convnext-Mistral-RSICD-without-captioning

swadhindas324/convnext-Mistral-RSICD-without-captioning

convnext-Mistral-RSICD-without-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 15, 2026
Updated: Jul 15, 2026
SHA: 05468c7e

Model card

From the Hugging Face model README

convnext-Mistral-RSICD-without-captioning

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

Loss: 1.7262
Accuracy: 82.42
Bleu-1: 0.6502
Bleu-2: 0.5182
Bleu-3: 0.4283
Bleu-4: 0.3617
Meteor: 0.6100
Rouge-l: 0.5789
Cider: 1.9325

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	1.3567	79.43	0.5159	0.3505	0.2526	0.1874	0.4593	0.4496	0.6915
2.4285	2.0	1536	1.2939	79.72	0.5731	0.4267	0.3287	0.2551	0.5497	0.5140	1.1645
0.9411	3.0	2304	1.2522	81.95	0.6864	0.5638	0.4696	0.3948	0.6723	0.6212	2.1220
0.7487	4.0	3072	1.3267	82.15	0.6896	0.5674	0.4718	0.3962	0.6822	0.6352	2.1771
0.7487	5.0	3840	1.4171	81.5	0.6543	0.5272	0.4349	0.3663	0.6425	0.5917	1.9704
0.5341	6.0	4608	1.5311	81.76	0.6421	0.5107	0.4177	0.3488	0.6200	0.5742	1.9267
0.4147	7.0	5376	1.6184	81.19	0.6232	0.4906	0.3963	0.3264	0.6132	0.5581	1.7155
0.3572	8.0	6144	1.6795	81.16	0.6154	0.4823	0.3871	0.3168	0.6009	0.5490	1.7236
0.3572	9.0	6912	1.7262	82.42	0.6502	0.5182	0.4283	0.3617	0.6100	0.5789	1.9325

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 15, 2026
Updated: Jul 15, 2026