Downloads · 30 days
9
28% of all-time downloads
Vit-GPT2-UCA-UCF-01
Image-Captioning-ML/Vit-GPT2-UCA-UCF-01
Vit-GPT2-UCA-UCF-01 is a image-text-to-text model from Image-Captioning-ML. 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. --
All-time downloads
32
Public
Parameters
239M
1.9 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors957 MB · 99%
At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- License
- apache-2.0
- Model type
- vision-encoder-decoder
- Access
- Public
- Created
- Mar 24, 2025
- Updated
- Mar 24, 2025
- SHA
- df620d2a
Try a prompt
Base models
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. -->
Vit-GPT2-UCA-UCF-01
This model is a fine-tuned version of NourFakih/Vit-GPT2-COCO2017Flickr-85k-09 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0808
- Rouge1: 57.3757
- Rouge2: 45.4285
- Rougel: 54.5391
- Rougelsum: 54.8713
- Gen Len: 15.7384
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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|---|---|---|---|---|---|---|---|---|
| No log | 0.3604 | 200 | 0.1535 | 39.243 | 22.9624 | 35.1916 | 35.6871 | 15.2289 |
| No log | 0.7207 | 400 | 0.1014 | 42.1983 | 25.2146 | 38.1711 | 38.7239 | 15.9897 |
| 0.0777 | 1.0811 | 600 | 0.0907 | 46.7294 | 31.005 | 42.8959 | 43.3674 | 15.8967 |
| 0.0777 | 1.4414 | 800 | 0.0861 | 50.849 | 36.5323 | 47.6738 | 48.0898 | 16.2324 |
| 0.0642 | 1.8018 | 1000 | 0.0835 | 52.9082 | 39.1634 | 49.4618 | 50.0549 | 15.6093 |
| 0.0642 | 2.1622 | 1200 | 0.0837 | 55.0496 | 42.0646 | 52.1721 | 52.5506 | 16.1463 |
| 0.0642 | 2.5225 | 1400 | 0.0824 | 57.0383 | 44.9584 | 53.9845 | 54.3247 | 15.9880 |
| 0.039 | 2.8829 | 1600 | 0.0808 | 57.3757 | 45.4285 | 54.5391 | 54.8713 | 15.7384 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- vision-encoder-decoder
- License
- apache-2.0
- Created
- Mar 24, 2025
- Updated
- Mar 24, 2025