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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"

* YOLOv8 models automatically remember their training settings, so you can validate a model at the same image size and on the original dataset easily with just `yolo val model=yolov8n.pt` or `model('yolov8n.pt').val()`

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.

!!! example ""

=== "Python"

    ```python
    from ultralytics import YOLO
    
    # Load a model
    model = YOLO('yolov8n.pt')  # load an official model
    model = YOLO('path/to/best.pt')  # load a custom model
    
    # Validate the model
    metrics = model.val()  # no arguments needed, dataset and settings remembered
    metrics.box.map    # map50-95
    metrics.box.map50  # map50
    metrics.box.map75  # map75
    metrics.box.maps   # a list contains map50-95 of each category
    ```
=== "CLI"

    ```bash
    yolo detect val model=yolov8n.pt  # val official model
    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.

Key Value Description
data None path to data file, i.e. coco128.yaml
imgsz 640 image size as scalar or (h, w) list, i.e. (640, 480)
batch 16 number of images per batch (-1 for AutoBatch)
save_json False save results to JSON file
save_hybrid False save hybrid version of labels (labels + additional predictions)
conf 0.001 object confidence threshold for detection
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 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 - yolov8n.pt -
TorchScript torchscript yolov8n.torchscript imgsz, optimize
ONNX onnx yolov8n.onnx imgsz, half, dynamic, simplify, opset
OpenVINO openvino yolov8n_openvino_model/ imgsz, half
TensorRT engine yolov8n.engine imgsz, half, dynamic, simplify, workspace
CoreML coreml yolov8n.mlmodel imgsz, half, int8, nms
TF SavedModel saved_model yolov8n_saved_model/ imgsz, keras
TF GraphDef pb yolov8n.pb imgsz
TF Lite tflite yolov8n.tflite imgsz, half, int8
TF Edge TPU edgetpu yolov8n_edgetpu.tflite imgsz
TF.js tfjs yolov8n_web_model/ imgsz
PaddlePaddle paddle yolov8n_paddle_model/ imgsz