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ahishamm/
vit-base-binary-isic-sharpened-patch-16

Name: vit-base-binary-isic-sharpened-patch-16
Availability: InStock

generated_from_trainer

apache-2.0

autotrain_compatible

endpoints_compatible

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vit-base-binary-isic-sharpened-patch-16

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

Loss: 0.2583
Accuracy: 0.8912
Recall: 0.8912
F1: 0.8912
Precision: 0.8912

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

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	F1	Precision
0.3281	0.09	100	0.4381	0.8183	0.8183	0.8183	0.8183
0.3212	0.18	200	0.3179	0.8503	0.8503	0.8503	0.8503
0.2864	0.28	300	0.3126	0.8655	0.8655	0.8655	0.8655
0.2692	0.37	400	0.3217	0.8599	0.8599	0.8599	0.8599
0.3195	0.46	500	0.3061	0.8694	0.8694	0.8694	0.8694
0.2095	0.55	600	0.2910	0.8669	0.8669	0.8669	0.8669
0.2168	0.65	700	0.3248	0.8730	0.8730	0.8730	0.8730
0.2288	0.74	800	0.3067	0.8553	0.8553	0.8553	0.8553
0.2521	0.83	900	0.2723	0.8689	0.8689	0.8689	0.8689
0.1953	0.92	1000	0.2729	0.8724	0.8724	0.8724	0.8724
0.2845	1.02	1100	0.4392	0.8666	0.8666	0.8666	0.8666
0.1484	1.11	1200	0.3031	0.8884	0.8884	0.8884	0.8884
0.153	1.2	1300	0.2849	0.8992	0.8992	0.8992	0.8992
0.1648	1.29	1400	0.2583	0.8912	0.8912	0.8912	0.8912
0.1627	1.39	1500	0.2706	0.8933	0.8933	0.8933	0.8933
0.0943	1.48	1600	0.2783	0.9034	0.9034	0.9034	0.9034
0.0624	1.57	1700	0.2921	0.8926	0.8926	0.8926	0.8926
0.12	1.66	1800	0.2915	0.9006	0.9006	0.9006	0.9006
0.0735	1.76	1900	0.3103	0.8897	0.8897	0.8897	0.8897
0.0609	1.85	2000	0.3382	0.8971	0.8971	0.8971	0.8971
0.1645	1.94	2100	0.2675	0.8901	0.8901	0.8901	0.8901
0.0839	2.03	2200	0.3941	0.8962	0.8962	0.8962	0.8962
0.0571	2.13	2300	0.3888	0.9047	0.9047	0.9047	0.9047
0.0929	2.22	2400	0.3773	0.9009	0.9009	0.9009	0.9009
0.0378	2.31	2500	0.4577	0.9029	0.9029	0.9029	0.9029
0.0085	2.4	2600	0.3183	0.9203	0.9203	0.9203	0.9203
0.06	2.5	2700	0.3548	0.9126	0.9126	0.9126	0.9126
0.0139	2.59	2800	0.3213	0.9198	0.9198	0.9198	0.9198
0.056	2.68	2900	0.3558	0.9131	0.9131	0.9131	0.9131
0.0433	2.77	3000	0.3101	0.9215	0.9215	0.9215	0.9215
0.0074	2.87	3100	0.3140	0.9176	0.9176	0.9176	0.9176
0.0216	2.96	3200	0.3657	0.9186	0.9186	0.9186	0.9186
0.0118	3.05	3300	0.3722	0.9195	0.9195	0.9195	0.9195
0.0014	3.14	3400	0.4089	0.9141	0.9141	0.9141	0.9141
0.001	3.23	3500	0.4045	0.9189	0.9189	0.9189	0.9189
0.0009	3.33	3600	0.3932	0.9230	0.9230	0.9230	0.9230
0.0009	3.42	3700	0.4257	0.9174	0.9174	0.9174	0.9174
0.03	3.51	3800	0.3981	0.9222	0.9222	0.9222	0.9222
0.0007	3.6	3900	0.4211	0.9189	0.9189	0.9189	0.9189
0.0494	3.7	4000	0.4029	0.9207	0.9207	0.9207	0.9207
0.0009	3.79	4100	0.3951	0.9226	0.9226	0.9226	0.9226
0.0319	3.88	4200	0.3944	0.9221	0.9221	0.9221	0.9221
0.0013	3.97	4300	0.3894	0.9225	0.9225	0.9225	0.9225

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

ahishamm/vit-base-binary-isic-sharpened-patch-16

vit-base-binary-isic-sharpened-patch-16

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ahishamm/
vit-base-binary-isic-sharpened-patch-16