vit-gpt-NWPU-captioning

swadhindas324/vit-gpt-NWPU-captioning

vit-gpt-NWPU-captioning is a image-text-to-text model from swadhindas324. 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: Jul 1, 2026
Updated: Jul 1, 2026
SHA: 5e4f819e

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Model card

From the Hugging Face model README

vit-gpt-NWPU-captioning

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

Loss: 0.6254
Accuracy: 85.1
Bleu-1: 0.8526
Bleu-2: 0.7615
Bleu-3: 0.6873
Bleu-4: 0.6274
Meteor: 0.7682
Rouge-l: 0.7527
Cider: 1.7556

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: 0.0001
train_batch_size: 64
eval_batch_size: 64
seed: 50
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 128
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Bleu-1	Bleu-2	Bleu-3	Bleu-4	Meteor	Rouge-l	Cider
0.8241	1.0	1969	0.5023	85.06	0.8202	0.7130	0.6327	0.5699	0.7179	0.7020	1.6309
0.4855	2.0	3938	0.4788	84.88	0.8371	0.7374	0.6566	0.5926	0.7398	0.7235	1.6648
0.4499	3.0	5907	0.4754	85.1	0.8476	0.7561	0.6821	0.6236	0.7543	0.7413	1.7213
0.4260	4.0	7876	0.4710	84.49	0.8477	0.7575	0.6852	0.6268	0.7705	0.7533	1.7417
0.4066	5.0	9845	0.4763	85.14	0.8618	0.7728	0.7017	0.6441	0.7703	0.7583	1.8203
0.3883	6.0	11814	0.4791	85.28	0.8654	0.7775	0.7076	0.6513	0.7669	0.7625	1.8222
0.3686	7.0	13783	0.4883	84.91	0.8567	0.7669	0.6941	0.6351	0.7770	0.7606	1.7645
0.3499	8.0	15752	0.4983	85.09	0.8609	0.7744	0.7048	0.6480	0.7752	0.7632	1.8206
0.3313	9.0	17721	0.5119	84.99	0.8604	0.7729	0.7027	0.6456	0.7758	0.7621	1.8067
0.3115	10.0	19690	0.5315	85.26	0.8652	0.7799	0.7112	0.6549	0.7835	0.7700	1.8623
0.2940	11.0	21659	0.5515	85.11	0.8549	0.7636	0.6921	0.6345	0.7664	0.7537	1.7845
0.2775	12.0	23628	0.5757	85.1	0.8508	0.7575	0.6832	0.6235	0.7588	0.7468	1.7401
0.2458	13.0	25597	0.5890	85.13	0.8510	0.7551	0.6800	0.6201	0.7577	0.7445	1.7500
0.2295	14.0	27566	0.6115	85.25	0.8500	0.7584	0.6859	0.6272	0.7593	0.7503	1.7433
0.2204	15.0	29535	0.6254	85.1	0.8526	0.7615	0.6873	0.6274	0.7682	0.7527	1.7556

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

Task: Image-Text-to-Text
Library: transformers
Type: vision-encoder-decoder
Created: Jul 1, 2026
Updated: Jul 1, 2026