YOLOv12源码：https://github.com/sunsmarterjie/yolov12

第一步：更改Val.py文件

地址：该文件在yolov12-main\ultralytics\models\yolo\detect下

首先定位到def get_desc(self):这个函数上

代码修正如下：

    def get_desc(self):
        """Return a formatted string summarizing class metrics of YOLO model."""
        return ("%22s" + "%11s" * 7) % ("Class", "Images", "Instances", "Box(P", "R", "mAP50", "mAP75", "mAP50-95)")

然后再定位到def eval_json(self, stats):这个函数上，这个函数的末尾

代码修正如下：

                # update mAP50 mAP75 and mAP50-95
                stats[self.metrics.keys[-1]], stats[self.metrics.keys[-2]], stats[self.metrics.keys[-3]]= (
                    val.stats[:3] if self.is_coco else [val.results["AP50"], val.results["AP75"], val.results["AP"]]
                )

第二步：更改metrics.py文件

首先定位到这个def ap50(self):函数

在ap50和ap中间增加一个函数如下所示

    @property
    def ap75(self):
        """
        Returns the Average Precision (AP) at an IoU threshold of 0.5 for all classes.

        Returns:
            (np.ndarray, list): Array of shape (nc,) with AP50 values per class, or an empty list if not available.
        """
        return self.all_ap[:, 5] if len(self.all_ap) else []

然后再定位到mean_results(self):这个函数上

更改上图中的这三个函数     mean_results(self):      class_result(self, i):      fitness(self):

    def mean_results(self):
        """Mean of results, return mp, mr, map50, map75, map."""
        return [self.mp, self.mr, self.map50, self.map75, self.map]

    def class_result(self, i):
        """Class-aware result, return p[i], r[i], ap50[i], ap75[i], ap[i]."""
        return self.p[i], self.r[i], self.ap50[i], self.ap75[i], self.ap[i]

    def fitness(self):
        """Model fitness as a weighted combination of metrics."""
        w = [0.0, 0.0, 0.1, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.75, mAP@0.5:0.95]
        return (np.array(self.mean_results()) * w).sum()

再往下定位到def keys(self):这个函数上

    def keys(self):
        """Returns a list of keys for accessing specific metrics."""
        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", 'metrics/mAP75(B)', "metrics/mAP50-95(B)"]

再往下定位def keys(self):还是这个函数

    def keys(self):
        """Returns a list of keys for accessing metrics."""
        return [
            "metrics/precision(B)",
            "metrics/recall(B)",
            "metrics/mAP50(B)",
            "metrics/mAP50-95(B)",
            "metrics/mAP75(B)",
            "metrics/precision(M)",
            "metrics/recall(M)",
            "metrics/mAP50(M)",
            "metrics/mAP75(M)",
            "metrics/mAP50-95(M)",
        ]

    def keys(self):
        """Returns list of evaluation metric keys."""
        return [
            "metrics/precision(B)",
            "metrics/recall(B)",
            "metrics/mAP50(B)",
            "metrics/mAP75(B)",
            "metrics/mAP50-95(B)",
            "metrics/precision(P)",
            "metrics/recall(P)",
            "metrics/mAP50(P)",
            "metrics/mAP75(P)",
            "metrics/mAP50-95(P)",
        ]

    def keys(self):
        """Returns a list of keys for accessing specific metrics."""
        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP75(B)", "metrics/mAP50-95(B)"]

到此代码更改完毕，训练的时候就能出现map75指标了