skin_cancer_classification

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the Pranavkpba2000/skin_cancer_dataset dataset. It achieves the following results on the evaluation set:

• Loss: 0.5151
• Accuracy: 0.8260

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: 32
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8727	0.11	100	0.9507	0.6690
0.7811	0.22	200	0.8702	0.6843
0.7222	0.34	300	0.7517	0.7319
0.7191	0.45	400	0.7081	0.7474
0.638	0.56	500	0.6894	0.7407
0.6001	0.67	600	0.7046	0.7565
0.5818	0.78	700	0.7262	0.7422
0.5796	0.9	800	0.6466	0.7681
0.4426	1.01	900	0.6218	0.7730
0.4675	1.12	1000	0.6566	0.7569
0.4604	1.23	1100	0.5782	0.7938
0.4426	1.35	1200	0.5478	0.7996
0.3967	1.46	1300	0.5586	0.8001
0.4287	1.57	1400	0.5743	0.7948
0.4066	1.68	1500	0.5839	0.7980
0.4107	1.79	1600	0.5630	0.8032
0.3894	1.91	1700	0.5591	0.8004
0.2612	2.02	1800	0.5176	0.8173
0.346	2.13	1900	0.5796	0.7986
0.2242	2.24	2000	0.5196	0.8239
0.2262	2.35	2100	0.5568	0.8103
0.2764	2.47	2200	0.5428	0.8156
0.2795	2.58	2300	0.5354	0.8234
0.3017	2.69	2400	0.5231	0.8197
0.3295	2.8	2500	0.5550	0.8120
0.3014	2.91	2600	0.5151	0.8260
0.1332	3.03	2700	0.5651	0.8182
0.1519	3.14	2800	0.5824	0.8277
0.1537	3.25	2900	0.6195	0.8281
0.187	3.36	3000	0.6482	0.8204
0.1428	3.48	3100	0.5512	0.8463
0.1743	3.59	3200	0.6152	0.8252
0.1765	3.7	3300	0.5390	0.8418
0.1612	3.81	3400	0.5550	0.8279
0.0938	3.92	3500	0.6105	0.8380
0.0698	4.04	3600	0.6672	0.8273
0.1087	4.15	3700	0.6583	0.8184
0.0558	4.26	3800	0.6141	0.8304
0.0841	4.37	3900	0.6157	0.8377
0.0662	4.48	4000	0.7013	0.8289
0.0883	4.6	4100	0.7606	0.8180
0.0721	4.71	4200	0.6966	0.8322
0.0332	4.82	4300	0.7743	0.8189
0.0994	4.93	4400	0.6767	0.8303
0.0362	5.04	4500	0.7643	0.8321
0.0507	5.16	4600	0.8140	0.8348
0.0322	5.27	4700	0.7166	0.8397
0.0219	5.38	4800	0.7363	0.8339
0.0346	5.49	4900	0.7343	0.8435
0.0638	5.61	5000	0.7396	0.8441
0.1143	5.72	5100	0.8020	0.8338
0.024	5.83	5200	0.7715	0.8446
0.0203	5.94	5300	0.7485	0.8439
0.0172	6.05	5400	0.7893	0.8467
0.0115	6.17	5500	0.7671	0.8505
0.0628	6.28	5600	0.7690	0.8477
0.0475	6.39	5700	0.7534	0.8488
0.0193	6.5	5800	0.8026	0.8497
0.0163	6.61	5900	0.7703	0.8562
0.0232	6.73	6000	0.8823	0.8471
0.0157	6.84	6100	0.7953	0.8435
0.0211	6.95	6200	0.8299	0.8391
0.0007	7.06	6300	0.7862	0.8532
0.0085	7.17	6400	0.8090	0.8548
0.0153	7.29	6500	0.8183	0.8576
0.0031	7.4	6600	0.8853	0.8425
0.0112	7.51	6700	0.8415	0.8574
0.0012	7.62	6800	0.8442	0.8569
0.0015	7.74	6900	0.8475	0.8564
0.0043	7.85	7000	0.8748	0.8452
0.0099	7.96	7100	0.8873	0.8411
0.0008	8.07	7200	0.8584	0.8559
0.0004	8.18	7300	0.8598	0.8522
0.0004	8.3	7400	0.8667	0.8536
0.0003	8.41	7500	0.8667	0.8576
0.0073	8.52	7600	0.8736	0.8602
0.0003	8.63	7700	0.8760	0.8566
0.0007	8.74	7800	0.9347	0.8540
0.0073	8.86	7900	0.9070	0.8532
0.0002	8.97	8000	0.9208	0.8536
0.0002	9.08	8100	0.9304	0.8555
0.0003	9.19	8200	0.9301	0.8546
0.0002	9.3	8300	0.9298	0.8557
0.0002	9.42	8400	0.9300	0.8557
0.0003	9.53	8500	0.9283	0.8560
0.0002	9.64	8600	0.9205	0.8562
0.0002	9.75	8700	0.9216	0.8566
0.0002	9.87	8800	0.9204	0.8556
0.0002	9.98	8900	0.9207	0.8555

Framework versions

• Transformers 4.39.3
• Pytorch 2.1.2
• Datasets 2.18.0
• Tokenizers 0.15.2