The VisionEncoderDecoderModel can be used to initialize an image-to-text model with any pretrained Transformer-based vision model as the encoder (e.g. ViT, BEiT, DeiT, Swin) and any pretrained language model as the decoder (e.g. RoBERTa, GPT2, BERT, DistilBERT).

The [VisionEncoderDecoderModel] can be used to initialize an image-to-text model with any pretrained Transformer-based vision model as the encoder (e.g. ViT, BEiT, DeiT, Swin) and any pretrained language model as the decoder (e.g. RoBERTa, GPT2, BERT, DistilBERT).

Jul 18, 2022 · When we initialize the vision encoder decoder with our pretrained models (Vision transformer & Roberta in above example), it creates an image encoder & language decoder instance and ties...

Jul 7, 2022 · In this tutorial we will learn to create our very own image captioning model using Hugging face library. Along with the code walkthrough, we will discuss key concepts in brief to get an...

Apr 18, 2025 · We introduce Perception Encoder (PE), a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. Traditionally, vision encoders have relied on a variety of pretraining objectives, each tailored to specific downstream tasks such as classification, captioning, or localization. Surprisingly, after scaling our carefully tuned image pretraining recipe ...

May 4, 2023 · We presented MaMMUT, a simple and compact vision-encoder language-decoder model that jointly trains a number of conflicting objectives to reconcile contrastive-like and text-generative tasks.

EVE is an encoder-free vision-language model (VLM) that directly processes images and text within a unified decoder-only architecture, eliminating the need for a separate vision encoder.

Dec 3, 2024 · The blog provides hands-on tutorials on three different Transformer-based encoder-decoder image captioning models: ViT-GPT2, BLIP, and Alpha- CLIP, showing you how to deploy the models on AMD GPUs using ROCm, automatically generating relevant output text captions for given input images.

The VisionEncoderDecoderModel is an image-to-text model that combines the characteristics learned by a Transformer-based vision model (encoder) with the language comprehension skills of a pre-trained language model (decoder).

Apr 11, 2024 · GLID achieves competitive performance on various vision tasks, including object detection, image segmentation, pose estimation, and depth estimation, outperforming or matching specialist models such as Mask2Former, DETR, ViTPose, and BinsFormer.