deit_fold_3

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: 1.0647
• Accuracy: 0.9759
• F1 Score: 0.9781
• Recall: 0.9806

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
2.8115	1.0	19	2.7735	0.3034	0.2529	0.2630
2.7100	2.0	38	2.6285	0.4069	0.2911	0.3284
2.5177	3.0	57	2.4014	0.5207	0.4125	0.4375
2.2202	4.0	76	2.0460	0.7069	0.7150	0.6830
1.7906	5.0	95	1.6558	0.8414	0.8552	0.8490
1.4703	6.0	114	1.4065	0.8724	0.8851	0.8883
1.2977	7.0	133	1.3043	0.9138	0.9200	0.9203
1.2124	8.0	152	1.2551	0.9345	0.9395	0.9427
1.1651	9.0	171	1.2009	0.9448	0.9490	0.9512
1.1434	10.0	190	1.1668	0.9483	0.9520	0.9536
1.1184	11.0	209	1.1541	0.9586	0.9611	0.9623
1.0736	12.0	228	1.1619	0.9517	0.9555	0.9598
1.0636	13.0	247	1.1813	0.9448	0.9499	0.9561
1.0464	14.0	266	1.1243	0.9655	0.9686	0.9721
1.0363	15.0	285	1.1079	0.9586	0.9615	0.9621
1.0317	16.0	304	1.1095	0.9655	0.9675	0.9708
1.0205	17.0	323	1.1498	0.9552	0.9593	0.9648
1.0146	18.0	342	1.1132	0.9655	0.9686	0.9721
1.0132	19.0	361	1.1042	0.9552	0.9586	0.9569
1.0060	20.0	380	1.0980	0.9552	0.9594	0.9549
0.9965	21.0	399	1.0731	0.9759	0.9780	0.9780
0.9954	22.0	418	1.0879	0.9621	0.9654	0.9631
1.0041	23.0	437	1.1282	0.9621	0.9656	0.9697
0.9945	24.0	456	1.0968	0.9655	0.9686	0.9721
0.9823	25.0	475	1.1093	0.9621	0.9652	0.9697
0.9811	26.0	494	1.0782	0.9690	0.9715	0.9746
0.9701	27.0	513	1.0829	0.9586	0.9615	0.9621
0.9695	28.0	532	1.0792	0.9724	0.9751	0.9782
0.9779	29.0	551	1.0625	0.9655	0.9684	0.9699
0.9825	30.0	570	1.0647	0.9759	0.9781	0.9806
0.9718	31.0	589	1.0718	0.9724	0.9750	0.9769
0.9798	32.0	608	1.0673	0.9759	0.9781	0.9806
0.9833	33.0	627	1.0644	0.9724	0.9750	0.9769
0.9665	34.0	646	1.0789	0.9724	0.9741	0.9770
0.9802	35.0	665	1.0593	0.9759	0.9781	0.9793
0.9624	36.0	684	1.0595	0.9759	0.9776	0.9806
0.9674	37.0	703	1.0814	0.9655	0.9681	0.9721
0.9723	38.0	722	1.0604	0.9759	0.9781	0.9806
0.9589	39.0	741	1.0768	0.9621	0.9655	0.9686
0.9655	40.0	760	1.0548	0.9655	0.9688	0.9694
0.9814	41.0	779	1.0676	0.9724	0.9751	0.9782
0.9627	42.0	798	1.0547	0.9759	0.9781	0.9806
0.9588	43.0	817	1.1152	0.9586	0.9626	0.9672
0.9665	44.0	836	1.0859	0.9690	0.9721	0.9757
0.9627	45.0	855	1.0683	0.9690	0.9721	0.9757
0.9576	46.0	874	1.0634	0.9759	0.9776	0.9806
0.9559	47.0	893	1.0620	0.9690	0.9718	0.9719
0.9642	48.0	912	1.0653	0.9724	0.9750	0.9756
0.9751	49.0	931	1.0574	0.9759	0.9781	0.9793
0.9688	50.0	950	1.0704	0.9690	0.9720	0.9744
0.9596	51.0	969	1.0635	0.9724	0.9750	0.9769
0.9716	52.0	988	1.0674	0.9759	0.9776	0.9806

Framework versions

• Transformers 5.0.0
• Pytorch 2.10.0+cu128
• Datasets 5.0.0
• Tokenizers 0.22.2