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ultralytics 8.0.129 add YOLOv8 Tencent NCNN export (#3529)

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docs/modes/export.md
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@ -1,7 +1,7 @@
---
comments: true
description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, PaddlePaddle, TorchScript, ONNX format, OpenVINO format, TensorRT format, CoreML format, TF SavedModel format, PaddlePaddle format
keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, PaddlePaddle, TorchScript, ONNX format, OpenVINO format, TensorRT format, CoreML format, TF SavedModel format, PaddlePaddle format, Tencent NCNN, NCNN
---
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@ -84,4 +84,5 @@ i.e. `format='onnx'` or `format='engine'`.
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz` |

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docs/modes/val.md
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@ -6,9 +6,7 @@ keywords: Ultralytics, YOLO, YOLOv8, Val, Validation, Hyperparameters, Performan
<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a
validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters
of the model to improve its performance.
**Val mode** is used for validating a YOLOv8 model after it has been trained. In this mode, the model is evaluated on a validation set to measure its accuracy and generalization performance. This mode can be used to tune the hyperparameters of the model to improve its performance.
!!! tip "Tip"
@ -16,8 +14,7 @@ of the model to improve its performance.
## Usage Examples
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's
training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need to passed as the `model` retains it's training `data` and arguments as model attributes. See Arguments section below for a full list of export arguments.
!!! example ""
@ -46,13 +43,7 @@ training `data` and arguments as model attributes. See Arguments section below f
## Arguments
Validation settings for YOLO models refer to the various hyperparameters and configurations used to
evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and
accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed
during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation
process include the size and composition of the validation dataset and the specific task the model is being used for. It
is important to carefully tune and experiment with these settings to ensure that the model is performing well on the
validation dataset and to detect and prevent overfitting.
Validation settings for YOLO models refer to the various hyperparameters and configurations used to evaluate the model's performance on a validation dataset. These settings can affect the model's performance, speed, and accuracy. Some common YOLO validation settings include the batch size, the frequency with which validation is performed during training, and the metrics used to evaluate the model's performance. Other factors that may affect the validation process include the size and composition of the validation dataset and the specific task the model is being used for. It is important to carefully tune and experiment with these settings to ensure that the model is performing well on the validation dataset and to detect and prevent overfitting.
| Key | Value | Description |
|---------------|---------|--------------------------------------------------------------------|
@ -70,23 +61,4 @@ validation dataset and to detect and prevent overfitting.
| `plots` | `False` | show plots during training |
| `rect` | `False` | rectangular val with each batch collated for minimum padding |
| `split` | `val` | dataset split to use for validation, i.e. 'val', 'test' or 'train' |
## 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'`.
| 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` |
| [ONNX](https://onnx.ai/) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset` |
| [OpenVINO](https://docs.openvino.ai/latest/index.html) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half` |
| [TensorRT](https://developer.nvidia.com/tensorrt) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace` |
| [CoreML](https://github.com/apple/coremltools) | `coreml` | `yolov8n.mlmodel` | ✅ | `imgsz`, `half`, `int8`, `nms` |
| [TF SavedModel](https://www.tensorflow.org/guide/saved_model) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras` |
| [TF GraphDef](https://www.tensorflow.org/api_docs/python/tf/Graph) | `pb` | `yolov8n.pb` | ❌ | `imgsz` |
| [TF Lite](https://www.tensorflow.org/lite) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8` |
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
|

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docs/tasks/classify.md
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@ -176,5 +176,6 @@ i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your mo
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-cls_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-cls_ncnn_model/` | ✅ | `imgsz` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

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docs/tasks/detect.md
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@ -167,5 +167,6 @@ Available YOLOv8 export formats are in the table below. You can predict or valid
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

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docs/tasks/pose.md
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@ -181,5 +181,6 @@ i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your m
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-pose_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-pose_ncnn_model/` | ✅ | `imgsz` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.

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docs/tasks/segment.md
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@ -181,5 +181,6 @@ i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your mo
| [TF Edge TPU](https://coral.ai/docs/edgetpu/models-intro/) | `edgetpu` | `yolov8n-seg_edgetpu.tflite` | ✅ | `imgsz` |
| [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz` |
| [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz` |
| [NCNN](https://github.com/Tencent/ncnn) | `ncnn` | `yolov8n-seg_ncnn_model/` | ✅ | `imgsz` |
See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page.