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: 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
2.7557	1.0	20	2.7625	0.2692	0.2594	0.2612
2.7387	2.0	40	2.7482	0.3141	0.2617	0.2847
2.6979	3.0	60	2.7262	0.3878	0.2740	0.3281
2.6486	4.0	80	2.6881	0.4231	0.2920	0.3538
2.5751	5.0	100	2.6298	0.5064	0.4160	0.4486
2.4857	6.0	120	2.5060	0.5994	0.5319	0.5598
2.3204	7.0	140	2.3467	0.6795	0.6336	0.6488
2.1366	8.0	160	2.1263	0.7532	0.7320	0.7322
1.8969	9.0	180	1.8718	0.8077	0.7996	0.7961
1.7379	10.0	200	1.6131	0.8590	0.8563	0.8539
1.5923	11.0	220	1.4873	0.8846	0.8826	0.8816
1.3941	12.0	240	1.3840	0.8942	0.8898	0.8890
1.3773	13.0	260	1.3154	0.9038	0.8982	0.8958
1.3183	14.0	280	1.2813	0.9199	0.9171	0.9182
1.2701	15.0	300	1.2505	0.9327	0.9301	0.9294
1.2894	16.0	320	1.2321	0.9327	0.9314	0.9300
1.2239	17.0	340	1.2421	0.9263	0.9244	0.9235
1.2053	18.0	360	1.2268	0.9295	0.9289	0.9284
1.1995	19.0	380	1.2101	0.9391	0.9385	0.9386
1.2248	20.0	400	1.2013	0.9391	0.9372	0.9346
1.1555	21.0	420	1.1986	0.9359	0.9352	0.9349
1.1670	22.0	440	1.1890	0.9455	0.9456	0.9453
1.2071	23.0	460	1.1926	0.9423	0.9418	0.9434
1.1267	24.0	480	1.2246	0.9391	0.9383	0.9409
1.1614	25.0	500	1.1970	0.9455	0.9458	0.9465
1.1558	26.0	520	1.2032	0.9423	0.9418	0.9428
1.1441	27.0	540	1.1938	0.9487	0.9488	0.9502
1.1256	28.0	560	1.1955	0.9423	0.9426	0.9441
1.0955	29.0	580	1.2166	0.9327	0.9307	0.9311
1.0886	30.0	600	1.2201	0.9199	0.9176	0.9183
1.1150	31.0	620	1.1919	0.9423	0.9427	0.9441
1.1152	32.0	640	1.1871	0.9391	0.9393	0.9379
1.0707	33.0	660	1.2011	0.9327	0.9330	0.9318
1.0944	34.0	680	1.1869	0.9423	0.9425	0.9416
1.1264	35.0	700	1.1817	0.9487	0.9485	0.9477
1.0992	36.0	720	1.1838	0.9423	0.9425	0.9416
1.0791	37.0	740	1.1749	0.9455	0.9458	0.9453
1.0995	38.0	760	1.1811	0.9551	0.9559	0.9575
1.0542	39.0	780	1.1732	0.9519	0.9522	0.9514
1.1183	40.0	800	1.1655	0.9583	0.9590	0.9612
1.0785	41.0	820	1.1727	0.9455	0.9457	0.9465
1.0837	42.0	840	1.1645	0.9455	0.9458	0.9465
1.0621	43.0	860	1.1836	0.9487	0.9491	0.9514
1.0465	44.0	880	1.1845	0.9423	0.9426	0.9428
1.0469	45.0	900	1.1702	0.9455	0.9461	0.9453
1.0297	46.0	920	1.1686	0.9551	0.9554	0.9575
1.0551	47.0	940	1.1659	0.9583	0.9585	0.9612
1.0465	48.0	960	1.1744	0.9519	0.9526	0.9563
1.0796	49.0	980	1.1688	0.9519	0.9522	0.9539
1.0476	50.0	1000	1.1593	0.9551	0.9552	0.9563
1.0353	51.0	1020	1.1657	0.9487	0.9486	0.9477
1.0581	52.0	1040	1.1620	0.9519	0.9524	0.9514
1.1047	53.0	1060	1.1604	0.9551	0.9550	0.9551
1.0317	54.0	1080	1.1661	0.9455	0.9452	0.9441
1.0229	55.0	1100	1.1587	0.9487	0.9492	0.9477
1.0533	56.0	1120	1.1624	0.9551	0.9552	0.9563
1.0438	57.0	1140	1.1634	0.9551	0.9552	0.9563
1.0314	58.0	1160	1.1543	0.9551	0.9552	0.9563
1.0473	59.0	1180	1.1591	0.9551	0.9550	0.9551
1.0623	60.0	1200	1.1580	0.9647	0.9654	0.9673
1.0197	61.0	1220	1.1587	0.9615	0.9615	0.9637
1.0110	62.0	1240	1.1541	0.9551	0.9552	0.9563
1.0129	63.0	1260	1.1455	0.9615	0.9623	0.9637
1.0515	64.0	1280	1.1501	0.9583	0.9592	0.9600
1.0202	65.0	1300	1.1541	0.9519	0.9520	0.9526
1.0114	66.0	1320	1.1557	0.9583	0.9592	0.9600
1.0040	67.0	1340	1.1573	0.9551	0.9552	0.9563
1.0181	68.0	1360	1.1559	0.9551	0.9552	0.9563
1.0284	69.0	1380	1.1523	0.9551	0.9552	0.9563
1.0234	70.0	1400	1.1595	0.9551	0.9552	0.9563
1.0203	71.0	1420	1.1545	0.9551	0.9554	0.9575
1.0216	72.0	1440	1.1493	0.9551	0.9550	0.9551
1.0271	73.0	1460	1.1642	0.9551	0.9560	0.9563

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/resnet50_fold_1_v3

resnet50_fold_1_v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
resnet50_fold_1_v3