readme: ---
  tags:
  - image-to-text
  - image-captioning
  license: apache-2.0
  widget:
  - src: https://pixabay.com/get/ga187b8f146a9fa30b1f553d63fa94271e023868cd247fbad7ce02b6ffb5718a52fc04809be440f997f57dad90614dde2e9821edf8e628925f0042c6584fc04ec809421a040e3bc9561324249ab6e09c4_1280.jpg
    example_title: Horse Riding
  - src:  https://static1.bigstockphoto.com/6/8/2/large1500/286059499.jpg
    example_title: Bicycle 
  ---
  
  
  This is an image captioning model training by Zayn
  
  
  ```python
  
  from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
  
  model = VisionEncoderDecoderModel.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
  feature_extractor = ViTFeatureExtractor.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
  tokenizer = AutoTokenizer.from_pretrained("Zayn/AICVTG_What_if_a_machine_could_create_captions_automatically")
  
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  model.to(device)
  
  
  
  max_length = 20
  num_beams = 8
  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(['Image URL.jpg'])
  
  