Docs Prettier reformat (#13483)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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90 changed files with 763 additions and 742 deletions
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@ -74,7 +74,7 @@ Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT
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Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` provide users with the flexibility to fine-tune the benchmarks to their specific needs and compare the performance of different export formats with ease.
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| Key | Default Value | Description |
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|-----------|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
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| --------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `model` | `None` | Specifies the path to the model file. Accepts both `.pt` and `.yaml` formats, e.g., `"yolov8n.pt"` for pre-trained models or configuration files. |
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| `data` | `None` | Path to a YAML file defining the dataset for benchmarking, typically including paths and settings for validation data. Example: `"coco8.yaml"`. |
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| `imgsz` | `640` | The input image size for the model. Can be a single integer for square images or a tuple `(width, height)` for non-square, e.g., `(640, 480)`. |
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@ -88,19 +88,19 @@ Arguments such as `model`, `data`, `imgsz`, `half`, `device`, and `verbose` prov
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Benchmarks will attempt to run automatically on all possible export formats below.
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| Format | `format` Argument | Model | Metadata | Arguments |
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|---------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
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| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
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| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
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| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
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| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
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| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
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| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
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| ------------------------------------------------- | ----------------- | ------------------------- | -------- | -------------------------------------------------------------------- |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
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| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
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| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
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| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
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| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
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| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
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| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
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See full `export` details in the [Export](../modes/export.md) page.
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@ -75,7 +75,7 @@ Export a YOLOv8n model to a different format like ONNX or TensorRT. See Argument
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This table details the configurations and options available for exporting YOLO models to different formats. These settings are critical for optimizing the exported model's performance, size, and compatibility across various platforms and environments. Proper configuration ensures that the model is ready for deployment in the intended application with optimal efficiency.
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| Argument | Type | Default | Description |
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|-------------|------------------|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| ----------- | ---------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `format` | `str` | `'torchscript'` | Target format for the exported model, such as `'onnx'`, `'torchscript'`, `'tensorflow'`, or others, defining compatibility with various deployment environments. |
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| `imgsz` | `int` or `tuple` | `640` | Desired image size for the model input. Can be an integer for square images or a tuple `(height, width)` for specific dimensions. |
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| `keras` | `bool` | `False` | Enables export to Keras format for TensorFlow SavedModel, providing compatibility with TensorFlow serving and APIs. |
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@ -83,11 +83,11 @@ This table details the configurations and options available for exporting YOLO m
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| `half` | `bool` | `False` | Enables FP16 (half-precision) quantization, reducing model size and potentially speeding up inference on supported hardware. |
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| `int8` | `bool` | `False` | Activates INT8 quantization, further compressing the model and speeding up inference with minimal accuracy loss, primarily for edge devices. |
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| `dynamic` | `bool` | `False` | Allows dynamic input sizes for ONNX and TensorRT exports, enhancing flexibility in handling varying image dimensions. |
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| `simplify` | `bool` | `False` | Simplifies the model graph for ONNX exports with `onnxslim`, potentially improving performance and compatibility. |
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| `simplify` | `bool` | `False` | Simplifies the model graph for ONNX exports with `onnxslim`, potentially improving performance and compatibility. |
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| `opset` | `int` | `None` | Specifies the ONNX opset version for compatibility with different ONNX parsers and runtimes. If not set, uses the latest supported version. |
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| `workspace` | `float` | `4.0` | Sets the maximum workspace size in GiB for TensorRT optimizations, balancing memory usage and performance. |
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| `nms` | `bool` | `False` | Adds Non-Maximum Suppression (NMS) to the CoreML export, essential for accurate and efficient detection post-processing. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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| `batch` | `int` | `1` | Specifies export model batch inference size or the max number of images the exported model will process concurrently in `predict` mode. |
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Adjusting these parameters allows for customization of the export process to fit specific requirements, such as deployment environment, hardware constraints, and performance targets. Selecting the appropriate format and settings is essential for achieving the best balance between model size, speed, and accuracy.
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@ -96,17 +96,17 @@ Adjusting these parameters allows for customization of the export process to fit
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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.
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| Format | `format` Argument | Model | Metadata | Arguments |
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|---------------------------------------------------|-------------------|---------------------------|----------|----------------------------------------------------------------------|
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
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| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
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| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
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| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
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| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
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| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
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| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
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| ------------------------------------------------- | ----------------- | ------------------------- | -------- | -------------------------------------------------------------------- |
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| [PyTorch](https://pytorch.org/) | - | `yolov8n.pt` | ✅ | - |
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| [TorchScript](../integrations/torchscript.md) | `torchscript` | `yolov8n.torchscript` | ✅ | `imgsz`, `optimize`, `batch` |
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| [ONNX](../integrations/onnx.md) | `onnx` | `yolov8n.onnx` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `opset`, `batch` |
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| [OpenVINO](../integrations/openvino.md) | `openvino` | `yolov8n_openvino_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TensorRT](../integrations/tensorrt.md) | `engine` | `yolov8n.engine` | ✅ | `imgsz`, `half`, `dynamic`, `simplify`, `workspace`, `int8`, `batch` |
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| [CoreML](../integrations/coreml.md) | `coreml` | `yolov8n.mlpackage` | ✅ | `imgsz`, `half`, `int8`, `nms`, `batch` |
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| [TF SavedModel](../integrations/tf-savedmodel.md) | `saved_model` | `yolov8n_saved_model/` | ✅ | `imgsz`, `keras`, `int8`, `batch` |
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| [TF GraphDef](../integrations/tf-graphdef.md) | `pb` | `yolov8n.pb` | ❌ | `imgsz`, `batch` |
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| [TF Lite](../integrations/tflite.md) | `tflite` | `yolov8n.tflite` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [TF Edge TPU](../integrations/edge-tpu.md) | `edgetpu` | `yolov8n_edgetpu.tflite` | ✅ | `imgsz` |
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| [TF.js](../integrations/tfjs.md) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz`, `half`, `int8`, `batch` |
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| [PaddlePaddle](../integrations/paddlepaddle.md) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz`, `batch` |
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| [NCNN](../integrations/ncnn.md) | `ncnn` | `yolov8n_ncnn_model/` | ✅ | `imgsz`, `half`, `batch` |
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@ -26,7 +26,7 @@ In the world of machine learning and computer vision, the process of making sens
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## Real-world Applications
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| Manufacturing | Sports | Safety |
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|:-------------------------------------------------:|:----------------------------------------------------:|:-------------------------------------------:|
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| :-----------------------------------------------: | :--------------------------------------------------: | :-----------------------------------------: |
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| ![Vehicle Spare Parts Detection][car spare parts] | ![Football Player Detection][football player detect] | ![People Fall Detection][human fall detect] |
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| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection |
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@ -104,16 +104,16 @@ YOLOv8 can process different types of input sources for inference, as shown in t
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Use `stream=True` for processing long videos or large datasets to efficiently manage memory. When `stream=False`, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. In contrast, `stream=True` utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues.
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| Source | Argument | Type | Notes |
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|----------------|--------------------------------------------|-----------------|---------------------------------------------------------------------------------------------|
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| image | `'image.jpg'` | `str` or `Path` | Single image file. |
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| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
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| screenshot | `'screen'` | `str` | Capture a screenshot. |
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| PIL | `Image.open('im.jpg')` | `PIL.Image` | HWC format with RGB channels. |
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| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| numpy | `np.zeros((640,1280,3))` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` | BCHW format with RGB channels `float32 (0.0-1.0)`. |
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| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
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| Source | Argument | Type | Notes |
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| --------------- | ------------------------------------------ | --------------- | ------------------------------------------------------------------------------------------- |
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| image | `'image.jpg'` | `str` or `Path` | Single image file. |
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| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
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| screenshot | `'screen'` | `str` | Capture a screenshot. |
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| PIL | `Image.open('im.jpg')` | `PIL.Image` | HWC format with RGB channels. |
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| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| numpy | `np.zeros((640,1280,3))` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` | BCHW format with RGB channels `float32 (0.0-1.0)`. |
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| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
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| video ✅ | `'video.mp4'` | `str` or `Path` | Video file in formats like MP4, AVI, etc. |
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| directory ✅ | `'path/'` | `str` or `Path` | Path to a directory containing images or videos. |
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| glob ✅ | `'path/*.jpg'` | `str` | Glob pattern to match multiple files. Use the `*` character as a wildcard. |
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@ -368,7 +368,7 @@ Below are code examples for using each source type:
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Inference arguments:
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| Argument | Type | Default | Description |
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|-----------------|----------------|------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| --------------- | -------------- | ---------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `source` | `str` | `'ultralytics/assets'` | Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across different types of input. |
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| `conf` | `float` | `0.25` | Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives. |
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| `iou` | `float` | `0.7` | Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates. |
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@ -388,7 +388,7 @@ Inference arguments:
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Visualization arguments:
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| Argument | Type | Default | Description |
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|---------------|---------------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| ------------- | ------------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `show` | `bool` | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing. |
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| `save` | `bool` | `False` | Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results. |
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| `save_frames` | `bool` | `False` | When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis. |
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@ -409,7 +409,7 @@ YOLOv8 supports various image and video formats, as specified in [ultralytics/da
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The below table contains valid Ultralytics image formats.
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| Image Suffixes | Example Predict Command | Reference |
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|----------------|----------------------------------|----------------------------------------------------------------------------|
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| -------------- | -------------------------------- | -------------------------------------------------------------------------- |
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| `.bmp` | `yolo predict source=image.bmp` | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format) |
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| `.dng` | `yolo predict source=image.dng` | [Adobe DNG](https://en.wikipedia.org/wiki/Digital_Negative) |
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| `.jpeg` | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) |
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@ -426,7 +426,7 @@ The below table contains valid Ultralytics image formats.
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The below table contains valid Ultralytics video formats.
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| Video Suffixes | Example Predict Command | Reference |
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|----------------|----------------------------------|----------------------------------------------------------------------------------|
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| -------------- | -------------------------------- | -------------------------------------------------------------------------------- |
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| `.asf` | `yolo predict source=video.asf` | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) |
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| `.avi` | `yolo predict source=video.avi` | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave) |
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| `.gif` | `yolo predict source=video.gif` | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF) |
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@ -460,7 +460,7 @@ All Ultralytics `predict()` calls will return a list of `Results` objects:
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`Results` objects have the following attributes:
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| Attribute | Type | Description |
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|--------------|-----------------------|------------------------------------------------------------------------------------------|
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| ------------ | --------------------- | ---------------------------------------------------------------------------------------- |
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| `orig_img` | `numpy.ndarray` | The original image as a numpy array. |
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| `orig_shape` | `tuple` | The original image shape in (height, width) format. |
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| `boxes` | `Boxes, optional` | A Boxes object containing the detection bounding boxes. |
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@ -475,7 +475,7 @@ All Ultralytics `predict()` calls will return a list of `Results` objects:
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`Results` objects have the following methods:
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| Method | Return Type | Description |
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|---------------|-----------------|-------------------------------------------------------------------------------------|
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| ------------- | --------------- | ----------------------------------------------------------------------------------- |
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| `update()` | `None` | Update the boxes, masks, and probs attributes of the Results object. |
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| `cpu()` | `Results` | Return a copy of the Results object with all tensors on CPU memory. |
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| `numpy()` | `Results` | Return a copy of the Results object with all tensors as numpy arrays. |
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@ -515,7 +515,7 @@ For more details see the [`Results` class documentation](../reference/engine/res
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Here is a table for the `Boxes` class methods and properties, including their name, type, and description:
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| Name | Type | Description |
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|-----------|---------------------------|--------------------------------------------------------------------|
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| --------- | ------------------------- | ------------------------------------------------------------------ |
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| `cpu()` | Method | Move the object to CPU memory. |
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| `numpy()` | Method | Convert the object to a numpy array. |
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| `cuda()` | Method | Move the object to CUDA memory. |
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@ -553,7 +553,7 @@ For more details see the [`Boxes` class documentation](../reference/engine/resul
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Here is a table for the `Masks` class methods and properties, including their name, type, and description:
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| Name | Type | Description |
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|-----------|---------------------------|-----------------------------------------------------------------|
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| --------- | ------------------------- | --------------------------------------------------------------- |
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| `cpu()` | Method | Returns the masks tensor on CPU memory. |
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| `numpy()` | Method | Returns the masks tensor as a numpy array. |
|
||||
| `cuda()` | Method | Returns the masks tensor on GPU memory. |
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@ -586,7 +586,7 @@ For more details see the [`Masks` class documentation](../reference/engine/resul
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|||
Here is a table for the `Keypoints` class methods and properties, including their name, type, and description:
|
||||
|
||||
| Name | Type | Description |
|
||||
|-----------|---------------------------|-------------------------------------------------------------------|
|
||||
| --------- | ------------------------- | ----------------------------------------------------------------- |
|
||||
| `cpu()` | Method | Returns the keypoints tensor on CPU memory. |
|
||||
| `numpy()` | Method | Returns the keypoints tensor as a numpy array. |
|
||||
| `cuda()` | Method | Returns the keypoints tensor on GPU memory. |
|
||||
|
|
@ -620,7 +620,7 @@ For more details see the [`Keypoints` class documentation](../reference/engine/r
|
|||
Here's a table summarizing the methods and properties for the `Probs` class:
|
||||
|
||||
| Name | Type | Description |
|
||||
|------------|---------------------------|-------------------------------------------------------------------------|
|
||||
| ---------- | ------------------------- | ----------------------------------------------------------------------- |
|
||||
| `cpu()` | Method | Returns a copy of the probs tensor on CPU memory. |
|
||||
| `numpy()` | Method | Returns a copy of the probs tensor as a numpy array. |
|
||||
| `cuda()` | Method | Returns a copy of the probs tensor on GPU memory. |
|
||||
|
|
@ -655,7 +655,7 @@ For more details see the [`Probs` class documentation](../reference/engine/resul
|
|||
Here is a table for the `OBB` class methods and properties, including their name, type, and description:
|
||||
|
||||
| Name | Type | Description |
|
||||
|-------------|---------------------------|-----------------------------------------------------------------------|
|
||||
| ----------- | ------------------------- | --------------------------------------------------------------------- |
|
||||
| `cpu()` | Method | Move the object to CPU memory. |
|
||||
| `numpy()` | Method | Convert the object to a numpy array. |
|
||||
| `cuda()` | Method | Move the object to CUDA memory. |
|
||||
|
|
@ -705,7 +705,7 @@ The `plot()` method in `Results` objects facilitates visualization of prediction
|
|||
The `plot()` method supports various arguments to customize the output:
|
||||
|
||||
| Argument | Type | Description | Default |
|
||||
|--------------|-----------------|----------------------------------------------------------------------------|---------------|
|
||||
| ------------ | --------------- | -------------------------------------------------------------------------- | ------------- |
|
||||
| `conf` | `bool` | Include detection confidence scores. | `True` |
|
||||
| `line_width` | `float` | Line width of bounding boxes. Scales with image size if `None`. | `None` |
|
||||
| `font_size` | `float` | Text font size. Scales with image size if `None`. | `None` |
|
||||
|
|
@ -800,7 +800,5 @@ Here's a Python script using OpenCV (`cv2`) and YOLOv8 to run inference on video
|
|||
This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.
|
||||
|
||||
[car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1
|
||||
|
||||
[football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442
|
||||
|
||||
[human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43
|
||||
|
|
|
|||
|
|
@ -33,7 +33,7 @@ The output from Ultralytics trackers is consistent with standard object detectio
|
|||
## Real-world Applications
|
||||
|
||||
| Transportation | Retail | Aquaculture |
|
||||
|:----------------------------------:|:--------------------------------:|:----------------------------:|
|
||||
| :--------------------------------: | :------------------------------: | :--------------------------: |
|
||||
| ![Vehicle Tracking][vehicle track] | ![People Tracking][people track] | ![Fish Tracking][fish track] |
|
||||
| Vehicle Tracking | People Tracking | Fish Tracking |
|
||||
|
||||
|
|
@ -365,7 +365,5 @@ To initiate your contribution, please refer to our [Contributing Guide](../help/
|
|||
Together, let's enhance the tracking capabilities of the Ultralytics YOLO ecosystem 🙏!
|
||||
|
||||
[fish track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142
|
||||
|
||||
[people track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527
|
||||
|
||||
[vehicle track]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab
|
||||
|
|
|
|||
|
|
@ -176,7 +176,7 @@ Remember that checkpoints are saved at the end of every epoch by default, or at
|
|||
The training settings for YOLO models encompass various hyperparameters and configurations used during the training process. These settings influence the model's performance, speed, and accuracy. Key training settings include batch size, learning rate, momentum, and weight decay. Additionally, the choice of optimizer, loss function, and training dataset composition can impact the training process. Careful tuning and experimentation with these settings are crucial for optimizing performance.
|
||||
|
||||
| Argument | Default | Description |
|
||||
|-------------------|----------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| ----------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `model` | `None` | Specifies the model file for training. Accepts a path to either a `.pt` pretrained model or a `.yaml` configuration file. Essential for defining the model structure or initializing weights. |
|
||||
| `data` | `None` | Path to the dataset configuration file (e.g., `coco8.yaml`). This file contains dataset-specific parameters, including paths to training and validation data, class names, and number of classes. |
|
||||
| `epochs` | `100` | Total number of training epochs. Each epoch represents a full pass over the entire dataset. Adjusting this value can affect training duration and model performance. |
|
||||
|
|
@ -227,9 +227,9 @@ The training settings for YOLO models encompass various hyperparameters and conf
|
|||
| `plots` | `False` | Generates and saves plots of training and validation metrics, as well as prediction examples, providing visual insights into model performance and learning progression. |
|
||||
|
||||
!!! info "Note on Batch-size Settings"
|
||||
|
||||
|
||||
The `batch` argument can be configured in three ways:
|
||||
|
||||
|
||||
- **Fixed Batch Size**: Set an integer value (e.g., `batch=16`), specifying the number of images per batch directly.
|
||||
- **Auto Mode (60% GPU Memory)**: Use `batch=-1` to automatically adjust batch size for approximately 60% CUDA memory utilization.
|
||||
- **Auto Mode with Utilization Fraction**: Set a fraction value (e.g., `batch=0.70`) to adjust batch size based on the specified fraction of GPU memory usage.
|
||||
|
|
@ -239,7 +239,7 @@ The training settings for YOLO models encompass various hyperparameters and conf
|
|||
Augmentation techniques are essential for improving the robustness and performance of YOLO models by introducing variability into the training data, helping the model generalize better to unseen data. The following table outlines the purpose and effect of each augmentation argument:
|
||||
|
||||
| Argument | Type | Default | Range | Description |
|
||||
|-----------------|---------|---------------|---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| --------------- | ------- | ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `hsv_h` | `float` | `0.015` | `0.0 - 1.0` | Adjusts the hue of the image by a fraction of the color wheel, introducing color variability. Helps the model generalize across different lighting conditions. |
|
||||
| `hsv_s` | `float` | `0.7` | `0.0 - 1.0` | Alters the saturation of the image by a fraction, affecting the intensity of colors. Useful for simulating different environmental conditions. |
|
||||
| `hsv_v` | `float` | `0.4` | `0.0 - 1.0` | Modifies the value (brightness) of the image by a fraction, helping the model to perform well under various lighting conditions. |
|
||||
|
|
|
|||
|
|
@ -80,7 +80,7 @@ Validate trained YOLOv8n model accuracy on the COCO8 dataset. No argument need t
|
|||
When validating YOLO models, several arguments can be fine-tuned to optimize the evaluation process. These arguments control aspects such as input image size, batch processing, and performance thresholds. Below is a detailed breakdown of each argument to help you customize your validation settings effectively.
|
||||
|
||||
| Argument | Type | Default | Description |
|
||||
|---------------|---------|---------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| ------------- | ------- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `data` | `str` | `None` | Specifies the path to the dataset configuration file (e.g., `coco8.yaml`). This file includes paths to validation data, class names, and number of classes. |
|
||||
| `imgsz` | `int` | `640` | Defines the size of input images. All images are resized to this dimension before processing. |
|
||||
| `batch` | `int` | `16` | Sets the number of images per batch. Use `-1` for AutoBatch, which automatically adjusts based on GPU memory availability. |
|
||||
|
|
|
|||
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Reference in a new issue