@ -111,7 +111,7 @@ Model validation on a dataset is streamlined as follows:
!!! Note
The YOLO-World models provided by Ultralytics come pre-configured with [COCO dataset](../datasets/detect/coco) categories as part of their offline vocabulary, enhancing efficiency for immediate application. This integration allows the YOLOv8-World models to directly recognize and predict the 80 standard categories defined in the COCO dataset without requiring additional setup or customization.
The YOLO-World models provided by Ultralytics come pre-configured with [COCO dataset](../datasets/detect/coco.md) categories as part of their offline vocabulary, enhancing efficiency for immediate application. This integration allows the YOLOv8-World models to directly recognize and predict the 80 standard categories defined in the COCO dataset without requiring additional setup or customization.
@ -73,15 +73,15 @@ Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT
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.
| `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. |
| `data` | `None` | Path to a YAML file defining the dataset for benchmarking, typically including paths and settings for validation data. Example: `"coco128.yaml"`. |
| `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)`. |
| `half` | `False` | Enables FP16 (half-precision) inference, reducing memory usage and possibly increasing speed on compatible hardware. Use `half=True` to enable. |
| `int8` | `False` | Activates INT8 quantization for further optimized performance on supported devices, especially useful for edge devices. Set `int8=True` to use. |
| `device` | `None` | Defines the computation device(s) for benchmarking, such as `"cpu"`, `"cuda:0"`, or a list of devices like `"cuda:0,1"` for multi-GPU setups. |
| `verbose` | `False` | Controls the level of detail in logging output. A boolean value; set `verbose=True` for detailed logs or a float for thresholding errors. |
@ -72,21 +72,23 @@ Export a YOLOv8n model to a different format like ONNX or TensorRT. See Argument
## Arguments
Export settings for YOLO models refer to the various configurations and options used to save or export the model for use in other environments or platforms. These settings can affect the model's performance, size, and compatibility with different systems. Some common YOLO export settings include the format of the exported model file (e.g. ONNX, TensorFlow SavedModel), the device on which the model will be run (e.g. CPU, GPU), and the presence of additional features such as masks or multiple labels per box. Other factors that may affect the export process include the specific task the model is being used for and the requirements or constraints of the target environment or platform. It is important to carefully consider and configure these settings to ensure that the exported model is optimized for the intended use case and can be used effectively in the target environment.
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.
| `format` | `'torchscript'` | Specifies the export format. Supported values include `'torchscript'`, `'onnx'`, `'coreml'`, `'engine'` (TensorRT), `'saved_model'` (TensorFlow SavedModel), etc. |
| `imgsz` | `640` | Defines the image size for export. Accepts an integer for square images or a tuple `(height, width)` for non-square images. |
| `keras` | `False` | When exporting to TensorFlow SavedModel, setting this to `True` utilizes Keras for the export process. |
| `optimize` | `False` | Applies to TorchScript exports, enabling optimization for mobile deployments. |
| `half` | `False` | Enables half-precision (FP16) quantization for the exported model, reducing size and potentially increasing inference speed on compatible hardware. |
| `int8` | `False` | Activates INT8 quantization, further reducing model size and increasing inference speed at the cost of precision. Useful for edge devices. |
| `dynamic` | `False` | For ONNX and TensorRT formats, enables dynamic axes, allowing variable input sizes for inference. |
| `simplify` | `False` | Simplifies the model structure for ONNX and TensorRT formats, potentially improving efficiency and compatibility. |
| `opset` | `None` | Specifies the ONNX opset version for export. If not set, uses the latest supported version. Useful for ensuring compatibility with older ONNX parsers. |
| `workspace` | `4` | Defines the maximum workspace size in GB for TensorRT exports, affecting the optimization process and memory usage. |
| `nms` | `False` | When exporting to CoreML, adds a Non-Maximum Suppression (NMS) layer to the model, useful for filtering overlapping detections. |
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.
@ -75,23 +75,54 @@ Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need
yolo detect val model=path/to/best.pt # val custom model
```
## 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.
| `iou` | `0.6` | intersection over union (IoU) threshold for NMS |
| `max_det` | `300` | maximum number of detections per image |
| `half` | `True` | use half precision (FP16) |
| `device` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu |
| `dnn` | `False` | use OpenCV DNN for ONNX inference |
| `plots` | `False` | save plots and images during train/val |
| `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' |
## Arguments for YOLO Model Validation
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.
| `data` | `None` | The path to the dataset configuration file (e.g., `coco128.yaml`). This file specifies the dataset's structure, including the classes, train, and validation set paths. |
| `imgsz` | `640` | The input image size as an integer. This size is used to resize images during validation, impacting detection accuracy and inference speed. |
| `batch` | `16` | The number of images processed in each batch. A larger batch size can speed up validation but requires more memory. Use `-1` for AutoBatch to automatically adjust based on available memory. |
| `save_json` | `False` | If set to `True`, validation results are saved in a JSON format, useful for further analysis or submission to evaluation servers. |
| `save_hybrid` | `False` | When `True`, saves a hybrid version of labels combining ground truth with model predictions. This can be useful for visualizing model performance or training enhancements. |
| `conf` | `0.001` | The minimum confidence threshold for considering detections. Increasing this value may reduce false positives but could also miss less confident detections. |
| `iou` | `0.6` | The Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Higher values result in fewer detections by eliminating more overlapping boxes. |
| `max_det` | `300` | The maximum number of detections allowed per image. Useful for limiting outputs in images with many objects. |
| `half` | `True` | Enables half precision (FP16) to speed up validation on compatible hardware without significantly affecting accuracy. |
| `device` | `None` | Specifies the computation device, such as a specific GPU (`cuda:0`) or CPU (`cpu`). This setting allows for model validation on different hardware configurations. |
| `dnn` | `False` | If `True`, uses OpenCV's DNN module for ONNX model inference. This option can be beneficial for environments where CUDA is unavailable. |
| `plots` | `False` | Enables the generation of plots and saved images during validation, providing visual insights into model performance. |
| `rect` | `False` | Applies rectangular inference, minimizing padding by processing images in their original aspect ratio. This can improve accuracy and speed but may require more memory. |
| `split` | `val` | Defines the dataset split to use for validation (e.g., 'val', 'test', 'train'). This allows for flexible validation across different parts of the dataset. |
Each of these settings plays a vital role in the validation process, allowing for a customizable and efficient evaluation of YOLO models. Adjusting these parameters according to your specific needs and resources can help achieve the best balance between accuracy and performance.
### Example Validation with Arguments
The below examples showcase YOLO model validation with custom arguments in Python and CLI.
!!! Example
=== "Python"
```python
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n.pt')
# Customize validation settings
validation_results = model.val(data='coco8.yaml',
imgsz=640,
batch=16,
conf=0.25,
iou=0.6,
device='0')
```
=== "CLI"
```bash
yolo val model=yolov8n.pt data=coco8.yaml imgsz=640 batch=16 conf=0.25 iou=0.6 device=0
Glenn Jocher
11 months ago
committed by
GitHub