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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.


  • 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 or model('').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.

from ultralytics import YOLO

# Load a model
model = YOLO('')  # load an official model
model = YOLO('path/to/')  # load a custom model

# Validate the model
metrics = model.val()  # no arguments needed, dataset and settings remembered    # map50-95  # map50  # map75   # a list contains map50-95 of each category
yolo detect val  # val official model
yolo detect val model=path/to/  # val custom model


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
PyTorch -
TorchScript torchscript yolov8n.torchscript
ONNX onnx yolov8n.onnx
OpenVINO openvino yolov8n_openvino_model/
TensorRT engine yolov8n.engine
CoreML coreml yolov8n.mlmodel
TF SavedModel saved_model yolov8n_saved_model/
TF GraphDef pb yolov8n.pb
TF Lite tflite yolov8n.tflite
TF Edge TPU edgetpu yolov8n_edgetpu.tflite
TF.js tfjs yolov8n_web_model/
PaddlePaddle paddle yolov8n_paddle_model/