200"
```
```
### Batch Logging Interval

10
docs/en/models/rtdetr.md
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@ -71,11 +71,11 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
### Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
## Citations and Acknowledgements

10
docs/en/models/sam.md
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@ -131,11 +131,11 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
## Operating Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :x: |
| Training | :x: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ❌ |
| Training | ❌ |
## SAM comparison vs YOLOv8

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docs/en/models/yolo-nas.md
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@ -94,11 +94,11 @@ The YOLO-NAS models are primarily designed for object detection tasks. You can d
The YOLO-NAS models support both inference and validation modes, allowing you to predict and validate results with ease. Training mode, however, is currently not supported.
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :x: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ❌ |
Harness the power of the YOLO-NAS models to drive your object detection tasks to new heights of performance and speed.

10
docs/en/models/yolov5.md
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@ -28,11 +28,11 @@ YOLOv5u represents an advancement in object detection methodologies. Originating
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
!!! Performance

10
docs/en/models/yolov6.md
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@ -85,11 +85,11 @@ You can use YOLOv6 for object detection tasks using the Ultralytics pip package.
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
## Citations and Acknowledgements

10
docs/en/models/yolov8.md
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@ -30,11 +30,11 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
!!! Performance

20
docs/en/quickstart.md
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@ -148,12 +148,13 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
Ultralytics `yolo` commands use the following syntax:
```bash
yolo TASK MODE ARGS
Where TASK (optional) is one of [detect, segment, classify]
MODE (required) is one of [train, val, predict, export, track]
ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
```
See all ARGS in the full [Configuration Guide](usage/cfg.md) or with `yolo cfg`
- `TASK` (optional) is one of ([detect](tasks/detect.md), [segment](tasks/segment.md), [classify](tasks/classify.md), [pose](tasks/pose.md))
- `MODE` (required) is one of ([train](modes/train.md), [val](modes/val.md), [predict](modes/predict.md), [export](modes/export.md), [track](modes/track.md))
- `ARGS` (optional) are `arg=value` pairs like `imgsz=640` that override defaults.
See all `ARGS` in the full [Configuration Guide](usage/cfg.md) or with the `yolo cfg` CLI command.
=== "Train"
@ -197,11 +198,12 @@ The Ultralytics command line interface (CLI) allows for simple single-line comma
!!! warning "Warning"
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces ` ` between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
Arguments must be passed as `arg=val` pairs, split by an equals `=` sign and delimited by spaces between pairs. Do not use `--` argument prefixes or commas `,` between arguments.
- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25` &nbsp; ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` &nbsp; ❌
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` &nbsp; ❌
- `yolo predict model=yolov8n.pt imgsz=640 conf=0.25` ✅
- `yolo predict model yolov8n.pt imgsz 640 conf 0.25` ❌ (missing `=`)
- `yolo predict model=yolov8n.pt, imgsz=640, conf=0.25` ❌ (do not use `,`)
- `yolo predict --model yolov8n.pt --imgsz 640 --conf 0.25` ❌ (do not use `--`)
[CLI Guide](usage/cli.md){ .md-button .md-button--primary}

5
docs/en/reference/nn/tasks.md
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@ -1,3 +1,8 @@
---
description: Dive into the intricacies of YOLO tasks.py. Learn about DetectionModel, PoseModel and more for powerful AI development.
keywords: Ultralytics, YOLO, nn tasks, DetectionModel, PoseModel, RTDETRDetectionModel, model weights, parse model, AI development
---
# Reference for `ultralytics/nn/tasks.py`
!!! note

4
docs/en/reference/utils/__init__.md
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@ -21,10 +21,6 @@ keywords: Ultralytics, Utils, utilitarian functions, colorstr, yaml_save, set_lo
## ::: ultralytics.utils.IterableSimpleNamespace
<br><br>
---
## ::: ultralytics.utils.EmojiFilter
<br><br>
---
## ::: ultralytics.utils.ThreadingLocked
<br><br>

5
docs/en/reference/utils/triton.md
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@ -1,3 +1,8 @@
---
description: Deploy ML models effortlessly with Ultralytics TritonRemoteModel. Simplify serving with our comprehensive utils guide.
keywords: Ultralytics, YOLO, TritonRemoteModel, machine learning, model serving, deployment, utils, documentation
---
# Reference for `ultralytics/utils/triton.py`
!!! note

15
docs/update_translations.py
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@ -23,10 +23,9 @@ class MarkdownLinkFixer:
self.update_links = update_links
self.update_frontmatter = update_frontmatter
self.update_iframes = update_iframes
self.md_link_regex = re.compile(r'\[([^\]]+)\]\(([^:\)]+)\.md\)')
self.front_matter_regex = re.compile(r'^(comments|description|keywords):.*$', re.MULTILINE)
self.md_link_regex = re.compile(r'\[([^]]+)]\(([^:)]+)\.md\)')
self.translations = {
'zh': ['评论', '描述', '关键词'], # Mandarin Chinese (Simplified)
'zh': ['评论', '描述', '关键词'], # Mandarin Chinese (Simplified) warning, sometimes translates as 关键字
'es': ['comentarios', 'descripción', 'palabras clave'], # Spanish
'ru': ['комментарии', 'описание', 'ключевые слова'], # Russian
'pt': ['comentários', 'descrição', 'palavras-chave'], # Portuguese
@ -44,15 +43,17 @@ class MarkdownLinkFixer:
for term, eng_key in zip(terms, english_keys):
if eng_key == 'comments':
# Replace comments key and set its value to 'true'
content = re.sub(rf'{term} *:.*', f'{eng_key}: true', content)
content = re.sub(rf'{term} *[：:].*', f'{eng_key}: true', content)
else:
content = re.sub(rf'{term} *:', f'{eng_key}:', content)
content = re.sub(rf'{term} *[：:] *', f'{eng_key}: ', content)
return content
def update_iframe(self, content):
@staticmethod
def update_iframe(content):
"""Update the 'allow' attribute of iframe if it does not contain the specific English permissions."""
english_permissions = 'accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share'
english_permissions = \
'accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share'
pattern = re.compile(f'allow="(?!{re.escape(english_permissions)}).+?"')
return pattern.sub(f'allow="{english_permissions}"', content)

6
docs/zh/quickstart.md
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@ -1,7 +1,7 @@
---
评论：真
描述：探索使用pip、conda、git和Docker安装Ultralytics的各种方法。了解如何在命令行界面或Python项目中使用Ultralytics。
关键字：Ultralytics安装，pip安装Ultralytics，Docker安装Ultralytics，Ultralytics命令行界面，Ultralytics Python接口
comments: true
description: 探索使用pip、conda、git和Docker安装Ultralytics的各种方法。了解如何在命令行界面或Python项目中使用Ultralytics。
keywords: Ultralytics安装，pip安装Ultralytics，Docker安装Ultralytics，Ultralytics命令行界面，Ultralytics Python接口
---
## 安装Ultralytics