vit-base-binary-isic-patch-32

ahishamm/vit-base-binary-isic-patch-32

vit-base-binary-isic-patch-32 is a image classification model from ahishamm. 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: Jul 1, 2023
Updated: Jul 1, 2023
SHA: e46612d8

Model card

From the Hugging Face model README

vit-base-binary-isic-patch-32

This model is a fine-tuned version of google/vit-base-patch32-224-in21k on the ahishamm/isic_binary_augmented dataset. It achieves the following results on the evaluation set:

Loss: 0.2618
Accuracy: 0.8897
Recall: 0.8897
F1: 0.8897
Precision: 0.8897

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
0.4617	0.09	100	0.4375	0.8083	0.8083	0.8083	0.8083
0.28	0.19	200	0.3352	0.8575	0.8575	0.8575	0.8575
0.2684	0.28	300	0.3081	0.8521	0.8521	0.8521	0.8521
0.2755	0.37	400	0.3489	0.8433	0.8433	0.8433	0.8433
0.2683	0.46	500	0.3533	0.8120	0.8120	0.8120	0.8120
0.2013	0.56	600	0.3404	0.8683	0.8683	0.8683	0.8683
0.2892	0.65	700	0.2950	0.8688	0.8688	0.8688	0.8688
0.2326	0.74	800	0.2858	0.8875	0.8875	0.8875	0.8875
0.2642	0.84	900	0.3176	0.8666	0.8666	0.8666	0.8666
0.1676	0.93	1000	0.3049	0.8726	0.8726	0.8726	0.8726
0.1002	1.02	1100	0.3512	0.8797	0.8797	0.8797	0.8797
0.1888	1.12	1200	0.3286	0.8566	0.8566	0.8566	0.8566
0.134	1.21	1300	0.3178	0.8661	0.8661	0.8661	0.8661
0.1085	1.3	1400	0.2618	0.8897	0.8897	0.8897	0.8897
0.189	1.39	1500	0.3490	0.8737	0.8737	0.8737	0.8737
0.1781	1.49	1600	0.3033	0.8728	0.8728	0.8728	0.8728
0.0788	1.58	1700	0.3623	0.8774	0.8774	0.8774	0.8774
0.1135	1.67	1800	0.3341	0.8688	0.8688	0.8688	0.8688
0.0607	1.77	1900	0.2954	0.8874	0.8874	0.8874	0.8874
0.058	1.86	2000	0.3921	0.8780	0.8780	0.8780	0.8780
0.0873	1.95	2100	0.3094	0.8883	0.8883	0.8883	0.8883
0.0584	2.04	2200	0.3688	0.8892	0.8892	0.8892	0.8892
0.0978	2.14	2300	0.3874	0.8916	0.8916	0.8916	0.8916
0.0958	2.23	2400	0.3679	0.8927	0.8927	0.8927	0.8927
0.0559	2.32	2500	0.4649	0.8874	0.8874	0.8874	0.8874
0.0125	2.42	2600	0.4350	0.8943	0.8943	0.8943	0.8943
0.0636	2.51	2700	0.4195	0.8906	0.8906	0.8906	0.8906
0.0458	2.6	2800	0.4127	0.8980	0.8980	0.8980	0.8980
0.055	2.7	2900	0.4086	0.8993	0.8993	0.8993	0.8993
0.03	2.79	3000	0.3710	0.9013	0.9013	0.9013	0.9013
0.027	2.88	3100	0.4148	0.8940	0.8940	0.8940	0.8940
0.0384	2.97	3200	0.4036	0.8938	0.8938	0.8938	0.8938
0.0095	3.07	3300	0.4830	0.8892	0.8892	0.8892	0.8892
0.0489	3.16	3400	0.4590	0.8945	0.8945	0.8945	0.8945
0.0198	3.25	3500	0.4732	0.8972	0.8972	0.8972	0.8972
0.0604	3.35	3600	0.5283	0.8916	0.8916	0.8916	0.8916
0.0028	3.44	3700	0.5160	0.8931	0.8931	0.8931	0.8931
0.0018	3.53	3800	0.5038	0.8966	0.8966	0.8966	0.8966
0.0192	3.62	3900	0.5112	0.8975	0.8975	0.8975	0.8975
0.0455	3.72	4000	0.4954	0.8985	0.8985	0.8985	0.8985
0.0014	3.81	4100	0.4984	0.8994	0.8994	0.8994	0.8994
0.0012	3.9	4200	0.4987	0.9007	0.9007	0.9007	0.9007
0.0009	4.0	4300	0.5004	0.8998	0.8998	0.8998	0.8998

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Jul 1, 2023
Updated: Jul 1, 2023