skin_cancer_classification

karoladelk/skin_cancer_classification

skin_cancer_classification is a image classification model from karoladelk. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

Downloads · 30 days

4% of all-time downloads

All-time downloads

376

Public

Parameters

85.8M

19.9 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors343 MB · 100%

At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Apr 22, 2024
Updated: Apr 22, 2024
SHA: 3aa168dd

Base models

google/vit-base-patch16-224-in21k

Model card

From the Hugging Face model README

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

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Apr 22, 2024
Updated: Apr 22, 2024