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

Loss: 1.1385
Accuracy: 0.9682
F1 Score: 0.9692
Recall: 0.9731

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: 50
mixed_precision_training: Native AMP
label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
2.9037	1.0	20	2.9992	0.2229	0.1992	0.2055
2.6661	2.0	40	2.8086	0.3248	0.2916	0.2944
2.3527	3.0	60	2.5086	0.4713	0.4337	0.4307
1.9695	4.0	80	2.1277	0.6306	0.6090	0.5984
1.6318	5.0	100	1.7679	0.7452	0.7420	0.7276
1.3582	6.0	120	1.5133	0.8535	0.8567	0.8552
1.2231	7.0	140	1.3629	0.9045	0.9072	0.9136
1.2005	8.0	160	1.2944	0.9236	0.9249	0.9308
1.0999	9.0	180	1.2554	0.9363	0.9373	0.9462
1.0716	10.0	200	1.2232	0.9363	0.9366	0.9406
1.0572	11.0	220	1.2030	0.9427	0.9430	0.9486
1.0421	12.0	240	1.1992	0.9490	0.9491	0.9560
1.0298	13.0	260	1.1828	0.9490	0.9490	0.9536
1.0341	14.0	280	1.1787	0.9554	0.9550	0.9585
1.0144	15.0	300	1.1642	0.9554	0.9550	0.9585
1.0131	16.0	320	1.1651	0.9554	0.9560	0.9596
1.0032	17.0	340	1.1606	0.9554	0.9560	0.9596
0.9809	18.0	360	1.1584	0.9554	0.9572	0.9608
0.9980	19.0	380	1.1463	0.9682	0.9691	0.9706
0.9842	20.0	400	1.1544	0.9618	0.9631	0.9657
0.9821	21.0	420	1.1542	0.9682	0.9692	0.9731
0.9810	22.0	440	1.1553	0.9554	0.9574	0.9633
0.9764	23.0	460	1.1354	0.9682	0.9692	0.9731
0.9810	24.0	480	1.1282	0.9745	0.9752	0.9780
0.9744	25.0	500	1.1473	0.9682	0.9692	0.9731
0.9775	26.0	520	1.1464	0.9554	0.9562	0.9621
0.9709	27.0	540	1.1477	0.9554	0.9562	0.9621
0.9733	28.0	560	1.1382	0.9682	0.9692	0.9731
0.9766	29.0	580	1.1407	0.9618	0.9633	0.9682
0.9810	30.0	600	1.1458	0.9618	0.9633	0.9682
0.9704	31.0	620	1.1332	0.9682	0.9692	0.9731
0.9707	32.0	640	1.1292	0.9745	0.9752	0.9780
0.9691	33.0	660	1.1380	0.9682	0.9692	0.9731
0.9709	34.0	680	1.1385	0.9682	0.9692	0.9731

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/vit-rice-disease

vit-rice-disease

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
vit-rice-disease