len of Predicted bounding boxes are not equal to len of true bounding boxes

I am trying to to evaluate a model that I have already trained for two classes in pytorch. I have the checkpoint and extracted its values. However when I try to predict, the length of predicted bounding boxes are more than the length true bounding boxes. I have tried different ways but they were not helpful.

Here is my code,

checkpoint = torch.load(checkpoint_path,map_location=torch.device('cpu'))
model = torchvision.models.detection.retinanet_resnet50_fpn(
        pretrained=False, num_classes=2
optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
epoch = checkpoint['epoch']
loss = checkpoint['loss']

after loading the data, I have

with torch.no_grad():
    for i,(images, targets) in enumerate(tqdm(test_loader)):
        if i ==0: # try the first iteration just for test

            outputs = model(images,targets) # get the predictions on the image
            # predictions
            scores = outputs[0]['scores'].cpu().numpy()
            pboxes = outputs[0]['boxes'].cpu().numpy()
            plabels = outputs[0]['labels'].cpu().numpy()
            # true BB and labels
            oboxes = targets[0]['boxes'].cpu().numpy()
            olabels = targets[0]['labels'].cpu().numpy()

            print(' len oboxes-->',len(oboxes)) #56
            print(' len olabels-->',len(olabels)) #56

            print(' len scores-->',len(scores)) #231
            print(' len pboxes-->',len(pboxes))#231
            print(' len plabels-->',len(plabels))#231

predictions and true values dont have same length. Any help is appreciated.