model_converter support for getitune models

[mgumowsk]

What does this PR do?

Fixes most of the getitune model parameters for exported and quantized models.

Conversion Summary Report

Model Full Name	Model Type	FP32 Accuracy	FP16 Accuracy	INT8 Accuracy	Status
DINOv2 Classification	Classification	1.00%	1.00%	1.00%	OK
ViT-Tiny Classification	Classification	77.80%	77.80%	77.60%	OK
YOLOX-S	YOLOX	33.99%	33.99%	32.98%	OK
YOLOX-L	YOLOX	43.77%	43.77%	42.62%	OK
YOLOX-X	YOLOX	45.58%	45.58%	44.53%	OK
ATSS MobileNet-V2	SSD	36.18%	36.18%	24.28%	ACCURACY DROP >5%
YOLOX-Tiny	YOLOX	29.62%	29.62%	29.21%	OK
DEIM D-FINE-L	SSD	51.11%	51.11%	46.11%	ACCURACY DROP >5%
DEIM D-FINE-M	SSD	48.94%	48.94%	43.81%	ACCURACY DROP >5%
DEIM D-FINE-X	SSD	51.85%	51.85%	46.51%	ACCURACY DROP >5%
DEIMv2-L	SSD	45.80%	45.80%	44.70%	OK
DEIMv2-M	SSD	28.24%	28.24%	30.57%	OK
DEIMv2-S	SSD	25.15%	25.15%	24.93%	OK
D-FINE-X	SSD	52.40%	52.40%	46.81%	ACCURACY DROP >5%
RF-DETR Large	SSD	52.10%	52.10%	46.32%	ACCURACY DROP >5%
RF-DETR Medium	SSD	50.07%	50.07%	44.35%	ACCURACY DROP >5%
RF-DETR Small	SSD	48.85%	48.85%	42.55%	ACCURACY DROP >5%
RT-DETR ResNet-50	SSD	49.42%	49.42%	47.44%	OK
SSD MobileNet-V2	SSD	0.88%	0.88%	0.84%	OK
RTMDet Instance Tiny	MaskRCNN	44.00%	44.00%	41.51%	OK
RF-DETR Segmentation Small	DETRInstSeg	37.71%	37.71%	37.63%	OK
RF-DETR Segmentation Medium	DETRInstSeg	38.87%	38.87%	38.76%	OK
RF-DETR Segmentation Large	DETRInstSeg	40.33%	40.33%	40.25%	OK
RF-DETR Segmentation X-Large	DETRInstSeg	42.22%	42.22%	41.95%	OK
Mask R-CNN ResNet-50	MaskRCNN	27.41%	27.41%	25.75%	OK
Mask R-CNN EfficientNet-B2B	MaskRCNN	0.00%	0.00%	0.00%	OK
Mask R-CNN Swin-Tiny	MaskRCNN	26.76%	26.76%	26.28%	OK

still to investigate

Mask R-CNN EfficientNet-B2B

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?