smids_5x_deit_base_sgd_001_fold1

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

• Loss: 0.2661
• Accuracy: 0.8932

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
0.7985	1.0	376	0.8182	0.6978
0.594	2.0	752	0.5849	0.7746
0.4653	3.0	1128	0.4811	0.8197
0.4509	4.0	1504	0.4265	0.8264
0.406	5.0	1880	0.3929	0.8447
0.3758	6.0	2256	0.3696	0.8581
0.3147	7.0	2632	0.3531	0.8698
0.3421	8.0	3008	0.3417	0.8664
0.3606	9.0	3384	0.3307	0.8798
0.2866	10.0	3760	0.3222	0.8865
0.2912	11.0	4136	0.3153	0.8798
0.2629	12.0	4512	0.3094	0.8865
0.2464	13.0	4888	0.3048	0.8848
0.2413	14.0	5264	0.3005	0.8881
0.3125	15.0	5640	0.2955	0.8932
0.226	16.0	6016	0.2931	0.8865
0.2346	17.0	6392	0.2899	0.8915
0.2997	18.0	6768	0.2867	0.8881
0.2564	19.0	7144	0.2849	0.8898
0.1951	20.0	7520	0.2838	0.8898
0.2828	21.0	7896	0.2817	0.8898
0.2327	22.0	8272	0.2806	0.8898
0.2604	23.0	8648	0.2786	0.8865
0.2065	24.0	9024	0.2780	0.8881
0.2338	25.0	9400	0.2766	0.8881
0.2197	26.0	9776	0.2745	0.8898
0.1797	27.0	10152	0.2743	0.8898
0.199	28.0	10528	0.2732	0.8915
0.2002	29.0	10904	0.2724	0.8898
0.1586	30.0	11280	0.2714	0.8932
0.1861	31.0	11656	0.2710	0.8932
0.2539	32.0	12032	0.2706	0.8948
0.1906	33.0	12408	0.2700	0.8948
0.1642	34.0	12784	0.2697	0.8915
0.1856	35.0	13160	0.2694	0.8915
0.2084	36.0	13536	0.2691	0.8932
0.1812	37.0	13912	0.2681	0.8948
0.2073	38.0	14288	0.2680	0.8948
0.1854	39.0	14664	0.2677	0.8915
0.1953	40.0	15040	0.2671	0.8932
0.1912	41.0	15416	0.2672	0.8948
0.1646	42.0	15792	0.2669	0.8932
0.1689	43.0	16168	0.2666	0.8932
0.1894	44.0	16544	0.2664	0.8932
0.173	45.0	16920	0.2663	0.8932
0.2186	46.0	17296	0.2661	0.8932
0.1671	47.0	17672	0.2661	0.8932
0.1916	48.0	18048	0.2661	0.8932
0.1583	49.0	18424	0.2661	0.8932
0.137	50.0	18800	0.2661	0.8932

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2