---
license: apache-2.0
base_model: nlpconnect/vit-gpt2-image-captioning
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: image-captioning-output
  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. -->

# image-captioning-output

This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5164
- Rouge1: 35.5267
- Rouge2: 12.254
- Rougel: 32.968
- Rougelsum: 32.9723
- Gen Len: 12.395

## 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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.5193        | 0.25  | 500  | 0.5171          | 33.0319 | 10.364  | 30.6939 | 30.6888   | 12.1    |
| 0.4842        | 0.5   | 1000 | 0.5102          | 33.7318 | 10.8199 | 31.1842 | 31.18     | 11.3    |
| 0.4724        | 0.75  | 1500 | 0.5028          | 34.6981 | 11.4074 | 31.9128 | 31.9158   | 12.02   |
| 0.4632        | 1.0   | 2000 | 0.5012          | 35.9443 | 12.8742 | 33.4061 | 33.377    | 11.04   |
| 0.377         | 1.25  | 2500 | 0.5026          | 35.7745 | 12.2309 | 33.3234 | 33.3353   | 11.735  |
| 0.3819        | 1.5   | 3000 | 0.5018          | 36.0145 | 13.0296 | 33.5985 | 33.6182   | 12.285  |
| 0.3788        | 1.75  | 3500 | 0.5030          | 35.9016 | 12.5276 | 33.4995 | 33.5033   | 11.305  |
| 0.3654        | 2.0   | 4000 | 0.5020          | 36.2476 | 12.945  | 33.6453 | 33.6595   | 11.9    |
| 0.3102        | 2.25  | 4500 | 0.5146          | 36.1507 | 13.0072 | 33.3889 | 33.3786   | 12.305  |
| 0.3137        | 2.5   | 5000 | 0.5166          | 35.7413 | 12.5693 | 33.2646 | 33.2508   | 12.71   |
| 0.3111        | 2.75  | 5500 | 0.5171          | 35.5658 | 12.511  | 33.0581 | 33.0518   | 12.55   |
| 0.3023        | 3.0   | 6000 | 0.5164          | 35.5267 | 12.254  | 32.968  | 32.9723   | 12.395  |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1