vit-resnet-mistral-RSICD-with-3-captioning

swadhindas324/vit-resnet-mistral-RSICD-with-3-captioning

vit-resnet-mistral-RSICD-with-3-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: 6081f110

Model card

From the Hugging Face model README

vit-resnet-mistral-RSICD-with-3-captioning

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

Loss: 0.8332
Accuracy: 81.61
Bleu-1: 0.6172
Bleu-2: 0.4856
Bleu-3: 0.3941
Bleu-4: 0.3277
Meteor: 0.5872
Rouge-l: 0.5473
Cider: 1.7764

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	0.6875	79.76	0.4668	0.3027	0.2115	0.1537	0.4085	0.3975	0.4800
0.9613	2.0	1536	0.6617	79.95	0.5408	0.3865	0.2900	0.2251	0.5063	0.4725	0.8810
0.4688	3.0	2304	0.6203	81.41	0.6967	0.5742	0.4819	0.4076	0.6830	0.6362	2.2115
0.3476	4.0	3072	0.6622	81.63	0.6703	0.5444	0.4542	0.3856	0.6507	0.6073	2.1154
0.3476	5.0	3840	0.7266	80.53	0.6206	0.4886	0.3989	0.3344	0.6006	0.5550	1.7074
0.2386	6.0	4608	0.7691	82.47	0.6587	0.5282	0.4352	0.3655	0.6262	0.5896	1.9587
0.1875	7.0	5376	0.8124	81.71	0.6256	0.4936	0.4024	0.3350	0.5991	0.5542	1.7928
0.1642	8.0	6144	0.8332	81.61	0.6172	0.4856	0.3941	0.3277	0.5872	0.5473	1.7764

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