readme: ---
  language:
  - en
  tags:
  - image-to-text
  license: mit
  datasets:
  - coco2017
  ---
  
  # Vit2-DistilGPT2
  This model takes in an image and outputs a caption. It was trained using the Coco dataset and the full training script can be found in [this kaggle kernel](https://www.kaggle.com/sachin/visionencoderdecoder-model-training)
  
  ## Usage
  ```python
  import Image
  from transformers import AutoModel, GPT2Tokenizer, ViTFeatureExtractor
  model = AutoModel.from_pretrained("sachin/vit2distilgpt2")
  vit_feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
  # make sure GPT2 appends EOS in begin and end
  def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
      outputs = [self.bos_token_id] + token_ids_0 + [self.eos_token_id]
      return outputs
      
  GPT2Tokenizer.build_inputs_with_special_tokens = build_inputs_with_special_tokens
  gpt2_tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
  # set pad_token_id to unk_token_id -> be careful here as unk_token_id == eos_token_id == bos_token_id
  gpt2_tokenizer.pad_token = gpt2_tokenizer.unk_token
  image = (Image.open(image_path).convert("RGB"), return_tensors="pt").pixel_values
  encoder_outputs = model.generate(image.unsqueeze(0))
  generated_sentences = gpt2_tokenizer.batch_decode(encoder_outputs, skip_special_tokens=True)
  ```
  Note that the output sentence may be repeated, hence a post processing step may be required.
  
  ## Bias Warning
  This model may be biased due to dataset, lack of long training and the model itself. The following gender bias is an example.
  ![](https://i.imgur.com/9zVN022.png)
  
  ## Results
  <iframe src="https://wandb.ai/sachinruk/Vit2GPT2/reports/Shared-panel-22-01-27-23-01-56--VmlldzoxNDkyMTM3?highlightShare" style="border:none;height:1024px;width:100%">
  
  