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
2.7783	1.0	20	2.7710	0.2949	0.2710	0.2759
2.7580	2.0	40	2.7556	0.3365	0.2639	0.2926
2.7216	3.0	60	2.7397	0.3622	0.2276	0.2952
2.6886	4.0	80	2.7127	0.3654	0.2024	0.2903
2.6295	5.0	100	2.6748	0.3942	0.2501	0.3210
2.5506	6.0	120	2.5983	0.4231	0.3071	0.3553
2.4231	7.0	140	2.4871	0.5353	0.4822	0.4890
2.2373	8.0	160	2.3257	0.7147	0.7002	0.6936
2.0301	9.0	180	2.0683	0.8077	0.8000	0.7966
1.8538	10.0	200	1.8067	0.8397	0.8329	0.8345
1.6776	11.0	220	1.6026	0.8526	0.8482	0.8519
1.5205	12.0	240	1.4569	0.8846	0.8807	0.8830
1.4183	13.0	260	1.3538	0.9071	0.9047	0.9062
1.3339	14.0	280	1.3228	0.9135	0.9116	0.9143
1.3037	15.0	300	1.2856	0.9295	0.9273	0.9286
1.3341	16.0	320	1.2510	0.9359	0.9345	0.9361
1.2451	17.0	340	1.2533	0.9295	0.9283	0.9299
1.2560	18.0	360	1.2248	0.9327	0.9326	0.9349
1.1929	19.0	380	1.2038	0.9327	0.9326	0.9336
1.2412	20.0	400	1.2002	0.9359	0.9354	0.9362
1.1767	21.0	420	1.1933	0.9423	0.9418	0.9436
1.1864	22.0	440	1.1789	0.9359	0.9361	0.9380
1.1552	23.0	460	1.1636	0.9391	0.9393	0.9405
1.1383	24.0	480	1.1652	0.9519	0.9514	0.9516
1.1679	25.0	500	1.1609	0.9487	0.9484	0.9491
1.1492	26.0	520	1.1551	0.9487	0.9482	0.9498
1.1360	27.0	540	1.1494	0.9551	0.9550	0.9560
1.1375	28.0	560	1.1476	0.9519	0.9514	0.9516
1.0980	29.0	580	1.1590	0.9455	0.9446	0.9428
1.1126	30.0	600	1.1373	0.9551	0.9551	0.9547
1.0821	31.0	620	1.1378	0.9519	0.9524	0.9535
1.1267	32.0	640	1.1283	0.9551	0.9555	0.9534
1.0791	33.0	660	1.1331	0.9519	0.9526	0.9535
1.1392	34.0	680	1.1391	0.9423	0.9433	0.9435
1.1060	35.0	700	1.1312	0.9583	0.9599	0.9591
1.0965	36.0	720	1.1337	0.9583	0.9595	0.9584
1.0718	37.0	740	1.1323	0.9551	0.9568	0.9573
1.0722	38.0	760	1.1476	0.9615	0.9626	0.9647
1.0748	39.0	780	1.1248	0.9583	0.9601	0.9604
1.1088	40.0	800	1.1177	0.9583	0.9597	0.9597
1.0842	41.0	820	1.1172	0.9551	0.9567	0.9554
1.0672	42.0	840	1.1210	0.9583	0.9593	0.9572
1.0817	43.0	860	1.1199	0.9583	0.9597	0.9597
1.0688	44.0	880	1.1203	0.9551	0.9557	0.9572
1.0564	45.0	900	1.1182	0.9647	0.9657	0.9666
1.0478	46.0	920	1.1175	0.9615	0.9624	0.9641
1.0500	47.0	940	1.1093	0.9583	0.9590	0.9591
1.0721	48.0	960	1.1337	0.9551	0.9572	0.9598
1.0933	49.0	980	1.1275	0.9551	0.9568	0.9573
1.0512	50.0	1000	1.1120	0.9583	0.9597	0.9597
1.0703	51.0	1020	1.1175	0.9615	0.9622	0.9628
1.0695	52.0	1040	1.1178	0.9551	0.9564	0.9547
1.1348	53.0	1060	1.1195	0.9583	0.9593	0.9584
1.0354	54.0	1080	1.1152	0.9615	0.9620	0.9610
1.0183	55.0	1100	1.1135	0.9647	0.9652	0.9647
1.0415	56.0	1120	1.1125	0.9615	0.9626	0.9622
1.0584	57.0	1140	1.1110	0.9583	0.9596	0.9598

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/resnet50_fold_3_v3

resnet50_fold_3_v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
resnet50_fold_3_v3