Darkflow: Perte de résultats d'entraînement : nan Perte moyenne : nan

Salut tout le monde,
Toutes mes excuses, c'est la première fois que j'utilise la détection d'objets de quelque sorte que ce soit, et en fait la première fois que j'utilise python. En essayant d'entraîner mon ensemble de données, j'obtiens des résultats en tant que nan.
L'erreur que je reçois est :
RuntimeWarning : valeur invalide rencontrée dans sqrt
obj[4] = np.sqrt(obj[4])

Exemple de fichier XML d'annotation

<annotation>
  <folder>images</folder>
  <filename>000006.png</filename>
  <segmented>0</segmented>
  <size>
    <width>225</width>
    <height>225</height>
    <depth>3</depth>
  </size>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>35</xmin>
      <ymin>93</ymin>
      <xmax>45</xmax>
      <ymax>45</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>69</xmin>
      <ymin>94</ymin>
      <xmax>77</xmax>
      <ymax>77</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>129</xmin>
      <ymin>85</ymin>
      <xmax>136</xmax>
      <ymax>136</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>145</xmin>
      <ymin>98</ymin>
      <xmax>153</xmax>
      <ymax>153</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>81</xmin>
      <ymin>143</ymin>
      <xmax>92</xmax>
      <ymax>92</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>185</xmin>
      <ymin>188</ymin>
      <xmax>195</xmax>
      <ymax>195</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>77</xmin>
      <ymin>189</ymin>
      <xmax>93</xmax>
      <ymax>93</ymax>
    </bndbox>
  </object>
  <object>
    <name>person</name>
    <pose>Unspecified</pose>
    <truncated>0</truncated>
    <difficult>0</difficult>
    <bndbox>
      <xmin>75</xmin>
      <ymin>204</ymin>
      <xmax>89</xmax>
      <ymax>89</ymax>
    </bndbox>
  </object>
</annotation>

fichier cfg

[net]
batch=64
subdivisions=8
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.001
max_batches = 40100
policy=steps
steps=-1,100,20000,30000
scales=.1,10,.1,.1

[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=1

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

###########

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=30
activation=linear

[region]
anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
bias_match=1
classes=1
coords=4
num=5
softmax=1
jitter=.2
rescore=1

object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1

absolute=1
thresh = .5
random=1

J'espère que quelqu'un pourra m'orienter dans la bonne direction. C'est la première fois que j'essaie de former mon propre modèle.
Merci,
Lee