(next mAP calculation at 6175 iterations)
Last accuracy [email protected] = 62.27 %, best = 66.48 %
6000: 1.022357, 1.471952 avg loss, 0.000010 rate, 4.679000 seconds, 192000 images, 0.091826 hours left
Resizing to initial size: 416 x 416 try to allocate additional workspace_size = 52.43 MB
CUDA allocate done!
calculation mAP (mean average precision)...
Detection layer: 139 - type = 28
Detection layer: 150 - type = 28
Detection layer: 161 - type = 28
260
detections_count = 1069, unique_truth_count = 413
class_id = 0, name = fire, ap = 61.61% (TP = 258, FP = 106)
for conf_thresh = 0.25, precision = 0.71, recall = 0.62, F1-score = 0.66
for conf_thresh = 0.25, TP = 258, FP = 106, FN = 155, average IoU = 54.18 %
IoU threshold = 50 %, used Area-Under-Curve for each unique Recall
mean average precision ([email protected]) = 0.616056, or 61.61 %
Total Detection Time: 9 Seconds
感觉指标是不是有点低啊=-=