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ViTGPT2I2A
gagan3012/ViTGPT2I2A
ViTGPT2I2A is a image-text-to-text model from gagan3012. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- License
- apache-2.0
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Feb 8, 2022
- SHA
- 1a1ef7d3
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Model card
From the Hugging Face model README
<!-- 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. -->
ViTGPT2I2A
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the vizwiz dataset. It achieves the following results on the evaluation set:
- Loss: 0.0708
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: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1528 | 0.17 | 1000 | 0.0869 |
| 0.0899 | 0.34 | 2000 | 0.0817 |
| 0.084 | 0.51 | 3000 | 0.0790 |
| 0.0814 | 0.68 | 4000 | 0.0773 |
| 0.0803 | 0.85 | 5000 | 0.0757 |
| 0.077 | 1.02 | 6000 | 0.0745 |
| 0.0739 | 1.19 | 7000 | 0.0740 |
| 0.0719 | 1.37 | 8000 | 0.0737 |
| 0.0717 | 1.54 | 9000 | 0.0730 |
| 0.0731 | 1.71 | 10000 | 0.0727 |
| 0.0708 | 1.88 | 11000 | 0.0720 |
| 0.0697 | 2.05 | 12000 | 0.0717 |
| 0.0655 | 2.22 | 13000 | 0.0719 |
| 0.0653 | 2.39 | 14000 | 0.0719 |
| 0.0657 | 2.56 | 15000 | 0.0712 |
| 0.0663 | 2.73 | 16000 | 0.0710 |
| 0.0654 | 2.9 | 17000 | 0.0708 |
| 0.0645 | 3.07 | 18000 | 0.0716 |
| 0.0616 | 3.24 | 19000 | 0.0712 |
| 0.0607 | 3.41 | 20000 | 0.0712 |
| 0.0611 | 3.58 | 21000 | 0.0711 |
| 0.0615 | 3.76 | 22000 | 0.0711 |
| 0.0614 | 3.93 | 23000 | 0.0710 |
| 0.0594 | 4.1 | 24000 | 0.0716 |
| 0.0587 | 4.27 | 25000 | 0.0715 |
| 0.0574 | 4.44 | 26000 | 0.0715 |
| 0.0579 | 4.61 | 27000 | 0.0715 |
| 0.0581 | 4.78 | 28000 | 0.0715 |
| 0.0579 | 4.95 | 29000 | 0.0715 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.10.2+cu113
- Datasets 1.18.3
- Tokenizers 0.11.0
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- apache-2.0
- Created
- Mar 2, 2022
- Updated
- Feb 8, 2022