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: 1.1298
Accuracy: 0.9519
F1 Score: 0.9537
Recall: 0.9579

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
2.8252	1.0	20	2.8294	0.2532	0.1757	0.2799
2.6170	2.0	40	2.6009	0.4231	0.3643	0.4176
2.3206	3.0	60	2.3003	0.6282	0.6023	0.6150
1.9316	4.0	80	1.9340	0.7468	0.7536	0.7418
1.6434	5.0	100	1.6591	0.8301	0.8350	0.8313
1.4153	6.0	120	1.4709	0.8718	0.8757	0.8768
1.2545	7.0	140	1.3896	0.8846	0.8842	0.8805
1.2233	8.0	160	1.3060	0.9006	0.9020	0.8985
1.1294	9.0	180	1.2383	0.9295	0.9305	0.9327
1.1183	10.0	200	1.2321	0.9167	0.9188	0.9187
1.1030	11.0	220	1.2141	0.9263	0.9277	0.9273
1.0461	12.0	240	1.1977	0.9327	0.9341	0.9346
1.0356	13.0	260	1.1742	0.9327	0.9345	0.9359
1.0387	14.0	280	1.1660	0.9295	0.9322	0.9366
1.0136	15.0	300	1.1681	0.9295	0.9318	0.9359
1.0312	16.0	320	1.1628	0.9295	0.9316	0.9346
1.0127	17.0	340	1.1365	0.9391	0.9409	0.9420
1.0043	18.0	360	1.1322	0.9423	0.9439	0.9444
0.9970	19.0	380	1.1653	0.9455	0.9473	0.9532
1.0022	20.0	400	1.1536	0.9359	0.9380	0.9415
1.0002	21.0	420	1.1603	0.9391	0.9412	0.9469
1.0062	22.0	440	1.1409	0.9455	0.9470	0.9518
0.9826	23.0	460	1.1523	0.9423	0.9437	0.9415
0.9858	24.0	480	1.1611	0.9455	0.9472	0.9513
0.9753	25.0	500	1.1412	0.9455	0.9472	0.9506
0.9730	26.0	520	1.1605	0.9423	0.9441	0.9427
0.9833	27.0	540	1.1281	0.9519	0.9531	0.9555
0.9765	28.0	560	1.1411	0.9487	0.9503	0.9501
0.9791	29.0	580	1.1365	0.9487	0.9511	0.9555
0.9748	30.0	600	1.1481	0.9391	0.9415	0.9450
0.9725	31.0	620	1.1567	0.9359	0.9370	0.9366
0.9645	32.0	640	1.1298	0.9519	0.9537	0.9579
0.9599	33.0	660	1.1275	0.9487	0.9500	0.9518
0.9701	34.0	680	1.1281	0.9519	0.9530	0.9543
0.9785	35.0	700	1.1293	0.9455	0.9472	0.9506
0.9714	36.0	720	1.1466	0.9455	0.9473	0.9513
0.9687	37.0	740	1.1502	0.9423	0.9447	0.9501
0.9581	38.0	760	1.1606	0.9391	0.9415	0.9464
0.9675	39.0	780	1.1502	0.9423	0.9442	0.9462
0.9697	40.0	800	1.1641	0.9391	0.9414	0.9476
0.9664	41.0	820	1.1442	0.9455	0.9475	0.9523
0.9567	42.0	840	1.1395	0.9423	0.9441	0.9462
0.9716	43.0	860	1.1537	0.9359	0.9380	0.9415

Framework versions

Transformers 5.0.0
Pytorch 2.10.0+cu128
Datasets 5.0.0
Tokenizers 0.22.2

nqvii/deit_fold_2_v3

deit_fold_2_v3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nqvii/
deit_fold_2_v3