google-bert

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bert-base-uncased

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Some weights of the model checkpoint at facebook/detr-resnet-50 were not used when initializing DetrForObjectDetection: ['model.backbone.conv_encoder.model.layer3.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer4.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer2.0.downsample.1.num_batches_tracked', 'model.backbone.conv_encoder.model.layer1.0.downsample.1.num_batches_tracked']

• This IS expected if you are initializing DetrForObjectDetection from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing DetrForObjectDetection from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  Some weights of DetrForObjectDetection were not initialized from the model checkpoint at facebook/detr-resnet-50 and are newly initialized because the shapes did not match:
• class_labels_classifier.weight: found shape torch.Size([92, 256]) in the checkpoint and torch.Size([3, 256]) in the model instantiated
• class_labels_classifier.bias: found shape torch.Size([92]) in the checkpoint and torch.Size([3]) in the model instantiated
  You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.