deit_fold_2_v3

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