diff --git a/docs/build_docs.py b/docs/build_docs.py index 825df8033d..3d4eadb6f0 100644 --- a/docs/build_docs.py +++ b/docs/build_docs.py @@ -32,6 +32,7 @@ from pathlib import Path from tqdm import tqdm +os.environ["JUPYTER_PLATFORM_DIRS"] = "1" # fix DeprecationWarning: Jupyter is migrating to use standard platformdirs DOCS = Path(__file__).parent.resolve() SITE = DOCS.parent / "site" diff --git a/docs/coming_soon_template.md b/docs/coming_soon_template.md index f8d78022e7..1b610d4d5c 100644 --- a/docs/coming_soon_template.md +++ b/docs/coming_soon_template.md @@ -5,7 +5,7 @@ keywords: Ultralytics, coming soon, under construction, new features, AI updates # 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 🎉 @@ -23,7 +23,7 @@ This placeholder page is your first stop for upcoming developments. Keep an eye ## 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! 🌍 diff --git a/docs/en/datasets/explorer/api.md b/docs/en/datasets/explorer/api.md index aa24f55ede..0aa60034e2 100644 --- a/docs/en/datasets/explorer/api.md +++ b/docs/en/datasets/explorer/api.md @@ -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. 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 @@ -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") ``` -## 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` diff --git a/docs/en/hub/integrations.md b/docs/en/hub/integrations.md index 7615f9f8cb..c383ba00bf 100644 --- a/docs/en/hub/integrations.md +++ b/docs/en/hub/integrations.md @@ -29,21 +29,23 @@ Welcome to the Integrations guide for [Ultralytics HUB](https://hub.ultralytics. ### Export Integrations -| Format | `format` Argument | Model | Metadata | Arguments | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | -| [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` | -| [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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +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`. + +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `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 diff --git a/docs/en/integrations/index.md b/docs/en/integrations/index.md index 834e4089e7..927cf32856 100644 --- a/docs/en/integrations/index.md +++ b/docs/en/integrations/index.md @@ -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: -| Format | `format` Argument | Model | Metadata | Arguments | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | -| [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` | -| [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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `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` | -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 diff --git a/docs/en/modes/benchmark.md b/docs/en/modes/benchmark.md index bc5ce6fbd5..199b397fbf 100644 --- a/docs/en/modes/benchmark.md +++ b/docs/en/modes/benchmark.md @@ -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. -| Format | `format` Argument | Model | Metadata | Arguments | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | -| [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` | -| [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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `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` | -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. diff --git a/docs/en/modes/export.md b/docs/en/modes/export.md index 2a944c2eb4..526eecd45b 100644 --- a/docs/en/modes/export.md +++ b/docs/en/modes/export.md @@ -93,20 +93,20 @@ Adjusting these parameters allows for customization of the export process to fit ## 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 | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | -| [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` | -| [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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `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` | diff --git a/docs/en/tasks/classify.md b/docs/en/tasks/classify.md index e9bef079bb..b916c23f92 100644 --- a/docs/en/tasks/classify.md +++ b/docs/en/tasks/classify.md @@ -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 ``` -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 | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - | -| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | -| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-cls.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `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.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|-------------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n-cls.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-cls.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-cls.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-cls.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-cls_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-cls.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-cls.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz`, `batch` | +| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz`, `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. diff --git a/docs/en/tasks/detect.md b/docs/en/tasks/detect.md index 17c3bfba3a..ec3a914b78 100644 --- a/docs/en/tasks/detect.md +++ b/docs/en/tasks/detect.md @@ -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 ``` -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 | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | -| [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` | -| [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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `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` | -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. diff --git a/docs/en/tasks/obb.md b/docs/en/tasks/obb.md index 7c976108a2..e96dd73ecd 100644 --- a/docs/en/tasks/obb.md +++ b/docs/en/tasks/obb.md @@ -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 ``` -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 | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | ✅ | - | -| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | -| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-obb.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `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.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-obb_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|-------------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n-obb.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-obb.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-obb.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-obb.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-obb_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-obb.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-obb.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-obb_edgetpu.tflite` | ✅ | `imgsz`, `batch` | +| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-obb_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-obb_paddle_model/` | ✅ | `imgsz`, `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. diff --git a/docs/en/tasks/pose.md b/docs/en/tasks/pose.md index baad6274ce..bd9ae02ea0 100644 --- a/docs/en/tasks/pose.md +++ b/docs/en/tasks/pose.md @@ -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 ``` -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 | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - | -| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | -| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-pose.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `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.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|--------------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n-pose.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-pose.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-pose.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-pose.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-pose_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-pose.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-pose.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz`, `batch` | +| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz`, `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. diff --git a/docs/en/tasks/segment.md b/docs/en/tasks/segment.md index d25800196a..7ec390e57e 100644 --- a/docs/en/tasks/segment.md +++ b/docs/en/tasks/segment.md @@ -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 ``` -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 | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ | - | -| [TorchScript](https://pytorch.org/docs/stable/jit.html) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize`, `batch` | -| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n-seg.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `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.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|-------------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n-seg.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n-seg.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n-seg.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n-seg.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n-seg_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n-seg.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n-seg.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz`, `batch` | +| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz`, `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. diff --git a/docs/en/usage/cli.md b/docs/en/usage/cli.md index 9e1a096336..1ec5f3ece6 100644 --- a/docs/en/usage/cli.md +++ b/docs/en/usage/cli.md @@ -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'`. -| Format | `format` Argument | Model | Metadata | Arguments | -|--------------------------------------------------------------------|-------------------|---------------------------|-----------|--------------------------------------------------------------| -| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | -| [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` | -| [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` | -| [CoreML](https://github.com/apple/coremltools) | `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 GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | -| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `batch` | -| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` | -| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` | -| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` | +| Format | `format` Argument | Model | Metadata | Arguments | +|---------------------------------------------------|-------------------|---------------------------|----------|--------------------------------------------------------------| +| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - | +| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `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` | +| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `batch` | +| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` | +| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` | +| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` | +| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` | +| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz`, `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` | + +See full `export` details in the [Export](../modes/export.md) page. ## Overriding default arguments diff --git a/docs/en/usage/simple-utilities.md b/docs/en/usage/simple-utilities.md index a2d40d0ffe..4dff6b5a94 100644 --- a/docs/en/usage/simple-utilities.md +++ b/docs/en/usage/simple-utilities.md @@ -154,7 +154,7 @@ for f in Path('path/to/dataset').rglob('*.jpg'): ### 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 } from ultralytics.data.utils import autosplit diff --git a/docs/mkdocs_github_authors.yaml b/docs/mkdocs_github_authors.yaml index 2ac8014e12..72a924740c 100644 --- a/docs/mkdocs_github_authors.yaml +++ b/docs/mkdocs_github_authors.yaml @@ -1,4 +1,5 @@ 1185102784@qq.com: Laughing-q +130829914+IvorZhu331@users.noreply.github.com: IvorZhu331 1579093407@qq.com: null 17216799+ouphi@users.noreply.github.com: ouphi 17316848+maianumerosky@users.noreply.github.com: maianumerosky @@ -7,6 +8,7 @@ 39910262+ChaoningZhang@users.noreply.github.com: ChaoningZhang 40165666+berry-ding@users.noreply.github.com: berry-ding 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 62214284+Burhan-Q@users.noreply.github.com: Burhan-Q 75611662+tensorturtle@users.noreply.github.com: tensorturtle @@ -22,4 +24,5 @@ not.committed.yet: null plashchynski@gmail.com: plashchynski priytosh.revolution@live.com: priytosh-tripathi shuizhuyuanluo@126.com: null +stormsson@users.noreply.github.com: stormsson xinwang614@gmail.com: GreatV diff --git a/examples/heatmaps.ipynb b/examples/heatmaps.ipynb index b49e69bc8a..0488e3d894 100644 --- a/examples/heatmaps.ipynb +++ b/examples/heatmaps.ipynb @@ -24,9 +24,11 @@ " \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", + " \"Ultralytics\n", " \"Open\n", + " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the heatmaps and understand its features and capabilities.\n", "\n", @@ -142,4 +144,4 @@ } } ] -} \ No newline at end of file +} diff --git a/examples/hub.ipynb b/examples/hub.ipynb index c6fec03efa..db2b4fb385 100644 --- a/examples/hub.ipynb +++ b/examples/hub.ipynb @@ -27,12 +27,13 @@ "\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", " \"CI\n", " \n", " \"Open\n", + " \"Discord\"\n", "\n", "Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook!\n", "\n", @@ -58,7 +59,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "824edff2-510a-4575-c749-396dafb36a47" + "outputId": "e1d713ec-e8a6-4422-fe61-c76ec9f03df5" }, "source": [ "%pip install ultralytics # install\n", @@ -71,8 +72,8 @@ "output_type": "stream", "name": "stdout", "text": [ - "Ultralytics YOLOv8.1.47 🚀 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" + "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, 28.8/78.2 GB disk)\n" ] } ] @@ -85,7 +86,7 @@ "source": [ "# Start\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" }, "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", - "model = YOLO('https://hub.ultralytics.com/models/MODEL_ID') # use your model URL\n", - "results = model.train() # train model" + "# Load your model from HUB (replace 'YOUR_MODEL_ID' with your model ID)\n", + "model = YOLO('https://hub.ultralytics.com/models/YOUR_MODEL_ID')\n", + "\n", + "# Train the model\n", + "results = model.train()" ], "execution_count": null, "outputs": [] } ] -} \ No newline at end of file +} diff --git a/examples/object_counting.ipynb b/examples/object_counting.ipynb index 83ad8736ac..d6afd3f234 100644 --- a/examples/object_counting.ipynb +++ b/examples/object_counting.ipynb @@ -24,9 +24,11 @@ " \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", + " \"Ultralytics\n", " \"Open\n", + " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the Object Counting and understand its features and capabilities.\n", "\n", @@ -144,4 +146,4 @@ } } ] -} \ No newline at end of file +} diff --git a/examples/object_tracking.ipynb b/examples/object_tracking.ipynb index 1825f693b2..610514eafd 100644 --- a/examples/object_tracking.ipynb +++ b/examples/object_tracking.ipynb @@ -24,9 +24,11 @@ " \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", + " \"Ultralytics\n", " \"Open\n", + " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the Object Tracking and understand its features and capabilities.\n", "\n", diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb index 69eb09209a..2a5f1d831e 100644 --- a/examples/tutorial.ipynb +++ b/examples/tutorial.ipynb @@ -25,11 +25,13 @@ " \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", + " \"Ultralytics\n", " \"Run\n", " \"Open\n", " \"Open\n", + " \"Discord\"\n", "\n", "Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLOv8 and understand its features and capabilities.\n", "\n", @@ -58,7 +60,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "51d15672-e688-4fb8-d9d0-00d1916d3532" + "outputId": "96335d4c-20a9-4864-f7a4-bb2eb0077a9d" }, "source": [ "%pip install ultralytics\n", @@ -71,8 +73,8 @@ "output_type": "stream", "name": "stdout", "text": [ - "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", - "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)\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, 28.8/78.2 GB disk)\n" ] } ] @@ -95,7 +97,7 @@ "colab": { "base_uri": "https://localhost:8080/" }, - "outputId": "37738db7-4284-47de-b3ed-b82f2431ed23" + "outputId": "84f32db2-80b0-4f35-9a2a-a56d11f7863f" }, "source": [ "# Run inference on an image with YOLOv8n\n", @@ -108,14 +110,14 @@ "name": "stdout", "text": [ "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", - "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n", + "100% 6.23M/6.23M [00:00<00:00, 83.2MB/s]\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", "\n", "Downloading https://ultralytics.com/images/zidane.jpg to 'zidane.jpg'...\n", - "100% 165k/165k [00:00<00:00, 7.05MB/s]\n", - "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 162.0ms\n", - "Speed: 13.9ms preprocess, 162.0ms inference, 1259.5ms postprocess per image at shape (1, 3, 384, 640)\n", + "100% 165k/165k [00:00<00:00, 11.1MB/s]\n", + "image 1/1 /content/zidane.jpg: 384x640 2 persons, 1 tie, 21.4ms\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", "💡 Learn more at https://docs.ultralytics.com/modes/predict\n" ] @@ -160,7 +162,7 @@ "cell_type": "code", "metadata": { "id": "X58w8JLpMnjH", - "outputId": "61001937-ccd2-4157-a373-156a57495231", + "outputId": "bed10d45-ceb6-4b6f-86b7-9428208b142a", "colab": { "base_uri": "https://localhost:8080/" } @@ -175,20 +177,20 @@ "output_type": "stream", "name": "stdout", "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", "\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", - "100% 433k/433k [00:00<00:00, 12.5MB/s]\n", - "Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 4546.38file/s]\n", - "Dataset download success ✅ (0.9s), saved to \u001b[1m/content/datasets\u001b[0m\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, 1093.93file/s]\n", + "Dataset download success ✅ (1.3s), saved to \u001b[1m/content/datasets\u001b[0m\n", "\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", - "\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", + "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, 157.00it/s]\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", " person 4 10 0.721 0.5 0.519 0.269\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", " umbrella 4 1 0.564 1 0.995 0.995\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", "💡 Learn more at https://docs.ultralytics.com/modes/val\n" ] @@ -239,7 +241,7 @@ "cell_type": "code", "metadata": { "id": "1NcFxRcFdJ_O", - "outputId": "1ec62d53-41eb-444f-e2f7-cef5c18b9a27", + "outputId": "9f60c6cb-fa9c-4785-cb7a-71d40abeaf38", "colab": { "base_uri": "https://localhost:8080/" } @@ -254,8 +256,11 @@ "output_type": "stream", "name": "stdout", "text": [ - "Ultralytics YOLOv8.1.23 🚀 Python-3.10.12 torch-2.1.0+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", + "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, 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", " from n params module arguments \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", "\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[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[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=1.6.0", "mkdocs-material>=9.5.9", "mkdocstrings[python]", "mkdocs-jupyter", # for notebooks