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vit

Image-Text-to-Text
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PyTorch
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vision-encoder-decoder
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vit

This model is a fine-tuned version of nlpconnect/vit-gpt2-image-captioning on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2973
  • Rouge1: 67.9673
  • Rouge2: 58.9518
  • Rougel: 67.1789
  • Rougelsum: 67.324
  • Bleu: 54.4707
  • Gen Len: 7.7647

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 4

Training results

Framework versions

  • Transformers 4.28.0
  • Pytorch 2.0.0+cu118
  • Datasets 2.11.0
  • Tokenizers 0.13.3
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Evaluation results

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