smids_5x_deit_small_rms_001_fold2

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: 0.5587
• Accuracy: 0.7571

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
1.1114	1.0	375	1.0890	0.3910
0.8594	2.0	750	0.8338	0.5258
0.7942	3.0	1125	0.8187	0.5857
0.8295	4.0	1500	0.8518	0.5441
0.7679	5.0	1875	0.8037	0.5574
0.78	6.0	2250	0.8086	0.5641
0.8051	7.0	2625	0.8475	0.5707
0.7931	8.0	3000	0.7986	0.5740
0.787	9.0	3375	0.8111	0.6273
0.7865	10.0	3750	0.7854	0.5874
0.7545	11.0	4125	0.7499	0.6156
0.8266	12.0	4500	0.7256	0.6373
0.699	13.0	4875	0.7372	0.6456
0.7725	14.0	5250	0.7195	0.6439
0.7834	15.0	5625	0.7235	0.6489
0.6954	16.0	6000	0.7061	0.6589
0.6911	17.0	6375	0.8229	0.5973
0.667	18.0	6750	0.6746	0.6772
0.6889	19.0	7125	0.6831	0.6955
0.6628	20.0	7500	0.6921	0.6922
0.7228	21.0	7875	0.6764	0.6656
0.7022	22.0	8250	0.6797	0.6905
0.6549	23.0	8625	0.6709	0.6772
0.7183	24.0	9000	0.6429	0.6955
0.6612	25.0	9375	0.6503	0.6988
0.6901	26.0	9750	0.7018	0.6739
0.7038	27.0	10125	0.6168	0.7271
0.6364	28.0	10500	0.6219	0.7121
0.6477	29.0	10875	0.6546	0.7188
0.5753	30.0	11250	0.6252	0.7221
0.6932	31.0	11625	0.6174	0.7271
0.6245	32.0	12000	0.6281	0.7255
0.6083	33.0	12375	0.6211	0.7105
0.6277	34.0	12750	0.5911	0.7471
0.596	35.0	13125	0.5943	0.7304
0.5539	36.0	13500	0.5798	0.7121
0.6231	37.0	13875	0.5842	0.7321
0.5692	38.0	14250	0.5897	0.7288
0.5587	39.0	14625	0.6220	0.7155
0.5891	40.0	15000	0.6063	0.7338
0.561	41.0	15375	0.5930	0.7338
0.5901	42.0	15750	0.5990	0.7288
0.5194	43.0	16125	0.5632	0.7488
0.5311	44.0	16500	0.5715	0.7488
0.5414	45.0	16875	0.5640	0.7537
0.5291	46.0	17250	0.5674	0.7504
0.5724	47.0	17625	0.5765	0.7454
0.4849	48.0	18000	0.5625	0.7438
0.5463	49.0	18375	0.5558	0.7571
0.4844	50.0	18750	0.5587	0.7571

Framework versions

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