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