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

Loss: 1.4149
Accuracy: 0.7052

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: 0.0002
train_batch_size: 16
eval_batch_size: 32
seed: 42
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
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.7259	0.1209	100	2.6351	0.2344
2.4984	0.2418	200	2.4934	0.2759
2.4756	0.3628	300	2.4343	0.2956
2.3697	0.4837	400	2.2332	0.3569
2.3429	0.6046	500	2.2275	0.3449
2.2098	0.7255	600	2.1461	0.3740
2.097	0.8464	700	2.0719	0.4027
2.1721	0.9674	800	2.0249	0.4280
2.0066	1.0883	900	1.9906	0.4212
1.9893	1.2092	1000	1.9690	0.4250
1.9437	1.3301	1100	1.9122	0.4387
1.8935	1.4510	1200	1.8618	0.4482
1.947	1.5719	1300	1.8229	0.4520
1.983	1.6929	1400	1.8269	0.4554
1.8011	1.8138	1500	1.7483	0.4841
1.8234	1.9347	1600	1.8248	0.4529
1.6741	2.0556	1700	1.7227	0.4867
1.7061	2.1765	1800	1.7463	0.4837
1.5089	2.2975	1900	1.7026	0.4931
1.6389	2.4184	2000	1.6726	0.5073
1.5872	2.5393	2100	1.7186	0.4923
1.5651	2.6602	2200	1.6471	0.5171
1.54	2.7811	2300	1.6291	0.5189
1.6464	2.9021	2400	1.5212	0.5381
1.4522	3.0230	2500	1.5721	0.5300
1.3173	3.1439	2600	1.5308	0.5467
1.3144	3.2648	2700	1.5011	0.5608
1.38	3.3857	2800	1.5539	0.5394
1.2156	3.5067	2900	1.4841	0.5510
1.3692	3.6276	3000	1.4552	0.5656
1.4016	3.7485	3100	1.5167	0.5497
1.2821	3.8694	3200	1.4833	0.5690
1.1618	3.9903	3300	1.4224	0.5677
1.4415	4.1112	3400	1.4014	0.5870
1.1131	4.2322	3500	1.4399	0.5831
1.0672	4.3531	3600	1.4385	0.5668
1.2124	4.4740	3700	1.4179	0.5698
1.1765	4.5949	3800	1.3597	0.5943
1.0993	4.7158	3900	1.3415	0.6003
1.1414	4.8368	4000	1.3966	0.5968
1.1284	4.9577	4100	1.3665	0.5994
0.9258	5.0786	4200	1.3508	0.6165
0.684	5.1995	4300	1.3676	0.6058
1.0152	5.3204	4400	1.3588	0.6067
0.7438	5.4414	4500	1.3133	0.6170
0.8849	5.5623	4600	1.2907	0.6264
0.7456	5.6832	4700	1.4062	0.6058
1.1013	5.8041	4800	1.3282	0.6110
0.982	5.9250	4900	1.2998	0.6243
0.7012	6.0459	5000	1.3006	0.6230
0.6848	6.1669	5100	1.3672	0.6191
0.7298	6.2878	5200	1.3138	0.6290
0.542	6.4087	5300	1.3664	0.6217
0.7623	6.5296	5400	1.3301	0.6354
1.0472	6.6505	5500	1.2836	0.6298
0.964	6.7715	5600	1.3024	0.6345
0.7213	6.8924	5700	1.3025	0.6401
0.6109	7.0133	5800	1.3086	0.6384
0.5563	7.1342	5900	1.3405	0.6307
0.4472	7.2551	6000	1.2843	0.6470
0.5637	7.3761	6100	1.3159	0.6255
0.6429	7.4970	6200	1.3515	0.6298
0.4535	7.6179	6300	1.3600	0.6320
0.4351	7.7388	6400	1.3419	0.6431
0.6521	7.8597	6500	1.3131	0.6384
0.6632	7.9807	6600	1.3271	0.6320
0.6364	8.1016	6700	1.3336	0.6440
0.3828	8.2225	6800	1.4081	0.6337
0.5726	8.3434	6900	1.3465	0.6487
0.5724	8.4643	7000	1.3892	0.6397
0.6399	8.5852	7100	1.4268	0.6238
0.4594	8.7062	7200	1.3526	0.6495
0.4738	8.8271	7300	1.3674	0.6470
0.5154	8.9480	7400	1.3398	0.6414
0.3716	9.0689	7500	1.3825	0.6487
0.4229	9.1898	7600	1.3579	0.6525
0.396	9.3108	7700	1.4205	0.6474
0.4992	9.4317	7800	1.3717	0.6534
0.5165	9.5526	7900	1.3134	0.6594
0.3848	9.6735	8000	1.3695	0.6620
0.4414	9.7944	8100	1.3554	0.6624
0.5408	9.9154	8200	1.3660	0.6620
0.3946	10.0363	8300	1.3243	0.6658
0.3157	10.1572	8400	1.3912	0.6542
0.385	10.2781	8500	1.3961	0.6602
0.3742	10.3990	8600	1.3357	0.6611
0.3976	10.5200	8700	1.3715	0.6632
0.355	10.6409	8800	1.3365	0.6722
0.5399	10.7618	8900	1.3486	0.6787
0.4398	10.8827	9000	1.2953	0.6769
0.2445	11.0036	9100	1.2773	0.6847
0.3286	11.1245	9200	1.3179	0.6727
0.1964	11.2455	9300	1.3526	0.6817
0.3503	11.3664	9400	1.3517	0.6795
0.2261	11.4873	9500	1.3236	0.6787
0.4133	11.6082	9600	1.3401	0.6744
0.3857	11.7291	9700	1.3169	0.6834
0.3831	11.8501	9800	1.3116	0.6782
0.3891	11.9710	9900	1.3644	0.6740
0.4093	12.0919	10000	1.3590	0.6748
0.5045	12.2128	10100	1.3527	0.6791
0.2819	12.3337	10200	1.3897	0.6740
0.2815	12.4547	10300	1.3712	0.6847
0.4357	12.5756	10400	1.3475	0.6787
0.318	12.6965	10500	1.3712	0.6859
0.2357	12.8174	10600	1.3942	0.6782
0.3216	12.9383	10700	1.3630	0.6808
0.2873	13.0593	10800	1.4015	0.6727
0.2433	13.1802	10900	1.3585	0.6872
0.2962	13.3011	11000	1.4138	0.6795
0.2134	13.4220	11100	1.3382	0.6834
0.2922	13.5429	11200	1.3553	0.6898
0.2562	13.6638	11300	1.3986	0.6855
0.1831	13.7848	11400	1.4005	0.6855
0.2235	13.9057	11500	1.3770	0.6855
0.2411	14.0266	11600	1.4194	0.6637
0.1687	14.1475	11700	1.3968	0.6804
0.1913	14.2684	11800	1.4210	0.6787
0.2395	14.3894	11900	1.4085	0.6718
0.111	14.5103	12000	1.4555	0.6812
0.1616	14.6312	12100	1.3750	0.6859
0.2003	14.7521	12200	1.3594	0.6954
0.313	14.8730	12300	1.3914	0.6877
0.2766	14.9940	12400	1.3821	0.6855
0.2937	15.1149	12500	1.3909	0.6889
0.2221	15.2358	12600	1.4073	0.6907
0.1867	15.3567	12700	1.4243	0.6825
0.2371	15.4776	12800	1.4190	0.6872
0.215	15.5985	12900	1.4330	0.6851
0.2075	15.7195	13000	1.4656	0.6812
0.1663	15.8404	13100	1.4386	0.6791
0.2015	15.9613	13200	1.4236	0.6868
0.2444	16.0822	13300	1.4427	0.6872
0.2799	16.2031	13400	1.4151	0.6881
0.1378	16.3241	13500	1.4102	0.6949
0.2701	16.4450	13600	1.3858	0.7018
0.2951	16.5659	13700	1.4027	0.6954
0.1788	16.6868	13800	1.4067	0.6949
0.185	16.8077	13900	1.4164	0.6889
0.241	16.9287	14000	1.3851	0.7001
0.2172	17.0496	14100	1.4145	0.6924
0.1449	17.1705	14200	1.3958	0.6979
0.21	17.2914	14300	1.3992	0.6924
0.2003	17.4123	14400	1.3995	0.7027
0.1851	17.5333	14500	1.3837	0.7001
0.0763	17.6542	14600	1.3951	0.6949
0.2952	17.7751	14700	1.4049	0.6945
0.1609	17.8960	14800	1.4123	0.6932
0.1816	18.0169	14900	1.4050	0.6984
0.1211	18.1378	15000	1.4065	0.6962
0.1513	18.2588	15100	1.4139	0.6937
0.1249	18.3797	15200	1.4142	0.6988
0.1939	18.5006	15300	1.4139	0.7018
0.0724	18.6215	15400	1.4093	0.7018
0.2841	18.7424	15500	1.4191	0.6988
0.2753	18.8634	15600	1.4229	0.6954
0.0368	18.9843	15700	1.4186	0.6937
0.0901	19.1052	15800	1.4220	0.6979
0.153	19.2261	15900	1.4193	0.6954
0.1448	19.3470	16000	1.4176	0.6988
0.157	19.4680	16100	1.4154	0.7018
0.1827	19.5889	16200	1.4165	0.7014
0.0809	19.7098	16300	1.4149	0.7052
0.1651	19.8307	16400	1.4129	0.7044
0.1256	19.9516	16500	1.4133	0.7044

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

WahajRaza/finetuned-dermnet

finetuned-dermnet

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

WahajRaza/
finetuned-dermnet