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docs/en/yolov5/tutorials/hyperparameter_evolution.md
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@ -151,7 +151,7 @@ We recommend a minimum of 300 generations of evolution for best results. Note th
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/model_ensembling.md
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@ -132,7 +132,7 @@ Done. (0.223s)
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/model_export.md
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@ -36,7 +36,7 @@ YOLOv5 inference is officially supported in 11 formats:
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov5s.mlmodel` |
| [TensorFlow SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov5s_saved_model/` |
| [TensorFlow GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov5s.pb` |
| [TensorFlow Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov5s.tflite` |
| [TensorFlow Lite](https://ai.google.dev/edge/litert) | `tflite` | `yolov5s.tflite` |
| [TensorFlow Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov5s_edgetpu.tflite` |
| [TensorFlow.js](https://www.tensorflow.org/js) | `tfjs` | `yolov5s_web_model/` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov5s_paddle_model/` |
@ -224,7 +224,7 @@ YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:
YOLOv5 OpenVINO C++ inference examples:
- [https://github.com/dacquaviva/yolov5-openvino-cpp-python](https://github.com/dacquaviva/yolov5-openvino-cpp-python)
- [https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp](https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp)
- [https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp](https://github.com/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp)
## TensorFlow.js Web Browser Inference
@ -232,7 +232,7 @@ YOLOv5 OpenVINO C++ inference examples:
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/model_pruning_and_sparsity.md
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@ -95,7 +95,7 @@ In the results we can observe that we have achieved a **sparsity of 30%** in our
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/multi_gpu_training.md
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@ -171,7 +171,7 @@ If you went through all the above, feel free to raise an Issue by giving as much
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/pytorch_hub_model_loading.md
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@ -4,7 +4,7 @@ description: Learn how to load YOLOv5 from PyTorch Hub for seamless model infere
keywords: YOLOv5, PyTorch Hub, model loading, Ultralytics, object detection, machine learning, AI, tutorial, inference
---
📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).
📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5/).
## Before You Start
@ -359,7 +359,7 @@ model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_paddle_mode
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/roboflow_datasets_integration.md
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@ -12,14 +12,14 @@ You can now use Roboflow to organize, label, prepare, version, and host your dat
Ultralytics offers two licensing options:
- The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an [OSI-approved](https://opensource.org/licenses/) open-source license ideal for students and enthusiasts.
- The [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services.
- The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an [OSI-approved](https://opensource.org/license) open-source license ideal for students and enthusiasts.
- The [Enterprise License](https://www.ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services.
For more details see [Ultralytics Licensing](https://ultralytics.com/license).
For more details see [Ultralytics Licensing](https://www.ultralytics.com/license).
## Upload
You can upload your data to Roboflow via [web UI](https://docs.roboflow.com/adding-data), [REST API](https://docs.roboflow.com/adding-data/upload-api), or [Python](https://docs.roboflow.com/python).
You can upload your data to Roboflow via [web UI](https://docs.roboflow.com/adding-data?ref=ultralytics), [REST API](https://docs.roboflow.com/adding-data/upload-api?ref=ultralytics), or [Python](https://docs.roboflow.com/python?ref=ultralytics).
## Labeling
@ -52,13 +52,13 @@ We have released a custom training tutorial demonstrating all of the above capab
## Active Learning
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.
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/?ref=ultralytics) 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.
<p align=""><a href="https://roboflow.com/?ref=ultralytics"><img width="1000" src="https://github.com/ultralytics/docs/releases/download/0/roboflow-active-learning.avif" alt="Roboflow active learning"></a></p>
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/test_time_augmentation.md
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@ -125,7 +125,7 @@ Done. (0.156s)
### PyTorch Hub TTA
TTA is automatically integrated into all [YOLOv5 PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5) models, and can be accessed by passing `augment=True` at inference time.
TTA is automatically integrated into all [YOLOv5 PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/) models, and can be accessed by passing `augment=True` at inference time.
```python
import torch
@ -149,7 +149,7 @@ You can customize the TTA ops applied in the YOLOv5 `forward_augment()` method [
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)

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docs/en/yolov5/tutorials/train_custom_data.md
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@ -29,10 +29,10 @@ Creating a custom model to detect your objects is an iterative process of collec
Ultralytics offers two licensing options:
- The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an [OSI-approved](https://opensource.org/licenses/) open-source license ideal for students and enthusiasts.
- The [Enterprise License](https://ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services.
- The [AGPL-3.0 License](https://github.com/ultralytics/ultralytics/blob/main/LICENSE), an [OSI-approved](https://opensource.org/license) open-source license ideal for students and enthusiasts.
- The [Enterprise License](https://www.ultralytics.com/license) for businesses seeking to incorporate our AI models into their products and services.
For more details see [Ultralytics Licensing](https://ultralytics.com/license).
For more details see [Ultralytics Licensing](https://www.ultralytics.com/license).
YOLOv5 models must be trained on labelled data in order to learn classes of objects in that data. There are two options for creating your dataset before you start training:
@ -209,7 +209,7 @@ Once your model is trained you can use your best checkpoint `best.pt` to:
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)
@ -269,6 +269,6 @@ To convert annotated data to YOLOv5 format using Roboflow:
Ultralytics offers two licensing options:
- **AGPL-3.0 License**: An open-source license suitable for non-commercial use, ideal for students and enthusiasts.
- **Enterprise License**: Tailored for businesses seeking to integrate YOLOv5 into commercial products and services. For detailed information, visit our [Licensing page](https://ultralytics.com/license).
- **Enterprise License**: Tailored for businesses seeking to integrate YOLOv5 into commercial products and services. For detailed information, visit our [Licensing page](https://www.ultralytics.com/license).
For more details, refer to our guide on [Ultralytics Licensing](https://ultralytics.com/license).
For more details, refer to our guide on [Ultralytics Licensing](https://www.ultralytics.com/license).

2
docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md
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@ -139,7 +139,7 @@ Interestingly, the more modules are frozen the less GPU memory is required to tr
## Supported Environments
Ultralytics provides a range of ready-to-use environments, each pre-installed with essential dependencies such as [CUDA](https://developer.nvidia.com/cuda), [CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/), to kickstart your projects.
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**: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
- **Google Cloud**: [GCP Quickstart Guide](../environments/google_cloud_quickstart_tutorial.md)