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

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>

README.md

@@ -44,7 +44,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
 </div>
 </div>
 
-## Documentation
+## <div align="center">Documentation</div>
 
 See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for full documentation on training, validation, prediction and deployment.
 
@@ -98,7 +98,7 @@ See YOLOv8 [Python Docs](https://docs.ultralytics.com/usage/python) for more exa
 
 </details>
 
-## Models
+## <div align="center">Models</div>
 
 YOLOv8 [Detect](https://docs.ultralytics.com/tasks/detect), [Segment](https://docs.ultralytics.com/tasks/segment) and [Pose](https://docs.ultralytics.com/tasks/pose) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco) dataset are available here, as well as YOLOv8 [Classify](https://docs.ultralytics.com/tasks/classify) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet) dataset. [Track](https://docs.ultralytics.com/modes/track) mode is available for all Detect, Segment and Pose models.
 
@@ -203,7 +203,7 @@ See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for usag
 
 </details>
 
-## Integrations
+## <div align="center">Integrations</div>
 
 Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [Roboflow](https://roboflow.com/?ref=ultralytics), ClearML, [Comet](https://bit.ly/yolov8-readme-comet), Neural Magic and [OpenVINO](https://docs.ultralytics.com/integrations/openvino), can optimize your AI workflow.
 
@@ -231,14 +231,14 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
 | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
 | Label and export your custom datasets directly to YOLOv8 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv8 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov8-readme-comet) lets you save YOLOv8 models, resume training, and interactively visualize and debug predictions | Run YOLOv8 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
 
-## Ultralytics HUB
+## <div align="center">Ultralytics HUB</div>
 
 Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLOv5 and YOLOv8 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
 
-## Contribute
+## <div align="center">Contribute</div>
 
 We love your input! YOLOv5 and YOLOv8 would not be possible without help from our community. Please see our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started, and fill out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experience. Thank you 🙏 to all our contributors!
 
@@ -247,14 +247,14 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
 <a href="https://github.com/ultralytics/yolov5/graphs/contributors">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
 
-## License
+## <div align="center">License</div>
 
 Ultralytics offers two licensing options to accommodate diverse use cases:
 
 - **AGPL-3.0 License**: This [OSI-approved](https://opensource.org/licenses/) open-source license is ideal for students and enthusiasts, promoting open collaboration and knowledge sharing. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for more details.
 - **Enterprise License**: Designed for commercial use, this license permits seamless integration of Ultralytics software and AI models into commercial goods and services, bypassing the open-source requirements of AGPL-3.0. If your scenario involves embedding our solutions into a commercial offering, reach out through [Ultralytics Licensing](https://ultralytics.com/license).
 
-## Contact
+## <div align="center">Contact</div>
 
 For Ultralytics bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://ultralytics.com/discord) community for questions and discussions!
 

README.zh-CN.md

@@ -44,7 +44,7 @@
 </div>
 </div>
 
-## 文档
+## <div align="center">文档</div>
 
 请参阅下面的快速安装和使用示例，以及 [YOLOv8 文档](https://docs.ultralytics.com) 上有关训练、验证、预测和部署的完整文档。
 
@@ -98,7 +98,7 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 </details>
 
-## 模型
+## <div align="center">模型</div>
 
 在[COCO](https://docs.ultralytics.com/datasets/detect/coco)数据集上预训练的YOLOv8 [检测](https://docs.ultralytics.com/tasks/detect)，[分割](https://docs.ultralytics.com/tasks/segment)和[姿态](https://docs.ultralytics.com/tasks/pose)模型可以在这里找到，以及在[ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet)数据集上预训练的YOLOv8 [分类](https://docs.ultralytics.com/tasks/classify)模型。所有的检测，分割和姿态模型都支持[追踪](https://docs.ultralytics.com/modes/track)模式。
 
@@ -202,7 +202,7 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 
 </details>
 
-## 集成
+## <div align="center">集成</div>
 
 我们与领先的AI平台的关键整合扩展了Ultralytics产品的功能，增强了数据集标签化、训练、可视化和模型管理等任务。探索Ultralytics如何与[Roboflow](https://roboflow.com/?ref=ultralytics)、ClearML、[Comet](https://bit.ly/yolov8-readme-comet)、Neural Magic以及[OpenVINO](https://docs.ultralytics.com/integrations/openvino)合作，优化您的AI工作流程。
 
@@ -230,14 +230,14 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 | :--------------------------------------------------------------------------------: | :----------------------------------------------------------------------------: | :----------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------: |
 | 使用 [Roboflow](https://roboflow.com/?ref=ultralytics) 将您的自定义数据集直接标记并导出至 YOLOv8 进行训练 | 使用 [ClearML](https://cutt.ly/yolov5-readme-clearml)（开源！）自动跟踪、可视化，甚至远程训练 YOLOv8 | 免费且永久，[Comet](https://bit.ly/yolov8-readme-comet) 让您保存 YOLOv8 模型、恢复训练，并以交互式方式查看和调试预测 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 使 YOLOv8 推理速度提高多达 6 倍 |
 
-## Ultralytics HUB
+## <div align="center">Ultralytics HUB</div>
 
 体验 [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐ 带来的无缝 AI，这是一个一体化解决方案，用于数据可视化、YOLOv5 和即将推出的 YOLOv8 🚀 模型训练和部署，无需任何编码。通过我们先进的平台和用户友好的 [Ultralytics 应用程序](https://ultralytics.com/app_install)，轻松将图像转化为可操作的见解，并实现您的 AI 愿景。现在就开始您的**免费**之旅！
 
 <a href="https://bit.ly/ultralytics_hub" target="_blank">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png" alt="Ultralytics HUB preview image"></a>
 
-## 贡献
+## <div align="center">贡献</div>
 
 我们喜欢您的参与！没有社区的帮助，YOLOv5 和 YOLOv8 将无法实现。请参阅我们的[贡献指南](https://docs.ultralytics.com/help/contributing)以开始使用，并填写我们的[调查问卷](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey)向我们提供您的使用体验反馈。感谢所有贡献者的支持！🙏
 
@@ -246,14 +246,14 @@ success = model.export(format="onnx")  # 将模型导出为 ONNX 格式
 <a href="https://github.com/ultralytics/yolov5/graphs/contributors">
 <img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
 
-## 许可证
+## <div align="center">许可证</div>
 
 Ultralytics 提供两种许可证选项以适应各种使用场景：
 
 - **AGPL-3.0 许可证**：这个[OSI 批准](https://opensource.org/licenses/)的开源许可证非常适合学生和爱好者，可以推动开放的协作和知识分享。请查看[LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) 文件以了解更多细节。
 - **企业许可证**：专为商业用途设计，该许可证允许将 Ultralytics 的软件和 AI 模型无缝集成到商业产品和服务中，从而绕过 AGPL-3.0 的开源要求。如果您的场景涉及将我们的解决方案嵌入到商业产品中，请通过 [Ultralytics Licensing](https://ultralytics.com/license)与我们联系。
 
-## 联系方式
+## <div align="center">联系方式</div>
 
 对于 Ultralytics 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)，并加入我们的 [Discord](https://ultralytics.com/discord) 社区进行问题和讨论！
 

docs/en/guides/model-deployment-options.md

@@ -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
 ---
 

docs/en/guides/yolo-performance-metrics.md

@@ -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
 ---
 

docs/en/integrations/comet.md

@@ -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
 

docs/en/models/rtdetr.md

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

docs/en/models/sam.md

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

docs/en/models/yolo-nas.md

@@ -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          |
+| Mode       | Supported |
-|------------|--------------------|
+|------------|-----------|
-| Inference  | :heavy_check_mark: |
+| Inference  | ✅         |
-| Validation | :heavy_check_mark: |
+| Validation | ✅         |
-| Training   | :x:                |
+| Training   | ❌         |
 
 Harness the power of the YOLO-NAS models to drive your object detection tasks to new heights of performance and speed.
 

docs/en/models/yolov5.md

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

docs/en/models/yolov6.md

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

docs/en/models/yolov8.md

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

docs/en/quickstart.md

@@ -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`   ✅
+    - `yolo predict model=yolov8n.pt imgsz=640 conf=0.25`  ✅
-    - `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`   ❌
+    - `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}
 

docs/en/reference/nn/tasks.md

@@ -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

docs/en/reference/utils/__init__.md

@@ -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>

docs/en/reference/utils/triton.md

@@ -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

docs/update_translations.py

@@ -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.md_link_regex = re.compile(r'\[([^]]+)]\(([^:)]+)\.md\)')
-        self.front_matter_regex = re.compile(r'^(comments|description|keywords):.*$', re.MULTILINE)
         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)
 

docs/zh/quickstart.md

@@ -1,7 +1,7 @@
 ---
-评论：真
+comments: true
-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。
+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。
-关键字：Ultralytics安装，pip安装Ultralytics，Docker安装Ultralytics，Ultralytics命令行界面，Ultralytics Python接口
+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

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.211'
+__version__ = '8.0.212'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/utils/__init__.py

@@ -225,25 +225,29 @@ def plt_settings(rcparams=None, backend='Agg'):
 
 
 def set_logging(name=LOGGING_NAME, verbose=True):
-    """Sets up logging for the given name."""
+    """Sets up logging for the given name with UTF-8 encoding support."""
-    rank = int(os.getenv('RANK', -1))  # rank in world for Multi-GPU trainings
+    level = logging.INFO if verbose and RANK in {-1, 0} else logging.ERROR  # rank in world for Multi-GPU trainings
-    level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
+
-    logging.config.dictConfig({
+    # Configure the console (stdout) encoding to UTF-8
-        'version': 1,
+    if WINDOWS:  # for Windows
-        'disable_existing_loggers': False,
+        sys.stdout.reconfigure(encoding='utf-8')
-        'formatters': {
+
-            name: {
+    # Create and configure the StreamHandler
-                'format': '%(message)s'}},
+    stream_handler = logging.StreamHandler(sys.stdout)
-        'handlers': {
+    stream_handler.setFormatter(logging.Formatter('%(message)s'))
-            name: {
+    stream_handler.setLevel(level)
-                'class': 'logging.StreamHandler',
+
-                'formatter': name,
+    logger = logging.getLogger(name)
-                'level': level}},
+    logger.setLevel(level)
-        'loggers': {
+    logger.addHandler(stream_handler)
-            name: {
+    logger.propagate = False
-                'level': level,
+    return logger
-                'handlers': [name],
+
-                'propagate': False}}})
+
+# Set logger
+LOGGER = set_logging(LOGGING_NAME, verbose=VERBOSE)  # define globally (used in train.py, val.py, predict.py, etc.)
+for logger in 'sentry_sdk', 'urllib3.connectionpool':
+    logging.getLogger(logger).setLevel(logging.CRITICAL)
 
 
 def emojis(string=''):
@@ -251,29 +255,6 @@ def emojis(string=''):
     return string.encode().decode('ascii', 'ignore') if WINDOWS else string
 
 
-class EmojiFilter(logging.Filter):
-    """
-    A custom logging filter class for removing emojis in log messages.
-
-    This filter is particularly useful for ensuring compatibility with Windows terminals that may not support the
-    display of emojis in log messages.
-    """
-
-    def filter(self, record):
-        """Filter logs by emoji unicode characters on windows."""
-        record.msg = emojis(record.msg)
-        return super().filter(record)
-
-
-# Set logger
-set_logging(LOGGING_NAME, verbose=VERBOSE)  # run before defining LOGGER
-LOGGER = logging.getLogger(LOGGING_NAME)  # define globally (used in train.py, val.py, detect.py, etc.)
-if WINDOWS:  # emoji-safe logging
-    LOGGER.addFilter(EmojiFilter())
-for logger in 'sentry_sdk', 'urllib3.connectionpool':
-    logging.getLogger(logger).setLevel(logging.CRITICAL)
-
-
 class ThreadingLocked:
     """
     A decorator class for ensuring thread-safe execution of a function or method. This class can be used as a decorator