readme: ---
  tags:
  - image-to-text
  - image-captioning
  language:
  - ru
  metrics:
  - bleu
  library_name: transformers
  ---
  
  # First image captioning model for russian language vit-rugpt2-image-captioning
  
  This is an image captioning model trained on translated version (en-ru) of dataset COCO2014.
  
  # Model Details
  
  Model was initialized `google/vit-base-patch16-224-in21k` for encoder and `sberbank-ai/rugpt3large_based_on_gpt2` for decoder.
  
  # Metrics on test data
  
  * Bleu: 8.672
  * Bleu precision 1: 30.567
  * Bleu precision 2: 7.895
  * Bleu precision 3: 3.261
  
  # Sample running code
  
  ```python
  
  from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
  import torch
  from PIL import Image
  
  model = VisionEncoderDecoderModel.from_pretrained("vit-rugpt2-image-captioning")
  feature_extractor = ViTFeatureExtractor.from_pretrained("vit-rugpt2-image-captioning")
  tokenizer = AutoTokenizer.from_pretrained("vit-rugpt2-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_caption(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_caption(['train2014/COCO_train2014_000000295442.jpg']) # ['Самолет на взлетно-посадочной полосе аэропорта.']
  
  ```
  
  # Sample running code using transformers pipeline
  
  ```python
  
  from transformers import pipeline
  
  image_to_text = pipeline("image-to-text", model="vit-rugpt2-image-captioning")
  
  image_to_text("train2014/COCO_train2014_000000296754.jpg") # [{'generated_text': 'Человек идет по улице с зонтом.'}]
  
  ```
  
  
  # Contact for any help
  * https://huggingface.co/tuman
  * https://github.com/tumanov-a
  * https://t.me/tumanov_av