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
2.7675	1.0	19	2.7627	0.2276	0.1794	0.2296
2.7523	2.0	38	2.7540	0.3241	0.2311	0.2797
2.7348	3.0	57	2.7263	0.3828	0.2281	0.2988
2.6936	4.0	76	2.6837	0.3793	0.2013	0.2840
2.6260	5.0	95	2.6374	0.3828	0.1989	0.2850
2.5096	6.0	114	2.5647	0.4276	0.2991	0.3443
2.3841	7.0	133	2.4568	0.5966	0.5553	0.5545
2.2120	8.0	152	2.2170	0.7172	0.7022	0.6993
1.9531	9.0	171	1.9750	0.8	0.8021	0.8009
1.8001	10.0	190	1.7220	0.8310	0.8415	0.8368
1.6636	11.0	209	1.5142	0.8759	0.8851	0.8898
1.4820	12.0	228	1.3768	0.8828	0.8913	0.8960
1.3627	13.0	247	1.3274	0.8966	0.9016	0.9069
1.2971	14.0	266	1.2548	0.9345	0.9376	0.9411
1.2295	15.0	285	1.2514	0.9276	0.9347	0.9391
1.2444	16.0	304	1.2267	0.9345	0.9406	0.9427
1.2220	17.0	323	1.2259	0.9345	0.9369	0.9397
1.1872	18.0	342	1.2203	0.9241	0.9260	0.9349
1.2176	19.0	361	1.1870	0.9310	0.9358	0.9411
1.2911	20.0	380	1.1895	0.9414	0.9461	0.9446
1.1779	21.0	399	1.1768	0.9448	0.9488	0.9534
1.1306	22.0	418	1.1870	0.9345	0.9395	0.9371
1.1647	23.0	437	1.1661	0.9448	0.9502	0.9547
1.1289	24.0	456	1.1722	0.9345	0.9402	0.9448
1.1109	25.0	475	1.1681	0.9448	0.9499	0.9521
1.1047	26.0	494	1.1909	0.9345	0.9401	0.9473
1.1087	27.0	513	1.1579	0.9483	0.9523	0.9533
1.1312	28.0	532	1.1588	0.9448	0.9487	0.9521
1.1221	29.0	551	1.1549	0.9483	0.9538	0.9572
1.1603	30.0	570	1.1495	0.9448	0.9498	0.9534
1.1105	31.0	589	1.1553	0.9379	0.9443	0.9448
1.1005	32.0	608	1.1685	0.9517	0.9556	0.9609
1.1101	33.0	627	1.1662	0.9414	0.9466	0.9484
1.0823	34.0	646	1.1724	0.9345	0.9403	0.9409
1.1197	35.0	665	1.1616	0.9448	0.9499	0.9521
1.0557	36.0	684	1.1553	0.9379	0.9433	0.9434
1.0602	37.0	703	1.1578	0.9414	0.9463	0.9484
1.0646	38.0	722	1.1797	0.9276	0.9316	0.9348
1.0431	39.0	741	1.1758	0.9483	0.9526	0.9559
1.0977	40.0	760	1.1542	0.9414	0.9460	0.9471

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/resnet50_fold_3

resnet50_fold_3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
resnet50_fold_3