vilt b32 mlm

Introduction

The Vision-and-Language Transformer (ViLT) is a pre-trained model designed to process both visual and textual data without using convolution or region supervision. It was introduced by Kim et al. in the paper "ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision". The model is pre-trained on datasets such as GCC, SBU, COCO, and VG, totaling 200k steps, and it includes a language modeling head for masked language modeling tasks.

Architecture

ViLT is a transformer-based model that integrates vision and language processing capabilities. By omitting convolutional layers and region supervision, it simplifies the architecture while maintaining the ability to handle complex multimodal tasks.

Training

Training Data

The specific datasets used for pre-training ViLT include GCC, SBU, COCO, and VG. Further details on preprocessing and training procedures are currently marked as "to do."

Training Procedure

Information about the preprocessing steps, pretraining, and evaluation results is yet to be provided.

Guide: Running Locally

To use the ViLT model locally, you can follow these steps:

1. Install Required Libraries: Ensure you have the transformers, torch, Pillow, and requests libraries installed.
2. Load the Model and Processor:

   from transformers import ViltProcessor, ViltForMaskedLM
   processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
   model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")
   

3. Prepare Input Data: Use an image URL and text with [MASK] tokens.
4. Inference:

   # Prepare inputs and perform a forward pass
   encoding = processor(image, text, return_tensors="pt")
   outputs = model(**encoding)
   

5. Cloud GPUs Suggestion: For efficient computation, consider using cloud GPU services like AWS, Google Cloud, or Azure.

License

The ViLT model is released under the Apache 2.0 License, allowing for use and distribution with almost no restrictions.