EfficientNetV2_Small_v1

This model is a fine-tuned version of timm/tf_efficientnetv2_s.in21k on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.0340
• Accuracy: 0.9935
• Precision: 0.9981
• Recall: 0.9878
• F1: 0.9929
• Tp: 1618
• Tn: 1907
• Fp: 3
• Fn: 20

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: 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: linear
• lr_scheduler_warmup_steps: 442
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Tp	Tn	Fp	Fn
0.1995	1.0	222	0.1349	0.9628	0.9575	0.9621	0.9598	1576	1840	70	62
0.1442	2.0	444	0.0940	0.9789	0.9956	0.9585	0.9767	1570	1903	7	68
0.1625	3.0	666	0.0827	0.9837	0.9925	0.9719	0.9821	1592	1898	12	46
0.1592	4.0	888	0.0926	0.9752	0.9708	0.9756	0.9732	1598	1862	48	40
0.1100	5.0	1110	0.0544	0.9876	0.9950	0.9780	0.9865	1602	1902	8	36
0.1497	6.0	1332	0.0635	0.9868	0.9877	0.9835	0.9856	1611	1890	20	27
0.1125	7.0	1554	0.0485	0.9896	0.9957	0.9817	0.9886	1608	1903	7	30
0.1202	8.0	1776	0.0774	0.9794	0.9740	0.9817	0.9778	1608	1867	43	30
0.1031	9.0	1998	0.0507	0.9893	0.9938	0.9829	0.9883	1610	1900	10	28
0.1211	10.0	2220	0.0434	0.9915	0.9975	0.9841	0.9908	1612	1906	4	26
0.1239	11.0	2442	0.0400	0.9918	0.9975	0.9847	0.9911	1613	1906	4	25
0.1066	12.0	2664	0.0403	0.9927	0.9988	0.9853	0.9920	1614	1908	2	24
0.1065	13.0	2886	0.0363	0.9927	0.9994	0.9847	0.9920	1613	1909	1	25
0.1074	14.0	3108	0.0378	0.9930	0.9988	0.9860	0.9923	1615	1908	2	23
0.1128	15.0	3330	0.0327	0.9924	0.9981	0.9853	0.9917	1614	1907	3	24
0.0963	16.0	3552	0.0309	0.9930	0.9988	0.9860	0.9923	1615	1908	2	23
0.1379	17.0	3774	0.0366	0.9927	0.9969	0.9872	0.9920	1617	1905	5	21
0.1070	18.0	3996	0.0331	0.9930	0.9981	0.9866	0.9923	1616	1907	3	22
0.1332	19.0	4218	0.0343	0.9930	0.9981	0.9866	0.9923	1616	1907	3	22
0.1294	20.0	4440	0.0340	0.9935	0.9981	0.9878	0.9929	1618	1907	3	20

Framework versions

• Transformers 5.2.0
• Pytorch 2.9.0+cu126
• Datasets 4.0.0
• Tokenizers 0.22.2