
# 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
{"license": "apache-2.0", "tags": ["image-to-text", "image-captioning"], "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"}], "duplicated_from": "nlpconnect/vit-gpt2-image-captioning"}
transformers pytorch vision-encoder-decoder image-to-text image-captioning license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #license-apache-2.0 #endpoints_compatible #region-us 


# 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

![](URL

* URL


# Sample running code



# Sample running code using transformers pipeline




# Contact for any help
* URL
* URL
* URL
* URL
# 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

![](URL

* URL # Sample running code # Sample running code using transformers pipeline # Contact for any help
* URL
* URL
* URL
* URL
TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #license-apache-2.0 #endpoints_compatible #region-us 
 # 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

![](URL

* URL # Sample running code # Sample running code using transformers pipeline # Contact for any help
* URL
* URL
* URL
* URL
TAGS
#transformers #pytorch #vision-encoder-decoder #image-to-text #image-captioning #license-apache-2.0 #endpoints_compatible #region-us 
# 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

![](URL

* URL# Sample running code# Sample running code using transformers pipeline# Contact for any help
* URL
* URL
* URL
* URL