deit_fold_2

nqvii/deit_fold_2

deit_fold_2 is a image classification model from nqvii. 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: Aug 8, 2026
Updated: Aug 8, 2026
SHA: 8bcbb44f

Base models

facebook/deit-small-patch16-224

Datasets

imagefolder

Model card

From the Hugging Face model README

deit_fold_2

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

Loss: 1.1405
Accuracy: 0.9586
F1 Score: 0.9620
Recall: 0.9621

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
2.8182	1.0	19	2.7982	0.2966	0.2243	0.2444
2.7152	2.0	38	2.6468	0.3793	0.2689	0.3032
2.5094	3.0	57	2.4150	0.5241	0.4335	0.4481
2.1993	4.0	76	2.0760	0.7172	0.7150	0.6964
1.7513	5.0	95	1.7070	0.8069	0.8177	0.8141
1.4469	6.0	114	1.4631	0.8586	0.8728	0.8742
1.2482	7.0	133	1.3515	0.8759	0.8882	0.8890
1.2058	8.0	152	1.3018	0.8897	0.8990	0.8998
1.1470	9.0	171	1.2735	0.9069	0.9141	0.9169
1.0887	10.0	190	1.2533	0.9207	0.9291	0.9306
1.0903	11.0	209	1.2413	0.9103	0.9182	0.9159
1.0576	12.0	228	1.2260	0.9207	0.9286	0.9317
1.0231	13.0	247	1.2594	0.9207	0.9278	0.9303
1.0264	14.0	266	1.2164	0.9276	0.9339	0.9305
1.0175	15.0	285	1.2294	0.9241	0.9301	0.9267
1.0135	16.0	304	1.2143	0.9345	0.9406	0.9402
1.0178	17.0	323	1.2314	0.9276	0.9351	0.9380
0.9823	18.0	342	1.2160	0.9310	0.9373	0.9365
0.9965	19.0	361	1.2154	0.9172	0.9232	0.9206
0.9854	20.0	380	1.1846	0.9345	0.9411	0.9415
0.9807	21.0	399	1.2096	0.9345	0.9414	0.9414
0.9860	22.0	418	1.1996	0.9276	0.9338	0.9316
0.9796	23.0	437	1.1967	0.9310	0.9365	0.9317
0.9826	24.0	456	1.2180	0.9172	0.9239	0.9183
0.9809	25.0	475	1.2030	0.9345	0.9405	0.9378
0.9803	26.0	494	1.1866	0.9345	0.9403	0.9389
0.9748	27.0	513	1.1626	0.9448	0.9497	0.9487
0.9659	28.0	532	1.1405	0.9586	0.9620	0.9621
0.9718	29.0	551	1.1410	0.9483	0.9525	0.9525
0.9668	30.0	570	1.1485	0.9552	0.9589	0.9584
0.9715	31.0	589	1.1423	0.9448	0.9499	0.9502
0.9729	32.0	608	1.1560	0.9483	0.9535	0.9550
0.9766	33.0	627	1.1721	0.9483	0.9541	0.9561
0.9687	34.0	646	1.1706	0.9448	0.9509	0.9524
0.9769	35.0	665	1.1539	0.9345	0.9410	0.9403
0.9609	36.0	684	1.1552	0.9483	0.9539	0.9549
0.9587	37.0	703	1.1565	0.9517	0.9569	0.9597
0.9673	38.0	722	1.1675	0.9483	0.9537	0.9562

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

Task: Image Classification
Library: transformers
Type: vit
License: apache-2.0
Created: Aug 8, 2026
Updated: Aug 8, 2026