eval.py

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11s.yaml")
model = YOLO("yolo11s.pt")  # load an official model

# Validate the model
metrics = model.val(data="coco.yaml",batch=16, onnxpath='./yolo11s_qat_slim.onnx')  # 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

# yolov11官方精度
# Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.466
# Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.635
# Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.503
# Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.292
# Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.511
# Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.638
# Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.362
# Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.598
# Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.651
# Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.472
# Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.709
# Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.813