smids_3x_deit_small_sgd_001_fold1

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: 0.3034
• Accuracy: 0.8765

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
0.874	1.0	226	0.8422	0.6477
0.6808	2.0	452	0.6564	0.7362
0.5184	3.0	678	0.5592	0.7780
0.5173	4.0	904	0.5087	0.7813
0.5113	5.0	1130	0.4746	0.8047
0.4469	6.0	1356	0.4445	0.8214
0.3731	7.0	1582	0.4247	0.8364
0.413	8.0	1808	0.4105	0.8397
0.369	9.0	2034	0.3943	0.8464
0.3882	10.0	2260	0.3826	0.8548
0.3525	11.0	2486	0.3772	0.8648
0.3235	12.0	2712	0.3644	0.8598
0.2669	13.0	2938	0.3578	0.8715
0.2463	14.0	3164	0.3531	0.8681
0.3167	15.0	3390	0.3484	0.8698
0.3258	16.0	3616	0.3417	0.8715
0.2311	17.0	3842	0.3383	0.8748
0.2669	18.0	4068	0.3359	0.8748
0.2828	19.0	4294	0.3332	0.8731
0.2409	20.0	4520	0.3289	0.8731
0.3064	21.0	4746	0.3277	0.8715
0.2918	22.0	4972	0.3274	0.8731
0.3068	23.0	5198	0.3218	0.8748
0.2544	24.0	5424	0.3201	0.8715
0.2207	25.0	5650	0.3191	0.8781
0.2923	26.0	5876	0.3153	0.8748
0.2033	27.0	6102	0.3156	0.8765
0.2492	28.0	6328	0.3128	0.8731
0.2136	29.0	6554	0.3157	0.8715
0.2344	30.0	6780	0.3143	0.8765
0.2394	31.0	7006	0.3111	0.8781
0.2259	32.0	7232	0.3105	0.8781
0.2144	33.0	7458	0.3085	0.8781
0.1831	34.0	7684	0.3101	0.8748
0.2542	35.0	7910	0.3071	0.8781
0.211	36.0	8136	0.3058	0.8781
0.2498	37.0	8362	0.3047	0.8815
0.2414	38.0	8588	0.3058	0.8765
0.1832	39.0	8814	0.3053	0.8765
0.1872	40.0	9040	0.3039	0.8781
0.1641	41.0	9266	0.3048	0.8765
0.244	42.0	9492	0.3047	0.8748
0.1877	43.0	9718	0.3040	0.8765
0.1335	44.0	9944	0.3029	0.8781
0.2501	45.0	10170	0.3036	0.8781
0.2161	46.0	10396	0.3032	0.8765
0.222	47.0	10622	0.3033	0.8781
0.2279	48.0	10848	0.3033	0.8765
0.1815	49.0	11074	0.3033	0.8765
0.1776	50.0	11300	0.3034	0.8765

Framework versions

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