README.md

metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
metrics:
  - accuracy
  - recall
  - f1
  - precision
model-index:
  - name: vit-base-binary-isic-sharpened-patch-16
    results: []

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