nlpconnect/vit-gpt2-image-captioning is a forked repo from huggingface. License: apache-2-0

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Ankur Singh dc68f91c06 Updates README.md sample running code to remove FutureWarning deprecation error for `ViTFeatureExtractor`. (#14 ) - Updates README.md sample running code to remove FutureWarning deprecation error for `ViTFeatureExtractor`. (496b5b1656cab6f6f963d1db47724f3cc56e6c7a) Co-authored-by: E <commonslash@users.noreply.huggingface.co>		2023-02-27 15:00:09 +00:00
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README.md

nlpconnect/vit-gpt2-image-captioning

This is an image captioning model trained by @ydshieh in flax this is pytorch version of this.

The Illustrated Image Captioning using transformers

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

Sample running code


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


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 '}]

README.md

nlpconnect/vit-gpt2-image-captioning

The Illustrated Image Captioning using transformers

Sample running code

Sample running code using transformers pipeline

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