Custom Detection Model Training: Validation values schow always 0.000000

[S13Drifter]

Hello there,
I'm training a Custom Detection Model with two types of objects in the images to test out ImageAI.
This is the Code:
from imageai.Detection.Custom import DetectionModelTrainer
``
trainer = DetectionModelTrainer()
`trainer.setModelTypeAsYOLOv3()`
`trainer.setDataDirectory(data_directory="/content/drive/My Drive/MelipoAI/Dataset/melipo_ai")`
`trainer.setTrainConfig(object_names_array=["jatai"], batch_size=4, num_experiments=200,` `train_from_pretrained_model="yolov3.pt")`
`trainer.trainModel()`

It starts training the model but validation values are always at 0.000000

Generating anchor boxes for training images...
thr=0.25: 1.0000 best possible recall, 7.42 anchors past thr
n=9, img_size=416, metric_all=0.467/0.833-mean/best, past_thr=0.527-mean:
pretrained weight loading failed. Defaulting to using random weight.

Pretrained YOLOv3 model loaded to initialize weights

Epoch 1/200

Train:
25it [00:03, 7.41it/s]
box loss-> 0.10574, object loss-> 0.57634, class loss-> 0.00000
Validation:
16it [01:14, 4.64s/it]
recall: 0.000000 precision: 0.000000 mAP@0.5: 0.000000, mAP@0.5-0.95: 0.000000

Epoch 2/200

Train:
25it [00:03, 7.57it/s]
box loss-> 0.10004, object loss-> 0.18873, class loss-> 0.00000
Validation:
16it [01:17, 4.82s/it]
recall: 0.000000 precision: 0.000000 mAP@0.5: 0.000000, mAP@0.5-0.95: 0.000000`

...and so on.
What could be the reason? How can I fix this?
Thanks

[S13Drifter]

Seems like the issue was that I only included one type of object. With more than one object it shows the validation values