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Vit Base Patch16 224 Distilgpt2
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Vit Base Patch16 224 Distilgpt2

Developed by tarekziade
DistilViT is an image caption generation model based on Vision Transformer (ViT) and distilled GPT-2, capable of converting images into textual descriptions.
Image-to-Text
Transformers
Open Source License:Apache-2.0#Image Caption Generation#Distilled Model#Multi-dataset Fine-tuning
Downloads 17
Release Time : 6/19/2024

Model Overview

This model combines the image encoding capability of Vision Transformer with the text generation ability of distilled GPT-2, specifically designed for image-to-text tasks to generate descriptive text for images.

Model Features

Efficient Image Understanding
Uses VIT model as the image encoder to effectively understand image content
Lightweight Text Generation
Employs distilled GPT-2 as the text decoder to reduce model size while maintaining performance
Multi-dataset Training
Trained on multiple datasets including Flickr30k and COCO 2017 to enhance generalization capability

Model Capabilities

Image Content Understanding
Image Caption Generation
Vision-Language Conversion

Use Cases

Assistive Technology
Generating Image Descriptions for the Visually Impaired
Automatically generates textual descriptions for images to help visually impaired individuals understand image content
Content Management
Automatic Image Tagging
Automatically generates descriptive tags for large volumes of images to facilitate search and management
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