This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.5286
F1 Macro: 0.4619
F1 Micro: 0.5455
F1 Weighted: 0.5156
Precision Macro: 0.4696
Precision Micro: 0.5455
Precision Weighted: 0.5176
Recall Macro: 0.4841
Recall Micro: 0.5455
Recall Weighted: 0.5455
Accuracy: 0.5455

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: 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
1.934	1.0	58	1.8780	0.0664	0.2045	0.0901	0.1708	0.2045	0.2415	0.1534	0.2045	0.2045	0.2045
1.8145	2.0	116	1.8828	0.0691	0.1742	0.0755	0.0608	0.1742	0.0658	0.1575	0.1742	0.1742	0.1742
1.8527	3.0	174	1.7131	0.2503	0.3788	0.3053	0.2573	0.3788	0.3062	0.3094	0.3788	0.3788	0.3788
1.6734	4.0	232	1.7940	0.1621	0.2803	0.2087	0.2145	0.2803	0.2624	0.2076	0.2803	0.2803	0.2803
1.6408	5.0	290	1.6808	0.1570	0.3333	0.1965	0.1432	0.3333	0.1858	0.2702	0.3333	0.3333	0.3333
1.5696	6.0	348	1.5061	0.3172	0.4470	0.3802	0.3895	0.4470	0.4186	0.3618	0.4470	0.4470	0.4470
1.4543	7.0	406	1.3674	0.4113	0.5152	0.4708	0.4077	0.5152	0.4630	0.4479	0.5152	0.5152	0.5152
1.2349	8.0	464	1.3137	0.4024	0.5	0.4550	0.4050	0.5	0.4606	0.4479	0.5	0.5	0.5
1.2544	9.0	522	1.3322	0.4209	0.5076	0.4748	0.4224	0.5076	0.4737	0.4480	0.5076	0.5076	0.5076
1.206	10.0	580	1.3818	0.3555	0.4621	0.4009	0.3931	0.4621	0.4372	0.4129	0.4621	0.4621	0.4621
1.0416	11.0	638	1.3142	0.4610	0.5606	0.5249	0.5218	0.5606	0.5872	0.4951	0.5606	0.5606	0.5606
1.1494	12.0	696	1.3793	0.4106	0.4773	0.4652	0.4619	0.4773	0.5256	0.4227	0.4773	0.4773	0.4773
0.7366	13.0	754	1.1936	0.5656	0.6515	0.6383	0.5708	0.6515	0.6446	0.5790	0.6515	0.6515	0.6515
1.3729	14.0	812	1.2285	0.5151	0.6061	0.5861	0.5714	0.6061	0.6314	0.5225	0.6061	0.6061	0.6061
1.3638	15.0	870	1.1742	0.5389	0.6212	0.6055	0.5617	0.6212	0.6334	0.5513	0.6212	0.6212	0.6212
0.9063	16.0	928	1.2325	0.5079	0.5985	0.5770	0.5077	0.5985	0.5715	0.5215	0.5985	0.5985	0.5985
0.4584	17.0	986	1.1497	0.5515	0.6364	0.6210	0.5676	0.6364	0.6286	0.5575	0.6364	0.6364	0.6364
0.86	18.0	1044	1.2673	0.4925	0.5909	0.5719	0.4968	0.5909	0.5681	0.5031	0.5909	0.5909	0.5909
0.2113	19.0	1102	1.2132	0.5180	0.6212	0.5986	0.5386	0.6212	0.6049	0.5257	0.6212	0.6212	0.6212
0.1168	20.0	1160	1.2442	0.5543	0.6136	0.6070	0.5742	0.6136	0.6164	0.5517	0.6136	0.6136	0.6136
0.3149	21.0	1218	1.2900	0.5446	0.6288	0.6146	0.5463	0.6288	0.6120	0.5534	0.6288	0.6288	0.6288
0.0793	22.0	1276	1.3290	0.5692	0.6288	0.6210	0.5960	0.6288	0.6359	0.5651	0.6288	0.6288	0.6288
0.1761	23.0	1334	1.4284	0.5572	0.6212	0.6032	0.6454	0.6212	0.6563	0.5516	0.6212	0.6212	0.6212
0.1714	24.0	1392	1.2994	0.5782	0.6288	0.6344	0.5899	0.6288	0.6461	0.5728	0.6288	0.6288	0.6288
0.465	25.0	1450	1.4011	0.5581	0.6136	0.6134	0.5662	0.6136	0.6188	0.5556	0.6136	0.6136	0.6136
0.2203	26.0	1508	1.4701	0.5741	0.6288	0.6266	0.6167	0.6288	0.6553	0.5676	0.6288	0.6288	0.6288
0.0574	27.0	1566	1.4511	0.5800	0.6364	0.6352	0.6073	0.6364	0.6546	0.5738	0.6364	0.6364	0.6364
0.0399	28.0	1624	1.4921	0.5674	0.6061	0.6133	0.5933	0.6061	0.6390	0.5645	0.6061	0.6061	0.6061
0.0269	29.0	1682	1.4752	0.5563	0.6288	0.6283	0.5686	0.6288	0.6350	0.5515	0.6288	0.6288	0.6288
0.0267	30.0	1740	1.5353	0.5621	0.6136	0.6142	0.5859	0.6136	0.6324	0.5565	0.6136	0.6136	0.6136
0.1094	31.0	1798	1.5126	0.5912	0.6515	0.6529	0.6028	0.6515	0.6604	0.5867	0.6515	0.6515	0.6515
0.0243	32.0	1856	1.4900	0.5985	0.6591	0.6563	0.6103	0.6591	0.6604	0.5935	0.6591	0.6591	0.6591
0.0366	33.0	1914	1.4680	0.6275	0.6894	0.6851	0.6369	0.6894	0.6855	0.6241	0.6894	0.6894	0.6894
0.0235	34.0	1972	1.4772	0.6216	0.6818	0.6795	0.6324	0.6818	0.6836	0.6173	0.6818	0.6818	0.6818
0.0345	35.0	2030	1.4754	0.6556	0.6970	0.6961	0.6722	0.6970	0.7038	0.6479	0.6970	0.6970	0.6970

Framework versions

Transformers 4.48.2
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

corranm/square_run_min_loss

square_run_min_loss

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

corranm/
square_run_min_loss