06f1fd69a6
2.5 KiB
This model was published in HF papers on 2021-11-15 and contributed to Hugging Face Transformers on 2021-11-30.
VisionTextDualEncoder
Overview
The [VisionTextDualEncoderModel] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (e.g. ViT, BEiT, DeiT) and any pretrained text autoencoding model as the text encoder (e.g. RoBERTa, BERT). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
training and then can be used for zero-shot vision tasks such image-classification or retrieval.
In LiT: Zero-Shot Transfer with Locked-image Text Tuning it is shown how leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on new zero-shot vision tasks such as image classification or retrieval.
VisionTextDualEncoderConfig
autodoc VisionTextDualEncoderConfig
VisionTextDualEncoderProcessor
autodoc VisionTextDualEncoderProcessor - call
VisionTextDualEncoderModel
autodoc VisionTextDualEncoderModel - forward - get_text_features - get_image_features