diff --git a/README.md b/README.md index 819f98230..4914bef67 100644 --- a/README.md +++ b/README.md @@ -139,7 +139,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp - **mAPval** values are for single-model single-scale on [Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/) dataset.
Reproduce by `yolo val detect data=open-images-v7.yaml device=0` -- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. +- **Speed** averaged over Open Image V7 val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance.
Reproduce by `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` diff --git a/README.zh-CN.md b/README.zh-CN.md index 84f264ce5..7f25522d9 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -139,7 +139,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式 - **mAP验证** 值适用于在[Open Image V7](https://docs.ultralytics.com/datasets/detect/open-images-v7/)数据集上的单模型单尺度。
通过 `yolo val detect data=open-images-v7.yaml device=0` 以复现。 -- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对COCO验证图像进行平均测算。 +- **速度** 在使用[Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)实例对Open Image V7验证图像进行平均测算。
通过 `yolo val detect data=open-images-v7.yaml batch=1 device=0|cpu` 以复现。 diff --git a/docs/en/datasets/pose/tiger-pose.md b/docs/en/datasets/pose/tiger-pose.md index c00fafb6f..4d419ca9e 100644 --- a/docs/en/datasets/pose/tiger-pose.md +++ b/docs/en/datasets/pose/tiger-pose.md @@ -15,6 +15,17 @@ Despite its manageable size of 210 images, tiger-pose dataset offers diversity, This dataset is intended for use with [Ultralytics HUB](https://hub.ultralytics.com) and [YOLOv8](https://github.com/ultralytics/ultralytics). +

+
+ +
+ Watch: Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB +

+ ## Dataset YAML A YAML (Yet Another Markup Language) file serves as the means to specify the configuration details of a dataset. It encompasses crucial data such as file paths, class definitions, and other pertinent information. Specifically, for the `tiger-pose.yaml` file, you can check [Ultralytics Tiger-Pose Dataset Configuration File](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/tiger-pose.yaml). diff --git a/docs/en/guides/isolating-segmentation-objects.md b/docs/en/guides/isolating-segmentation-objects.md index 866c750d1..334367f2c 100644 --- a/docs/en/guides/isolating-segmentation-objects.md +++ b/docs/en/guides/isolating-segmentation-objects.md @@ -143,7 +143,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab ### Object Isolation Options - !!! example "" + !!! Example "" === "Black Background Pixels" diff --git a/docs/en/hub/projects.md b/docs/en/hub/projects.md index f71294233..c0cbf208d 100644 --- a/docs/en/hub/projects.md +++ b/docs/en/hub/projects.md @@ -10,6 +10,17 @@ keywords: Ultralytics, HUB projects, Create project, Edit project, Share project This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models. +

+
+ +
+ Watch: Train YOLOv8 Pose Model on Tiger-Pose Dataset Using Ultralytics HUB +

+ ## Create Project Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar. diff --git a/docs/en/integrations/clearml.md b/docs/en/integrations/clearml.md index 933cc2ab0..1c65d4ab8 100644 --- a/docs/en/integrations/clearml.md +++ b/docs/en/integrations/clearml.md @@ -13,7 +13,7 @@ MLOps bridges the gap between creating and deploying machine learning models in ## ClearML

- ClearML Overview + ClearML Overview

[ClearML](https://clear.ml/) is an innovative open-source MLOps platform that is skillfully designed to automate, monitor, and orchestrate machine learning workflows. Its key features include automated logging of all training and inference data for full experiment reproducibility, an intuitive web UI for easy data visualization and analysis, advanced hyperparameter optimization algorithms, and robust model management for efficient deployment across various platforms. @@ -119,24 +119,24 @@ By clicking on the URL link to the ClearML results page in the output of the usa #### Key Features of the ClearML Results Page - **Real-Time Metrics Tracking** - - Track critical metrics like loss, accuracy, and validation scores as they occur. - - Provides immediate feedback for timely model performance adjustments. + - Track critical metrics like loss, accuracy, and validation scores as they occur. + - Provides immediate feedback for timely model performance adjustments. - **Experiment Comparison** - - Compare different training runs side-by-side. - - Essential for hyperparameter tuning and identifying the most effective models. + - Compare different training runs side-by-side. + - Essential for hyperparameter tuning and identifying the most effective models. - **Detailed Logs and Outputs** - - Access comprehensive logs, graphical representations of metrics, and console outputs. - - Gain a deeper understanding of model behavior and issue resolution. + - Access comprehensive logs, graphical representations of metrics, and console outputs. + - Gain a deeper understanding of model behavior and issue resolution. - **Resource Utilization Monitoring** - - Monitor the utilization of computational resources, including CPU, GPU, and memory. - - Key to optimizing training efficiency and costs. + - Monitor the utilization of computational resources, including CPU, GPU, and memory. + - Key to optimizing training efficiency and costs. - **Model Artifacts Management** - - View, download, and share model artifacts like trained models and checkpoints. - - Enhances collaboration and streamlines model deployment and sharing. + - View, download, and share model artifacts like trained models and checkpoints. + - Enhances collaboration and streamlines model deployment and sharing. For a visual walkthrough of what the ClearML Results Page looks like, watch the video below: diff --git a/docs/en/integrations/index.md b/docs/en/integrations/index.md index a6a0ce1e8..4cb89d017 100644 --- a/docs/en/integrations/index.md +++ b/docs/en/integrations/index.md @@ -18,7 +18,7 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of - [Comet ML](comet.md): Enhance your model development with Ultralytics by tracking, comparing, and optimizing your machine learning experiments. -- [ClearML](https://clear.ml/): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration. +- [ClearML](clearml.md): Automate your Ultralytics ML workflows, monitor experiments, and foster team collaboration. - [DVC](https://dvc.org/): Implement version control for your Ultralytics machine learning projects, synchronizing data, code, and models effectively. diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 6f6f3c1fd..ccf9fa71e 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -272,6 +272,7 @@ nav: - Ray Tune: integrations/ray-tune.md - Roboflow: integrations/roboflow.md - MLflow: integrations/mlflow.md + - ClearML: integrations/clearml.md - Usage: - CLI: usage/cli.md - Python: usage/python.md diff --git a/ultralytics/data/converter.py b/ultralytics/data/converter.py index 2e97d9658..2f9a1dc1b 100644 --- a/ultralytics/data/converter.py +++ b/ultralytics/data/converter.py @@ -168,18 +168,18 @@ def convert_dota_to_yolo_obb(dota_root_path: str): Notes: The directory structure assumed for the DOTA dataset: - DOTA - - images - - train - - val - - labels - - train_original - - val_original - - After the function execution, the new labels will be saved in: + ├─ images + │ ├─ train + │ └─ val + └─ labels + ├─ train_original + └─ val_original + + After execution, the function will organize the labels into: - DOTA - - labels - - train - - val + └─ labels + ├─ train + └─ val """ dota_root_path = Path(dota_root_path)