readme: ---
  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, ViTFeatureExtractor, AutoTokenizer
  import torch
  from PIL import Image
  
  model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
  feature_extractor = ViTFeatureExtractor.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