vit-base-skin

khleeloo/vit-base-skin

vit-base-skin is a image classification model from khleeloo. 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. --

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At a glance

Task: Image Classification
Library: transformers
License: apache-2.0
Model type: vit
Access: Public
Created: Oct 18, 2023
Updated: Oct 27, 2023
SHA: 32567ef7

Model card

From the Hugging Face model README

vit-base-skin

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

Loss: 0.6917
Accuracy: 0.8549
F1: 0.8552
Precision: 0.8560
Recall: 0.8549

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: 6
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.9322	0.16	100	0.8109	0.6943	0.6290	0.5939	0.6943
0.7518	0.32	200	0.6722	0.7409	0.6832	0.6945	0.7409
0.6616	0.48	300	0.7126	0.7358	0.7077	0.7039	0.7358
0.8264	0.64	400	0.6001	0.8135	0.8092	0.8178	0.8135
0.5767	0.8	500	0.6306	0.7772	0.7619	0.7945	0.7772
0.5939	0.96	600	0.4621	0.8290	0.7988	0.8397	0.8290
0.4351	1.12	700	0.5544	0.7979	0.7894	0.8410	0.7979
0.4737	1.28	800	0.5151	0.8238	0.8334	0.8708	0.8238
0.428	1.44	900	0.4980	0.8238	0.8170	0.8299	0.8238
0.4596	1.6	1000	0.5650	0.7927	0.8032	0.8428	0.7927
0.4096	1.76	1100	0.4544	0.8342	0.8178	0.8567	0.8342
0.4328	1.92	1200	0.4524	0.8290	0.8294	0.8482	0.8290
0.2272	2.08	1300	0.4808	0.8290	0.8304	0.8409	0.8290
0.2415	2.24	1400	0.5585	0.7927	0.7916	0.8057	0.7927
0.2743	2.4	1500	0.4144	0.8497	0.8484	0.8497	0.8497
0.1943	2.56	1600	0.3977	0.8705	0.8722	0.8761	0.8705
0.1839	2.72	1700	0.4784	0.8394	0.8382	0.8517	0.8394
0.1905	2.88	1800	0.4314	0.8653	0.8669	0.8724	0.8653
0.111	3.04	1900	0.5080	0.8290	0.8309	0.8348	0.8290
0.0872	3.19	2000	0.5320	0.8549	0.8520	0.8649	0.8549
0.1169	3.35	2100	0.5110	0.8342	0.8386	0.8477	0.8342
0.1181	3.51	2200	0.4916	0.8446	0.8482	0.8563	0.8446
0.0879	3.67	2300	0.5428	0.8601	0.8657	0.8829	0.8601
0.1896	3.83	2400	0.5534	0.8497	0.8484	0.8536	0.8497
0.0794	3.99	2500	0.6542	0.8342	0.8259	0.8270	0.8342
0.0398	4.15	2600	0.5962	0.8187	0.8243	0.8338	0.8187
0.0512	4.31	2700	0.6286	0.8497	0.8447	0.8457	0.8497
0.0106	4.47	2800	0.6446	0.8394	0.8372	0.8377	0.8394
0.0058	4.63	2900	0.5754	0.8653	0.8616	0.8618	0.8653
0.0268	4.79	3000	0.5966	0.8653	0.8651	0.8658	0.8653
0.0146	4.95	3100	0.6707	0.8601	0.8535	0.8577	0.8601
0.0325	5.11	3200	0.6543	0.8549	0.8518	0.8511	0.8549
0.0063	5.27	3300	0.6780	0.8497	0.8519	0.8583	0.8497
0.003	5.43	3400	0.6675	0.8601	0.8577	0.8562	0.8601
0.0143	5.59	3500	0.6967	0.8601	0.8554	0.8539	0.8601
0.004	5.75	3600	0.6992	0.8601	0.8573	0.8552	0.8601
0.003	5.91	3700	0.6917	0.8549	0.8552	0.8560	0.8549

Framework versions

Transformers 4.29.2
Pytorch 1.13.1
Datasets 2.14.5
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Oct 18, 2023
Updated: Oct 27, 2023