vit-resnet-mistral-RSICD-with-6-captioning

swadhindas324/vit-resnet-mistral-RSICD-with-6-captioning

vit-resnet-mistral-RSICD-with-6-captioning is a machine learning model from swadhindas324. Use it for the machine learning 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

Library: transformers
Model type: vision-encoder-decoder
Access: Public
Created: Jul 3, 2026
Updated: Jul 3, 2026
SHA: 9a9356c8

Model card

From the Hugging Face model README

vit-resnet-mistral-RSICD-with-6-captioning

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

Loss: 1.0749
Accuracy: 83.69
Bleu-1: 0.7129
Bleu-2: 0.5909
Bleu-3: 0.4955
Bleu-4: 0.4213
Meteor: 0.6677
Rouge-l: 0.6364
Cider: 2.2411

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: 0.1
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
No log	1.0	768	0.6911	80.18	0.4668	0.3027	0.2115	0.1537	0.4085	0.3975	0.4800
0.9634	2.0	1536	0.6668	80.0	0.5394	0.3880	0.2931	0.2275	0.5084	0.4761	0.8994
0.4727	3.0	2304	0.6267	81.01	0.7016	0.5789	0.4859	0.4116	0.6863	0.6354	2.2058
0.3503	4.0	3072	0.6738	82.3	0.7048	0.5808	0.4869	0.4135	0.6742	0.6340	2.2838
0.3503	5.0	3840	0.7228	80.8	0.6741	0.5502	0.4577	0.3871	0.6539	0.6097	2.1323
0.2434	6.0	4608	0.7652	82.97	0.7096	0.5857	0.4930	0.4206	0.6704	0.6376	2.2930
0.1932	7.0	5376	0.8145	82.46	0.6879	0.5653	0.4734	0.4019	0.6713	0.6244	2.1774
0.1692	8.0	6144	0.8359	82.7	0.6868	0.5636	0.4715	0.3996	0.6713	0.6243	2.2719
0.1692	9.0	6912	0.8724	83.21	0.6891	0.5638	0.4692	0.3949	0.6685	0.6272	2.1536
0.1510	10.0	7680	0.8612	83.31	0.7111	0.5896	0.4942	0.4196	0.6844	0.6435	2.3086
0.1461	11.0	8448	0.9065	83.18	0.6983	0.5737	0.4806	0.4097	0.6660	0.6240	2.2408
0.1426	12.0	9216	0.9210	83.13	0.6999	0.5739	0.4788	0.4049	0.6728	0.6297	2.1826
0.1426	13.0	9984	0.9393	83.65	0.7133	0.5897	0.4951	0.4221	0.6770	0.6403	2.2777
0.1358	14.0	10752	0.9753	83.58	0.7189	0.5983	0.5059	0.4324	0.6893	0.6496	2.4369
0.1278	15.0	11520	0.9849	83.49	0.7123	0.5919	0.4995	0.4256	0.6884	0.6468	2.3441
0.1207	16.0	12288	1.0215	83.64	0.7169	0.5932	0.4992	0.4240	0.6777	0.6392	2.3050
0.1207	17.0	13056	1.0412	83.2	0.6969	0.5727	0.4785	0.4060	0.6759	0.6289	2.2425
0.1145	18.0	13824	1.0660	83.19	0.7011	0.5772	0.4834	0.4109	0.6777	0.6316	2.2432
0.1111	19.0	14592	1.0749	83.69	0.7129	0.5909	0.4955	0.4213	0.6677	0.6364	2.2411

Framework versions

Transformers 5.12.1
Pytorch 2.12.1+cu130
Datasets 5.0.0
Tokenizers 0.22.2

Library: transformers
Type: vision-encoder-decoder
Created: Jul 3, 2026
Updated: Jul 3, 2026