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

Loss: 0.5205
Accuracy: 0.8642
Precision: 0.8742
Recall: 0.8642
F1: 0.8636

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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.3382	0.1667	100	0.7037	0.7592	0.8533	0.7592	0.7413
0.2441	0.3333	200	0.5509	0.8167	0.8354	0.8167	0.8179
0.1033	0.5	300	0.5433	0.8508	0.8663	0.8508	0.8492
0.0863	0.6667	400	0.5815	0.8104	0.8328	0.8104	0.7969
0.1032	0.8333	500	0.7683	0.7908	0.8394	0.7908	0.7771
0.0681	1.0	600	0.6216	0.8392	0.8451	0.8392	0.8393
0.0098	1.1667	700	0.8241	0.8087	0.8317	0.8087	0.8010
0.1486	1.3333	800	0.5205	0.8642	0.8742	0.8642	0.8636
0.0552	1.5	900	0.8228	0.8092	0.8290	0.8092	0.8074
0.1194	1.6667	1000	0.9466	0.7479	0.8266	0.7479	0.7067
0.1081	1.8333	1100	0.7999	0.8379	0.8586	0.8379	0.8334
0.0024	2.0	1200	0.8330	0.8438	0.8629	0.8438	0.8434
0.0799	2.1667	1300	0.7392	0.8588	0.8771	0.8588	0.8560
0.0018	2.3333	1400	0.9487	0.8158	0.8222	0.8158	0.8153
0.0052	2.5	1500	0.6795	0.8712	0.8739	0.8712	0.8678
0.0012	2.6667	1600	0.7281	0.8821	0.8859	0.8821	0.8812
0.0022	2.8333	1700	1.2392	0.795	0.7874	0.795	0.7857
0.0835	3.0	1800	1.0174	0.8163	0.8503	0.8163	0.8178
0.063	3.1667	1900	0.6986	0.8275	0.8288	0.8275	0.8258
0.0124	3.3333	2000	1.3449	0.7354	0.7889	0.7354	0.7215
0.0751	3.5	2100	0.9783	0.8292	0.8578	0.8292	0.8224
0.0089	3.6667	2200	0.6416	0.8871	0.8909	0.8871	0.8851
0.0833	3.8333	2300	0.9829	0.8025	0.8282	0.8025	0.8019
0.024	4.0	2400	0.7989	0.8508	0.8659	0.8508	0.8475
0.0221	4.1667	2500	0.6812	0.8842	0.8845	0.8842	0.8837
0.0005	4.3333	2600	0.9451	0.8429	0.8614	0.8429	0.8360
0.0005	4.5	2700	0.6669	0.8875	0.8882	0.8875	0.8865
0.0005	4.6667	2800	1.2303	0.8017	0.8330	0.8017	0.7984
0.0071	4.8333	2900	0.7767	0.8725	0.8790	0.8725	0.8725
0.1049	5.0	3000	0.7006	0.8646	0.8834	0.8646	0.8665
0.0761	5.1667	3100	0.7335	0.8892	0.8912	0.8892	0.8867
0.0007	5.3333	3200	0.6957	0.8867	0.8934	0.8867	0.8861
0.0006	5.5	3300	0.7774	0.8629	0.8739	0.8629	0.8637
0.0387	5.6667	3400	1.3677	0.7971	0.8275	0.7971	0.7944
0.0032	5.8333	3500	0.7322	0.8729	0.8836	0.8729	0.8710
0.0008	6.0	3600	0.9531	0.8517	0.8768	0.8517	0.8438
0.0014	6.1667	3700	0.8285	0.8654	0.8687	0.8654	0.8632
0.0004	6.3333	3800	0.7225	0.8875	0.8897	0.8875	0.8865
0.0009	6.5	3900	0.8248	0.87	0.8797	0.87	0.8705
0.0003	6.6667	4000	0.8972	0.8658	0.8805	0.8658	0.8665
0.0002	6.8333	4100	0.8997	0.8654	0.8800	0.8654	0.8662
0.0002	7.0	4200	0.8968	0.8667	0.8808	0.8667	0.8674
0.0002	7.1667	4300	0.8712	0.8725	0.8839	0.8725	0.8728
0.0002	7.3333	4400	0.8688	0.8838	0.8971	0.8838	0.8827
0.0002	7.5	4500	0.8917	0.8712	0.8818	0.8712	0.8686
0.0477	7.6667	4600	0.8017	0.8692	0.8832	0.8692	0.8703
0.0002	7.8333	4700	0.9936	0.85	0.8654	0.85	0.8445
0.0004	8.0	4800	0.9378	0.8396	0.8719	0.8396	0.8411
0.0007	8.1667	4900	1.2102	0.8013	0.8376	0.8013	0.7975
0.0004	8.3333	5000	0.7613	0.8883	0.9041	0.8883	0.8885
0.0005	8.5	5100	0.9156	0.8571	0.8821	0.8571	0.8573
0.0002	8.6667	5200	0.6973	0.8996	0.9065	0.8996	0.8969
0.0002	8.8333	5300	0.9252	0.8625	0.8938	0.8625	0.8636
0.0002	9.0	5400	0.7714	0.8854	0.9038	0.8854	0.8857
0.0001	9.1667	5500	0.7521	0.8892	0.9048	0.8892	0.8893
0.0002	9.3333	5600	0.7296	0.8971	0.9053	0.8971	0.8961
0.0002	9.5	5700	0.8592	0.8812	0.8882	0.8812	0.8807
0.027	9.6667	5800	1.0926	0.8346	0.8684	0.8346	0.8350
0.0002	9.8333	5900	0.8884	0.8654	0.8749	0.8654	0.8650
0.0255	10.0	6000	0.8784	0.8708	0.8809	0.8708	0.8704
0.0002	10.1667	6100	1.2491	0.7992	0.8409	0.7992	0.7816
0.0003	10.3333	6200	0.6981	0.8796	0.8850	0.8796	0.8776
0.0002	10.5	6300	0.8654	0.8725	0.8861	0.8725	0.8679
0.0002	10.6667	6400	0.5566	0.9012	0.9041	0.9012	0.8998
0.0002	10.8333	6500	0.6042	0.9025	0.9048	0.9025	0.9010
0.0002	11.0	6600	0.6078	0.9042	0.9062	0.9042	0.9027
0.0001	11.1667	6700	0.6105	0.9046	0.9066	0.9046	0.9030
0.0001	11.3333	6800	0.6138	0.9025	0.9047	0.9025	0.9010
0.0001	11.5	6900	0.6188	0.9025	0.9047	0.9025	0.9010
0.0001	11.6667	7000	0.6243	0.9017	0.9038	0.9017	0.9001
0.0001	11.8333	7100	0.6208	0.8992	0.9001	0.8992	0.8982
0.0067	12.0	7200	0.7476	0.8846	0.8948	0.8846	0.8835
0.0139	12.1667	7300	0.6116	0.9025	0.9042	0.9025	0.9013
0.0001	12.3333	7400	0.6976	0.8971	0.9053	0.8971	0.8962
0.0001	12.5	7500	0.7213	0.8946	0.9041	0.8946	0.8938
0.0001	12.6667	7600	0.7205	0.8954	0.9047	0.8954	0.8946
0.0001	12.8333	7700	0.6671	0.9029	0.9075	0.9029	0.9008
0.0001	13.0	7800	0.6448	0.9071	0.9130	0.9071	0.9059
0.0001	13.1667	7900	0.6449	0.9071	0.9130	0.9071	0.9059
0.0001	13.3333	8000	0.6453	0.9071	0.9130	0.9071	0.9059
0.0001	13.5	8100	0.6340	0.9087	0.9136	0.9087	0.9075
0.0001	13.6667	8200	0.6347	0.9087	0.9136	0.9087	0.9075
0.0001	13.8333	8300	0.6350	0.9092	0.9141	0.9092	0.9079
0.0001	14.0	8400	0.6355	0.9096	0.9144	0.9096	0.9084
0.0001	14.1667	8500	0.6358	0.9092	0.9139	0.9092	0.9080
0.0001	14.3333	8600	0.6360	0.9092	0.9139	0.9092	0.9080
0.0001	14.5	8700	0.6363	0.9092	0.9139	0.9092	0.9080
0.0001	14.6667	8800	0.6365	0.9096	0.9143	0.9096	0.9084
0.0001	14.8333	8900	0.6367	0.9096	0.9143	0.9096	0.9084
0.0001	15.0	9000	0.6369	0.9096	0.9143	0.9096	0.9084

Framework versions

Transformers 4.48.2
Pytorch 2.6.0+cu126
Datasets 3.1.0
Tokenizers 0.21.0

Ivanrs/vit-base-kidney-stone-2-Jonathan_El-Beze_-w256_1k_v1-_MIX

vit-base-kidney-stone-2-Jonathan_El-Beze_-w256_1k_v1-_MIX

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Ivanrs/
vit-base-kidney-stone-2-Jonathan_El-Beze_-w256_1k_v1-_MIX