This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the ahishamm/HAM_db_enhanced_balanced_reduced_50_20_20_50 dataset. It achieves the following results on the evaluation set:

Loss: 0.5296
Accuracy: 0.8344
Recall: 0.8344
F1: 0.8344
Precision: 0.8344

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: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	F1	Precision
1.4855	0.12	50	1.3519	0.5093	0.5093	0.5093	0.5093
1.044	0.23	100	1.0515	0.6268	0.6268	0.6268	0.6268
1.0774	0.35	150	1.2104	0.5681	0.5681	0.5681	0.5681
0.9508	0.46	200	1.0624	0.6061	0.6061	0.6061	0.6061
0.9522	0.58	250	0.9338	0.6449	0.6449	0.6449	0.6449
0.774	0.69	300	0.8939	0.6676	0.6676	0.6676	0.6676
0.7675	0.81	350	0.7742	0.7183	0.7183	0.7183	0.7183
0.7167	0.92	400	0.7695	0.7216	0.7216	0.7216	0.7216
0.5204	1.04	450	0.8005	0.7303	0.7303	0.7303	0.7303
0.456	1.15	500	0.8523	0.6903	0.6903	0.6903	0.6903
0.5421	1.27	550	0.6753	0.7543	0.7543	0.7543	0.7543
0.4446	1.38	600	0.6042	0.7810	0.7810	0.7810	0.7810
0.455	1.5	650	0.6913	0.7410	0.7410	0.7410	0.7410
0.4175	1.61	700	0.6142	0.7810	0.7810	0.7810	0.7810
0.3626	1.73	750	0.5831	0.8004	0.8004	0.8004	0.8004
0.4816	1.84	800	0.5586	0.7891	0.7891	0.7891	0.7891
0.3257	1.96	850	0.5759	0.7991	0.7991	0.7991	0.7991
0.3111	2.07	900	0.6100	0.7931	0.7931	0.7931	0.7931
0.2052	2.19	950	0.5674	0.8111	0.8111	0.8111	0.8111
0.2273	2.3	1000	0.5975	0.8017	0.8017	0.8017	0.8017
0.3007	2.42	1050	0.5714	0.8204	0.8204	0.8204	0.8204
0.2812	2.53	1100	0.6081	0.8004	0.8004	0.8004	0.8004
0.2661	2.65	1150	0.5653	0.8224	0.8224	0.8224	0.8224
0.1796	2.76	1200	0.5447	0.8338	0.8338	0.8338	0.8338
0.1882	2.88	1250	0.5357	0.8284	0.8284	0.8284	0.8284
0.1596	3.0	1300	0.5296	0.8344	0.8344	0.8344	0.8344
0.075	3.11	1350	0.5876	0.8198	0.8198	0.8198	0.8198
0.1128	3.23	1400	0.5612	0.8338	0.8338	0.8338	0.8338
0.0677	3.34	1450	0.5911	0.8331	0.8331	0.8331	0.8331
0.0794	3.46	1500	0.5971	0.8304	0.8304	0.8304	0.8304
0.0367	3.57	1550	0.5634	0.8378	0.8378	0.8378	0.8378
0.0279	3.69	1600	0.5674	0.8391	0.8391	0.8391	0.8391
0.0216	3.8	1650	0.5777	0.8358	0.8358	0.8358	0.8358
0.0161	3.92	1700	0.5608	0.8438	0.8438	0.8438	0.8438

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

ahishamm/vit-base-16-thesis-demo-HAM10000

vit-base-16-thesis-demo-HAM10000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ahishamm/
vit-base-16-thesis-demo-HAM10000