vit-riceleafbd-7november2025v2

nqvii/vit-riceleafbd-7november2025v2

vit-riceleafbd-7november2025v2 is a image classification model from nqvii. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Aug 8, 2026
Updated: Aug 8, 2026
SHA: 405d8181

Base models

google/vit-base-patch16-224

Datasets

rice-diseases

Model card

From the Hugging Face model README

vit-riceleafbd-7november2025v2

This model is a fine-tuned version of google/vit-base-patch16-224 on the rice-diseases-image-dataset dataset. It achieves the following results on the evaluation set:

Loss: 1.0941
Accuracy: 0.9793
F1 Score: 0.9818
Recall: 0.9831

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: 1e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
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: cosine
lr_scheduler_warmup_steps: 150
num_epochs: 100
label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
3.2388	1.0	19	3.1369	0.2207	0.1485	0.2041
3.0146	2.0	38	2.8588	0.3310	0.2437	0.2877
2.5872	3.0	57	2.5202	0.4552	0.4004	0.4039
2.1867	4.0	76	2.1757	0.6621	0.6470	0.6264
1.8151	5.0	95	1.8032	0.8138	0.8278	0.8189
1.5088	6.0	114	1.5041	0.8483	0.8664	0.8681
1.2751	7.0	133	1.3375	0.8897	0.9038	0.9093
1.1891	8.0	152	1.2427	0.9310	0.9385	0.9387
1.1147	9.0	171	1.2279	0.9379	0.9447	0.9485
1.0932	10.0	190	1.1802	0.9310	0.9387	0.9412
1.0565	11.0	209	1.1744	0.9379	0.9447	0.9485
1.0276	12.0	228	1.1759	0.9379	0.9447	0.9485
1.0484	13.0	247	1.1875	0.9379	0.9464	0.9536
1.0223	14.0	266	1.1503	0.9586	0.9624	0.9632
1.0341	15.0	285	1.1367	0.9655	0.9699	0.9732
0.9960	16.0	304	1.1431	0.9586	0.9639	0.9683
1.0004	17.0	323	1.1354	0.9586	0.9639	0.9683
0.9870	18.0	342	1.1230	0.9724	0.9758	0.9782
0.9848	19.0	361	1.1146	0.9724	0.9758	0.9782
0.9705	20.0	380	1.1073	0.9724	0.9758	0.9782
0.9817	21.0	399	1.1136	0.9655	0.9699	0.9732
0.9827	22.0	418	1.1386	0.9448	0.9522	0.9585
0.9770	23.0	437	1.1081	0.9655	0.9699	0.9732
0.9720	24.0	456	1.0941	0.9793	0.9818	0.9831
0.9762	25.0	475	1.0859	0.9793	0.9818	0.9831
0.9693	26.0	494	1.1249	0.9517	0.9581	0.9634
0.9670	27.0	513	1.0965	0.9724	0.9758	0.9782
0.9640	28.0	532	1.1014	0.9655	0.9699	0.9732
0.9712	29.0	551	1.1083	0.9655	0.9699	0.9732
0.9704	30.0	570	1.1001	0.9655	0.9699	0.9732
0.9707	31.0	589	1.0955	0.9655	0.9699	0.9732
0.9662	32.0	608	1.1017	0.9586	0.9639	0.9683
0.9667	33.0	627	1.1039	0.9586	0.9639	0.9683
0.9603	34.0	646	1.0957	0.9655	0.9699	0.9732
0.9680	35.0	665	1.0971	0.9655	0.9699	0.9732

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Aug 8, 2026
Updated: Aug 8, 2026