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---
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
---
# distilvit
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 get that checkpoint using the 3083a3cef6e3c8dd90df3f088074bbe836b0f403 commit.
It was then further fine-tuned on :
- Flickr30k debiased: https://huggingface.co/datasets/Mozilla/flickr30k-transformed-captions
- DocOrNot: https://huggingface.co/datasets/Mozilla/docornot
You can find the code used to create the model here: https://github.com/mozilla/distilvit
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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