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: 1.1935
Accuracy: 0.7099

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: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.2171	0.12	100	1.5449	0.5390
1.1934	0.24	200	1.5524	0.5330
1.0425	0.36	300	1.4836	0.5570
1.2195	0.48	400	1.5463	0.5428
1.2398	0.6	500	1.5507	0.5548
1.0992	0.73	600	1.4974	0.5578
0.9141	0.85	700	1.4526	0.5801
0.9695	0.97	800	1.4464	0.5741
0.8629	1.09	900	1.5265	0.5677
0.7845	1.21	1000	1.5754	0.5583
0.7901	1.33	1100	1.5343	0.5690
0.8336	1.45	1200	1.4265	0.5891
0.7639	1.57	1300	1.5037	0.5750
0.8555	1.69	1400	1.4346	0.6011
0.8874	1.81	1500	1.3850	0.6003
0.7824	1.93	1600	1.4507	0.5891
0.6257	2.06	1700	1.4597	0.5925
0.6028	2.18	1800	1.4626	0.6054
0.6019	2.3	1900	1.5333	0.5664
0.5468	2.42	2000	1.4553	0.6007
0.5237	2.54	2100	1.4363	0.6015
0.6603	2.66	2200	1.4913	0.5750
0.5703	2.78	2300	1.4628	0.6071
0.4992	2.9	2400	1.4719	0.6011
0.4853	3.02	2500	1.4663	0.5887
0.3463	3.14	2600	1.5019	0.5947
0.4537	3.26	2700	1.5044	0.6037
0.4989	3.39	2800	1.4753	0.6135
0.4843	3.51	2900	1.4336	0.6221
0.4864	3.63	3000	1.4612	0.6161
0.3936	3.75	3100	1.4786	0.6217
0.6484	3.87	3200	1.4947	0.6148
0.766	3.99	3300	1.4022	0.6255
0.5875	4.11	3400	1.3863	0.6315
0.6366	4.23	3500	1.4059	0.6418
0.4798	4.35	3600	1.3654	0.6362
0.5828	4.47	3700	1.4061	0.6260
0.5378	4.59	3800	1.3399	0.6560
0.5519	4.72	3900	1.3586	0.6350
0.6189	4.84	4000	1.3274	0.6465
0.6252	4.96	4100	1.2417	0.6650
0.4329	5.08	4200	1.3092	0.6628
0.5504	5.2	4300	1.3120	0.6547
0.5053	5.32	4400	1.3241	0.6482
0.4077	5.44	4500	1.2671	0.6684
0.5016	5.56	4600	1.3034	0.6641
0.4671	5.68	4700	1.3233	0.6525
0.5919	5.8	4800	1.3478	0.6607
0.5295	5.93	4900	1.3041	0.6577
0.3118	6.05	5000	1.2377	0.6731
0.3774	6.17	5100	1.2894	0.6607
0.405	6.29	5200	1.2821	0.6735
0.3187	6.41	5300	1.2697	0.6727
0.4335	6.53	5400	1.3005	0.6645
0.3935	6.65	5500	1.2890	0.6701
0.5328	6.77	5600	1.3079	0.6752
0.3797	6.89	5700	1.2841	0.6787
0.353	7.01	5800	1.2331	0.6808
0.3576	7.13	5900	1.2487	0.6787
0.3157	7.26	6000	1.2325	0.6834
0.3551	7.38	6100	1.2531	0.6817
0.261	7.5	6200	1.2243	0.6979
0.3384	7.62	6300	1.2787	0.6821
0.1776	7.74	6400	1.2401	0.7001
0.3227	7.86	6500	1.2233	0.6941
0.1673	7.98	6600	1.2653	0.6958
0.1985	8.1	6700	1.2421	0.6911
0.2384	8.22	6800	1.2494	0.6915
0.3055	8.34	6900	1.2675	0.6937
0.3417	8.46	7000	1.2517	0.6967
0.3827	8.59	7100	1.2827	0.6911
0.2781	8.71	7200	1.2234	0.6979
0.3134	8.83	7300	1.1935	0.7099
0.2248	8.95	7400	1.2028	0.7044
0.2491	9.07	7500	1.2043	0.7108
0.2153	9.19	7600	1.2054	0.7057
0.2619	9.31	7700	1.2102	0.7035
0.2425	9.43	7800	1.2161	0.7078
0.2068	9.55	7900	1.2068	0.7069
0.222	9.67	8000	1.2035	0.7091
0.0899	9.79	8100	1.2022	0.7112
0.2154	9.92	8200	1.1999	0.7108

Framework versions

Transformers 4.38.1
Pytorch 2.1.2
Datasets 2.1.0
Tokenizers 0.15.2

Muzmmillcoste/finetuned-dermnet

finetuned-dermnet

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Muzmmillcoste/
finetuned-dermnet