hushem_40x_deit_tiny_rms_001_fold4

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

• Loss: 3.6233
• Accuracy: 0.6905

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: 0.001
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.1473	1.0	219	0.8756	0.6190
0.9719	2.0	438	0.9893	0.5714
0.7611	3.0	657	0.7217	0.7619
0.6995	4.0	876	0.7516	0.6429
0.6928	5.0	1095	1.0447	0.5952
0.6114	6.0	1314	0.9410	0.6667
0.4906	7.0	1533	1.4457	0.5238
0.4956	8.0	1752	1.1229	0.6429
0.3708	9.0	1971	0.5610	0.7381
0.3213	10.0	2190	1.1632	0.6667
0.279	11.0	2409	0.8853	0.7381
0.26	12.0	2628	1.0316	0.6905
0.2004	13.0	2847	0.8001	0.7619
0.2396	14.0	3066	1.0495	0.7381
0.1937	15.0	3285	1.2736	0.7381
0.1386	16.0	3504	0.9949	0.7381
0.1459	17.0	3723	1.2302	0.6905
0.0754	18.0	3942	1.9238	0.6667
0.0996	19.0	4161	1.4396	0.6905
0.0438	20.0	4380	1.1891	0.7143
0.1349	21.0	4599	1.4228	0.7381
0.0058	22.0	4818	1.2340	0.7619
0.0345	23.0	5037	1.1630	0.6667
0.0461	24.0	5256	2.1318	0.6429
0.0595	25.0	5475	1.7499	0.6905
0.004	26.0	5694	1.6488	0.6905
0.0014	27.0	5913	1.8134	0.6905
0.0335	28.0	6132	2.3351	0.6905
0.0071	29.0	6351	2.4170	0.5714
0.0006	30.0	6570	1.5965	0.7381
0.0014	31.0	6789	2.0937	0.7381
0.0419	32.0	7008	1.8845	0.6905
0.042	33.0	7227	3.6234	0.6190
0.0018	34.0	7446	1.5177	0.6905
0.027	35.0	7665	1.3824	0.7857
0.0005	36.0	7884	2.3915	0.7619
0.0226	37.0	8103	1.6001	0.7381
0.001	38.0	8322	2.3141	0.6905
0.0	39.0	8541	2.5460	0.7143
0.0	40.0	8760	2.6724	0.6905
0.0	41.0	8979	2.7005	0.6905
0.0	42.0	9198	2.8171	0.7143
0.0	43.0	9417	2.9876	0.7143
0.0	44.0	9636	3.1125	0.7143
0.0	45.0	9855	3.2479	0.7143
0.0	46.0	10074	3.4344	0.7143
0.0	47.0	10293	3.4573	0.7143
0.0	48.0	10512	3.5752	0.6905
0.0	49.0	10731	3.5910	0.6905
0.0	50.0	10950	3.6233	0.6905

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2