vit-rugpt2-image-captioning
| Property | Value |
|---|---|
| Model Type | Vision Encoder-Decoder |
| Architecture | ViT + ruGPT2 |
| Primary Language | Russian |
| BLEU Score | 8.672 |
What is vit-rugpt2-image-captioning?
vit-rugpt2-image-captioning is a groundbreaking image captioning model specifically designed for the Russian language. It combines a Vision Transformer (ViT) encoder with a Russian GPT-2 decoder to generate natural language descriptions of images. The model was trained on a Russian-translated version of the COCO2014 dataset, marking it as the first dedicated image captioning model for Russian language content.
Implementation Details
The model architecture consists of google/vit-base-patch16-224-in21k as the encoder and sberbank-ai/rugpt3large_based_on_gpt2 as the decoder. It achieves a BLEU score of 8.672, with specific precision metrics of 30.567 for unigrams, 7.895 for bigrams, and 3.261 for trigrams.
- Utilizes transformer-based architecture for both vision and text processing
- Supports batch processing of images
- Implements beam search with configurable parameters
- Compatible with HuggingFace's transformers library
Core Capabilities
- Russian language image caption generation
- Support for RGB image processing
- Beam search optimization for better caption quality
- Easy integration through transformers pipeline
Frequently Asked Questions
Q: What makes this model unique?
This is the first image captioning model specifically trained for Russian language output, filling a crucial gap in non-English language image processing capabilities.
Q: What are the recommended use cases?
The model is ideal for automated image description in Russian content management systems, accessibility applications, and content cataloging where Russian language output is required.
