smids_10x_deit_tiny_sgd_001_fold2

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: 0.3073
• Accuracy: 0.8902

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
0.5172	1.0	750	0.5682	0.7687
0.3347	2.0	1500	0.4469	0.8153
0.3177	3.0	2250	0.3953	0.8469
0.3776	4.0	3000	0.3688	0.8502
0.2886	5.0	3750	0.3556	0.8519
0.2396	6.0	4500	0.3328	0.8502
0.2545	7.0	5250	0.3237	0.8586
0.2435	8.0	6000	0.3188	0.8569
0.2366	9.0	6750	0.3065	0.8686
0.232	10.0	7500	0.3041	0.8652
0.2399	11.0	8250	0.2971	0.8785
0.2717	12.0	9000	0.2941	0.8769
0.2579	13.0	9750	0.2863	0.8852
0.1661	14.0	10500	0.2895	0.8802
0.1655	15.0	11250	0.2865	0.8785
0.1921	16.0	12000	0.2897	0.8802
0.1525	17.0	12750	0.2854	0.8835
0.1653	18.0	13500	0.2861	0.8819
0.1849	19.0	14250	0.2939	0.8702
0.1923	20.0	15000	0.2850	0.8835
0.1967	21.0	15750	0.2874	0.8802
0.1373	22.0	16500	0.2916	0.8802
0.1229	23.0	17250	0.2891	0.8869
0.1054	24.0	18000	0.2911	0.8802
0.1456	25.0	18750	0.2869	0.8869
0.2052	26.0	19500	0.2987	0.8835
0.1723	27.0	20250	0.2918	0.8835
0.1277	28.0	21000	0.2937	0.8902
0.1569	29.0	21750	0.2956	0.8902
0.1514	30.0	22500	0.2954	0.8885
0.1603	31.0	23250	0.2954	0.8902
0.1428	32.0	24000	0.2940	0.8918
0.1564	33.0	24750	0.3002	0.8835
0.1386	34.0	25500	0.3023	0.8852
0.1564	35.0	26250	0.2982	0.8869
0.183	36.0	27000	0.3004	0.8885
0.1456	37.0	27750	0.3058	0.8869
0.1394	38.0	28500	0.3047	0.8869
0.121	39.0	29250	0.3021	0.8902
0.1192	40.0	30000	0.3035	0.8852
0.1706	41.0	30750	0.3048	0.8918
0.1421	42.0	31500	0.3036	0.8885
0.1223	43.0	32250	0.3066	0.8852
0.1116	44.0	33000	0.3060	0.8885
0.1122	45.0	33750	0.3075	0.8885
0.1411	46.0	34500	0.3066	0.8885
0.1644	47.0	35250	0.3072	0.8885
0.0953	48.0	36000	0.3070	0.8902
0.1109	49.0	36750	0.3072	0.8902
0.1061	50.0	37500	0.3073	0.8902

Framework versions

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