square_run_second_vote_full_pic_age_gender

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

• Loss: 1.0942
• F1 Macro: 0.4946
• F1 Micro: 0.5909
• F1 Weighted: 0.5567
• Precision Macro: 0.5352
• Precision Micro: 0.5909
• Precision Weighted: 0.6194
• Recall Macro: 0.5362
• Recall Micro: 0.5909
• Recall Weighted: 0.5909
• Accuracy: 0.5909

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: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	F1 Macro	F1 Micro	F1 Weighted	Precision Macro	Precision Micro	Precision Weighted	Recall Macro	Recall Micro	Recall Weighted	Accuracy
1.7962	1.0	58	1.7841	0.2169	0.3409	0.2791	0.2076	0.3409	0.2573	0.2540	0.3409	0.3409	0.3409
1.7195	2.0	116	1.8915	0.2134	0.3409	0.2372	0.1677	0.3409	0.1872	0.3116	0.3409	0.3409	0.3409
1.4056	3.0	174	1.5382	0.3310	0.4470	0.3785	0.4080	0.4470	0.4904	0.3773	0.4470	0.4470	0.4470
1.3472	4.0	232	1.3224	0.4005	0.5530	0.4824	0.4535	0.5530	0.5284	0.4615	0.5530	0.5530	0.5530
1.3266	5.0	290	1.3801	0.3815	0.5	0.4467	0.3655	0.5	0.4261	0.4203	0.5	0.5	0.5
1.3514	6.0	348	1.4009	0.3982	0.4697	0.4703	0.5142	0.4697	0.6060	0.4123	0.4697	0.4697	0.4697
1.0789	7.0	406	1.0679	0.5153	0.6136	0.5974	0.5624	0.6136	0.6350	0.5174	0.6136	0.6136	0.6136
0.9054	8.0	464	1.0248	0.5610	0.6591	0.6392	0.5675	0.6591	0.6528	0.5790	0.6591	0.6591	0.6591
0.9475	9.0	522	1.0533	0.5533	0.6439	0.6313	0.5630	0.6439	0.6377	0.5627	0.6439	0.6439	0.6439
0.7595	10.0	580	1.2404	0.5064	0.6061	0.5985	0.5585	0.6061	0.6540	0.5220	0.6061	0.6061	0.6061
0.6635	11.0	638	1.2577	0.5481	0.6515	0.6358	0.5697	0.6515	0.6554	0.5644	0.6515	0.6515	0.6515
0.6638	12.0	696	1.1971	0.5943	0.6894	0.6847	0.6031	0.6894	0.6966	0.6035	0.6894	0.6894	0.6894
1.3747	13.0	754	1.3014	0.5376	0.6136	0.6094	0.5734	0.6136	0.6522	0.5461	0.6136	0.6136	0.6136
0.3888	14.0	812	1.3645	0.5671	0.6364	0.6212	0.6146	0.6364	0.6701	0.5834	0.6364	0.6364	0.6364
0.3119	15.0	870	1.4637	0.5753	0.6591	0.6482	0.5955	0.6591	0.6694	0.5839	0.6591	0.6591	0.6591
0.1874	16.0	928	1.4016	0.5387	0.6288	0.6220	0.5523	0.6288	0.6300	0.5383	0.6288	0.6288	0.6288
0.0585	17.0	986	1.5412	0.5895	0.6894	0.6834	0.6173	0.6894	0.7082	0.5938	0.6894	0.6894	0.6894
0.0425	18.0	1044	1.5022	0.6341	0.6970	0.6980	0.6602	0.6970	0.7305	0.6383	0.6970	0.6970	0.6970
0.1893	19.0	1102	1.5766	0.6294	0.6818	0.6736	0.6630	0.6818	0.6847	0.6225	0.6818	0.6818	0.6818
0.0059	20.0	1160	1.5288	0.6187	0.7273	0.7173	0.6260	0.7273	0.7246	0.6302	0.7273	0.7273	0.7273
0.0019	21.0	1218	1.5794	0.6116	0.7121	0.7044	0.6149	0.7121	0.7040	0.6158	0.7121	0.7121	0.7121
0.0043	22.0	1276	1.6290	0.5979	0.6970	0.6910	0.6144	0.6970	0.6984	0.5944	0.6970	0.6970	0.6970
0.0012	23.0	1334	1.6983	0.6387	0.6894	0.6835	0.6647	0.6894	0.6874	0.6310	0.6894	0.6894	0.6894
0.0007	24.0	1392	1.6381	0.6084	0.6970	0.6986	0.6195	0.6970	0.7039	0.6007	0.6970	0.6970	0.6970
0.0035	25.0	1450	1.6691	0.6100	0.6970	0.6975	0.6162	0.6970	0.7018	0.6077	0.6970	0.6970	0.6970
0.0318	26.0	1508	1.6443	0.6116	0.7045	0.7036	0.6223	0.7045	0.7080	0.6055	0.7045	0.7045	0.7045
0.0005	27.0	1566	1.6647	0.6203	0.7121	0.7117	0.6312	0.7121	0.7167	0.6139	0.7121	0.7121	0.7121
0.0059	28.0	1624	1.6387	0.6243	0.7273	0.7198	0.6269	0.7273	0.7211	0.6309	0.7273	0.7273	0.7273
0.0009	29.0	1682	1.6511	0.5960	0.6894	0.6864	0.5988	0.6894	0.6864	0.5963	0.6894	0.6894	0.6894
0.003	30.0	1740	1.6608	0.6046	0.6970	0.6945	0.6080	0.6970	0.6954	0.6047	0.6970	0.6970	0.6970

Framework versions

• Transformers 4.49.0
• Pytorch 2.6.0+cu124
• Datasets 3.3.1
• Tokenizers 0.21.0