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b090b79022
* Add attribute_map * Add mention in docs * Set hidden_size attribute correctly * Add note about Transformer-based models only Co-authored-by: Niels Rogge <nielsrogge@Nielss-MBP.localdomain>
47 lines
1.8 KiB
Plaintext
47 lines
1.8 KiB
Plaintext
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# Vision Encoder Decoder Models
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The [`VisionEncoderDecoderModel`] can be used to initialize an image-to-text-sequence model with any
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pretrained Transformer-based vision autoencoding model as the encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit), [Swin](swin))
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and any pretrained language model as the decoder (*e.g.* [RoBERTa](roberta), [GPT2](gpt2), [BERT](bert), [DistilBERT](distilbert)).
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The effectiveness of initializing image-to-text-sequence models with pretrained checkpoints has been shown in (for
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example) [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang,
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Zhoujun Li, Furu Wei.
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An example of how to use a [`VisionEncoderDecoderModel`] for inference can be seen in [TrOCR](trocr).
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## VisionEncoderDecoderConfig
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[[autodoc]] VisionEncoderDecoderConfig
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## VisionEncoderDecoderModel
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[[autodoc]] VisionEncoderDecoderModel
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- forward
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- from_encoder_decoder_pretrained
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## TFVisionEncoderDecoderModel
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[[autodoc]] TFVisionEncoderDecoderModel
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- call
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- from_encoder_decoder_pretrained
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## FlaxVisionEncoderDecoderModel
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[[autodoc]] FlaxVisionEncoderDecoderModel
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- __call__
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- from_encoder_decoder_pretrained
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