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MkDocs validation and Export internal linking (#10368)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/build_docs.py
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@ -32,6 +32,7 @@ from pathlib import Path
from tqdm import tqdm from tqdm import tqdm
os.environ["JUPYTER_PLATFORM_DIRS"] = "1" # fix DeprecationWarning: Jupyter is migrating to use standard platformdirs
DOCS = Path(__file__).parent.resolve() DOCS = Path(__file__).parent.resolve()
SITE = DOCS.parent / "site" SITE = DOCS.parent / "site"

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@ -5,7 +5,7 @@ keywords: Ultralytics, coming soon, under construction, new features, AI updates
# Under Construction 🏗️🌟 # Under Construction 🏗️🌟
Welcome to the Ultralytics "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you! Welcome to the [Ultralytics](https://ultralytics.com) "Under Construction" page! Here, we're hard at work developing the next generation of AI and ML innovations. This page serves as a teaser for the exciting updates and new features we're eager to share with you!
## Exciting New Features on the Way 🎉 ## Exciting New Features on the Way 🎉
@ -23,7 +23,7 @@ This placeholder page is your first stop for upcoming developments. Keep an eye
## We Value Your Input 🗣️ ## We Value Your Input 🗣️
Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/contact). Your feedback shapes our future releases. Share your thoughts and suggestions [here](https://ultralytics.com/survey).
## Thank You, Community! 🌍 ## Thank You, Community! 🌍

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@ -53,7 +53,8 @@ dataframe = explorer.get_similar(idx=0)
Embeddings table for a given dataset and model pair is only created once and reused. These use [LanceDB](https://lancedb.github.io/lancedb/) under the hood, which scales on-disk, so you can create and reuse embeddings for large datasets like COCO without running out of memory. Embeddings table for a given dataset and model pair is only created once and reused. These use [LanceDB](https://lancedb.github.io/lancedb/) under the hood, which scales on-disk, so you can create and reuse embeddings for large datasets like COCO without running out of memory.
In case you want to force update the embeddings table, you can pass `force=True` to `create_embeddings_table` method. In case you want to force update the embeddings table, you can pass `force=True` to `create_embeddings_table` method.
You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in [Working with table section](#4-advanced---working-with-embeddings-table)
You can directly access the LanceDB table object to perform advanced analysis. Learn more about it in the [Working with Embeddings Table section](#4-working-with-embeddings-table)
## 1. Similarity Search ## 1. Similarity Search
@ -196,7 +197,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
exp.plot_sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%' LIMIT 10") exp.plot_sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%' LIMIT 10")
``` ```
## 4. Advanced - Working with Embeddings Table ## 4. Working with Embeddings Table
You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the `Explorer.table` You can also work with the embeddings table directly. Once the embeddings table is created, you can access it using the `Explorer.table`

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@ -29,21 +29,23 @@ Welcome to the Integrations guide for [Ultralytics HUB](https://hub.ultralytics.
### Export Integrations ### Export Integrations
| Format | `format` Argument | Model | Metadata | Arguments | Available export formats are in the table below. You can predict or validate directly on exported models using the `ultralytics` Python package, i.e. `yolo predict model=yolov8n.onnx`.
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | Format | `format` Argument | Model | Metadata | Arguments |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | |---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
## Coming Soon ## Coming Soon

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@ -85,23 +85,23 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
We also support a variety of model export formats for deployment in different environments. Here are the available formats: We also support a variety of model export formats for deployment in different environments. Here are the available formats:
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
Explore the links to learn more about each integration and how to get the most out of them with Ultralytics. Explore the links to learn more about each integration and how to get the most out of them with Ultralytics. See full `export` details in the [Export](../modes/export.md) page.
## Contribute to Our Integrations ## Contribute to Our Integrations

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@ -87,20 +87,20 @@ Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` prov
Benchmarks will attempt to run automatically on all possible export formats below. Benchmarks will attempt to run automatically on all possible export formats below.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. See full `export` details in the [Export](../modes/export.md) page.

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@ -93,20 +93,20 @@ Adjusting these parameters allows for customization of the export process to fit
## Export Formats ## Export Formats
Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |

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@ -160,22 +160,22 @@ Export a YOLOv8n-cls model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model yolo export model=path/to/best.pt format=onnx # export custom trained model
``` ```
Available YOLOv8-cls export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes. Available YOLOv8-cls export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|-------------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-cls_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-cls.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-cls.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. See full `export` details in the [Export](../modes/export.md) page.

34
docs/en/tasks/detect.md
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@ -161,22 +161,22 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model yolo export model=path/to/best.pt format=onnx # export custom trained model
``` ```
Available YOLOv8 export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n.onnx`. Usage examples are shown for your model after export completes. Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. See full `export` details in the [Export](../modes/export.md) page.

34
docs/en/tasks/obb.md
View file

@ -182,22 +182,22 @@ Export a YOLOv8n-obb model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model yolo export model=path/to/best.pt format=onnx # export custom trained model
``` ```
Available YOLOv8-obb export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-obb.onnx`. Usage examples are shown for your model after export completes. Available YOLOv8-obb export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-obb.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|-------------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-obb.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-obb.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-obb_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-obb_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-obb.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-obb.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-obb.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-obb.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-obb_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-obb_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-obb.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-obb.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-obb_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-obb_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. See full `export` details in the [Export](../modes/export.md) page.

34
docs/en/tasks/pose.md
View file

@ -176,22 +176,22 @@ Export a YOLOv8n Pose model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model yolo export model=path/to/best.pt format=onnx # export custom trained model
``` ```
Available YOLOv8-pose export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your model after export completes. Available YOLOv8-pose export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|--------------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-pose_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-pose.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-pose.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. See full `export` details in the [Export](../modes/export.md) page.

34
docs/en/tasks/segment.md
View file

@ -166,22 +166,22 @@ Export a YOLOv8n-seg model to a different format like ONNX, CoreML, etc.
yolo export model=path/to/best.pt format=onnx # export custom trained model yolo export model=path/to/best.pt format=onnx # export custom trained model
``` ```
Available YOLOv8-seg export formats are in the table below. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your model after export completes. Available YOLOv8-seg export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. You can predict or validate directly on exported models, i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your model after export completes.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|-------------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n-seg.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n-seg_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n-seg.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-seg.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n-seg.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-seg.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n-seg.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-seg.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. See full `export` details in the [Export](../modes/export.md) page.

32
docs/en/usage/cli.md
View file

@ -170,21 +170,23 @@ Export a YOLOv8n model to a different format like ONNX, CoreML, etc.
Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`. Available YOLOv8 export formats are in the table below. You can export to any format using the `format` argument, i.e. `format='onnx'` or `format='engine'`.
| Format | `format` Argument | Model | Metadata | Arguments | | Format | `format` Argument | Model | Metadata | Arguments |
|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| |---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|
| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | | [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | | [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` | | [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | | [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | | [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | | [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | | [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | | [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | | [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | | [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | | [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
See full `export` details in the [Export](../modes/export.md) page.
## Overriding default arguments ## Overriding default arguments

2
docs/en/usage/simple-utilities.md
View file

@ -154,7 +154,7 @@ for f in Path('path/to/dataset').rglob('*.jpg'):
### Auto-split Dataset ### Auto-split Dataset
Automatically split a dataset into `train`/`val`/`test` splits and save the resulting splits into `autosplit_*.txt` files. This function will use random sampling, which is not included when using [`fraction` argument for training](../modes/train.md#arguments). Automatically split a dataset into `train`/`val`/`test` splits and save the resulting splits into `autosplit_*.txt` files. This function will use random sampling, which is not included when using [`fraction` argument for training](../modes/train.md#train-settings).
```{ .py .annotate } ```{ .py .annotate }
from ultralytics.data.utils import autosplit from ultralytics.data.utils import autosplit

3
docs/mkdocs_github_authors.yaml
View file

@ -1,4 +1,5 @@
1185102784@qq.com: Laughing-q 1185102784@qq.com: Laughing-q
130829914+IvorZhu331@users.noreply.github.com: IvorZhu331
1579093407@qq.com: null 1579093407@qq.com: null
17216799+ouphi@users.noreply.github.com: ouphi 17216799+ouphi@users.noreply.github.com: ouphi
17316848+maianumerosky@users.noreply.github.com: maianumerosky 17316848+maianumerosky@users.noreply.github.com: maianumerosky
@ -7,6 +8,7 @@
39910262+ChaoningZhang@users.noreply.github.com: ChaoningZhang 39910262+ChaoningZhang@users.noreply.github.com: ChaoningZhang
40165666+berry-ding@users.noreply.github.com: berry-ding 40165666+berry-ding@users.noreply.github.com: berry-ding
47978446+sergiuwaxmann@users.noreply.github.com: sergiuwaxmann 47978446+sergiuwaxmann@users.noreply.github.com: sergiuwaxmann
49699333+dependabot[bot]@users.noreply.github.com: dependabot[bot]
61612323+Laughing-q@users.noreply.github.com: Laughing-q 61612323+Laughing-q@users.noreply.github.com: Laughing-q
62214284+Burhan-Q@users.noreply.github.com: Burhan-Q 62214284+Burhan-Q@users.noreply.github.com: Burhan-Q
75611662+tensorturtle@users.noreply.github.com: tensorturtle 75611662+tensorturtle@users.noreply.github.com: tensorturtle
@ -22,4 +24,5 @@ not.committed.yet: null
plashchynski@gmail.com: plashchynski plashchynski@gmail.com: plashchynski
priytosh.revolution@live.com: priytosh-tripathi priytosh.revolution@live.com: priytosh-tripathi
shuizhuyuanluo@126.com: null shuizhuyuanluo@126.com: null
stormsson@users.noreply.github.com: stormsson
xinwang614@gmail.com: GreatV xinwang614@gmail.com: GreatV

6
examples/heatmaps.ipynb
View file

@ -24,9 +24,11 @@
" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
"\n", "\n",
" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
"\n", "\n",
" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n", " <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/heatmaps.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n", "\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 <a href=\"https://docs.ultralytics.com/guides/heatmaps/\">heatmaps</a> and understand its features and capabilities.\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 <a href=\"https://docs.ultralytics.com/guides/heatmaps/\">heatmaps</a> and understand its features and capabilities.\n",
"\n", "\n",
@ -142,4 +144,4 @@
} }
} }
] ]
} }

23
examples/hub.ipynb
View file

@ -27,12 +27,13 @@
"\n", "\n",
"<div align=\"center\">\n", "<div align=\"center\">\n",
"\n", "\n",
"[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", "[中文](https://docs.ultralytics.com/zh/hub/) | [한국어](https://docs.ultralytics.com/ko/hub/) | [日本語](https://docs.ultralytics.com/ja/hub/) | [Русский](https://docs.ultralytics.com/ru/hub/) | [Deutsch](https://docs.ultralytics.com/de/hub/) | [Français](https://docs.ultralytics.com/fr/hub/) | [Español](https://docs.ultralytics.com/es/hub/) | [Português](https://docs.ultralytics.com/pt/hub/) | [Türkçe](https://docs.ultralytics.com/tr/hub/) | [Tiếng Việt](https://docs.ultralytics.com/vi/hub/) | [हिन्दी](https://docs.ultralytics.com/hi/hub/) | [العربية](https://docs.ultralytics.com/ar/hub/)\n",
"\n", "\n",
" <a href=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml\">\n", " <a href=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml\">\n",
" <img src=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n", " <img src=\"https://github.com/ultralytics/hub/actions/workflows/ci.yaml/badge.svg\" alt=\"CI CPU\"></a>\n",
" <a href=\"https://colab.research.google.com/github/ultralytics/hub/blob/main/hub.ipynb\">\n", " <a href=\"https://colab.research.google.com/github/ultralytics/hub/blob/main/hub.ipynb\">\n",
" <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n", " <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n", "\n",
"Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook!\n", "Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook!\n",
"\n", "\n",
@ -58,7 +59,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "824edff2-510a-4575-c749-396dafb36a47" "outputId": "e1d713ec-e8a6-4422-fe61-c76ec9f03df5"
}, },
"source": [ "source": [
"%pip install ultralytics # install\n", "%pip install ultralytics # install\n",
@ -71,8 +72,8 @@
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.1.47 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 29.0/78.2 GB disk)\n" "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n"
] ]
} }
] ]
@ -85,7 +86,7 @@
"source": [ "source": [
"# Start\n", "# Start\n",
"\n", "\n",
"Login with your [API key](https://hub.ultralytics.com/settings?tab=api+keys), select your YOLO 🚀 model and start training!" "⚡ Login with your API key, load your YOLO 🚀 model and start training in 3 lines of code!"
] ]
}, },
{ {
@ -94,13 +95,17 @@
"id": "XSlZaJ9Iw_iZ" "id": "XSlZaJ9Iw_iZ"
}, },
"source": [ "source": [
"hub.login('API_KEY') # use your API key\n", "# Log in to HUB using your API key (https://hub.ultralytics.com/settings?tab=api+keys)\n",
"hub.login('YOUR_API_KEY')\n",
"\n", "\n",
"model = YOLO('https://hub.ultralytics.com/models/MODEL_ID') # use your model URL\n", "# Load your model from HUB (replace 'YOUR_MODEL_ID' with your model ID)\n",
"results = model.train() # train model" "model = YOLO('https://hub.ultralytics.com/models/YOUR_MODEL_ID')\n",
"\n",
"# Train the model\n",
"results = model.train()"
], ],
"execution_count": null, "execution_count": null,
"outputs": [] "outputs": []
} }
] ]
} }

6
examples/object_counting.ipynb
View file

@ -24,9 +24,11 @@
" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
"\n", "\n",
" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
"\n", "\n",
" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n", " <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_counting.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n", "\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Object Counting</a> and understand its features and capabilities.\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 <a href=\"https://docs.ultralytics.com/guides/object-counting/\">Object Counting</a> and understand its features and capabilities.\n",
"\n", "\n",
@ -144,4 +146,4 @@
} }
} }
] ]
} }

4
examples/object_tracking.ipynb
View file

@ -24,9 +24,11 @@
" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
"\n", "\n",
" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
"\n", "\n",
" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
" <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n", " <a href=\"https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/object_tracking.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n", "\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 <a href=\"https://docs.ultralytics.com/modes/track/\">Object Tracking</a> and understand its features and capabilities.\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 <a href=\"https://docs.ultralytics.com/modes/track/\">Object Tracking</a> and understand its features and capabilities.\n",
"\n", "\n",

131
examples/tutorial.ipynb
View file

@ -25,11 +25,13 @@
" <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n", " <a href=\"https://ultralytics.com/yolov8\" target=\"_blank\">\n",
" <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n", " <img width=\"1024\", src=\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\"></a>\n",
"\n", "\n",
" [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n", " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
"\n", "\n",
" <a href=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml\"><img src=\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg\" alt=\"Ultralytics CI\"></a>\n",
" <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://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://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/ultralytics/yolov8\"><img src=\"https://kaggle.com/static/images/open-in-kaggle.svg\" alt=\"Open In Kaggle\"></a>\n",
" <a href=\"https://ultralytics.com/discord\"><img alt=\"Discord\" src=\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\"></a>\n",
"\n", "\n",
"Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 YOLOv8 and understand its features and capabilities.\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! <a href=\"https://github.com/ultralytics/ultralytics\">YOLOv8</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 YOLOv8 and understand its features and capabilities.\n",
"\n", "\n",
@ -58,7 +60,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "51d15672-e688-4fb8-d9d0-00d1916d3532" "outputId": "96335d4c-20a9-4864-f7a4-bb2eb0077a9d"
}, },
"source": [ "source": [
"%pip install ultralytics\n", "%pip install ultralytics\n",
@ -71,8 +73,8 @@
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)\n" "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 28.8/78.2 GB disk)\n"
] ]
} }
] ]
@ -95,7 +97,7 @@
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"outputId": "37738db7-4284-47de-b3ed-b82f2431ed23" "outputId": "84f32db2-80b0-4f35-9a2a-a56d11f7863f"
}, },
"source": [ "source": [
"# Run inference on an image with YOLOv8n\n", "# Run inference on an image with YOLOv8n\n",
@ -108,14 +110,14 @@
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n", "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n",
"100% 6.23M/6.23M [00:00<00:00, 72.6MB/s]\n", "100% 6.23M/6.23M [00:00<00:00, 83.2MB/s]\n",
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n", "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n", "\n",
"Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n", "Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n",
"100% 165k/165k [00:00<00:00, 7.05MB/s]\n", "100% 165k/165k [00:00<00:00, 11.1MB/s]\n",
"image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 162.0ms\n", "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 21.4ms\n",
"Speed: 13.9ms preprocess, 162.0ms inference, 1259.5ms postprocess per image at shape (1, 3, 384, 640)\n", "Speed: 1.9ms preprocess, 21.4ms inference, 6.2ms postprocess per image at shape (1, 3, 384, 640)\n",
"Results saved to \u001b[1mruns/detect/predict\u001b[0m\n", "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/predict\n" "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
] ]
@ -160,7 +162,7 @@
"cell_type": "code", "cell_type": "code",
"metadata": { "metadata": {
"id": "X58w8JLpMnjH", "id": "X58w8JLpMnjH",
"outputId": "61001937-ccd2-4157-a373-156a57495231", "outputId": "bed10d45-ceb6-4b6f-86b7-9428208b142a",
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
} }
@ -175,20 +177,20 @@
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n", "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n", "\n",
"Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n", "Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
"Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n", "Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
"100% 433k/433k [00:00<00:00, 12.5MB/s]\n", "100% 433k/433k [00:00<00:00, 14.2MB/s]\n",
"Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 4546.38file/s]\n", "Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1093.93file/s]\n",
"Dataset download success ✅ (0.9s), saved to \u001b[1m/content/datasets\u001b[0m\n", "Dataset download success ✅ (1.3s), saved to \u001b[1m/content/datasets\u001b[0m\n",
"\n", "\n",
"Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n", "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
"100% 755k/755k [00:00<00:00, 17.8MB/s]\n", "100% 755k/755k [00:00<00:00, 17.4MB/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 275.94it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 157.00it/s]\n",
"\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n", "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:02<00:00, 2.23s/it]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:06<00:00, 6.89s/it]\n",
" all 4 17 0.621 0.833 0.888 0.63\n", " all 4 17 0.621 0.833 0.888 0.63\n",
" person 4 10 0.721 0.5 0.519 0.269\n", " person 4 10 0.721 0.5 0.519 0.269\n",
" dog 4 1 0.37 1 0.995 0.597\n", " dog 4 1 0.37 1 0.995 0.597\n",
@ -196,7 +198,7 @@
" elephant 4 2 0.505 0.5 0.828 0.394\n", " elephant 4 2 0.505 0.5 0.828 0.394\n",
" umbrella 4 1 0.564 1 0.995 0.995\n", " umbrella 4 1 0.564 1 0.995 0.995\n",
" potted plant 4 1 0.814 1 0.995 0.895\n", " potted plant 4 1 0.814 1 0.995 0.895\n",
"Speed: 0.3ms preprocess, 56.9ms inference, 0.0ms loss, 222.8ms postprocess per image\n", "Speed: 0.3ms preprocess, 4.9ms inference, 0.0ms loss, 1.3ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/val\u001b[0m\n", "Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/val\n" "💡 Learn more at https://docs.ultralytics.com/modes/val\n"
] ]
@ -239,7 +241,7 @@
"cell_type": "code", "cell_type": "code",
"metadata": { "metadata": {
"id": "1NcFxRcFdJ_O", "id": "1NcFxRcFdJ_O",
"outputId": "1ec62d53-41eb-444f-e2f7-cef5c18b9a27", "outputId": "9f60c6cb-fa9c-4785-cb7a-71d40abeaf38",
"colab": { "colab": {
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
} }
@ -254,8 +256,11 @@
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n", "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
"2024-04-27 18:41:11.160690: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-04-27 18:41:11.160751: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-04-27 18:41:11.162138: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"\n", "\n",
" from n params module arguments \n", " from n params module arguments \n",
" 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n", " 0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2] \n",
@ -288,9 +293,11 @@
"Freezing layer 'model.22.dfl.conv.weight'\n", "Freezing layer 'model.22.dfl.conv.weight'\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n", "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
"\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n", "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 43351.98it/s]\n", "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/coco8/labels/train... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 837.19it/s]\n",
"\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\n", "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/coco8/labels/train.cache\n",
"\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n", "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
"/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
" self.pid = os.fork()\n",
"\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n", "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<?, ?it/s]\n",
"Plotting labels to runs/detect/train/labels.jpg... \n", "Plotting labels to runs/detect/train/labels.jpg... \n",
"\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n", "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
@ -302,36 +309,36 @@
"Starting training for 3 epochs...\n", "Starting training for 3 epochs...\n",
"\n", "\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 1/3 0.77G 0.9308 3.155 1.291 32 640: 100% 1/1 [00:01<00:00, 1.70s/it]\n", " 1/3 0.81G 1.039 3.146 1.498 25 640: 100% 1/1 [00:01<00:00, 1.51s/it]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 1.90it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 2.32it/s]\n",
" all 4 17 0.858 0.54 0.726 0.51\n", " all 4 17 0.62 0.885 0.888 0.621\n",
"\n", "\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 2/3 0.78G 1.162 3.127 1.518 33 640: 100% 1/1 [00:00<00:00, 8.18it/s]\n", " 2/3 0.772G 1.169 2.779 1.442 36 640: 100% 1/1 [00:00<00:00, 8.14it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.71it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 3.22it/s]\n",
" all 4 17 0.904 0.526 0.742 0.5\n", " all 4 17 0.595 0.903 0.888 0.616\n",
"\n", "\n",
" Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n", " Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size\n",
" 3/3 0.759G 0.925 2.507 1.254 17 640: 100% 1/1 [00:00<00:00, 7.53it/s]\n", " 3/3 0.776G 0.6701 3.697 1.096 17 640: 100% 1/1 [00:00<00:00, 6.45it/s]\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 6.80it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 5.66it/s]\n",
" all 4 17 0.906 0.532 0.741 0.513\n", " all 4 17 0.577 0.833 0.874 0.614\n",
"\n", "\n",
"3 epochs completed in 0.002 hours.\n", "3 epochs completed in 0.002 hours.\n",
"Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n", "Optimizer stripped from runs/detect/train/weights/last.pt, 6.5MB\n",
"Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n", "Optimizer stripped from runs/detect/train/weights/best.pt, 6.5MB\n",
"\n", "\n",
"Validating runs/detect/train/weights/best.pt...\n", "Validating runs/detect/train/weights/best.pt...\n",
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
"Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n", "Model summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
" Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 16.31it/s]\n", " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:00<00:00, 18.23it/s]\n",
" all 4 17 0.906 0.533 0.755 0.515\n", " all 4 17 0.617 0.884 0.888 0.622\n",
" person 4 10 0.942 0.3 0.519 0.233\n", " person 4 10 0.67 0.5 0.52 0.278\n",
" dog 4 1 1 0 0.332 0.162\n", " dog 4 1 0.361 1 0.995 0.597\n",
" horse 4 2 1 0.9 0.995 0.698\n", " horse 4 2 0.728 1 0.995 0.631\n",
" elephant 4 2 1 0 0.695 0.206\n", " elephant 4 2 0.602 0.805 0.828 0.332\n",
" umbrella 4 1 0.755 1 0.995 0.895\n", " umbrella 4 1 0.553 1 0.995 0.995\n",
" potted plant 4 1 0.739 1 0.995 0.895\n", " potted plant 4 1 0.789 1 0.995 0.895\n",
"Speed: 0.3ms preprocess, 6.1ms inference, 0.0ms loss, 2.5ms postprocess per image\n", "Speed: 0.3ms preprocess, 4.1ms inference, 0.0ms loss, 1.2ms postprocess per image\n",
"Results saved to \u001b[1mruns/detect/train\u001b[0m\n", "Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
"💡 Learn more at https://docs.ultralytics.com/modes/train\n" "💡 Learn more at https://docs.ultralytics.com/modes/train\n"
] ]
@ -348,21 +355,21 @@
"- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup. \n", "- 💡 ProTip: Export to [ONNX](https://docs.ultralytics.com/integrations/onnx/) or [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) for up to 3x CPU speedup. \n",
"- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n", "- 💡 ProTip: Export to [TensorRT](https://docs.ultralytics.com/integrations/tensorrt/) for up to 5x GPU speedup.\n",
"\n", "\n",
"| Format | `format` Argument | Model | Metadata | Arguments |\n", "| Format | `format` Argument | Model | Metadata | Arguments |\n",
"|--------------------------------------------------------------------|-------------------|---------------------------|----------|-----------------------------------------------------|\n", "|--------------------------------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------|\n",
"| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n", "| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |\n",
"| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize` |\n", "| [TorchScript](https://docs.ultralytics.com/integrations/torchscript) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |\n",
"| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |\n", "| [ONNX](https://docs.ultralytics.com/integrations/onnx) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |\n",
"| [OpenVINO](https://docs.openvino.ai/) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8` |\n", "| [OpenVINO](https://docs.ultralytics.com/integrations/openvino) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |\n", "| [TensorRT](https://docs.ultralytics.com/integrations/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` |\n",
"| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms` |\n", "| [CoreML](https://docs.ultralytics.com/integrations/coreml) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |\n",
"| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8` |\n", "| [TF SavedModel](https://docs.ultralytics.com/integrations/tf-savedmodel) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |\n",
"| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |\n", "| [TF GraphDef](https://docs.ultralytics.com/integrations/tf-graphdef) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |\n",
"| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |\n", "| [TF Lite](https://docs.ultralytics.com/integrations/tflite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |\n", "| [TF Edge TPU](https://docs.ultralytics.com/integrations/edge-tpu) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` |\n",
"| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8` |\n", "| [TF.js](https://docs.ultralytics.com/integrations/tfjs) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |\n",
"| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |\n", "| [PaddlePaddle](https://docs.ultralytics.com/integrations/paddlepaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |\n",
"| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half` |\n" "| [NCNN](https://docs.ultralytics.com/integrations/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |"
], ],
"metadata": { "metadata": {
"id": "nPZZeNrLCQG6" "id": "nPZZeNrLCQG6"
@ -378,7 +385,7 @@
"base_uri": "https://localhost:8080/" "base_uri": "https://localhost:8080/"
}, },
"id": "CYIjW4igCjqD", "id": "CYIjW4igCjqD",
"outputId": "f6d45666-07b4-4214-86c0-4e83e70ac096" "outputId": "947e65cc-79c8-4713-bfd4-3139903ac05a"
}, },
"execution_count": 5, "execution_count": 5,
"outputs": [ "outputs": [
@ -386,15 +393,15 @@
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": [ "text": [
"Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CPU (Intel Xeon 2.30GHz)\n", "Ultralytics YOLOv8.2.3 🚀 Python-3.10.12 torch-2.2.1+cu121 CPU (Intel Xeon 2.00GHz)\n",
"YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n", "YOLOv8n summary (fused): 168 layers, 3151904 parameters, 0 gradients, 8.7 GFLOPs\n",
"\n", "\n",
"\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n", "\u001b[34m\u001b[1mPyTorch:\u001b[0m starting from 'yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
"\n", "\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.1.0+cu121...\n", "\u001b[34m\u001b[1mTorchScript:\u001b[0m starting export with torch 2.2.1+cu121...\n",
"\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.4s, saved as 'yolov8n.torchscript' (12.4 MB)\n", "\u001b[34m\u001b[1mTorchScript:\u001b[0m export success ✅ 2.0s, saved as 'yolov8n.torchscript' (12.4 MB)\n",
"\n", "\n",
"Export complete (4.5s)\n", "Export complete (4.0s)\n",
"Results saved to \u001b[1m/content\u001b[0m\n", "Results saved to \u001b[1m/content\u001b[0m\n",
"Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n", "Predict: yolo predict task=detect model=yolov8n.torchscript imgsz=640 \n",
"Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n", "Validate: yolo val task=detect model=yolov8n.torchscript imgsz=640 data=coco.yaml \n",

52
mkdocs.yml
View file

@ -1,5 +1,10 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license # Ultralytics YOLO 🚀, AGPL-3.0 license
# Configuration file for building the Ultralytics YOLO documentation site using MkDocs.
# Provides settings to control site metadata, customize the appearance using the
# Material theme, define the navigation structure, and enable various plugins.
# Site metadata
site_name: Ultralytics YOLOv8 Docs site_name: Ultralytics YOLOv8 Docs
site_description: Explore Ultralytics YOLOv8, a cutting-edge real-time object detection and image segmentation model for various applications and hardware platforms. site_description: Explore Ultralytics YOLOv8, a cutting-edge real-time object detection and image segmentation model for various applications and hardware platforms.
site_url: https://docs.ultralytics.com site_url: https://docs.ultralytics.com
@ -11,6 +16,7 @@ remote_name: https://github.com/ultralytics/docs
docs_dir: "docs/en/" # where to find the markdown files docs_dir: "docs/en/" # where to find the markdown files
site_dir: "site/" # where to publish to site_dir: "site/" # where to publish to
# Theme customization
theme: theme:
name: material name: material
language: en language: en
@ -42,7 +48,6 @@ theme:
icon: material/brightness-7 icon: material/brightness-7
name: Switch to dark mode name: Switch to dark mode
features: features:
# - announce.dismiss
- content.action.edit - content.action.edit
- content.code.annotate - content.code.annotate
- content.code.copy - content.code.copy
@ -71,40 +76,6 @@ extra: # version:
analytics: analytics:
provider: google provider: google
property: G-2M5EHKC0BH property: G-2M5EHKC0BH
# alternate: # language drop-down
# - name: 🇬🇧 English
# link: /
# lang: en
# - name: 🇨🇳 简体中文
# link: /zh/
# lang: zh
# - name: 🇰🇷 한국어
# link: /ko/
# lang: ko
# - name: 🇯🇵 日本語
# link: /ja/
# lang: ja
# - name: 🇷🇺 Русский
# link: /ru/
# lang: ru
# - name: 🇩🇪 Deutsch
# link: /de/
# lang: de
# - name: 🇫🇷 Français
# link: /fr/
# lang: fr
# - name: 🇪🇸 Español
# link: /es/
# lang: es
# - name: 🇵🇹 Português
# link: /pt/
# lang: pt
# - name: 🇮🇳 हिन्दी
# link: /hi/
# lang: hi
# - name: 🇸🇦 العربية
# link: /ar/
# lang: ar
social: social:
- icon: fontawesome/brands/github - icon: fontawesome/brands/github
link: https://github.com/ultralytics link: https://github.com/ultralytics
@ -150,6 +121,17 @@ markdown_extensions:
- pymdownx.tabbed: - pymdownx.tabbed:
alternate_style: true alternate_style: true
# Validation settings https://www.mkdocs.org/user-guide/configuration/#validation
validation:
nav:
omitted_files: info
not_found: warn
absolute_links: info
links:
absolute_links: relative_to_docs
anchors: warn
unrecognized_links: warn
# Primary navigation --------------------------------------------------------------------------------------------------- # Primary navigation ---------------------------------------------------------------------------------------------------
nav: nav:
- Home: - Home:

1
pyproject.toml
View file

@ -89,6 +89,7 @@ dev = [
"pytest", "pytest",
"pytest-cov", "pytest-cov",
"coverage[toml]", "coverage[toml]",
"mkdocs>=1.6.0",
"mkdocs-material>=9.5.9", "mkdocs-material>=9.5.9",
"mkdocstrings[python]", "mkdocstrings[python]",
"mkdocs-jupyter", # for notebooks "mkdocs-jupyter", # for notebooks