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---

tags:
- image-to-text
- image-captioning
license: apache-2.0
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
---


# nlpconnect/vit-gpt2-image-captioning

This is an image captioning model trained by @ydshieh in [flax ](https://github.com/huggingface/transformers/tree/main/examples/flax/image-captioning) this is pytorch version of [this](https://huggingface.co/ydshieh/vit-gpt2-coco-en-ckpts).


# The Illustrated Image Captioning using transformers

![](https://ankur3107.github.io/assets/images/vision-encoder-decoder.png)

* https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/


# Sample running code

```python



from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer

import torch

from PIL import Image



model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")

tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")



device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model.to(device)







max_length = 16

num_beams = 4

gen_kwargs = {"max_length": max_length, "num_beams": num_beams}

def predict_step(image_paths):

  images = []

  for image_path in image_paths:

    i_image = Image.open(image_path)

    if i_image.mode != "RGB":

      i_image = i_image.convert(mode="RGB")



    images.append(i_image)



  pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values

  pixel_values = pixel_values.to(device)



  output_ids = model.generate(pixel_values, **gen_kwargs)



  preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)

  preds = [pred.strip() for pred in preds]

  return preds





predict_step(['doctor.e16ba4e4.jpg']) # ['a woman in a hospital bed with a woman in a hospital bed']



```

# Sample running code using transformers pipeline

```python



from transformers import pipeline



image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")



image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png")



# [{'generated_text': 'a soccer game with a player jumping to catch the ball '}]





```


# Contact for any help
* https://huggingface.co/ankur310794
* https://twitter.com/ankur310794
* http://github.com/ankur3107
* https://www.linkedin.com/in/ankur310794