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:<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Notebooks**with free GPU:<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/models/ultralytics/yolo11"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **GoogleCloud** 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/)
<ahref="https://console.paperspace.com/github/ultralytics/ultralytics"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run Ultralytics on Gradient"></a>
<ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Ultralytics In Colab"></a>
<ahref="https://www.kaggle.com/ultralytics/yolov8"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open Ultralytics In Kaggle"></a>
<ahref="https://www.kaggle.com/models/ultralytics/yolo11"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open Ultralytics In Kaggle"></a>
</div>
<br>
@ -229,9 +229,9 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
| 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) |
| 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) |
<ahref="https://console.paperspace.com/github/ultralytics/ultralytics"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run Ultralytics on Gradient"></a>
<ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open Ultralytics In Colab"></a>
<ahref="https://www.kaggle.com/ultralytics/yolov8"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open Ultralytics In Kaggle"></a>
<ahref="https://www.kaggle.com/models/ultralytics/yolo11"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open Ultralytics In Kaggle"></a>
" <a href=\"https://console.paperspace.com/github/ultralytics/ultralytics\"><img src=\"https://assets.paperspace.io/img/gradient-badge.svg\" alt=\"Run on Gradient\"/></a>\n",
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
" <a href=\"https://www.kaggle.com/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
" <a href=\"https://www.kaggle.com/models/ultralytics/yolo11\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\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",
@ -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)⭐
@ -18,7 +18,7 @@ One of the most important steps when working on a [computer vision project](./st
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> Model Training Tips | How to Handle Large Datasets | Batch Size, GPU Utilization and [Mixed Precision](https://www.ultralytics.com/glossary/mixed-precision)
<strong>Watch:</strong> Model Training Tips | How to Handle Large Datasets | Batch Size, GPU Utilization and <ahref="https://www.ultralytics.com/glossary/mixed-precision">Mixed Precision</a>
</p>
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.
@ -18,15 +18,11 @@ Computer vision is a subfield of [artificial intelligence](https://www.ultralyti
allowfullscreen>
</iframe>
<br>
<strong>Watch:</strong> How to Do [Computer Vision](https://www.ultralytics.com/glossary/computer-vision-cv) Projects | A Step-by-Step Guide
<strong>Watch:</strong> How to Do <ahref="https://www.ultralytics.com/glossary/computer-vision-cv">Computer Vision</a> Projects | A Step-by-Step Guide
</p>
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.
<palign="center">
<imgwidth="100%"src="https://media.licdn.com/dms/image/D4D12AQGf61lmNOm3xA/article-cover_image-shrink_720_1280/0/1656513646049?e=1722470400&v=beta&t=23Rqohhxfie38U5syPeL2XepV2QZe6_HSSC-4rAAvt4"alt="Overview of computer vision techniques">
</p>
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!
<ahref="https://console.paperspace.com/github/ultralytics/ultralytics"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a>
<ahref="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a>
<ahref="https://www.kaggle.com/ultralytics/yolov8"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
<ahref="https://www.kaggle.com/models/ultralytics/yolo11"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
</div>
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.
@ -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.
@ -28,7 +28,7 @@ Once you sign in to your Kaggle account, you can click on the option to copy and
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.
@ -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?
@ -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) <ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <ahref="https://hub.docker.com/r/ultralytics/yolov5"><imgsrc="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker"alt="Docker Pulls"></a>.
Other quickstart options for YOLOv5 include our [Colab Notebook](https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb) <ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and our Docker image at [Docker Hub](https://hub.docker.com/r/ultralytics/yolov5) <ahref="https://hub.docker.com/r/ultralytics/yolov5"><imgsrc="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker"alt="Docker Pulls"></a>.
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) <ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>, [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) <ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>, [GCP Deep Learning VM](./google_cloud_quickstart_tutorial.md), and [Amazon AWS](./aws_quickstart_tutorial.md).
<ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a>
<ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a>
<ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
<ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
<br>
<br>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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.
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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, ..]
💡 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. <ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
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. <ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
## 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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
@ -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**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
- **Free GPU Notebooks**: <ahref="https://bit.ly/yolov5-paperspace-notebook"><imgsrc="https://assets.paperspace.io/img/gradient-badge.svg"alt="Run on Gradient"></a><ahref="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"></a><ahref="https://www.kaggle.com/models/ultralytics/yolov5"><imgsrc="https://kaggle.com/static/images/open-in-kaggle.svg"alt="Open In Kaggle"></a>
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. 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",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. 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",
"Welcome to the Ultralytics YOLO11 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLO11</a> is the latest version of the YOLO (You Only Look Once) AI models developed by <a href=\"https://ultralytics.com\">Ultralytics</a>. 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",
Ultralytics Assistant
1 month ago
committed by
GitHub