VIT_Captioning

Skyler215/VIT_Captioning

VIT_Captioning is a image-text-to-text model from Skyler215. 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.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image-Text-to-Text
Library: transformers
Model type: vision-encoder-decoder
Access: Public
Created: Nov 25, 2024
Updated: Nov 25, 2024
SHA: fe947f58

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Model card

From the Hugging Face model README

VIT_Captioning

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.0461
Rouge1: 0.4850
Rouge2: 0.2566
Rougel: 0.3589
Rougelsum: 0.3595

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: 8
eval_batch_size: 8
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
lr_scheduler_warmup_steps: 1024
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum
2.4042	1.0	1828	1.7451	0.4622	0.1906	0.3370	0.3422
1.5875	2.0	3656	1.5933	0.4599	0.2060	0.3451	0.3472
1.3882	3.0	5484	1.5322	0.4606	0.2082	0.3422	0.3442
1.2415	4.0	7312	1.5130	0.4687	0.2208	0.3458	0.3476
1.1113	5.0	9140	1.5186	0.4630	0.2146	0.3398	0.3402
0.9671	6.0	10968	1.5683	0.4720	0.2290	0.3517	0.3520
0.8528	7.0	12796	1.6352	0.4704	0.2281	0.3491	0.3496
0.7555	8.0	14624	1.7122	0.4725	0.2305	0.3477	0.3481
0.6567	9.0	16452	1.7814	0.4763	0.2389	0.3537	0.3543
0.5612	10.0	18280	1.8528	0.4777	0.2410	0.3515	0.3516
0.4953	11.0	20108	1.9072	0.4799	0.2487	0.3562	0.3565
0.4445	12.0	21936	1.9503	0.4829	0.2514	0.3571	0.3574
0.3976	13.0	23764	1.9928	0.4834	0.2543	0.3569	0.3573
0.3643	14.0	25592	2.0249	0.4820	0.2520	0.3575	0.3581
0.3263	15.0	27420	2.0461	0.4850	0.2566	0.3589	0.3595

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Created: Nov 25, 2024
Updated: Nov 25, 2024