smids_3x_deit_tiny_adamax_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: 1.1177
• Accuracy: 0.8681

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
0.5567	1.0	226	0.5713	0.7462
0.3828	2.0	452	0.3656	0.8564
0.3502	3.0	678	0.3812	0.8381
0.276	4.0	904	0.4744	0.8280
0.2886	5.0	1130	0.4090	0.8447
0.2624	6.0	1356	0.3974	0.8598
0.1626	7.0	1582	0.4030	0.8781
0.1074	8.0	1808	0.4449	0.8631
0.184	9.0	2034	0.3919	0.8831
0.0836	10.0	2260	0.5392	0.8564
0.0539	11.0	2486	0.6608	0.8381
0.0511	12.0	2712	0.6349	0.8564
0.046	13.0	2938	0.6675	0.8848
0.054	14.0	3164	0.6371	0.8664
0.0362	15.0	3390	0.8363	0.8414
0.0153	16.0	3616	0.7151	0.8664
0.0583	17.0	3842	0.7946	0.8514
0.0315	18.0	4068	0.8456	0.8614
0.0078	19.0	4294	0.7428	0.8431
0.0034	20.0	4520	0.8571	0.8614
0.0384	21.0	4746	0.7674	0.8731
0.0465	22.0	4972	0.6983	0.8715
0.0099	23.0	5198	1.0003	0.8581
0.0034	24.0	5424	0.9037	0.8631
0.0001	25.0	5650	0.9614	0.8548
0.0004	26.0	5876	1.0237	0.8548
0.0094	27.0	6102	0.8996	0.8698
0.0001	28.0	6328	0.9361	0.8765
0.0	29.0	6554	1.0528	0.8614
0.0001	30.0	6780	0.9933	0.8614
0.0001	31.0	7006	1.0600	0.8614
0.0	32.0	7232	1.0559	0.8664
0.0031	33.0	7458	1.0415	0.8598
0.0	34.0	7684	1.1002	0.8648
0.0	35.0	7910	1.0102	0.8731
0.0	36.0	8136	1.0422	0.8731
0.0	37.0	8362	1.0448	0.8681
0.0	38.0	8588	1.0235	0.8698
0.0	39.0	8814	1.0543	0.8648
0.0	40.0	9040	1.0808	0.8681
0.0029	41.0	9266	1.0840	0.8698
0.0032	42.0	9492	1.0669	0.8698
0.0	43.0	9718	1.1155	0.8631
0.0	44.0	9944	1.0986	0.8698
0.0	45.0	10170	1.1093	0.8664
0.0	46.0	10396	1.1032	0.8664
0.0	47.0	10622	1.1140	0.8664
0.0	48.0	10848	1.1176	0.8681
0.0	49.0	11074	1.1190	0.8681
0.0	50.0	11300	1.1177	0.8681

Framework versions

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