vit-convnext-mistral-SYDNEY-with-3-captioning

swadhindas324/vit-convnext-mistral-SYDNEY-with-3-captioning

vit-convnext-mistral-SYDNEY-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: Jun 30, 2026
Updated: Jun 30, 2026
SHA: e77bd500

Model card

From the Hugging Face model README

vit-convnext-mistral-SYDNEY-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: 0.4677
Accuracy: 67.84
Bleu-1: 0.8276
Bleu-2: 0.7647
Bleu-3: 0.7080
Bleu-4: 0.6529
Meteor: 0.7872
Rouge-l: 0.7522
Cider: 2.7295

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	44	0.9229	65.29	0.5334	0.4164	0.3336	0.2624	0.4445	0.4631	0.8656
No log	2.0	88	0.5094	62.53	0.5480	0.4380	0.3590	0.2933	0.4270	0.4606	0.7265
No log	3.0	132	0.4333	67.07	0.6516	0.5537	0.4747	0.4075	0.5585	0.5842	1.5430
No log	4.0	176	0.4120	66.53	0.7884	0.6896	0.5943	0.5038	0.6917	0.6649	2.2437
No log	5.0	220	0.3894	66.55	0.7856	0.6987	0.6136	0.5245	0.7038	0.6536	2.1245
No log	6.0	264	0.3879	68.31	0.7695	0.6675	0.5711	0.4875	0.6892	0.6815	2.1739
No log	7.0	308	0.3940	68.41	0.7771	0.7067	0.6375	0.5753	0.7588	0.7246	2.4446
No log	8.0	352	0.3873	65.2	0.8230	0.7558	0.6975	0.6429	0.7782	0.7331	2.8550
No log	9.0	396	0.4036	67.78	0.8054	0.7255	0.6513	0.5856	0.7640	0.7412	2.5325
No log	10.0	440	0.4154	67.94	0.8138	0.7411	0.6724	0.6097	0.7607	0.7260	2.7450
No log	11.0	484	0.4043	69.18	0.8103	0.7487	0.6900	0.6370	0.7911	0.7598	2.8851
No log	12.0	528	0.4204	69.5	0.8225	0.7419	0.6646	0.5952	0.7517	0.7362	2.5103
No log	13.0	572	0.4245	66.61	0.8175	0.7467	0.6643	0.5809	0.7490	0.7098	2.4963
No log	14.0	616	0.4374	67.67	0.7974	0.7284	0.6683	0.6113	0.7553	0.7264	2.5928
No log	15.0	660	0.4389	69.27	0.8190	0.7337	0.6654	0.6027	0.7817	0.7489	2.5667
No log	16.0	704	0.4502	68.05	0.7931	0.7241	0.6659	0.6142	0.7625	0.7302	2.7312
No log	17.0	748	0.4592	67.88	0.8162	0.7518	0.6933	0.6382	0.7797	0.7362	2.7089
No log	18.0	792	0.4661	68.02	0.8298	0.7607	0.6962	0.6353	0.7886	0.7552	2.7675
No log	19.0	836	0.4602	68.15	0.8132	0.7418	0.6774	0.6170	0.7466	0.7108	2.4385
No log	20.0	880	0.4673	69.33	0.8171	0.7486	0.6851	0.6271	0.7725	0.7492	2.7374
No log	21.0	924	0.4677	67.84	0.8276	0.7647	0.7080	0.6529	0.7872	0.7522	2.7295

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: Jun 30, 2026
Updated: Jun 30, 2026