smids_3x_deit_tiny_rms_001_fold1

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: 2.0122
• Accuracy: 0.8030

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
0.8369	1.0	226	0.8757	0.5359
0.8499	2.0	452	0.8448	0.5776
0.7754	3.0	678	0.9313	0.5142
0.8771	4.0	904	0.7652	0.6194
0.7781	5.0	1130	0.7375	0.6711
0.7866	6.0	1356	0.7599	0.6394
0.664	7.0	1582	0.7260	0.6711
0.6855	8.0	1808	0.8844	0.5376
0.6098	9.0	2034	0.7118	0.6778
0.6338	10.0	2260	0.6856	0.6962
0.5812	11.0	2486	0.6665	0.6962
0.5909	12.0	2712	0.8126	0.6260
0.5272	13.0	2938	0.6279	0.7329
0.5688	14.0	3164	0.7483	0.6494
0.5214	15.0	3390	0.6410	0.7312
0.5581	16.0	3616	0.6042	0.7412
0.4723	17.0	3842	0.6758	0.7145
0.5595	18.0	4068	0.6233	0.7412
0.5549	19.0	4294	0.6152	0.7329
0.5078	20.0	4520	0.6278	0.7195
0.5707	21.0	4746	0.5335	0.7780
0.4944	22.0	4972	0.6366	0.7396
0.5416	23.0	5198	0.5752	0.7663
0.5022	24.0	5424	0.5999	0.7479
0.5615	25.0	5650	0.5710	0.7596
0.5132	26.0	5876	0.5875	0.7730
0.3982	27.0	6102	0.5830	0.7763
0.4012	28.0	6328	0.7036	0.7563
0.37	29.0	6554	0.6641	0.7429
0.4588	30.0	6780	0.6124	0.7613
0.3873	31.0	7006	0.6238	0.7646
0.3153	32.0	7232	0.6857	0.7613
0.3038	33.0	7458	0.7385	0.7730
0.2793	34.0	7684	0.6805	0.7846
0.2405	35.0	7910	0.7592	0.7846
0.2843	36.0	8136	0.8044	0.7746
0.2771	37.0	8362	0.7613	0.7813
0.2263	38.0	8588	0.8328	0.7679
0.1499	39.0	8814	0.9707	0.7696
0.1482	40.0	9040	1.0206	0.7896
0.1303	41.0	9266	1.1237	0.7947
0.0595	42.0	9492	1.3060	0.7763
0.0163	43.0	9718	1.4374	0.7830
0.0383	44.0	9944	1.5230	0.7863
0.0303	45.0	10170	1.5896	0.7947
0.0051	46.0	10396	1.8469	0.7896
0.0006	47.0	10622	1.9434	0.7880
0.0004	48.0	10848	2.0244	0.7947
0.0004	49.0	11074	1.9864	0.7997
0.0002	50.0	11300	2.0122	0.8030

Framework versions

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