From 99f729a4e453151a14ad975012b0877ce42c07a0 Mon Sep 17 00:00:00 2001 From: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com> Date: Sat, 19 Oct 2024 18:20:57 +0200 Subject: [PATCH] Ultralytics Refactor https://ultralytics.com/actions (#17031) Signed-off-by: UltralyticsAssistant Co-authored-by: Glenn Jocher --- .github/workflows/format.yml | 2 +- README.md | 8 +++--- README.zh-CN.md | 8 +++--- docs/build_docs.py | 2 +- docs/en/datasets/explorer/explorer.ipynb | 2 +- .../guides/coral-edge-tpu-on-raspberry-pi.md | 2 +- docs/en/guides/model-training-tips.md | 2 +- docs/en/guides/steps-of-a-cv-project.md | 6 +---- docs/en/index.md | 2 +- docs/en/integrations/kaggle.md | 6 ++--- .../environments/aws_quickstart_tutorial.md | 2 +- .../docker_image_quickstart_tutorial.md | 2 +- docs/en/yolov5/index.md | 4 +-- .../tutorials/hyperparameter_evolution.md | 2 +- docs/en/yolov5/tutorials/model_ensembling.md | 2 +- docs/en/yolov5/tutorials/model_export.md | 2 +- .../tutorials/model_pruning_and_sparsity.md | 2 +- .../en/yolov5/tutorials/multi_gpu_training.md | 2 +- .../tutorials/pytorch_hub_model_loading.md | 2 +- .../roboflow_datasets_integration.md | 4 +-- .../tutorials/test_time_augmentation.md | 2 +- docs/en/yolov5/tutorials/train_custom_data.md | 6 ++--- .../transfer_learning_with_frozen_layers.md | 2 +- docs/overrides/partials/source-file.html | 26 ------------------- examples/heatmaps.ipynb | 2 +- examples/object_counting.ipynb | 2 +- examples/object_tracking.ipynb | 2 +- examples/tutorial.ipynb | 2 +- ultralytics/cfg/datasets/coco128-seg.yaml | 2 +- ultralytics/cfg/datasets/coco128.yaml | 2 +- 30 files changed, 41 insertions(+), 71 deletions(-) delete mode 100644 docs/overrides/partials/source-file.html diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml index f1e6ba90..acd28656 100644 --- a/.github/workflows/format.yml +++ b/.github/workflows/format.yml @@ -49,7 +49,7 @@ jobs: YOLO may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle + - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/) - **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) Docker Pulls diff --git a/README.md b/README.md index 291977d6..39fd7bac 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@
Run Ultralytics on Gradient Open Ultralytics In Colab - Open Ultralytics In Kaggle + Open Ultralytics In Kaggle
@@ -229,9 +229,9 @@ Our key integrations with leading AI platforms extend the functionality of Ultra NeuralMagic logo -| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic | -| :----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | -| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | +| Ultralytics HUB 🚀 | W&B | Comet ⭐ NEW | Neural Magic | +| :--------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | +| Streamline YOLO workflows: Label, train, and deploy effortlessly with [Ultralytics HUB](https://www.ultralytics.com/hub). Try now! | Track experiments, hyperparameters, and results with [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet) lets you save YOLO11 models, resume training, and interactively visualize and debug predictions | Run YOLO11 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) | ##
Ultralytics HUB
diff --git a/README.zh-CN.md b/README.zh-CN.md index ae2ded2e..ac87d1bd 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -16,7 +16,7 @@
Run Ultralytics on Gradient Open Ultralytics In Colab - Open Ultralytics In Kaggle + Open Ultralytics In Kaggle
@@ -229,9 +229,9 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d NeuralMagic logo -| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic | -| :------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | -| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 | +| Ultralytics HUB 🚀 | W&B | Comet ⭐ 全新 | Neural Magic | +| :----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------: | +| 简化 YOLO 工作流程：通过 [Ultralytics HUB](https://www.ultralytics.com/hub) 轻松标注、训练和部署。立即试用！ | 使用 [Weights & Biases](https://docs.wandb.ai/guides/integrations/ultralytics/) 跟踪实验、超参数和结果 | 永久免费，[Comet](https://bit.ly/yolov5-readme-comet) 允许您保存 YOLO11 模型、恢复训练，并交互式地可视化和调试预测结果 | 使用 [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) 运行 YOLO11 推理，速度提升至 6 倍 | ##
Ultralytics HUB
diff --git a/docs/build_docs.py b/docs/build_docs.py index 483a2dd0..82e4d6d3 100644 --- a/docs/build_docs.py +++ b/docs/build_docs.py @@ -199,7 +199,7 @@ def convert_plaintext_links_to_html(content): for text_node in paragraph.find_all(string=True, recursive=False): if text_node.parent.name not in {"a", "code"}: # Ignore links and code blocks new_text = re.sub( - r'(https?://[^\s()<>]+(?:\.[^\s()<>]+)+)(?]+)", r'\1', str(text_node), ) diff --git a/docs/en/datasets/explorer/explorer.ipynb b/docs/en/datasets/explorer/explorer.ipynb index 42da7a61..7349a082 100644 --- a/docs/en/datasets/explorer/explorer.ipynb +++ b/docs/en/datasets/explorer/explorer.ipynb @@ -17,7 +17,7 @@ "\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", "\n", "Welcome to the Ultralytics Explorer API notebook! This notebook serves as the starting point for exploring the various resources available to help you get started with using Ultralytics to explore your datasets using with the power of semantic search. You can utilities out of the box that allow you to examine specific types of labels using vector search or even SQL queries.\n", "\n", diff --git a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md index e2d2a03f..87154838 100644 --- a/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md +++ b/docs/en/guides/coral-edge-tpu-on-raspberry-pi.md @@ -27,7 +27,7 @@ The Coral Edge TPU is a compact device that adds an Edge TPU coprocessor to your ## Boost Raspberry Pi Model Performance with Coral Edge TPU -Many people want to run their models on an embedded or mobile device such as a Raspberry Pi, since they are very power efficient and can be used in many different applications. However, the inference performance on these devices is usually poor even when using formats like [onnx](../integrations/onnx.md) or [openvino](../integrations/openvino.md). The Coral Edge TPU is a great solution to this problem, since it can be used with a Raspberry Pi and accelerate inference performance greatly. +Many people want to run their models on an embedded or mobile device such as a Raspberry Pi, since they are very power efficient and can be used in many different applications. However, the inference performance on these devices is usually poor even when using formats like [ONNX](../integrations/onnx.md) or [OpenVINO](../integrations/openvino.md). The Coral Edge TPU is a great solution to this problem, since it can be used with a Raspberry Pi and accelerate inference performance greatly. ## Edge TPU on Raspberry Pi with TensorFlow Lite (New)⭐ diff --git a/docs/en/guides/model-training-tips.md b/docs/en/guides/model-training-tips.md index e7e39048..b0eada0d 100644 --- a/docs/en/guides/model-training-tips.md +++ b/docs/en/guides/model-training-tips.md @@ -18,7 +18,7 @@ One of the most important steps when working on a [computer vision project](./st allowfullscreen>
- Watch: Model Training Tips | How to Handle Large Datasets | Batch Size, GPU Utilization and [Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision) + Watch: Model Training Tips | How to Handle Large Datasets | Batch Size, GPU Utilization and Mixed Precision

So, what is [model training](../modes/train.md)? Model training is the process of teaching your model to recognize visual patterns and make predictions based on your data. It directly impacts the performance and accuracy of your application. In this guide, we'll cover best practices, optimization techniques, and troubleshooting tips to help you train your computer vision models effectively. diff --git a/docs/en/guides/steps-of-a-cv-project.md b/docs/en/guides/steps-of-a-cv-project.md index ca067547..72676d72 100644 --- a/docs/en/guides/steps-of-a-cv-project.md +++ b/docs/en/guides/steps-of-a-cv-project.md @@ -18,15 +18,11 @@ Computer vision is a subfield of [artificial intelligence](https://www.ultralyti allowfullscreen>
- Watch: How to Do [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) Projects | A Step-by-Step Guide + Watch: How to Do Computer Vision Projects | A Step-by-Step Guide

Computer vision techniques like [object detection](../tasks/detect.md), [image classification](../tasks/classify.md), and [instance segmentation](../tasks/segment.md) can be applied across various industries, from [autonomous driving](https://www.ultralytics.com/solutions/ai-in-self-driving) to [medical imaging](https://www.ultralytics.com/solutions/ai-in-healthcare) to gain valuable insights. -

- Overview of computer vision techniques -

- Working on your own computer vision projects is a great way to understand and learn more about computer vision. However, a computer vision project can consist of many steps, and it might seem confusing at first. By the end of this guide, you'll be familiar with the steps involved in a computer vision project. We'll walk through everything from the beginning to the end of a project, explaining why each part is important. Let's get started and make your computer vision project a success! ## An Overview of a Computer Vision Project diff --git a/docs/en/index.md b/docs/en/index.md index 5f861f7f..f796e4b4 100644 --- a/docs/en/index.md +++ b/docs/en/index.md @@ -28,7 +28,7 @@ keywords: Ultralytics, YOLO, YOLO11, object detection, image segmentation, deep
Run on Gradient Open In Colab -Open In Kaggle +Open In Kaggle Introducing [Ultralytics](https://www.ultralytics.com/) [YOLO11](https://github.com/ultralytics/ultralytics), the latest version of the acclaimed real-time object detection and image segmentation model. YOLO11 is built on cutting-edge advancements in [deep learning](https://www.ultralytics.com/glossary/deep-learning-dl) and [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv), offering unparalleled performance in terms of speed and [accuracy](https://www.ultralytics.com/glossary/accuracy). Its streamlined design makes it suitable for various applications and easily adaptable to different hardware platforms, from edge devices to cloud APIs. diff --git a/docs/en/integrations/kaggle.md b/docs/en/integrations/kaggle.md index 2e2c00ca..920c5dbc 100644 --- a/docs/en/integrations/kaggle.md +++ b/docs/en/integrations/kaggle.md @@ -20,7 +20,7 @@ With more than [10 million users](https://www.kaggle.com/discussions/general/332 Training YOLO11 models on Kaggle is simple and efficient, thanks to the platform's access to powerful GPUs. -To get started, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/code/ultralytics/yolov8). Kaggle's environment comes with pre-installed libraries like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making the setup process hassle-free. +To get started, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/code/glennjocherultralytics/yolo11). Kaggle's environment comes with pre-installed libraries like [TensorFlow](https://www.ultralytics.com/glossary/tensorflow) and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making the setup process hassle-free. ![What is the kaggle integration with respect to YOLO11?](https://github.com/ultralytics/docs/releases/download/0/kaggle-integration-yolov8.avif) @@ -28,7 +28,7 @@ Once you sign in to your Kaggle account, you can click on the option to copy and ![Using kaggle for machine learning model training with a GPU](https://github.com/ultralytics/docs/releases/download/0/using-kaggle-for-machine-learning-model-training-with-a-gpu.avif) -On the [official YOLO11 Kaggle notebook page](https://www.kaggle.com/code/ultralytics/yolov8), if you click on the three dots in the upper right-hand corner, you'll notice more options will pop up. +On the [official YOLO11 Kaggle notebook page](https://www.kaggle.com/code/glennjocherultralytics/yolo11), if you click on the three dots in the upper right-hand corner, you'll notice more options will pop up. ![Overview of Options From the Official YOLO11 Kaggle Notebook Page](https://github.com/ultralytics/docs/releases/download/0/overview-options-yolov8-kaggle-notebook.avif) @@ -95,7 +95,7 @@ Interested in more YOLO11 integrations? Check out the[ Ultralytics integration g ### How do I train a YOLO11 model on Kaggle? -Training a YOLO11 model on Kaggle is straightforward. First, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/ultralytics/yolov8). Sign in to your Kaggle account, copy and edit the notebook, and select a GPU under the accelerator settings. Run the notebook cells to start training. For more detailed steps, refer to our [YOLO11 Model Training guide](../modes/train.md). +Training a YOLO11 model on Kaggle is straightforward. First, access the [Kaggle YOLO11 Notebook](https://www.kaggle.com/code/glennjocherultralytics/yolo11). Sign in to your Kaggle account, copy and edit the notebook, and select a GPU under the accelerator settings. Run the notebook cells to start training. For more detailed steps, refer to our [YOLO11 Model Training guide](../modes/train.md). ### What are the benefits of using Kaggle for YOLO11 model training? diff --git a/docs/en/yolov5/environments/aws_quickstart_tutorial.md b/docs/en/yolov5/environments/aws_quickstart_tutorial.md index 0e5daf2f..387817dc 100644 --- a/docs/en/yolov5/environments/aws_quickstart_tutorial.md +++ b/docs/en/yolov5/environments/aws_quickstart_tutorial.md @@ -8,7 +8,7 @@ keywords: YOLOv5, AWS, Deep Learning, Machine Learning, AWS EC2, YOLOv5 setup, D Setting up a high-performance deep learning environment can be daunting for newcomers, but fear not! 🛠️ With this guide, we'll walk you through the process of getting YOLOv5 up and running on an AWS Deep Learning instance. By leveraging the power of Amazon Web Services (AWS), even those new to [machine learning](https://www.ultralytics.com/glossary/machine-learning-ml) can get started quickly and cost-effectively. The AWS platform's scalability is perfect for both experimentation and production deployment. -Other quickstart options for YOLOv5 include our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) Open In Colab Open In Kaggle, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) Docker Pulls. +Other quickstart options for YOLOv5 include our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) Open In Colab Open In Kaggle, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) Docker Pulls. ## Step 1: AWS Console Sign-In diff --git a/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md b/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md index 023f24c5..9063949b 100644 --- a/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md +++ b/docs/en/yolov5/environments/docker_image_quickstart_tutorial.md @@ -8,7 +8,7 @@ keywords: YOLOv5, Docker, Ultralytics, setup, guide, tutorial, machine learning, This tutorial will guide you through the process of setting up and running YOLOv5 in a Docker container. -You can also explore other quickstart options for YOLOv5, such as our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) Open In Colab Open In Kaggle, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md). +You can also explore other quickstart options for YOLOv5, such as our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) Open In Colab Open In Kaggle, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md). ## Prerequisites diff --git a/docs/en/yolov5/index.md b/docs/en/yolov5/index.md index ec520074..0e071299 100644 --- a/docs/en/yolov5/index.md +++ b/docs/en/yolov5/index.md @@ -18,7 +18,7 @@ keywords: YOLOv5, Ultralytics, object detection, computer vision, deep learning,
Run on Gradient Open In Colab -Open In Kaggle +Open In Kaggle

@@ -54,7 +54,7 @@ Here's a compilation of comprehensive tutorials that will guide you through diff Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/hyperparameter_evolution.md b/docs/en/yolov5/tutorials/hyperparameter_evolution.md index 3db460b1..9dff4ba3 100644 --- a/docs/en/yolov5/tutorials/hyperparameter_evolution.md +++ b/docs/en/yolov5/tutorials/hyperparameter_evolution.md @@ -153,7 +153,7 @@ We recommend a minimum of 300 generations of evolution for best results. Note th Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/model_ensembling.md b/docs/en/yolov5/tutorials/model_ensembling.md index 814c8969..cc76cc0c 100644 --- a/docs/en/yolov5/tutorials/model_ensembling.md +++ b/docs/en/yolov5/tutorials/model_ensembling.md @@ -134,7 +134,7 @@ Done. (0.223s) Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/model_export.md b/docs/en/yolov5/tutorials/model_export.md index e5f0c730..a3a945c1 100644 --- a/docs/en/yolov5/tutorials/model_export.md +++ b/docs/en/yolov5/tutorials/model_export.md @@ -234,7 +234,7 @@ YOLOv5 OpenVINO C++ inference examples: Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md b/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md index 8bda8772..0adfb32c 100644 --- a/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md +++ b/docs/en/yolov5/tutorials/model_pruning_and_sparsity.md @@ -97,7 +97,7 @@ In the results we can observe that we have achieved a **sparsity of 30%** in our Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/multi_gpu_training.md b/docs/en/yolov5/tutorials/multi_gpu_training.md index 53f3a1c1..d61fab83 100644 --- a/docs/en/yolov5/tutorials/multi_gpu_training.md +++ b/docs/en/yolov5/tutorials/multi_gpu_training.md @@ -173,7 +173,7 @@ If you went through all the above, feel free to raise an Issue by giving as much Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md b/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md index 27e26f14..0f464adf 100644 --- a/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md +++ b/docs/en/yolov5/tutorials/pytorch_hub_model_loading.md @@ -361,7 +361,7 @@ model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_paddle_mode Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md index 55728f21..a6f70069 100644 --- a/docs/en/yolov5/tutorials/roboflow_datasets_integration.md +++ b/docs/en/yolov5/tutorials/roboflow_datasets_integration.md @@ -60,7 +60,7 @@ The real world is messy and your model will invariably encounter situations your Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) @@ -102,4 +102,4 @@ Active learning is a machine learning strategy that iteratively improves a model ### How can I use Ultralytics environments for training YOLOv5 models on different platforms? -Ultralytics provides ready-to-use environments with pre-installed dependencies like CUDA, CUDNN, Python, and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making it easier to kickstart your training projects. These environments are available on various platforms such as Google Cloud, AWS, Azure, and Docker. You can also access free GPU notebooks via [Paperspace](https://bit.ly/yolov5-paperspace-notebook), [Google Colab](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb), and [Kaggle](https://www.kaggle.com/ultralytics/yolov5). For specific setup instructions, visit the [Supported Environments](#supported-environments) section of the documentation. +Ultralytics provides ready-to-use environments with pre-installed dependencies like CUDA, CUDNN, Python, and [PyTorch](https://www.ultralytics.com/glossary/pytorch), making it easier to kickstart your training projects. These environments are available on various platforms such as Google Cloud, AWS, Azure, and Docker. You can also access free GPU notebooks via [Paperspace](https://bit.ly/yolov5-paperspace-notebook), [Google Colab](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb), and [Kaggle](https://www.kaggle.com/models/ultralytics/yolov5). For specific setup instructions, visit the [Supported Environments](#supported-environments) section of the documentation. diff --git a/docs/en/yolov5/tutorials/test_time_augmentation.md b/docs/en/yolov5/tutorials/test_time_augmentation.md index 336ad3f7..30c53b72 100644 --- a/docs/en/yolov5/tutorials/test_time_augmentation.md +++ b/docs/en/yolov5/tutorials/test_time_augmentation.md @@ -151,7 +151,7 @@ You can customize the TTA ops applied in the YOLOv5 `forward_augment()` method [ Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/en/yolov5/tutorials/train_custom_data.md b/docs/en/yolov5/tutorials/train_custom_data.md index 8b465c52..c6f9d6f2 100644 --- a/docs/en/yolov5/tutorials/train_custom_data.md +++ b/docs/en/yolov5/tutorials/train_custom_data.md @@ -77,7 +77,7 @@ Export in `YOLOv5 Pytorch` format, then copy the snippet into your training scri ### 2.1 Create `dataset.yaml` -[COCO128](https://www.kaggle.com/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of [overfitting](https://www.ultralytics.com/glossary/overfitting). [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or `*.txt` files with image paths) and 2) a class `names` dictionary: +[COCO128](https://www.kaggle.com/datasets/ultralytics/coco128) is an example small tutorial dataset composed of the first 128 images in [COCO](https://cocodataset.org/) train2017. These same 128 images are used for both training and validation to verify our training pipeline is capable of [overfitting](https://www.ultralytics.com/glossary/overfitting). [data/coco128.yaml](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), shown below, is the dataset config file that defines 1) the dataset root directory `path` and relative paths to `train` / `val` / `test` image directories (or `*.txt` files with image paths) and 2) a class `names` dictionary: ```yaml # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] @@ -145,7 +145,7 @@ python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt 💡 Always train from a local dataset. Mounted or network drives like Google Drive will be very slow. -All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc. For more details see the Training section of our tutorial notebook. Open In Colab Open In Kaggle +All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc. For more details see the Training section of our tutorial notebook. Open In Colab Open In Kaggle ## 5. Visualize @@ -211,7 +211,7 @@ Once your model is trained you can use your best checkpoint `best.pt` to: Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) 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 9e689ad3..9a37ace1 100644 --- a/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md +++ b/docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md @@ -141,7 +141,7 @@ Interestingly, the more modules are frozen the less GPU memory is required to tr Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda-zone), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects. -- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle +- **Free GPU Notebooks**: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md) - **Amazon**: [AWS Quickstart Guide](../environments/aws_quickstart_tutorial.md) - **Azure**: [AzureML Quickstart Guide](../environments/azureml_quickstart_tutorial.md) diff --git a/docs/overrides/partials/source-file.html b/docs/overrides/partials/source-file.html deleted file mode 100644 index 84e2ab1f..00000000 --- a/docs/overrides/partials/source-file.html +++ /dev/null @@ -1,26 +0,0 @@ -{% import "partials/language.html" as lang with context %} - - - -
-
- - - - {% if page.meta.git_revision_date_localized %} - 📅 {{ lang.t("source.file.date.updated") }}: - {{ page.meta.git_revision_date_localized }} - {% if page.meta.git_creation_date_localized %} -
- 🎂 {{ lang.t("source.file.date.created") }}: - {{ page.meta.git_creation_date_localized }} - {% endif %} - - - {% elif page.meta.revision_date %} - 📅 {{ lang.t("source.file.date.updated") }}: - {{ page.meta.revision_date }} - {% endif %} - -
diff --git a/examples/heatmaps.ipynb b/examples/heatmaps.ipynb index 6ebf179b..11ffdc90 100644 --- a/examples/heatmaps.ipynb +++ b/examples/heatmaps.ipynb @@ -16,7 +16,7 @@ " \"Ultralytics\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n", diff --git a/examples/object_counting.ipynb b/examples/object_counting.ipynb index 8356d592..572f1033 100644 --- a/examples/object_counting.ipynb +++ b/examples/object_counting.ipynb @@ -16,7 +16,7 @@ " \"Ultralytics\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n", diff --git a/examples/object_tracking.ipynb b/examples/object_tracking.ipynb index 53ca6b25..7691fce9 100644 --- a/examples/object_tracking.ipynb +++ b/examples/object_tracking.ipynb @@ -16,7 +16,7 @@ " \"Ultralytics\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n", diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index c52b03c9..98c659b8 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -30,7 +30,7 @@ " \"Ultralytics\n", " \"Run\n", " \"Open\n", - " \"Open\n", + " \"Open\n", "\n", " \"Discord\"\n", " \"Ultralytics\n", diff --git a/ultralytics/cfg/datasets/coco128-seg.yaml b/ultralytics/cfg/datasets/coco128-seg.yaml index dcd961c6..c8349cee 100644 --- a/ultralytics/cfg/datasets/coco128-seg.yaml +++ b/ultralytics/cfg/datasets/coco128-seg.yaml @@ -1,5 +1,5 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Documentation: https://docs.ultralytics.com/datasets/segment/coco/ # Example usage: yolo train data=coco128.yaml # parent diff --git a/ultralytics/cfg/datasets/coco128.yaml b/ultralytics/cfg/datasets/coco128.yaml index 1b515592..a085f5e9 100644 --- a/ultralytics/cfg/datasets/coco128.yaml +++ b/ultralytics/cfg/datasets/coco128.yaml @@ -1,5 +1,5 @@ # Ultralytics YOLO 🚀, AGPL-3.0 license -# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics +# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics # Documentation: https://docs.ultralytics.com/datasets/detect/coco/ # Example usage: yolo train data=coco128.yaml # parent