我正在使用 YOLOv4-tiny 构建一个徽标检测系统。我构建了一个自定义合成数据集，在其中我在游戏画面顶部绘制了透明徽标。在背景上绘制徽标之前，已对徽标进行了增强（模糊、透视变换、调整大小、旋转等）。以下是我拥有的此类数据的一些示例。
我在 1,700 张图像的背景图像上定义的位置上粘贴 1-5 个徽标（随机）。每个徽标都是一个类，我有 16 个类。当我使用 YOLOv4-tiny 运行它时，这是我的输出示例。我的损失波动，我不明白为什么。
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 37 Avg (IOU: 0.281087), count: 1, class_loss = 0.738789, iou_loss = 15.317201, total_loss = 16.055990
total_bbox = 485600, rewritten_bbox = 1.175453 %
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 30 Avg (IOU: 0.547854), count: 5, class_loss = 4.434289, iou_loss = 2.494116, total_loss = 6.928406
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 37 Avg (IOU: 0.767385), count: 1, class_loss = 1.036910, iou_loss = 0.562877, total_loss = 1.599788
total_bbox = 485606, rewritten_bbox = 1.175439 %
(next mAP calculation at 3147 iterations)
Last accuracy mAP@0.5 = 3.82 %, best = 3.82 %
3038: 2.917786, 2.895705 avg loss, 0.002610 rate, 2.421050 seconds, 145824 images, 42.306447 hours left
Loaded: 1.968205 seconds - performance bottleneck on CPU or Disk HDD/SSD
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 30 Avg (IOU: 0.602211), count: 3, class_loss = 2.421113, iou_loss = 0.208666, total_loss = 2.629779
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 37 Avg (IOU: 0.000000), count: 1, class_loss = 0.000067, iou_loss = 0.000000, total_loss = 0.000067
total_bbox = 485609, rewritten_bbox = 1.175431 %
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 30 Avg (IOU: 0.583863), count: 4, class_loss = 3.290887, iou_loss = 0.876662, total_loss = 4.167549
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 37 Avg (IOU: 0.383844), count: 1, class_loss = 0.823941, iou_loss = 8.962539, total_loss = 9.786480
total_bbox = 485614, rewritten_bbox = 1.175419 %
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 30 Avg (IOU: 0.566122), count: 11, class_loss = 9.910147, iou_loss = 1.705420, total_loss = 11.615566
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 37 Avg (IOU: 0.000000), count: 1, class_loss = 0.000120, iou_loss = 0.000000, total_loss = 0.000120
total_bbox = 485625, rewritten_bbox = 1.175393 %
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 30 Avg (IOU: 0.582394), count: 5, class_loss = 3.849459, iou_loss = 0.561072, total_loss = 4.410531
v3 (iou loss, Normalizer: (iou: 0.07, obj: 1.00, cls: 1.00) Region 37 Avg (IOU: 0.365837), count: 1, class_loss = 0.918538, iou_loss = 6.407685, total_loss = 7.326223
我的问题是，这有什么收获？我什至没有针对测试集测试模型以查看它的性能如何。我是否需要改进徽标的增强？我怎样才能理解这个输出？
更新
这是模型的性能。它正确地检测到它是什么标志，但在边界框上完全搞砸了。
