smids_5x_deit_tiny_sgd_0001_fold3

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: 0.4818
• Accuracy: 0.8283

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
1.1768	1.0	375	1.1673	0.3967
1.0999	2.0	750	1.1000	0.4267
1.0421	3.0	1125	1.0522	0.4717
0.9929	4.0	1500	1.0115	0.5
0.9542	5.0	1875	0.9748	0.5233
0.8817	6.0	2250	0.9389	0.5417
0.8788	7.0	2625	0.9041	0.57
0.8581	8.0	3000	0.8691	0.59
0.7544	9.0	3375	0.8356	0.6033
0.7744	10.0	3750	0.8037	0.6317
0.7141	11.0	4125	0.7743	0.66
0.7231	12.0	4500	0.7462	0.6733
0.7108	13.0	4875	0.7217	0.7033
0.6884	14.0	5250	0.6985	0.7217
0.6146	15.0	5625	0.6770	0.7333
0.573	16.0	6000	0.6579	0.7467
0.6638	17.0	6375	0.6415	0.76
0.653	18.0	6750	0.6259	0.775
0.5464	19.0	7125	0.6119	0.7833
0.5679	20.0	7500	0.6000	0.79
0.5005	21.0	7875	0.5883	0.795
0.5248	22.0	8250	0.5790	0.8017
0.5865	23.0	8625	0.5701	0.8017
0.5913	24.0	9000	0.5617	0.8033
0.542	25.0	9375	0.5539	0.8033
0.5193	26.0	9750	0.5470	0.8033
0.5655	27.0	10125	0.5403	0.805
0.59	28.0	10500	0.5340	0.8117
0.4649	29.0	10875	0.5287	0.815
0.4871	30.0	11250	0.5240	0.8133
0.5615	31.0	11625	0.5192	0.815
0.5652	32.0	12000	0.5148	0.8167
0.5595	33.0	12375	0.5110	0.8183
0.4782	34.0	12750	0.5072	0.8167
0.4	35.0	13125	0.5039	0.8183
0.427	36.0	13500	0.5006	0.82
0.4646	37.0	13875	0.4979	0.82
0.4598	38.0	14250	0.4954	0.8217
0.4848	39.0	14625	0.4932	0.8233
0.4928	40.0	15000	0.4911	0.8267
0.4256	41.0	15375	0.4892	0.8267
0.4494	42.0	15750	0.4875	0.8267
0.5087	43.0	16125	0.4862	0.8267
0.4788	44.0	16500	0.4849	0.8267
0.4663	45.0	16875	0.4839	0.8267
0.4385	46.0	17250	0.4831	0.8267
0.4878	47.0	17625	0.4825	0.8283
0.4959	48.0	18000	0.4821	0.8283
0.4851	49.0	18375	0.4819	0.8283
0.4569	50.0	18750	0.4818	0.8283

Framework versions

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