Grounded-Segment-Anything

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Is it any solution to address?

Is it any solution to address?

You can check the network situation, this may be a network issue.

Is it any solution to address?

You can check the network situation, this may be a network issue.

Hello, can u provide the bert weight for me? I can't fixed the network issue.

Actually, I haved download the pretrained weight from huggingface, unfortunately, it raise some error: Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias']

• This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). _IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight'])

Actually, I haved download the pretrained weight from huggingface, unfortunately, it raise some error: Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias']

• This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). _IncompatibleKeys(missing_keys=[], unexpected_keys=['label_enc.weight'])

You may load models under unstrict mode. Just ignoring the incompatible keys is fine. See our examples for reference: https://github.com/IDEA-Research/Grounded-Segment-Anything/blob/main/grounded_sam.ipynb