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

Loss: 0.2499
Accuracy: 0.9318

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: 5e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	2	1.0896	0.2955
No log	2.0	4	0.6456	0.7273
No log	3.0	6	1.0355	0.7045
No log	4.0	8	0.9124	0.7045
0.7607	5.0	10	0.5809	0.7955
0.7607	6.0	12	0.6812	0.75
0.7607	7.0	14	0.6529	0.75
0.7607	8.0	16	0.7174	0.7273
0.7607	9.0	18	0.6619	0.6136
0.4221	10.0	20	0.8063	0.75
0.4221	11.0	22	0.6372	0.75
0.4221	12.0	24	0.5886	0.75
0.4221	13.0	26	0.6359	0.6364
0.4221	14.0	28	0.5585	0.75
0.3287	15.0	30	0.4541	0.7273
0.3287	16.0	32	0.7624	0.5682
0.3287	17.0	34	0.6806	0.75
0.3287	18.0	36	0.7708	0.7273
0.3287	19.0	38	0.4170	0.7273
0.3663	20.0	40	0.4282	0.7727
0.3663	21.0	42	0.5613	0.75
0.3663	22.0	44	0.4025	0.8409
0.3663	23.0	46	0.4109	0.7955
0.3663	24.0	48	0.4373	0.8409
0.2344	25.0	50	0.3211	0.8864
0.2344	26.0	52	0.5561	0.75
0.2344	27.0	54	0.3149	0.8636
0.2344	28.0	56	0.3166	0.7955
0.2344	29.0	58	0.4164	0.8636
0.2051	30.0	60	0.4345	0.8636
0.2051	31.0	62	0.3180	0.8636
0.2051	32.0	64	0.3673	0.8409
0.2051	33.0	66	0.4313	0.8409
0.2051	34.0	68	0.4359	0.8409
0.1694	35.0	70	0.3700	0.8182
0.1694	36.0	72	0.5843	0.7955
0.1694	37.0	74	0.4064	0.8636
0.1694	38.0	76	0.3992	0.8182
0.1694	39.0	78	0.3153	0.8636
0.1566	40.0	80	0.5581	0.8182
0.1566	41.0	82	0.2921	0.8636
0.1566	42.0	84	0.3217	0.8864
0.1566	43.0	86	0.3255	0.8864
0.1566	44.0	88	0.7238	0.75
0.1389	45.0	90	0.4053	0.8864
0.1389	46.0	92	0.2499	0.9318
0.1389	47.0	94	0.2584	0.8864
0.1389	48.0	96	0.4432	0.8409
0.1389	49.0	98	0.6965	0.7955
0.1311	50.0	100	0.3910	0.8409
0.1311	51.0	102	0.3017	0.8636
0.1311	52.0	104	0.3050	0.8636
0.1311	53.0	106	0.2193	0.8636
0.1311	54.0	108	0.2369	0.8409
0.1386	55.0	110	0.3143	0.8864
0.1386	56.0	112	0.2932	0.8864
0.1386	57.0	114	0.2725	0.9091
0.1386	58.0	116	0.5664	0.8409
0.1386	59.0	118	0.5875	0.8182
0.1194	60.0	120	0.4623	0.8636
0.1194	61.0	122	0.4716	0.8182
0.1194	62.0	124	0.5028	0.8182
0.1194	63.0	126	0.4558	0.8182
0.1194	64.0	128	0.4798	0.8182
0.1122	65.0	130	0.3827	0.8409
0.1122	66.0	132	0.3653	0.8409
0.1122	67.0	134	0.3972	0.8409
0.1122	68.0	136	0.5705	0.7727
0.1122	69.0	138	0.5935	0.7727
0.1041	70.0	140	0.3905	0.8636
0.1041	71.0	142	0.2791	0.8409
0.1041	72.0	144	0.2845	0.9091
0.1041	73.0	146	0.2401	0.8636
0.1041	74.0	148	0.2260	0.8864
0.0982	75.0	150	0.2454	0.8864
0.0982	76.0	152	0.3773	0.8864
0.0982	77.0	154	0.6185	0.8182
0.0982	78.0	156	0.7238	0.7727
0.0982	79.0	158	0.5469	0.8409
0.1065	80.0	160	0.4318	0.8636
0.1065	81.0	162	0.3348	0.8864
0.1065	82.0	164	0.3041	0.8636
0.1065	83.0	166	0.3350	0.8864
0.1065	84.0	168	0.3464	0.8864
0.0829	85.0	170	0.3375	0.8864
0.0829	86.0	172	0.3309	0.8864
0.0829	87.0	174	0.3325	0.8864
0.0829	88.0	176	0.3441	0.8864
0.0829	89.0	178	0.3456	0.8636
0.0902	90.0	180	0.3244	0.8636
0.0902	91.0	182	0.3126	0.8636
0.0902	92.0	184	0.3117	0.8636
0.0902	93.0	186	0.2877	0.8636
0.0902	94.0	188	0.2643	0.8636
0.0838	95.0	190	0.2525	0.8864
0.0838	96.0	192	0.2462	0.9091
0.0838	97.0	194	0.2417	0.9091
0.0838	98.0	196	0.2402	0.9091
0.0838	99.0	198	0.2409	0.9091
0.0747	100.0	200	0.2426	0.9091

Framework versions

Transformers 4.40.2
Pytorch 2.2.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

BilalMuftuoglu/beit-base-patch16-224-85-fold5

beit-base-patch16-224-85-fold5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

BilalMuftuoglu/
beit-base-patch16-224-85-fold5