bipin/image-caption-generator

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Image-caption-generator

This model is trained on 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
• —evalsamplesper_second: 63.818
• —evalstepsper_second: 8.002
• —epoch: 4.0
• —step: 3236

Running the model using transformers library

1. 1.Load the pre-trained model from the model hub

    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)

1. 1.Load the image for which the caption is to be generated(note: replace the value of img_name with image of your choice)

    ### replace the value with your image
    img_name = "flickr_data.jpg"
    img = Image.open(img_name)
    if img.mode != 'RGB':
        img = img.convert(mode="RGB")

1. 1.Pre-process the image

    pixel_values = feature_extractor(images=[img], return_tensors="pt").pixel_values
    pixel_values = pixel_values.to(device)

1. 1.Generate the caption

      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.

Training hyperparameters

The following hyperparameters were used during training:

• —learning_rate: 5e-05
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 5

Framework versions

• —Transformers 4.16.2
• —Pytorch 1.9.1
• —Datasets 1.18.4
• —Tokenizers 0.11.6