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: 2.4962
Accuracy: 0.3415

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: 0.001
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.501	1.0	28	1.3919	0.2439
1.3993	2.0	56	1.4008	0.2683
1.4258	3.0	84	1.4098	0.2439
1.4011	4.0	112	1.3674	0.2683
1.4153	5.0	140	1.3306	0.2683
1.3649	6.0	168	1.4784	0.2195
1.3525	7.0	196	1.2906	0.4390
1.3374	8.0	224	1.1798	0.5122
1.2661	9.0	252	1.3479	0.5122
1.3011	10.0	280	1.3054	0.4878
1.2212	11.0	308	1.1612	0.5122
1.2579	12.0	336	1.2572	0.2683
1.2438	13.0	364	1.1160	0.4634
1.2218	14.0	392	1.1291	0.4878
1.2455	15.0	420	1.4587	0.4390
1.2528	16.0	448	1.3009	0.5122
1.2445	17.0	476	1.1915	0.5122
1.1729	18.0	504	1.3461	0.4390
1.2917	19.0	532	1.3956	0.3659
1.2335	20.0	560	1.1161	0.4146
1.1787	21.0	588	1.4220	0.4390
1.1076	22.0	616	1.2157	0.5122
1.1837	23.0	644	1.2878	0.4634
1.065	24.0	672	1.3373	0.3659
1.0753	25.0	700	1.2968	0.4634
1.0288	26.0	728	1.2996	0.4146
1.0679	27.0	756	1.2975	0.3902
1.0591	28.0	784	1.3051	0.4634
1.0148	29.0	812	1.2575	0.5854
1.0668	30.0	840	1.3174	0.3415
0.9767	31.0	868	1.3259	0.4390
0.9254	32.0	896	1.3236	0.4878
0.9064	33.0	924	1.5265	0.3902
0.9504	34.0	952	1.2456	0.4390
0.8534	35.0	980	1.2811	0.5122
0.8361	36.0	1008	1.2101	0.6098
0.7846	37.0	1036	1.3727	0.4390
0.7661	38.0	1064	1.4030	0.4878
0.8237	39.0	1092	1.3385	0.4634
0.7652	40.0	1120	1.6174	0.4146
0.6764	41.0	1148	1.6358	0.4390
0.5675	42.0	1176	1.7675	0.4390
0.5777	43.0	1204	1.8573	0.4390
0.5704	44.0	1232	2.0252	0.3902
0.5677	45.0	1260	2.0725	0.3902
0.4676	46.0	1288	2.4159	0.3171
0.4167	47.0	1316	2.4083	0.3415
0.416	48.0	1344	2.4826	0.3415
0.3715	49.0	1372	2.4962	0.3415
0.368	50.0	1400	2.4962	0.3415

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

hkivancoral/hushem_5x_beit_base_rms_001_fold5

hushem_5x_beit_base_rms_001_fold5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
hushem_5x_beit_base_rms_001_fold5