I am using the a Faster RCNN model to learn an object detection task with one class of objects (plus the background).

The size of images is 1000x1000x3.
After training, the bounding boxes are always in the range [0,300]

Even after resizing the images to any other size, the predicted bounding boxes are always in this range

I need help with the following questions:

Why is this happening?
Is there a way to avoid this? Nobody else seems to be having this problem, so is there anything that I am supposed to do while constructing the model, or at any other step?

Code for loading and training the model:
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
num_classes = 2  # 1 class (insulator) + background
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
model.transform = torchvision.models.detection.transform.GeneralizedRCNNTransform(min_size=1000,
                                                                                  max_size=1000,
                                                                                  image_mean=[0.485, 0.456, 0.406],
                                                                                  image_std=[0.229, 0.224, 0.225])

params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=1e-2, momentum=0.9, weight_decay=0.005)

num_epochs = 10
for epoch in range(num_epochs):
        train_one_epoch(model, optimizer, data_loader_train, torch.device('cpu'), epoch, print_freq=10)

As mentioned, I have changed the size to different combinations, like [500x500], [800x800], [500x800], [800x500]. The result is still always the same - the predicted bounding boxes are in the range [0,300].
A sample set of predicted bounding boxes:
tensor([[108,   8, 140, 128],
        [ 39, 213,  61,  62],
        [ 36, 172,  56,  11],
        [ 97, 113, 115, 193],
        [247, 148,   8, 234],
        [229, 220, 254,  74],
        [ 28,  86,  44, 141],
        [213, 122, 233, 177],
        [138, 100, 209, 128],
        [239, 219,   1,  61],
        [ 44, 172,  58, 253],
        [ 29, 176,  63,  56],
        [227, 220, 244,  62],
        [  4, 240,  26,  12],
        [ 37, 177,  56, 213],
        [ 40, 187,  60, 225],
        [ 90, 114, 107, 188],
        [ 75, 112, 118, 186]], dtype=torch.uint8)

The ground truth for this sample:
tensor([[367., 782., 394., 899.],
        [552., 716., 573., 829.],
        [488., 466., 508., 583.],
        [807., 427., 823., 524.],
        [760., 661., 777., 750.],
        [866., 624., 879., 706.],
        [797., 858., 812., 910.]])