resnet-Mistral-NWPU-without-captioning

swadhindas324/resnet-Mistral-NWPU-without-captioning

resnet-Mistral-NWPU-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 16, 2026
Updated: Jul 16, 2026
SHA: 1e829491

Model card

From the Hugging Face model README

resnet-Mistral-NWPU-without-captioning

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

Loss: 0.6734
Accuracy: 85.81
Bleu-1: 0.8454
Bleu-2: 0.7530
Bleu-3: 0.6787
Bleu-4: 0.6185
Meteor: 0.7628
Rouge-l: 0.7459
Cider: 1.7133

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
0.9140	1.0	2215	0.8423	84.93	0.6232	0.4660	0.3643	0.2934	0.5122	0.4914	0.6281
0.7188	2.0	4430	0.6733	85.14	0.8170	0.7101	0.6264	0.5598	0.7170	0.7011	1.6003
0.6307	3.0	6645	0.6352	84.85	0.8156	0.7147	0.6351	0.5718	0.7339	0.7189	1.6351
0.5816	4.0	8860	0.6199	85.47	0.8478	0.7590	0.6859	0.6258	0.7567	0.7437	1.7018
0.5472	5.0	11075	0.6150	85.72	0.8534	0.7622	0.6889	0.6294	0.7619	0.7520	1.7586
0.5181	6.0	13290	0.6200	85.88	0.8502	0.7589	0.6839	0.6222	0.7658	0.7529	1.7791
0.5136	7.0	15505	0.6292	85.86	0.8484	0.7569	0.6829	0.6229	0.7693	0.7508	1.7448
0.4969	8.0	17720	0.6366	86.0	0.8494	0.7585	0.6856	0.6270	0.7635	0.7525	1.7570
0.4822	9.0	19935	0.6396	86.05	0.8511	0.7584	0.6819	0.6196	0.7729	0.7540	1.7505
0.4699	10.0	22150	0.6504	85.99	0.8489	0.7573	0.6851	0.6267	0.7590	0.7488	1.7718
0.4587	11.0	24365	0.6524	86.12	0.8489	0.7585	0.6868	0.6284	0.7674	0.7524	1.7675
0.4477	12.0	26580	0.6663	86.03	0.8521	0.7600	0.6864	0.6271	0.7624	0.7506	1.7499
0.4650	13.0	28795	0.6658	86.3	0.8477	0.7576	0.6853	0.6263	0.7605	0.7482	1.7700
0.4477	14.0	31010	0.6734	85.81	0.8454	0.7530	0.6787	0.6185	0.7628	0.7459	1.7133

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 16, 2026
Updated: Jul 16, 2026