Runtime Error while trying to train RTDetrV2 with Transformer

Question

I am trying to train RTDetrV2 for detection on water meter digit. I use an ipynb file form here https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-rt-detr-on-custom-dataset-with-transformers.ipynb#scrollTo=a7kZr8OtGkIO. But when I run through the code:

model = AutoModelForObjectDetection.from_pretrained(
CHECKPOINT,
id2label=id2label,
label2id=label2id,
anchor_image_size=None,
ignore_mismatched_sizes=True,)

This Warning appear

RuntimeError: Error(s) in loading state_dict for RTDetrV2ForObjectDetection:
size mismatch for model.denoising_class_embed.weight: copying a param with shape torch.Size([81, 256]) from checkpoint, the shape in current model is torch.Size([12, 256]).
size mismatch for model.enc_score_head.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.enc_score_head.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).
size mismatch for model.decoder.class_embed.0.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.decoder.class_embed.0.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).
size mismatch for model.decoder.class_embed.1.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.decoder.class_embed.1.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).
size mismatch for model.decoder.class_embed.2.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.decoder.class_embed.2.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).
size mismatch for model.decoder.class_embed.3.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.decoder.class_embed.3.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).
size mismatch for model.decoder.class_embed.4.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.decoder.class_embed.4.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).
size mismatch for model.decoder.class_embed.5.weight: copying a param with shape torch.Size([80, 256]) from checkpoint, the shape in current model is torch.Size([11, 256]).
size mismatch for model.decoder.class_embed.5.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([11]).

I running this ipynb on pycharm.

I try to delete id2label and label2id. It does not show the error but when I try to train for only one epoch it still training. IDK how to check if it works or not.