cleanup

utils/metrics.py

@@ -38,7 +38,6 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision
 
     # Create Precision-Recall curve and compute AP for each class
     px, py = np.linspace(0, 1, 1000), []  # for plotting
-    # pr_score = np.linspace(0, 1, 100)  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
     s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
     ap, p, r = np.zeros(s), np.zeros(s + [1000]), np.zeros(s + [1000])
     for ci, c in enumerate(unique_classes):
@@ -210,9 +209,9 @@ def plot_mc_curve(px, py, save_dir='.', names=(), xlabel='Confidence', ylabel='M
 
     if 0 < len(names) < 21:  # display per-class legend if < 21 classes
         for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(recall, precision)
+            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
     else:
-        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(confidence, metric)
 
     y = py.mean(1)
     ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')