square_run_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: 1.4067
• F1 Macro: 0.4365
• F1 Micro: 0.5152
• F1 Weighted: 0.4956
• Precision Macro: 0.4384
• Precision Micro: 0.5152
• Precision Weighted: 0.4986
• Recall Macro: 0.4561
• Recall Micro: 0.5152
• Recall Weighted: 0.5152
• Accuracy: 0.5152

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
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH 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: 35

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
1.8891	1.0	29	1.8671	0.1742	0.2576	0.2101	0.1681	0.2576	0.2045	0.2142	0.2576	0.2576	0.2576
1.8327	2.0	58	1.8124	0.1570	0.3182	0.1937	0.1335	0.3182	0.1611	0.2508	0.3182	0.3182	0.3182
1.9127	3.0	87	1.7830	0.2085	0.3182	0.2576	0.2128	0.3182	0.2618	0.2625	0.3182	0.3182	0.3182
1.4498	4.0	116	1.5796	0.2936	0.3864	0.3438	0.4342	0.3864	0.4527	0.3179	0.3864	0.3864	0.3864
1.2166	5.0	145	1.3485	0.3868	0.4773	0.4442	0.5068	0.4773	0.5373	0.4077	0.4773	0.4773	0.4773
1.5704	6.0	174	1.2560	0.4853	0.5606	0.5510	0.4906	0.5606	0.5679	0.5026	0.5606	0.5606	0.5606
1.2465	7.0	203	1.4968	0.3854	0.4924	0.4393	0.5611	0.4924	0.5975	0.4107	0.4924	0.4924	0.4924
1.2531	8.0	232	1.4663	0.4380	0.5	0.4841	0.4623	0.5	0.5302	0.4693	0.5	0.5	0.5
0.5318	9.0	261	1.1161	0.4938	0.5909	0.5646	0.4892	0.5909	0.5595	0.5176	0.5909	0.5909	0.5909
0.6824	10.0	290	1.1811	0.4802	0.5909	0.5515	0.4814	0.5909	0.5498	0.5148	0.5909	0.5909	0.5909
0.6324	11.0	319	1.2358	0.4927	0.5758	0.5506	0.5015	0.5758	0.5690	0.5226	0.5758	0.5758	0.5758
0.4145	12.0	348	1.1608	0.5846	0.6742	0.6643	0.5822	0.6742	0.6681	0.6005	0.6742	0.6742	0.6742
0.4805	13.0	377	1.3200	0.5276	0.5758	0.5689	0.5767	0.5758	0.6138	0.5269	0.5758	0.5758	0.5758
0.6232	14.0	406	1.3190	0.4790	0.5758	0.5517	0.5025	0.5758	0.5734	0.5006	0.5758	0.5758	0.5758
0.3475	15.0	435	1.1853	0.6303	0.6970	0.6894	0.6717	0.6970	0.7088	0.6312	0.6970	0.6970	0.6970
0.1956	16.0	464	1.5695	0.4323	0.5152	0.4974	0.4755	0.5152	0.5334	0.4358	0.5152	0.5152	0.5152
0.1519	17.0	493	1.4404	0.5819	0.6439	0.6317	0.6438	0.6439	0.6577	0.5706	0.6439	0.6439	0.6439
0.1031	18.0	522	1.4877	0.5370	0.6136	0.6041	0.5351	0.6136	0.5975	0.5422	0.6136	0.6136	0.6136
0.0615	19.0	551	1.4801	0.6013	0.6061	0.6106	0.6476	0.6061	0.6581	0.5951	0.6061	0.6061	0.6061
0.0249	20.0	580	1.6082	0.5198	0.5909	0.5825	0.5149	0.5909	0.5770	0.5272	0.5909	0.5909	0.5909
0.374	21.0	609	1.7594	0.6084	0.6288	0.6185	0.6712	0.6288	0.6679	0.6049	0.6288	0.6288	0.6288
0.025	22.0	638	1.4723	0.6446	0.6515	0.6520	0.6543	0.6515	0.6660	0.6479	0.6515	0.6515	0.6515
0.0096	23.0	667	1.5689	0.5899	0.6136	0.6089	0.6170	0.6136	0.6315	0.5878	0.6136	0.6136	0.6136
0.0661	24.0	696	1.6276	0.6056	0.6667	0.6576	0.6690	0.6667	0.6867	0.5949	0.6667	0.6667	0.6667
0.0463	25.0	725	1.6761	0.5591	0.6136	0.6085	0.6193	0.6136	0.6401	0.5521	0.6136	0.6136	0.6136
0.0118	26.0	754	1.6210	0.5353	0.6288	0.6075	0.5716	0.6288	0.6263	0.5410	0.6288	0.6288	0.6288
0.0018	27.0	783	1.6073	0.5860	0.6742	0.6575	0.5956	0.6742	0.6587	0.5929	0.6742	0.6742	0.6742
0.0336	28.0	812	1.5964	0.6086	0.6439	0.6411	0.6379	0.6439	0.6566	0.5979	0.6439	0.6439	0.6439
0.0014	29.0	841	1.5290	0.6873	0.7121	0.7083	0.7263	0.7121	0.7308	0.6734	0.7121	0.7121	0.7121
0.021	30.0	870	1.5440	0.6982	0.6970	0.6974	0.7076	0.6970	0.7170	0.7086	0.6970	0.6970	0.6970
0.0065	31.0	899	1.6576	0.6869	0.6970	0.6915	0.7430	0.6970	0.7270	0.6699	0.6970	0.6970	0.6970
0.0013	32.0	928	1.5603	0.7124	0.7197	0.7173	0.7508	0.7197	0.7411	0.6987	0.7197	0.7197	0.7197
0.0129	33.0	957	1.6028	0.6842	0.6894	0.6870	0.7153	0.6894	0.7059	0.6731	0.6894	0.6894	0.6894
0.0006	34.0	986	1.6075	0.6787	0.6818	0.6800	0.7094	0.6818	0.6991	0.6678	0.6818	0.6818	0.6818
0.0022	35.0	1015	1.6009	0.6848	0.6894	0.6869	0.7171	0.6894	0.7062	0.6731	0.6894	0.6894	0.6894

Framework versions

• Transformers 4.48.2
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0