---
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