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---
tags:
- generated_from_trainer
datasets:
- coco
metrics:
- rouge
- bleu
model-index:
- name: vit-swin-base-224-gpt2-image-captioning
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. -->
# vit-swin-base-224-gpt2-image-captioning
This model is a fine-tuned version of [](https://huggingface.co/) on the coco dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8174
- Rouge1: 41.4513
- Rouge2: 15.9705
- Rougel: 37.8534
- Rougelsum: 37.8514
- Bleu: 9.9633
- Gen Len: 11.3253
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:------:|:-------:|
| 1.091 | 0.19 | 2000 | 0.9783 | 35.5981 | 11.1245 | 32.4533 | 32.4622 | 6.1315 | 11.3253 |
| 0.9629 | 0.38 | 4000 | 0.9306 | 36.8386 | 12.0629 | 33.7446 | 33.7445 | 6.806 | 11.3253 |
| 0.9251 | 0.57 | 6000 | 0.9004 | 37.8439 | 13.1346 | 34.663 | 34.6608 | 7.6122 | 11.3253 |
| 0.9116 | 0.75 | 8000 | 0.8759 | 38.5078 | 13.477 | 35.1981 | 35.2143 | 7.6881 | 11.3253 |
| 0.8903 | 0.94 | 10000 | 0.8592 | 39.6087 | 14.2529 | 36.0992 | 36.1042 | 8.5688 | 11.3253 |
| 0.8381 | 1.13 | 12000 | 0.8480 | 40.3217 | 15.012 | 36.8038 | 36.8046 | 9.1783 | 11.3253 |
| 0.8066 | 1.32 | 14000 | 0.8383 | 40.7187 | 15.1971 | 37.15 | 37.148 | 9.2942 | 11.3253 |
| 0.7938 | 1.51 | 16000 | 0.8298 | 41.1227 | 15.635 | 37.423 | 37.4147 | 9.6574 | 11.3253 |
| 0.7854 | 1.7 | 18000 | 0.8232 | 41.5275 | 16.007 | 37.8586 | 37.8569 | 9.8936 | 11.3253 |
| 0.7837 | 1.88 | 20000 | 0.8190 | 41.2515 | 15.8468 | 37.6257 | 37.6252 | 9.8732 | 11.3253 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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