Update README.md

README.md

@@ -12,31 +12,24 @@ widget:
   example_title: Airport
 ---
 
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 # The Illustrated Image Captioning using transformers
 
 ![](https://ankur3107.github.io/assets/images/vision-encoder-decoder.png)
 
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+* https://ankur3107.github.io/blogs/the-illustrated-image-captioning-using-transformers/
 
 
 # Sample running code
 
 ```python
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 from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
 import torch
 from PIL import Image
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 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")
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 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.to(device)
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 max_length = 16
 num_beams = 4
 gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
@@ -46,36 +39,21 @@ def predict_step(image_paths):
     i_image = Image.open(image_path)
     if i_image.mode != "RGB":
       i_image = i_image.convert(mode="RGB")
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     images.append(i_image)
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   pixel_values = feature_extractor(images=images, return_tensors="pt").pixel_values
   pixel_values = pixel_values.to(device)
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   output_ids = model.generate(pixel_values, **gen_kwargs)
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   preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
   preds = [pred.strip() for pred in preds]
   return preds
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 predict_step(['doctor.e16ba4e4.jpg']) # ['a woman in a hospital bed with a woman in a hospital bed']
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 ```
 
 # Sample running code using transformers pipeline
 
 ```python
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 from transformers import pipeline
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 image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
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 image_to_text("https://ankur3107.github.io/assets/images/image-captioning-example.png")
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 # [{'generated_text': 'a soccer game with a player jumping to catch the ball '}]
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 ```
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