readme: ---
  tags:
  - image-captioning
  - image-to-text
  model-index:
  - name: image-caption-generator
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # Image-caption-generator
  
  This model is trained on [Flickr8k](https://www.kaggle.com/datasets/nunenuh/flickr8k) dataset to generate captions given an image.
  
  It achieves the following results on the evaluation set:
  - eval_loss: 0.2536
  - eval_runtime: 25.369
  - eval_samples_per_second: 63.818
  - eval_steps_per_second: 8.002
  - epoch: 4.0
  - step: 3236
  
  # Running the model using transformers library
  
  1. Load the pre-trained model from the model hub
      ```python
      from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
      import torch
      from PIL import Image
      
      model_name = "bipin/image-caption-generator"
  
      # load model
      model = VisionEncoderDecoderModel.from_pretrained(model_name)
      feature_extractor = ViTFeatureExtractor.from_pretrained(model_name)
      tokenizer = AutoTokenizer.from_pretrained("gpt2")
  
      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
      model.to(device)
      ```
  
  2. Load the image for which the caption is to be generated
      ```python
      img_name = "flickr_data.jpg"
      img = Image.open(img_name)
      if img.mode != 'RGB':
          img = img.convert(mode="RGB")
      ```
  
  3. Pre-process the image
      ```python
      pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values
      pixel_values = pixel_values.to(device)
      ```
  
  4. Generate the caption
       ```python
        max_length = 128
        num_beams = 4
  
        # get model prediction
        output_ids = model.generate(pixel_values, num_beams=num_beams, max_length=max_length)
  
        # decode the generated prediction
        preds = tokenizer.decode(output_ids[0], skip_special_tokens=True)
        print(preds)
       ```
  
  ## Training procedure
  The procedure used to train this model can be found [here](https://bipinkrishnan.github.io/ml-recipe-book/image_captioning.html).
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 5e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.9.1
  - Datasets 1.18.4
  - Tokenizers 0.11.6
  