---
tags:
- image-to-text
- image-captioning
license: apache-2.0
metrics:
- rouge
datasets:
- nlphuji/flickr30k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
  example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
  example_title: Football Match
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
  example_title: Airport
base_model: 
- google/vit-base-patch16-224-in21k

model-index:
- name: mozilla/distilvit
  results:
  - task:
      type: image-to-text
      name: Image To Text
    dataset:
      name: nlphuji/flickr30k
      type: nlphuji/flickr30k
    metrics:
    - name: ROUGE-1
      type: rouge
      value: 43.006
      verified: true
    - name: ROUGE-2
      type: rouge
      value: 16.9939
      verified: true
    - name: ROUGE-L
      type: rouge
      value: 38.8923
      verified: true
    - name: ROUGE-LSUM
      type: rouge
      value: 38.8877
      verified: true
    - name: loss
      type: loss
      value: 0.19939416646957397
    - name: gen_len
      type: gen_len
      value: 11.327256736227712
      verified: true
---

This model is a work in progress.  

Fine-tuned version of those base models:

- a VIT model for the image encoder:  https://huggingface.co/google/vit-base-patch16-224-in21k
- a Distilled GPT-2 model for the text decoder: https://huggingface.co/distilbert/distilgpt2 

This model was trained on:

- Flickr30k : https://huggingface.co/datasets/nlphuji/flickr30k
- COCO 2017: https://cocodataset.org

You can find the code used to create the model here: https://github.com/mozilla/distilvit