smids_10x_deit_tiny_adamax_0001_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: 0.7859
• Accuracy: 0.9182

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: 0.0001
• 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
0.2608	1.0	751	0.2844	0.8898
0.1304	2.0	1502	0.3294	0.8765
0.111	3.0	2253	0.3516	0.9015
0.1003	4.0	3004	0.4446	0.8932
0.034	5.0	3755	0.5205	0.8982
0.0067	6.0	4506	0.6326	0.9015
0.0227	7.0	5257	0.8411	0.8815
0.0262	8.0	6008	0.8754	0.8865
0.0488	9.0	6759	0.7139	0.9098
0.0361	10.0	7510	0.7866	0.8948
0.0107	11.0	8261	0.8081	0.9048
0.0056	12.0	9012	0.7555	0.8998
0.0	13.0	9763	0.8196	0.9015
0.0016	14.0	10514	0.8589	0.9032
0.0	15.0	11265	0.8346	0.9098
0.0	16.0	12016	0.7703	0.9115
0.0	17.0	12767	0.8587	0.9032
0.0	18.0	13518	0.8122	0.9115
0.0	19.0	14269	0.8002	0.9048
0.0	20.0	15020	0.8446	0.9115
0.0	21.0	15771	0.8926	0.9048
0.0	22.0	16522	0.8190	0.9065
0.0	23.0	17273	0.7943	0.9098
0.0	24.0	18024	0.7616	0.9098
0.0	25.0	18775	0.7566	0.9149
0.0	26.0	19526	0.7309	0.9149
0.0	27.0	20277	0.7760	0.9032
0.0	28.0	21028	0.7849	0.9132
0.008	29.0	21779	0.7826	0.9149
0.0	30.0	22530	0.7666	0.9199
0.0	31.0	23281	0.7402	0.9199
0.0	32.0	24032	0.7484	0.9199
0.0	33.0	24783	0.7616	0.9165
0.0	34.0	25534	0.7803	0.9149
0.0	35.0	26285	0.7685	0.9199
0.0	36.0	27036	0.7685	0.9165
0.0	37.0	27787	0.7687	0.9199
0.0	38.0	28538	0.7876	0.9199
0.0	39.0	29289	0.7749	0.9215
0.0	40.0	30040	0.7734	0.9165
0.0	41.0	30791	0.7803	0.9199
0.0	42.0	31542	0.7799	0.9182
0.0	43.0	32293	0.7798	0.9182
0.0	44.0	33044	0.7789	0.9182
0.0	45.0	33795	0.7827	0.9199
0.0	46.0	34546	0.7810	0.9182
0.0	47.0	35297	0.7840	0.9182
0.0	48.0	36048	0.7837	0.9199
0.0	49.0	36799	0.7839	0.9199
0.0	50.0	37550	0.7859	0.9182

Framework versions

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