yolov8评价指标，衡量模型大小

### YOLOv8 Evaluation Metrics For evaluating YOLOv8, several key metrics are utilized to assess the effectiveness and efficiency of object detection models. These include precision-recall curves, F1 score, mean Average Precision (mAP), and inference time which can be measured non-invasively and non-intrusively[^2]. The mAP metric evaluates how well a model performs across multiple classes by averaging the AP scores over all categories. Precision measures the proportion of true positive detections out of total predicted positives while recall gauges the fraction of actual objects correctly identified relative to all ground truth instances within images or videos being analyzed. Combining both yields the F1 score—a harmonic mean providing balanced insight into overall accuracy without favoring either aspect disproportionately. Inference speed represents another critical factor when assessing real-time applications where rapid processing times become essential for practical deployment scenarios involving live video streams or autonomous vehicles requiring instantaneous decision-making capabilities. ### Model Size Measurement Methods Model size plays a significant role in determining deployability especially concerning resource-constrained environments like mobile devices or embedded systems. To measure YOLOv8's footprint effectively: - **Parameter Count**: This involves counting trainable parameters present inside neural networks layers including convolutional filters weights alongside biases terms. - **Storage Footprint**: Calculating disk space required once saved as files post-training phase completion; typically reported using megabytes (MB). These measurements help gauge whether particular versions meet hardware limitations imposed during implementation stages ensuring optimal balance between computational power versus storage capacity available at target platforms. ```python import torch from yolov8 import YOLOv8 model = YOLOv8() total_params = sum(p.numel() for p in model.parameters()) print(f'Total number of parameters: {total_params}') ```