Yolo Darknet avec un vrai sémaphore Résultat des calculs
Calcul 1
calculation mAP (mean average precision)...
6000
detections_count = 17645, unique_truth_count = 5987
class_id = 0, name = a, ap = 97.74% (TP = 220, FP = 46)
class_id = 1, name = space, ap = 91.95% (TP = 205, FP = 75)
class_id = 2, name = b, ap = 99.55% (TP = 220, FP = 0)
class_id = 3, name = c, ap = 97.65% (TP = 214, FP = 14)
class_id = 4, name = d, ap = 100.00% (TP = 218, FP = 0)
class_id = 5, name = e, ap = 95.70% (TP = 210, FP = 8)
class_id = 6, name = f, ap = 100.00% (TP = 212, FP = 0)
class_id = 7, name = g, ap = 97.59% (TP = 209, FP = 25)
class_id = 8, name = h, ap = 100.00% (TP = 219, FP = 0)
class_id = 9, name = i, ap = 97.34% (TP = 209, FP = 60)
class_id = 10, name = j, ap = 100.00% (TP = 228, FP = 0)
class_id = 11, name = k, ap = 100.00% (TP = 241, FP = 26)
class_id = 12, name = l, ap = 98.39% (TP = 199, FP = 50)
class_id = 13, name = m, ap = 98.64% (TP = 218, FP = 0)
class_id = 14, name = n, ap = 97.77% (TP = 204, FP = 41)
class_id = 15, name = o, ap = 99.58% (TP = 237, FP = 16)
class_id = 16, name = p, ap = 98.38% (TP = 213, FP = 10)
class_id = 17, name = q, ap = 100.00% (TP = 207, FP = 17)
class_id = 18, name = r, ap = 100.00% (TP = 195, FP = 0)
class_id = 19, name = s, ap = 99.57% (TP = 229, FP = 61)
class_id = 20, name = t, ap = 86.73% (TP = 216, FP = 154)
class_id = 21, name = u, ap = 100.00% (TP = 213, FP = 10)
class_id = 22, name = v, ap = 94.88% (TP = 211, FP = 89)
class_id = 23, name = w, ap = 99.99% (TP = 235, FP = 14)
class_id = 24, name = x, ap = 93.46% (TP = 199, FP = 81)
class_id = 25, name = y, ap = 99.55% (TP = 223, FP = 0)
class_id = 26, name = z, ap = 95.30% (TP = 219, FP = 96)
for thresh = 0.25, precision = 0.87, recall = 0.97, F1-score = 0.92
for thresh = 0.25, TP = 5823, FP = 893, FN = 164, average IoU = 77.65 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision (mAP@0.50) = 0.977692, or 97.77 %
mean_average_precision (mAP@0.5) = 0.977692
Calcul 2
calculation mAP (mean average precision)...
6000
detections_count = 9682, unique_truth_count = 5987
class_id = 0, name = a, ap = 97.65% (TP = 232, FP = 118)
class_id = 1, name = space, ap = 91.08% (TP = 152, FP = 0)
class_id = 2, name = b, ap = 99.55% (TP = 220, FP = 0)
class_id = 3, name = c, ap = 97.35% (TP = 227, FP = 40)
class_id = 4, name = d, ap = 100.00% (TP = 218, FP = 0)
class_id = 5, name = e, ap = 94.33% (TP = 215, FP = 0)
class_id = 6, name = f, ap = 100.00% (TP = 212, FP = 0)
class_id = 7, name = g, ap = 96.39% (TP = 207, FP = 0)
class_id = 8, name = h, ap = 100.00% (TP = 219, FP = 0)
class_id = 9, name = i, ap = 97.12% (TP = 205, FP = 61)
class_id = 10, name = j, ap = 100.00% (TP = 228, FP = 0)
class_id = 11, name = k, ap = 100.00% (TP = 241, FP = 0)
class_id = 12, name = l, ap = 99.25% (TP = 180, FP = 3)
class_id = 13, name = m, ap = 98.64% (TP = 218, FP = 0)
class_id = 14, name = n, ap = 98.89% (TP = 218, FP = 96)
class_id = 15, name = o, ap = 99.58% (TP = 237, FP = 18)
class_id = 16, name = p, ap = 97.62% (TP = 220, FP = 58)
class_id = 17, name = q, ap = 100.00% (TP = 207, FP = 35)
class_id = 18, name = r, ap = 100.00% (TP = 195, FP = 0)
class_id = 19, name = s, ap = 99.57% (TP = 229, FP = 57)
class_id = 20, name = t, ap = 91.33% (TP = 215, FP = 138)
class_id = 21, name = u, ap = 99.99% (TP = 213, FP = 6)
class_id = 22, name = v, ap = 97.73% (TP = 180, FP = 0)
class_id = 23, name = w, ap = 99.99% (TP = 235, FP = 15)
class_id = 24, name = x, ap = 97.32% (TP = 177, FP = 0)
class_id = 25, name = y, ap = 99.55% (TP = 223, FP = 0)
class_id = 26, name = z, ap = 97.29% (TP = 199, FP = 10)
for thresh = 0.25, precision = 0.90, recall = 0.96, F1-score = 0.93
for thresh = 0.25, TP = 5722, FP = 655, FN = 265, average IoU = 83.17 %
mean_average_precision (mAP@0.5) = 0.981556
Calcul 3
calculation mAP (mean average precision)...
6000
detections_count = 17161, unique_truth_count = 5987
class_id = 0, name = a, 17161 ap = 82.72 %
class_id = 1, name = space, ap = 80.00 %
class_id = 2, name = b, ap = 100.00 %
class_id = 3, name = c, ap = 94.55 %
class_id = 4, name = d, ap = 100.00 %
class_id = 5, name = e, ap = 79.73 %
class_id = 6, name = f, ap = 100.00 %
class_id = 7, name = g, ap = 88.52 %
class_id = 8, name = h, ap = 99.72 %
class_id = 9, name = i, ap = 94.52 %
class_id = 10, name = j, ap = 99.88 %
class_id = 11, name = k, ap = 97.39 %
class_id = 12, name = l, ap = 96.58 %
class_id = 13, name = m, ap = 100.00 %
class_id = 14, name = n, ap = 93.39 %
class_id = 15, name = o, ap = 98.15 %
class_id = 16, name = p, ap = 94.77 %
class_id = 17, name = q, ap = 97.96 %
class_id = 18, name = r, ap = 100.00 %
class_id = 19, name = s, ap = 97.23 %
class_id = 20, name = t, ap = 81.34 %
class_id = 21, name = u, ap = 98.94 %
class_id = 22, name = v, ap = 65.49 %
class_id = 23, name = w, ap = 98.84 %
class_id = 24, name = x, ap = 88.67 %
class_id = 25, name = y, ap = 99.96 %
class_id = 26, name = z, ap = 81.75 %
for thresh = 0.25, precision = 0.94, recall = 0.86, F1-score = 0.90
for thresh = 0.25, TP = 5175, FP = 343, FN = 812, average IoU = 82.32 %
IoU threshold = 50 %
mean average precision (mAP@0.50) = 0.929660, or 92.97 %
mean_average_precision (mAP@0.5) = 0.929660