square_run_first_vote_full_pic_75_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: 1.7554
• F1 Macro: 0.3377
• F1 Micro: 0.4091
• F1 Weighted: 0.3702
• Precision Macro: 0.3432
• Precision Micro: 0.4091
• Precision Weighted: 0.3813
• Recall Macro: 0.3764
• Recall Micro: 0.4091
• Recall Weighted: 0.4091
• Accuracy: 0.4091

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
1.9621	1.0	58	1.9294	0.1188	0.2121	0.1307	0.0924	0.2121	0.1040	0.2009	0.2121	0.2121	0.2121
1.6897	2.0	116	1.8039	0.2212	0.3258	0.2698	0.2607	0.3258	0.3321	0.2817	0.3258	0.3258	0.3258
1.9018	3.0	174	1.8333	0.1488	0.2727	0.1810	0.1261	0.2727	0.1499	0.2142	0.2727	0.2727	0.2727
1.3206	4.0	232	1.8180	0.1941	0.2955	0.2237	0.2405	0.2955	0.2639	0.2493	0.2955	0.2955	0.2955
1.7794	5.0	290	1.4852	0.3258	0.4167	0.3866	0.3195	0.4167	0.3831	0.3576	0.4167	0.4167	0.4167
1.7676	6.0	348	1.4509	0.4341	0.4924	0.4845	0.4298	0.4924	0.4816	0.4437	0.4924	0.4924	0.4924
1.6212	7.0	406	1.5437	0.4281	0.4773	0.4424	0.4381	0.4773	0.4653	0.4668	0.4773	0.4773	0.4773
1.5547	8.0	464	1.4854	0.3596	0.4242	0.4034	0.3810	0.4242	0.4362	0.3863	0.4242	0.4242	0.4242
1.5353	9.0	522	1.3877	0.4190	0.5076	0.4726	0.4442	0.5076	0.5046	0.4533	0.5076	0.5076	0.5076
0.7126	10.0	580	1.4099	0.4492	0.5227	0.5096	0.4685	0.5227	0.5359	0.4685	0.5227	0.5227	0.5227
1.0466	11.0	638	1.5217	0.4805	0.5682	0.5464	0.4933	0.5682	0.5566	0.4972	0.5682	0.5682	0.5682
0.648	12.0	696	1.5465	0.4666	0.5303	0.5284	0.4870	0.5303	0.5655	0.4872	0.5303	0.5303	0.5303
0.6292	13.0	754	1.5671	0.4553	0.5152	0.5116	0.5059	0.5152	0.5740	0.4652	0.5152	0.5152	0.5152
0.3081	14.0	812	1.5835	0.5087	0.5909	0.5700	0.5276	0.5909	0.5752	0.5130	0.5909	0.5909	0.5909
0.349	15.0	870	1.7548	0.4364	0.5076	0.4959	0.4563	0.5076	0.5064	0.4397	0.5076	0.5076	0.5076
0.2594	16.0	928	1.9070	0.4717	0.5455	0.5287	0.4803	0.5455	0.5289	0.4780	0.5455	0.5455	0.5455
0.2384	17.0	986	1.8439	0.5212	0.5909	0.5797	0.5462	0.5909	0.5900	0.5211	0.5909	0.5909	0.5909
0.1308	18.0	1044	2.0280	0.5064	0.5758	0.5659	0.5375	0.5758	0.5885	0.5074	0.5758	0.5758	0.5758
0.042	19.0	1102	1.9038	0.5345	0.6136	0.6004	0.5672	0.6136	0.6168	0.5360	0.6136	0.6136	0.6136
0.0605	20.0	1160	2.1862	0.5215	0.5909	0.5855	0.5231	0.5909	0.5867	0.5264	0.5909	0.5909	0.5909
0.0153	21.0	1218	2.1651	0.5037	0.5682	0.5637	0.5110	0.5682	0.5718	0.5059	0.5682	0.5682	0.5682
0.0069	22.0	1276	2.1574	0.4779	0.5455	0.5377	0.4819	0.5455	0.5367	0.4793	0.5455	0.5455	0.5455
0.0138	23.0	1334	2.3532	0.4938	0.5606	0.5544	0.4944	0.5606	0.5692	0.5119	0.5606	0.5606	0.5606
0.0016	24.0	1392	2.3575	0.4795	0.5606	0.5442	0.4724	0.5606	0.5342	0.4922	0.5606	0.5606	0.5606
0.2167	25.0	1450	2.4082	0.5045	0.5758	0.5691	0.5056	0.5758	0.5715	0.5127	0.5758	0.5758	0.5758
0.0024	26.0	1508	2.4224	0.5014	0.5682	0.5612	0.5003	0.5682	0.5623	0.5102	0.5682	0.5682	0.5682
0.0042	27.0	1566	2.3936	0.5133	0.5833	0.5745	0.5123	0.5833	0.5716	0.5189	0.5833	0.5833	0.5833
0.0008	28.0	1624	2.3829	0.5239	0.5985	0.5885	0.5309	0.5985	0.5892	0.5290	0.5985	0.5985	0.5985
0.0009	29.0	1682	2.4101	0.4891	0.5606	0.5532	0.4833	0.5606	0.5484	0.4976	0.5606	0.5606	0.5606
0.0009	30.0	1740	2.4118	0.5015	0.5758	0.5656	0.4970	0.5758	0.5602	0.5108	0.5758	0.5758	0.5758

Framework versions

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