convnext-Mistral-NWPU-without-captioning

swadhindas324/convnext-Mistral-NWPU-without-captioning

convnext-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: 3448f0da

Model card

From the Hugging Face model README

convnext-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: 0.6620
Accuracy: 86.07
Bleu-1: 0.8490
Bleu-2: 0.7595
Bleu-3: 0.6866
Bleu-4: 0.6275
Meteor: 0.7640
Rouge-l: 0.7516
Cider: 1.7522

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
0.9173	1.0	2215	0.8352	84.14	0.6448	0.4677	0.3542	0.2779	0.5099	0.4842	0.4886
0.7188	2.0	4430	0.6649	85.64	0.8234	0.7204	0.6402	0.5760	0.7224	0.7116	1.6629
0.6309	3.0	6645	0.6222	85.03	0.8261	0.7273	0.6482	0.5841	0.7503	0.7312	1.6781
0.5804	4.0	8860	0.6049	85.5	0.8578	0.7753	0.7055	0.6474	0.7733	0.7608	1.7797
0.5463	5.0	11075	0.6012	85.94	0.8624	0.7756	0.7048	0.6465	0.7729	0.7618	1.7969
0.5173	6.0	13290	0.6060	85.8	0.8549	0.7668	0.6951	0.6368	0.7857	0.7651	1.8258
0.5068	7.0	15505	0.6140	85.94	0.8612	0.7775	0.7090	0.6526	0.7882	0.7719	1.8286
0.4875	8.0	17720	0.6223	86.14	0.8648	0.7816	0.7142	0.6588	0.7806	0.7693	1.8333
0.4685	9.0	19935	0.6283	86.12	0.8630	0.7765	0.7036	0.6440	0.7890	0.7705	1.8139
0.4531	10.0	22150	0.6428	86.16	0.8582	0.7712	0.7014	0.6448	0.7713	0.7630	1.8270
0.4381	11.0	24365	0.6506	86.23	0.8631	0.7771	0.7078	0.6512	0.7870	0.7701	1.8459
0.4241	12.0	26580	0.6620	86.07	0.8490	0.7595	0.6866	0.6275	0.7640	0.7516	1.7522

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