README.md

metadata

license: mit
language:
  - en
metrics:
  - code_eval
library_name: transformers
pipeline_tag: image-to-text
tags:
  - text-generation-inference

We are creating a spatial aware vision-language(VL) model.

This is a trained model on COCO dataset images including extra information regarding the spatial relationship between the entities of the image.

This is a sequence to sequence model for image-captioning. The architecture is ViT encoder and GPT2 decoder.

Requirements!

- 4GB GPU RAM. - CUDA enabled docker

The way to download and run this:

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
from transformers import pipeline
image_captioner = pipeline("image-to-text", model="sadassa17/rgb-language_cap", max_new_tokens=200, device=device)
filename = 'path/to/file'
generated_captions = image_captioner(filename)
print(generated_captions)

The model is trained to produce as many words as possible with a maximum of 200 tokens, which translates to roughly 5 sentences, while the 6th sentence is usually cropped.

The output is always of that form: "Object1" is to the "Left/Right etc." of the "Object2".

IF YOU WANT TO PRODUCE A SPECIFIC NUMBER OF CAPTIONS UP TO 5.

import os
def print_up_to_n_sentences(captions, n):
    for caption in captions:
        generated_text = caption.get('generated_text', '')
        sentences = generated_text.split('.')
        result = '.'.join(sentences[:n])
        #print(result)
    return result
filename = 'path/to/file'

generated_captions = image_captioner(filename)
captions = print_up_to_n_sentences(generated_captions, 5)
print(captions)