Deep learning YOLOv4在Colab Pro上报告了30小时的训练时间，只有340张训练图像

我试图在Colab Pro上测试我的模型，我只使用了340张16节课的训练图像进行测试。然而，Colab Pro告诉我还有大约30小时的训练时间：

(next mAP calculation at 1200 iterations) 
 Last accuracy mAP@0.5 = 0.37 %, best = 0.37 % 
 1187: 3.270728, 3.027621 avg loss, 0.010000 rate, 1.429193 seconds, 75968 images, 30.824708 hours left
Loaded: 1.136631 seconds - performance bottleneck on CPU or Disk HDD/SSD
...
...
...
 (next mAP calculation at 1300 iterations) 
 Last accuracy mAP@0.5 = 0.33 %, best = 0.37 % 
 1278: 3.231166, 2.967602 avg loss, 0.010000 rate, 2.552415 seconds, 81792 images, 30.512658 hours left
Loaded: 0.712928 seconds - performance bottleneck on CPU or Disk HDD/SSD

我不知道它为什么这样做。我只有一个小数据集

以下是我的cnfg参数：

[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=16
width=1024
height=1024
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
 
learning_rate=0.01
burn_in=1000
max_batches = {max_batches}
policy=steps
steps={steps_str}
scales=.1,.1
 
[convolutional]
batch_normalize=1
filters=32
size=3
stride=2
pad=1
activation=leaky
 
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
 
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
 
[route]
layers=-1
groups=2
group_id=1
 
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
 
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
 
[route]
layers = -1,-2
 
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
 
[route]
layers = -6,-1
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
 
[route]
layers=-1
groups=2
group_id=1
 
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
 
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
 
[route]
layers = -1,-2
 
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
 
[route]
layers = -6,-1
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
 
[route]
layers=-1
groups=2
group_id=1
 
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
 
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
 
[route]
layers = -1,-2
 
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
 
[route]
layers = -6,-1
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
 
##################################
 
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
 
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
 
[convolutional]
size=1
stride=1
pad=1
filters={num_filters}
activation=linear
 
 
 
[yolo]
mask = 3,4,5
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes={num_classes}
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
truth_thresh = 1
random=1
nms_kind=greedynms
beta_nms=0.6
ignore_thresh = .9 
iou_normalizer=0.5 
iou_loss=giou
 
[route]
layers = -4
 
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
 
[upsample]
stride=2
 
[route]
layers = -1, 23
 
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
 
[convolutional]
size=1
stride=1
pad=1
filters={num_filters}
activation=linear
 
[yolo]
mask = 1,2,3
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
classes={num_classes}
num=6
jitter=.3
scale_x_y = 1.05
cls_normalizer=1.0
ignore_thresh = .9 
iou_normalizer=0.5
iou_loss=giou
truth_thresh = 1
random=1
nms_kind=greedynms
beta_nms=0.6

---

您的培训取决于

max_batches

参数，该参数基本上是最大批次数

---

根据这一建议，

max_batches

应为

classes*2000

。在你的例子中，它是16*2000=32000。这就是为什么尽管数据集很小，但它需要更多的时间。

非常感谢。突然，我得到了一个输出：

v3（giou-loss，Normalizer:（iou:0.50，obj:1.00，cls:1.00）Region 30 Avg（iou:0.000000），count:6，class_-loss=-nan，iou-loss=-nan，total_-loss=-nan

为什么我的iou是0，其他一切都是-nan？