From 42bcf8c47f0bd9eef6da58409f036009e525c7ee Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Mon, 27 Nov 2023 17:46:29 +0100 Subject: [PATCH] Add missing HTML image alt tags (#6611) Signed-off-by: Glenn Jocher --- README.md | 48 +++++++++---------- README.zh-CN.md | 48 +++++++++---------- docs/ar/index.md | 14 +++--- docs/de/index.md | 14 +++--- docs/de/tasks/pose.md | 16 +++---- docs/en/hub/app/android.md | 14 +++--- docs/en/hub/app/index.md | 16 +++---- docs/en/hub/app/ios.md | 14 +++--- docs/en/hub/datasets.md | 2 +- docs/en/hub/index.md | 12 ++--- docs/en/index.md | 14 +++--- docs/en/integrations/openvino.md | 8 ++-- docs/en/integrations/roboflow.md | 46 +++++++++--------- .../docker_image_quickstart_tutorial.md | 2 +- docs/en/yolov5/index.md | 12 ++--- .../tutorials/architecture_description.md | 12 ++--- .../tutorials/clearml_logging_integration.md | 16 +++---- .../tutorials/comet_logging_integration.md | 2 +- docs/en/yolov5/tutorials/model_ensembling.md | 2 +- docs/en/yolov5/tutorials/model_export.md | 4 +- .../neural_magic_pruning_quantization.md | 6 +-- .../tutorials/pytorch_hub_model_loading.md | 3 +- .../roboflow_datasets_integration.md | 2 +- .../tutorials/running_on_jetson_nano.md | 4 +- .../tutorials/test_time_augmentation.md | 2 +- docs/en/yolov5/tutorials/train_custom_data.md | 26 +++++----- .../transfer_learning_with_frozen_layers.md | 10 ++-- docs/es/index.md | 14 +++--- docs/fr/index.md | 12 ++--- docs/hi/index.md | 14 +++--- docs/ja/index.md | 14 +++--- docs/ko/index.md | 14 +++--- docs/pt/index.md | 14 +++--- docs/ru/index.md | 14 +++--- docs/update_translations.py | 22 +++++++++ docs/zh/index.md | 14 +++--- examples/YOLOv8-Region-Counter/readme.md | 7 ++- .../README.md | 2 +- ultralytics/trackers/README.md | 2 +- 39 files changed, 267 insertions(+), 245 deletions(-) diff --git a/README.md b/README.md index d7c2b959..819f9823 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@

- + YOLO Vision banner

[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) @@ -13,7 +13,7 @@ YOLOv8 Citation Docker Pulls
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle
@@ -25,21 +25,21 @@ We hope that the resources here will help you get the most out of YOLOv8. Please To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license). - +YOLOv8 performance plots
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord
@@ -209,22 +209,22 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
- +Ultralytics active learning integrations

- - + Roboflow logo + space - - + ClearML logo + space - - + Comet ML logo + space - + NeuralMagic logo
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | @@ -245,7 +245,7 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou - +Ultralytics open-source contributors ##
License
@@ -261,16 +261,16 @@ For Ultralytics bug reports and feature requests please visit [GitHub Issues](ht
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord
diff --git a/README.zh-CN.md b/README.zh-CN.md index 386d63c7..84f264ce 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -1,7 +1,7 @@

- + YOLO Vision banner

[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/) @@ -13,7 +13,7 @@ YOLOv8 Citation Docker Pulls
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle
@@ -25,21 +25,21 @@ 如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格 - +YOLOv8 performance plots
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord
@@ -208,22 +208,22 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
- +Ultralytics active learning integrations

- - + Roboflow logo + space - - + ClearML logo + space - - + Comet ML logo + space - + NeuralMagic logo
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW | @@ -244,7 +244,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 - +Ultralytics open-source contributors ##
许可证
@@ -260,16 +260,16 @@ Ultralytics 提供两种许可证选项以适应各种使用场景：
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord
diff --git a/docs/ar/index.md b/docs/ar/index.md index 97e6332f..211f6673 100644 --- a/docs/ar/index.md +++ b/docs/ar/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics، YOLOv8، كشف الكائنات، تجزئة الصور Ultralytics YOLO banner

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -29,7 +29,7 @@ keywords: Ultralytics، YOLOv8، كشف الكائنات، تجزئة الصور YOLOv8 Citation Docker Pulls
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle diff --git a/docs/de/index.md b/docs/de/index.md index 367a7fc9..1216d92e 100644 --- a/docs/de/index.md +++ b/docs/de/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, Objekterkennung, Bildsegmentierung, maschinelles Ultralytics YOLO Banner

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, Objekterkennung, Bildsegmentierung, maschinelles YOLOv8 Zitation Docker Ziehungen
- Auf Gradient ausführen + Auf Gradient ausführen In Colab öffnen In Kaggle öffnen diff --git a/docs/de/tasks/pose.md b/docs/de/tasks/pose.md index 4e2ad3c5..14d0f25b 100644 --- a/docs/de/tasks/pose.md +++ b/docs/de/tasks/pose.md @@ -33,14 +33,14 @@ Hier werden vortrainierte YOLOv8 Pose-Modelle gezeigt. Erkennungs-, Segmentierun [Modelle](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) werden automatisch aus der neuesten Ultralytics-[Veröffentlichung](https://github.com/ultralytics/assets/releases) bei erstmaliger Verwendung heruntergeladen. -| Modell | Größe
(Pixel) | mAPpose
50-95 | mAPpose
50 | Geschwindigkeit
CPU ONNX
(ms) | Geschwindigkeit
A100 TensorRT
(ms) | Parameter
(M) | FLOPs
(B) | -|------------------------------------------------------------------------------------------------------|------------------------|------------------------|---------------------|--------------------------------------------|-------------------------------------------------|------------------------|--------------------| -| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50,4 | 80,1 | 131,8 | 1,18 | 3,3 | 9,2 | -| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60,0 | 86,2 | 233,2 | 1,42 | 11,6 | 30,2 | -| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65,0 | 88,8 | 456,3 | 2,00 | 26,4 | 81,0 | -| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67,6 | 90,0 | 784,5 | 2,59 | 44,4 | 168,6 | -| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69,2 | 90,2 | 1607,1 | 3,73 | 69,4 | 263,2 | -| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71,6 | 91,2 | 4088,7 | 10,04 | 99,1 | 1066,4 | +| Modell | Größe
(Pixel) | mAPpose
50-95 | mAPpose
50 | Geschwindigkeit
CPU ONNX
(ms) | Geschwindigkeit
A100 TensorRT
(ms) | Parameter
(M) | FLOPs
(B) | +|------------------------------------------------------------------------------------------------------|-----------------------|-----------------------|--------------------|------------------------------------------|-----------------------------------------------|-----------------------|-------------------| +| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50,4 | 80,1 | 131,8 | 1,18 | 3,3 | 9,2 | +| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60,0 | 86,2 | 233,2 | 1,42 | 11,6 | 30,2 | +| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65,0 | 88,8 | 456,3 | 2,00 | 26,4 | 81,0 | +| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67,6 | 90,0 | 784,5 | 2,59 | 44,4 | 168,6 | +| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69,2 | 90,2 | 1607,1 | 3,73 | 69,4 | 263,2 | +| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71,6 | 91,2 | 4088,7 | 10,04 | 99,1 | 1066,4 | - **mAPval** Werte gelten für ein einzelnes Modell mit einfacher Skala auf dem [COCO Keypoints val2017](http://cocodataset.org)-Datensatz.
Zu reproduzieren mit `yolo val pose data=coco-pose.yaml device=0`. diff --git a/docs/en/hub/app/android.md b/docs/en/hub/app/android.md index fa7cd855..0bff31c1 100644 --- a/docs/en/hub/app/android.md +++ b/docs/en/hub/app/android.md @@ -11,22 +11,22 @@ keywords: Ultralytics, Android App, real-time object detection, YOLO models, Ten
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

-   + Google Play store 
The Ultralytics Android App is a powerful tool that allows you to run YOLO models directly on your Android device for real-time object detection. This app utilizes TensorFlow Lite for model optimization and various hardware delegates for acceleration, enabling fast and efficient object detection. diff --git a/docs/en/hub/app/index.md b/docs/en/hub/app/index.md index 464c5ffc..ef962e83 100644 --- a/docs/en/hub/app/index.md +++ b/docs/en/hub/app/index.md @@ -11,24 +11,24 @@ keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object dete
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

- + Apple App store -   + Google Play store 
Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mobile app that allows you to run YOLOv5 and YOLOv8 models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) and [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) devices. With the HUB App, you can utilize hardware acceleration features like Apple's Neural Engine (ANE) or Android GPU and Neural Network API (NNAPI) delegates to achieve impressive performance on your mobile device. diff --git a/docs/en/hub/app/ios.md b/docs/en/hub/app/ios.md index 82a4e956..ac939c90 100644 --- a/docs/en/hub/app/ios.md +++ b/docs/en/hub/app/ios.md @@ -11,22 +11,22 @@ keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

- + Apple App store
The Ultralytics iOS App is a powerful tool that allows you to run YOLO models directly on your iPhone or iPad for real-time object detection. This app utilizes the Apple Neural Engine and Core ML for model optimization and acceleration, enabling fast and efficient object detection. diff --git a/docs/en/hub/datasets.md b/docs/en/hub/datasets.md index dfc3338d..1ab7c45f 100644 --- a/docs/en/hub/datasets.md +++ b/docs/en/hub/datasets.md @@ -25,7 +25,7 @@ zip -r coco8.zip coco8 You can download our [COCO8](https://github.com/ultralytics/hub/blob/main/example_datasets/coco8.zip) example dataset and unzip it to see exactly how to structure your dataset.

- COCO8 Dataset Structure + COCO8 Dataset Structure

The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format. diff --git a/docs/en/hub/index.md b/docs/en/hub/index.md index 9eedc724..69209539 100644 --- a/docs/en/hub/index.md +++ b/docs/en/hub/index.md @@ -11,17 +11,17 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pre
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

diff --git a/docs/en/index.md b/docs/en/index.md index 5126e78a..c69388b9 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine lea Ultralytics YOLO banner

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine lea YOLOv8 Citation Docker Pulls
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle
diff --git a/docs/en/integrations/openvino.md b/docs/en/integrations/openvino.md index c5f509b0..6f552ff1 100644 --- a/docs/en/integrations/openvino.md +++ b/docs/en/integrations/openvino.md @@ -114,7 +114,7 @@ The Intel® Data Center GPU Flex Series is a versatile and robust solution desig Benchmarks below run on Intel® Data Center GPU Flex 170 at FP32 precision.
- +Flex GPU benchmarks
| Model | Format | Status | Size (MB) | mAP50-95(B) | Inference time (ms/im) | @@ -153,7 +153,7 @@ Early reviews have praised the Arc™ series, particularly the integrated A770M Benchmarks below run on Intel® Arc 770 GPU at FP32 precision.
- +Arc GPU benchmarks
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) | @@ -188,7 +188,7 @@ Notably, Xeon® CPUs deliver high compute density and scalability, making them i Benchmarks below run on 4th Gen Intel® Xeon® Scalable CPU at FP32 precision.
- +Xeon CPU benchmarks
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) | @@ -221,7 +221,7 @@ The Intel® Core® series is a range of high-performance processors by Intel. Th Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.
- +Core CPU benchmarks
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) | diff --git a/docs/en/integrations/roboflow.md b/docs/en/integrations/roboflow.md index 2f015900..f640918a 100644 --- a/docs/en/integrations/roboflow.md +++ b/docs/en/integrations/roboflow.md @@ -27,20 +27,20 @@ Roboflow provides two services that can help you collect data for YOLOv8 models: Universe is an online repository with over 250,000 vision datasets totalling over 100 million images.

-Roboflow Universe +Roboflow Universe

With a [free Roboflow account](https://app.roboflow.com/?ref=ultralytics), you can export any dataset available on Universe. To export a dataset, click the "Download this Dataset" button on any dataset.

-Roboflow Universe dataset export +Roboflow Universe dataset export

For YOLOv8, select "YOLOv8" as the export format:

-Roboflow Universe dataset export +Roboflow Universe dataset export

Universe also has a page that aggregates all [public fine-tuned YOLOv8 models uploaded to Roboflow](https://universe.roboflow.com/search?q=model:yolov8). You can use this page to explore pre-trained models you can use for testing or [for automated data labeling](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling) or to prototype with [Roboflow inference](https://roboflow.com/inference?ref=ultralytics). @@ -54,13 +54,13 @@ If you want to gather images yourself, try [Collect](https://github.com/roboflow To label data for a YOLOv8 object detection, instance segmentation, or classification model, first create a project in Roboflow.

-Create a Roboflow project +Create a Roboflow project

Next, upload your images, and any pre-existing annotations you have from other tools ([using one of the 40+ supported import formats](https://roboflow.com/formats?ref=ultralytics)), into Roboflow.

-Upload images to Roboflow +Upload images to Roboflow

Select the batch of images you have uploaded on the Annotate page to which you are taken after uploading images. Then, click "Start Annotating" to label images. @@ -68,7 +68,7 @@ Select the batch of images you have uploaded on the Annotate page to which you a To label with bounding boxes, press the `B` key on your keyboard or click the box icon in the sidebar. Click on a point where you want to start your bounding box, then drag to create the box:

-Annotating an image in Roboflow +Annotating an image in Roboflow

A pop-up will appear asking you to select a class for your annotation once you have created an annotation. @@ -80,7 +80,7 @@ Roboflow offers a SAM-based label assistant with which you can label images fast To use the label assistant, click the cursor icon in the sidebar, SAM will be loaded for use in your project.

-Annotating an image in Roboflow with SAM-powered label assist +Annotating an image in Roboflow with SAM-powered label assist

Hover over any object in the image and SAM will recommend an annotation. You can hover to find the right place to annotate, then click to create your annotation. To amend your annotation to be more or less specific, you can click inside or outside of the annotation SAM has created on the document. @@ -88,7 +88,7 @@ Hover over any object in the image and SAM will recommend an annotation. You can You can also add tags to images from the Tags panel in the sidebar. You can apply tags to data from a particular area, taken from a specific camera, and more. You can then use these tags to search through data for images matching a tag and generate versions of a dataset with images that contain a particular tag or set of tags.

-Adding tags to an image in Roboflow +Adding tags to an image in Roboflow

Models hosted on Roboflow can be used with Label Assist, an automated annotation tool that uses your YOLOv8 model to recommend annotations. To use Label Assist, first upload a YOLOv8 model to Roboflow (see instructions later in the guide). Then, click the magic wand icon in the left sidebar and select your model for use in Label Assist. @@ -96,13 +96,13 @@ Models hosted on Roboflow can be used with Label Assist, an automated annotation Choose a model, then click "Continue" to enable Label Assist:

-Enabling Label Assist +Enabling Label Assist

When you open new images for annotation, Label Assist will trigger and recommend annotations.

-ALabel Assist recommending an annotation +ALabel Assist recommending an annotation

## Dataset Management for YOLOv8 @@ -114,13 +114,13 @@ First, you can use dataset search to find images that meet a semantic text descr For example, the following text query finds images that contain people in a dataset:

-Searching for an image +Searching for an image

You can narrow your search to images with a particular tag using the "Tags" selector:

-Filter images by tag +Filter images by tag

Before you start training a model with your dataset, we recommend using Roboflow [Health Check](https://docs.roboflow.com/datasets/dataset-health-check), a web tool that provides an insight into your dataset and how you can improve the dataset prior to training a vision model. @@ -128,7 +128,7 @@ Before you start training a model with your dataset, we recommend using Roboflow To use Health Check, click the "Health Check" sidebar link. A list of statistics will appear that show the average size of images in your dataset, class balance, a heatmap of where annotations are in your images, and more.

-Roboflow Health Check analysis +Roboflow Health Check analysis

Health Check may recommend changes to help enhance dataset performance. For example, the class balance feature may show that there is an imbalance in labels that, if solved, may boost performance or your model. @@ -138,19 +138,19 @@ Health Check may recommend changes to help enhance dataset performance. For exam To export your data, you will need a dataset version. A version is a state of your dataset frozen-in-time. To create a version, first click "Versions" in the sidebar. Then, click the "Create New Version" button. On this page, you will be able to choose augmentations and preprocessing steps to apply to your dataset:

-Creating a dataset version on Roboflow +Creating a dataset version on Roboflow

For each augmentation you select, a pop-up will appear allowing you to tune the augmentation to your needs. Here is an example of tuning a brightness augmentation within specified parameters:

-Applying augmentations to a dataset +Applying augmentations to a dataset

When your dataset version has been generated, you can export your data into a range of formats. Click the "Export Dataset" button on your dataset version page to export your data:

-Exporting a dataset +Exporting a dataset

You are now ready to train YOLOv8 on a custom dataset. Follow this [written guide](https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/) and [YouTube video](https://www.youtube.com/watch?v=wuZtUMEiKWY) for step-by-step instructions or refer to the [Ultralytics documentation](https://docs.ultralytics.com/modes/train/). @@ -181,7 +181,7 @@ When you run the code above, you will be asked to authenticate. Then, your model To test your model and find deployment instructions for supported SDKs, go to the "Deploy" tab in the Roboflow sidebar. At the top of this page, a widget will appear with which you can test your model. You can use your webcam for live testing or upload images or videos.

-Running inference on an example image +Running inference on an example image

You can also use your uploaded model as a [labeling assistant](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling). This feature uses your trained model to recommend annotations on images uploaded to Roboflow. @@ -195,13 +195,13 @@ Once you have uploaded a model to Roboflow, you can access our model evaluation To access a confusion matrix, go to your model page on the Roboflow dashboard, then click "View Detailed Evaluation":

-Start a Roboflow model evaluation +Start a Roboflow model evaluation

A pop-up will appear showing a confusion matrix:

-A confusion matrix +A confusion matrix

Hover over a box on the confusion matrix to see the value associated with the box. Click on a box to see images in the respective category. Click on an image to view the model predictions and ground truth data associated with that image. @@ -209,7 +209,7 @@ Hover over a box on the confusion matrix to see the value associated with the bo For more insights, click Vector Analysis. This will show a scatter plot of the images in your dataset, calculated using CLIP. The closer images are in the plot, the more similar they are, semantically. Each image is represented as a dot with a color between white and red. The more red the dot, the worse the model performed.

-A vector analysis plot +A vector analysis plot

You can use Vector Analysis to: @@ -233,7 +233,7 @@ Want to learn more about using Roboflow for creating YOLOv8 models? The followin Below are a few of the many pieces of feedback we have received for using YOLOv8 and Roboflow together to create computer vision models.

-Showcase image -Showcase image -Showcase image +Showcase image +Showcase image +Showcase image

diff --git a/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md b/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md index 44dcb174..5ff8797d 100644 --- a/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md +++ b/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md @@ -61,4 +61,4 @@ python detect.py --weights yolov5s.pt --source path/to/images # run inference o python export.py --weights yolov5s.pt --include onnx coreml tflite # export models to other formats ``` -

+

GCP running Docker

diff --git a/docs/en/yolov5/index.md b/docs/en/yolov5/index.md index 329c3641..d9303fc7 100644 --- a/docs/en/yolov5/index.md +++ b/docs/en/yolov5/index.md @@ -68,16 +68,16 @@ This badge signifies that all [YOLOv5 GitHub Actions](https://github.com/ultraly
Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord
diff --git a/docs/en/yolov5/tutorials/architecture_description.md b/docs/en/yolov5/tutorials/architecture_description.md index cdd79292..3c8c6612 100644 --- a/docs/en/yolov5/tutorials/architecture_description.md +++ b/docs/en/yolov5/tutorials/architecture_description.md @@ -165,7 +165,7 @@ The YOLOv5 architecture makes some important changes to the box prediction strat ![b_w](https://latex.codecogs.com/svg.image?b_w=p_w\cdot&space;e^{t_w}) ![b_h](https://latex.codecogs.com/svg.image?b_h=p_h\cdot&space;e^{t_h}) - +YOLOv5 grid computation However, in YOLOv5, the formula for predicting the box coordinates has been updated to reduce grid sensitivity and prevent the model from predicting unbounded box dimensions. @@ -178,11 +178,11 @@ The revised formulas for calculating the predicted bounding box are as follows: Compare the center point offset before and after scaling. The center point offset range is adjusted from (0, 1) to (-0.5, 1.5). Therefore, offset can easily get 0 or 1. - +YOLOv5 grid scaling Compare the height and width scaling ratio(relative to anchor) before and after adjustment. The original yolo/darknet box equations have a serious flaw. Width and Height are completely unbounded as they are simply out=exp(in), which is dangerous, as it can lead to runaway gradients, instabilities, NaN losses and ultimately a complete loss of training. [refer this issue](https://github.com/ultralytics/yolov5/issues/471#issuecomment-662009779) - +YOLOv5 unbounded scaling ### 4.4 Build Targets @@ -204,15 +204,15 @@ This process follows these steps: ![match](https://latex.codecogs.com/svg.image?r^{max}<{\rm&space;anchor_t}) - +YOLOv5 IoU computation - If the calculated ratio is within the threshold, match the ground truth box with the corresponding anchor. - +YOLOv5 grid overlap - Assign the matched anchor to the appropriate cells, keeping in mind that due to the revised center point offset, a ground truth box can be assigned to more than one anchor. Because the center point offset range is adjusted from (0, 1) to (-0.5, 1.5). GT Box can be assigned to more anchors. - +YOLOv5 anchor selection This way, the build targets process ensures that each ground truth object is properly assigned and matched during the training process, allowing YOLOv5 to learn the task of object detection more effectively. diff --git a/docs/en/yolov5/tutorials/clearml_logging_integration.md b/docs/en/yolov5/tutorials/clearml_logging_integration.md index 43c8395c..056f30c9 100644 --- a/docs/en/yolov5/tutorials/clearml_logging_integration.md +++ b/docs/en/yolov5/tutorials/clearml_logging_integration.md @@ -22,15 +22,15 @@ keywords: ClearML, YOLOv5, Ultralytics, AI toolbox, training data, remote traini 🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving -
+
And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! -
-
+
+
![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif) -
-
+
+
## 🦾 Setting Things Up @@ -52,7 +52,7 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t That's it! You're done 😎 -
+
## 🚀 Training YOLOv5 With ClearML @@ -95,7 +95,7 @@ That's a lot right? 🤯 Now, we can visualize all of this information in the Cl There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works! -
+
## 🔗 Dataset Version Management @@ -163,7 +163,7 @@ Now that you have a ClearML dataset, you can very simply use it to train custom python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache ``` -
+
## 👀 Hyperparameter Optimization diff --git a/docs/en/yolov5/tutorials/comet_logging_integration.md b/docs/en/yolov5/tutorials/comet_logging_integration.md index c70d2920..d66ee68e 100644 --- a/docs/en/yolov5/tutorials/comet_logging_integration.md +++ b/docs/en/yolov5/tutorials/comet_logging_integration.md @@ -4,7 +4,7 @@ description: Learn how to set up and use Comet to enhance your YOLOv5 model trai keywords: YOLOv5, Comet, Machine Learning, Ultralytics, Real time metrics tracking, Hyperparameters, Model checkpoints, Model predictions, YOLOv5 training, Comet Credentials --- - +![Comet](https://cdn.comet.ml/img/notebook_logo.png) # YOLOv5 with Comet diff --git a/docs/en/yolov5/tutorials/model_ensembling.md b/docs/en/yolov5/tutorials/model_ensembling.md index 3a3c2a7f..e7e12005 100644 --- a/docs/en/yolov5/tutorials/model_ensembling.md +++ b/docs/en/yolov5/tutorials/model_ensembling.md @@ -127,7 +127,7 @@ Results saved to runs/detect/exp2 Done. (0.223s) ``` - +YOLO inference result ## Environments diff --git a/docs/en/yolov5/tutorials/model_export.md b/docs/en/yolov5/tutorials/model_export.md index 192de827..05169f11 100644 --- a/docs/en/yolov5/tutorials/model_export.md +++ b/docs/en/yolov5/tutorials/model_export.md @@ -134,10 +134,10 @@ Visualize: https://netron.app/ ``` The 3 exported models will be saved alongside the original PyTorch model: -

+

YOLO export locations

[Netron Viewer](https://github.com/lutzroeder/netron) is recommended for visualizing exported models: -

+

YOLO model visualization

## Exported Model Usage Examples diff --git a/docs/en/yolov5/tutorials/neural_magic_pruning_quantization.md b/docs/en/yolov5/tutorials/neural_magic_pruning_quantization.md index a0754772..08b448c3 100644 --- a/docs/en/yolov5/tutorials/neural_magic_pruning_quantization.md +++ b/docs/en/yolov5/tutorials/neural_magic_pruning_quantization.md @@ -27,7 +27,7 @@ This guide explains how to deploy YOLOv5 with Neural Magic's DeepSparse. DeepSparse is an inference runtime with exceptional performance on CPUs. For instance, compared to the ONNX Runtime baseline, DeepSparse offers a 5.8x speed-up for YOLOv5s, running on the same machine!

- + YOLOv5 speed improvement

For the first time, your deep learning workloads can meet the performance demands of production without the complexity and costs of hardware accelerators. Put simply, DeepSparse gives you the performance of GPUs and the simplicity of software: @@ -43,7 +43,7 @@ DeepSparse takes advantage of model sparsity to gain its performance speedup. Sparsification through pruning and quantization is a broadly studied technique, allowing order-of-magnitude reductions in the size and compute needed to execute a network, while maintaining high accuracy. DeepSparse is sparsity-aware, meaning it skips the zeroed out parameters, shrinking amount of compute in a forward pass. Since the sparse computation is now memory bound, DeepSparse executes the network depth-wise, breaking the problem into Tensor Columns, vertical stripes of computation that fit in cache.

- + YOLO model pruning

Sparse networks with compressed computation, executed depth-wise in cache, allows DeepSparse to deliver GPU-class performance on CPUs! @@ -162,7 +162,7 @@ deepsparse.object_detection.annotate --model_filepath zoo:cv/detection/yolov5-s/ Running the above command will create an `annotation-results` folder and save the annotated image inside.

-annotated +annotated

## Benchmarking Performance diff --git a/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md b/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md index 5d9a10ad..31f0750c 100644 --- a/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md +++ b/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md @@ -76,7 +76,8 @@ results.pandas().xyxy[0] # im1 predictions (pandas) # 3 986.00 304.00 1028.0 420.0 0.286865 27 tie ``` - +YOLO inference results on zidane.jpg +YOLO inference results on bus.jpg For all inference options see YOLOv5 `AutoShape()` forward [method](https://github.com/ultralytics/yolov5/blob/30e4c4f09297b67afedf8b2bcd851833ddc9dead/models/common.py#L243-L252). diff --git a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md index 8f72af4e..80a28310 100644 --- a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md +++ b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md @@ -49,4 +49,4 @@ We have released a custom training tutorial demonstrating all of the above capab The real world is messy and your model will invariably encounter situations your dataset didn't anticipate. Using [active learning](https://blog.roboflow.com/what-is-active-learning/) is an important strategy to iteratively improve your dataset and model. With the Roboflow and YOLOv5 integration, you can quickly make improvements on your model deployments by using a battle tested machine learning pipeline. -

+

Roboflow active learning

diff --git a/docs/en/yolov5/tutorials/running_on_jetson_nano.md b/docs/en/yolov5/tutorials/running_on_jetson_nano.md index 1cb47454..86846b95 100644 --- a/docs/en/yolov5/tutorials/running_on_jetson_nano.md +++ b/docs/en/yolov5/tutorials/running_on_jetson_nano.md @@ -216,7 +216,7 @@ uri=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.m deepstream-app -c deepstream_app_config.txt ``` -
+
YOLOv5 with deepstream FP32
The above result is running on **Jetson Xavier NX** with **FP32** and **YOLOv5s 640x640**. We can see that the **FPS** is around **30**. @@ -299,7 +299,7 @@ network-mode=1 deepstream-app -c deepstream_app_config.txt ``` -
+
YOLOv5 with deepstream INT8
The above result is running on **Jetson Xavier NX** with **INT8** and **YOLOv5s 640x640**. We can see that the **FPS** is around **60**. diff --git a/docs/en/yolov5/tutorials/test_time_augmentation.md b/docs/en/yolov5/tutorials/test_time_augmentation.md index d9c00398..1ba33de6 100644 --- a/docs/en/yolov5/tutorials/test_time_augmentation.md +++ b/docs/en/yolov5/tutorials/test_time_augmentation.md @@ -121,7 +121,7 @@ Results saved to runs/detect/exp Done. (0.156s) ``` - +YOLOv5 test time augmentations ### PyTorch Hub TTA diff --git a/docs/en/yolov5/tutorials/train_custom_data.md b/docs/en/yolov5/tutorials/train_custom_data.md index 4713e05b..4fd52901 100644 --- a/docs/en/yolov5/tutorials/train_custom_data.md +++ b/docs/en/yolov5/tutorials/train_custom_data.md @@ -19,7 +19,7 @@ pip install -r requirements.txt # install ## Train On Custom Data - +Ultralytics active learning

@@ -46,7 +46,7 @@ If this is not possible, you can start from [a public dataset](https://universe. Once you have collected images, you will need to annotate the objects of interest to create a ground truth for your model to learn from. -

+

YOLOv5 accuracies

[Roboflow Annotate](https://roboflow.com/annotate?ref=ultralytics) is a simple web-based tool for managing and labeling your images with your team and exporting them in [YOLOv5's annotation format](https://roboflow.com/formats/yolov5-pytorch-txt?ref=ultralytics). @@ -59,18 +59,18 @@ and upload your dataset to a `Public` workspace, label any unannotated images, t Note: YOLOv5 does online augmentation during training, so we do not recommend applying any augmentation steps in Roboflow for training with YOLOv5. But we recommend applying the following preprocessing steps: -

+

Recommended Preprocessing Steps

* **Auto-Orient** - to strip EXIF orientation from your images. * **Resize (Stretch)** - to the square input size of your model (640x640 is the YOLOv5 default). Generating a version will give you a point in time snapshot of your dataset so you can always go back and compare your future model training runs against it, even if you add more images or change its configuration later. -

+

Export in YOLOv5 Format

Export in `YOLOv5 Pytorch` format, then copy the snippet into your training script or notebook to download your dataset. -

+

Roboflow dataset download snippet

Now continue with `2. Select a Model`. @@ -106,14 +106,14 @@ After using an annotation tool to label your images, export your labels to **YOL - One row per object - Each row is `class x_center y_center width height` format. -- Box coordinates must be in **normalized xywh** format (from 0 - 1). If your boxes are in pixels, divide `x_center` and `width` by image width, and `y_center` and `height` by image height. +- Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, divide `x_center` and `width` by image width, and `y_center` and `height` by image height. - Class numbers are zero-indexed (start from 0). -

+

Roboflow annotations

The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`): -

+

Roboflow dataset preprocessing

### 1.3 Organize Directories @@ -124,14 +124,14 @@ Organize your train and val images and labels according to the example below. YO ../datasets/coco128/labels/im0.txt # label ``` -

+

YOLOv5 dataset structure

### 2. Select a Model Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the second-smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models. -

YOLOv5 Models

+

YOLOv5 models

### 3. Train @@ -168,7 +168,7 @@ python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing) -yolo-ui +YOLO UI #### ClearML Logging and Automation 🌟 NEW @@ -182,7 +182,7 @@ You'll get all the great expected features from an experiment manager: live upda You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details! -ClearML Experiment Management UI +ClearML Experiment Management UI #### Local Logging @@ -190,7 +190,7 @@ Training results are automatically logged with [Tensorboard](https://www.tensorf This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. -Local logging results +Local logging results Results file `results.csv` is updated after each epoch, and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually: diff --git a/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md b/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md index a40fa4ba..5fd3376d 100644 --- a/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md +++ b/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md @@ -124,19 +124,19 @@ train.py --batch 48 --weights yolov5m.pt --data voc.yaml --epochs 50 --cache --i The results show that freezing speeds up training, but reduces final accuracy slightly. -![](https://user-images.githubusercontent.com/26833433/98394454-11579f80-205b-11eb-8e57-d8318e1cc2f8.png) +![Freezing training mAP50 results](https://user-images.githubusercontent.com/26833433/98394454-11579f80-205b-11eb-8e57-d8318e1cc2f8.png) -![](https://user-images.githubusercontent.com/26833433/98394459-13216300-205b-11eb-871b-49e20691a423.png) +![Freezing training mAP50-95 results](https://user-images.githubusercontent.com/26833433/98394459-13216300-205b-11eb-871b-49e20691a423.png) -Screenshot 2020-11-06 at 18 08 13 +Table results ### GPU Utilization Comparison Interestingly, the more modules are frozen the less GPU memory is required to train, and the lower GPU utilization. This indicates that larger models, or models trained at larger --image-size may benefit from freezing in order to train faster. -![](https://user-images.githubusercontent.com/26833433/98394920-c2f6d080-205b-11eb-9611-fd68522b4e0e.png) +![Training GPU memory allocated percent](https://user-images.githubusercontent.com/26833433/98394920-c2f6d080-205b-11eb-9611-fd68522b4e0e.png) -![](https://user-images.githubusercontent.com/26833433/98394918-bf634980-205b-11eb-948d-311036ef9325.png) +![Training GPU memory utilization percent](https://user-images.githubusercontent.com/26833433/98394918-bf634980-205b-11eb-948d-311036ef9325.png) ## Environments diff --git a/docs/es/index.md b/docs/es/index.md index 81ea747d..163f4c7d 100644 --- a/docs/es/index.md +++ b/docs/es/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, detección de objetos, segmentación de imágenes Banner de Ultralytics YOLO

GitHub de Ultralytics - + space LinkedIn de Ultralytics - + space Twitter de Ultralytics - + space YouTube de Ultralytics - + space TikTok de Ultralytics - + space Instagram de Ultralytics - + space Discord de Ultralytics

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, detección de objetos, segmentación de imágenes Cita de YOLOv8 Descargas de Docker
- Ejecutar en Gradient + Ejecutar en Gradient Abrir en Colab Abrir en Kaggle diff --git a/docs/fr/index.md b/docs/fr/index.md index 52717cdb..be3e9477 100644 --- a/docs/fr/index.md +++ b/docs/fr/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, détection d'objets, segmentation d'images, appre Bannière Ultralytics YOLO

GitHub Ultralytics - + space LinkedIn Ultralytics - + space Twitter Ultralytics - + space YouTube Ultralytics - + space TikTok Ultralytics - + space Instagram Ultralytics - + space Discord Ultralytics

diff --git a/docs/hi/index.md b/docs/hi/index.md index 35359b71..f06b6b8e 100644 --- a/docs/hi/index.md +++ b/docs/hi/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, वस्तु पता लगाना, छव Ultralytics YOLO banner

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, वस्तु पता लगाना, छव YOLOv8 Citation Docker Pulls
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle diff --git a/docs/ja/index.md b/docs/ja/index.md index 4eca5e41..97f5ec6e 100644 --- a/docs/ja/index.md +++ b/docs/ja/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, オブジェクト検出, 画像セグメンテ Ultralytics YOLOバナー

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, オブジェクト検出, 画像セグメンテ YOLOv8 引用情報 Docker プル
- Gradient上で実行 + Gradient上で実行 Colabで開く Kaggleで開く diff --git a/docs/ko/index.md b/docs/ko/index.md index 6706d45e..cf6acbe7 100644 --- a/docs/ko/index.md +++ b/docs/ko/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, 객체 탐지, 이미지 분할, 기계 학습, Ultralytics YOLO 배너

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, 객체 탐지, 이미지 분할, 기계 학습, YOLOv8 인용 Docker 당기기
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle diff --git a/docs/pt/index.md b/docs/pt/index.md index e709c04e..cc87e6ee 100644 --- a/docs/pt/index.md +++ b/docs/pt/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, detecção de objetos, segmentação de imagens, Banner Ultralytics YOLO

GitHub da Ultralytics - + space LinkedIn da Ultralytics - + space Twitter da Ultralytics - + space YouTube da Ultralytics - + space TikTok da Ultralytics - + space Instagram da Ultralytics - + space Discord da Ultralytics

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, detecção de objetos, segmentação de imagens, Citação do YOLOv8 Contagem de Pulls no Docker
- Executar no Gradient + Executar no Gradient Abrir no Colab Abrir no Kaggle diff --git a/docs/ru/index.md b/docs/ru/index.md index 1e07272a..aac44064 100644 --- a/docs/ru/index.md +++ b/docs/ru/index.md @@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, обнаружение объектов, сегм Ultralytics YOLO banner

GitHub Ultralytics - + space LinkedIn Ultralytics - + space Twitter Ultralytics - + space YouTube Ultralytics - + space TikTok Ultralytics - + space Instagram Ultralytics - + space Discord Ultralytics

@@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, обнаружение объектов, сегм Цитирование YOLOv8 Загрузки Docker
- Запустить на Gradient + Запустить на Gradient Открыть в Colab Открыть в Kaggle diff --git a/docs/update_translations.py b/docs/update_translations.py index f9676eae..9c27c700 100644 --- a/docs/update_translations.py +++ b/docs/update_translations.py @@ -121,6 +121,27 @@ class MarkdownLinkFixer: return match.group(0) + @staticmethod + def update_html_tags(content): + """Updates HTML tags in docs.""" + alt_tag = 'MISSING' + + # Remove closing slashes from self-closing HTML tags + pattern = re.compile(r'<([^>]+?)\s*/>') + content = re.sub(pattern, r'<\1>', content) + + # Find all images without alt tags and add placeholder alt text + pattern = re.compile(r'!\[(.*?)\]\((.*?)\)') + content, num_replacements = re.subn(pattern, lambda match: f'![{match.group(1) or alt_tag}]({match.group(2)})', + content) + + # Add missing alt tags to HTML images + pattern = re.compile(r']*src=["\'](.*?)["\'][^>]*>') + content, num_replacements = re.subn(pattern, lambda match: match.group(0).replace('>', f' alt="{alt_tag}">', 1), + content) + + return content + def process_markdown_file(self, md_file_path, lang_dir): """Process each markdown file in the language directory.""" print(f'Processing file: {md_file_path}') @@ -134,6 +155,7 @@ class MarkdownLinkFixer: content = self.replace_front_matter(content, lang_dir) content = self.replace_admonitions(content, lang_dir) content = self.update_iframe(content) + content = self.update_html_tags(content) with open(md_file_path, 'w', encoding='utf-8') as file: file.write(content) diff --git a/docs/zh/index.md b/docs/zh/index.md index a67abccc..b8c3ea80 100644 --- a/docs/zh/index.md +++ b/docs/zh/index.md @@ -12,17 +12,17 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度 Ultralytics YOLO banner

Ultralytics GitHub - + space Ultralytics LinkedIn - + space Ultralytics Twitter - + space Ultralytics YouTube - + space Ultralytics TikTok - + space Ultralytics Instagram - + space Ultralytics Discord

@@ -31,7 +31,7 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度 YOLOv8 Citation Docker Pulls
- Run on Gradient + Run on Gradient Open In Colab Open In Kaggle diff --git a/examples/YOLOv8-Region-Counter/readme.md b/examples/YOLOv8-Region-Counter/readme.md index 9c0ad168..2acf0a55 100644 --- a/examples/YOLOv8-Region-Counter/readme.md +++ b/examples/YOLOv8-Region-Counter/readme.md @@ -4,10 +4,9 @@ - Regions can be adjusted to suit the user's preferences and requirements.
-

- - -        +

+ YOLOv8 region counting visual 1 + YOLOv8 region counting visual 2

diff --git a/examples/YOLOv8-Segmentation-ONNXRuntime-Python/README.md b/examples/YOLOv8-Segmentation-ONNXRuntime-Python/README.md index 98e53ce3..9327f1fa 100644 --- a/examples/YOLOv8-Segmentation-ONNXRuntime-Python/README.md +++ b/examples/YOLOv8-Segmentation-ONNXRuntime-Python/README.md @@ -43,7 +43,7 @@ python main.py --model-path --source After running the command, you should see segmentation results similar to this: -Segmentation Demo +Segmentation Demo ## Advanced Usage diff --git a/ultralytics/trackers/README.md b/ultralytics/trackers/README.md index 7bbbaded..2cab3c04 100644 --- a/ultralytics/trackers/README.md +++ b/ultralytics/trackers/README.md @@ -1,6 +1,6 @@ # Multi-Object Tracking with Ultralytics YOLO - +YOLOv8 trackers visualization Object tracking in the realm of video analytics is a critical task that not only identifies the location and class of objects within the frame but also maintains a unique ID for each detected object as the video progresses. The applications are limitless—ranging from surveillance and security to real-time sports analytics.