smids_5x_deit_tiny_sgd_0001_fold4

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: 0.4936
• Accuracy: 0.8

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
1.1564	1.0	375	1.1576	0.3767
1.0853	2.0	750	1.0913	0.4
0.9971	3.0	1125	1.0409	0.44
0.9972	4.0	1500	0.9979	0.4683
0.9211	5.0	1875	0.9595	0.4967
0.885	6.0	2250	0.9224	0.535
0.8576	7.0	2625	0.8878	0.5583
0.8551	8.0	3000	0.8542	0.5783
0.8253	9.0	3375	0.8221	0.6017
0.8198	10.0	3750	0.7908	0.6283
0.6752	11.0	4125	0.7615	0.645
0.6508	12.0	4500	0.7343	0.6767
0.6556	13.0	4875	0.7097	0.69
0.7132	14.0	5250	0.6866	0.7083
0.6057	15.0	5625	0.6661	0.7183
0.5722	16.0	6000	0.6478	0.7283
0.5982	17.0	6375	0.6328	0.7333
0.5686	18.0	6750	0.6177	0.735
0.5939	19.0	7125	0.6046	0.7417
0.5225	20.0	7500	0.5938	0.7483
0.5314	21.0	7875	0.5829	0.7567
0.5367	22.0	8250	0.5746	0.765
0.506	23.0	8625	0.5665	0.77
0.5218	24.0	9000	0.5589	0.7717
0.5608	25.0	9375	0.5520	0.7767
0.5255	26.0	9750	0.5459	0.78
0.5248	27.0	10125	0.5406	0.78
0.496	28.0	10500	0.5353	0.78
0.4514	29.0	10875	0.5308	0.785
0.4878	30.0	11250	0.5266	0.785
0.4791	31.0	11625	0.5226	0.785
0.4601	32.0	12000	0.5192	0.785
0.527	33.0	12375	0.5161	0.7867
0.4682	34.0	12750	0.5130	0.785
0.4268	35.0	13125	0.5104	0.7917
0.4602	36.0	13500	0.5080	0.795
0.4456	37.0	13875	0.5057	0.7983
0.4657	38.0	14250	0.5038	0.7983
0.5191	39.0	14625	0.5021	0.7983
0.5029	40.0	15000	0.5005	0.8
0.4811	41.0	15375	0.4991	0.8
0.4466	42.0	15750	0.4979	0.8
0.4615	43.0	16125	0.4969	0.8017
0.4147	44.0	16500	0.4960	0.8
0.4484	45.0	16875	0.4953	0.8
0.4471	46.0	17250	0.4947	0.8
0.4839	47.0	17625	0.4942	0.8
0.4773	48.0	18000	0.4939	0.8
0.4334	49.0	18375	0.4937	0.8
0.4329	50.0	18750	0.4936	0.8

Framework versions

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