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Error Detection with unexpected labels: [CLS]and[SEP] #331
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Hi, See #321 for easy inference |
yeah, I've deal with this successfully, but still wondering why did this happens? Seems like caused by an abusing of wrong pth? |
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I use offlinemode, and
![pred](https://private-user-images.githubusercontent.com/1807012/329439737-84914b63-1dc1-4843-86dc-a6604d1036ff.jpg?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.d_GUATIk4iTk1TI52A-IlqfYvZwUitkS5fHwpxsKL58)
config_file = 'D:/deeplearning/Grounded-Segment-Anything-main/GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py' # change the path of the model config file
checkpoint_path = 'D:/deeplearning/Grounded-Segment-Anything-main/bert-base-uncased/groundingdino_swint_ogc.pth' # change the path of the model
image_path = 'D:/deeplearning/Grounded-Segment-Anything-main/1.jpg'
text_prompt = 'chair'
output_dir = 'D:/deeplearning/Grounded-Segment-Anything-main/output'
like this, in inference_on_a_image.py. However, the output results seems random. New labels like [CLS][SEP] apprear.
出现了很多的乱码标签[CLS][SEP]经查似乎与bert有关,且每次结果都是随机的
Anyone have any ideas? Thanks
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