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

Loss: 1.1556
Accuracy: 0.9516
F1 Score: 0.9547
Recall: 0.9558

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: 1e-05
train_batch_size: 64
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 150
num_epochs: 100
label_smoothing_factor: 0.15

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Recall
3.0831	1.0	19	2.9877	0.1834	0.1506	0.1750
2.9160	2.0	38	2.7975	0.2734	0.2517	0.2583
2.5816	3.0	57	2.5431	0.4671	0.4271	0.4325
2.1999	4.0	76	2.2368	0.6263	0.6019	0.6120
1.8110	5.0	95	1.9213	0.7301	0.7278	0.7223
1.4822	6.0	114	1.6147	0.8304	0.8407	0.8357
1.2515	7.0	133	1.4442	0.8581	0.8646	0.8541
1.1471	8.0	152	1.3540	0.8789	0.8857	0.8784
1.0819	9.0	171	1.2801	0.8962	0.9021	0.8978
1.0613	10.0	190	1.2465	0.9100	0.9142	0.9124
1.0395	11.0	209	1.2235	0.9343	0.9386	0.9387
1.0240	12.0	228	1.2106	0.9377	0.9410	0.9397
1.0257	13.0	247	1.1934	0.9308	0.9358	0.9361
1.0162	14.0	266	1.1946	0.9377	0.9402	0.9384
0.9940	15.0	285	1.1879	0.9377	0.9411	0.9397
1.0097	16.0	304	1.1848	0.9412	0.9449	0.9423
0.9937	17.0	323	1.1863	0.9377	0.9408	0.9384
0.9864	18.0	342	1.1935	0.9446	0.9471	0.9446
0.9830	19.0	361	1.1802	0.9446	0.9474	0.9459
0.9825	20.0	380	1.1508	0.9446	0.9490	0.9485
0.9733	21.0	399	1.1568	0.9343	0.9385	0.9385
0.9778	22.0	418	1.1570	0.9412	0.9455	0.9460
0.9657	23.0	437	1.1588	0.9446	0.9487	0.9497
0.9771	24.0	456	1.1637	0.9446	0.9495	0.9510
0.9647	25.0	475	1.1640	0.9481	0.9509	0.9509
0.9649	26.0	494	1.1572	0.9516	0.9546	0.9546
0.9686	27.0	513	1.1667	0.9377	0.9411	0.9397
0.9681	28.0	532	1.1614	0.9377	0.9417	0.9423
0.9642	29.0	551	1.1599	0.9412	0.9444	0.9434
0.9677	30.0	570	1.1556	0.9516	0.9547	0.9558

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/fold_5

fold_5

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
fold_5