smids_5x_deit_small_rms_0001_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: 1.0513
• Accuracy: 0.8902

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
0.2658	1.0	375	0.3126	0.8819
0.1908	2.0	750	0.3283	0.8918
0.0825	3.0	1125	0.4361	0.8702
0.0963	4.0	1500	0.5286	0.8619
0.0865	5.0	1875	0.5193	0.8885
0.0848	6.0	2250	0.5531	0.8835
0.072	7.0	2625	0.6299	0.8669
0.0017	8.0	3000	0.6200	0.8819
0.0259	9.0	3375	0.6078	0.9002
0.0609	10.0	3750	0.5800	0.8918
0.045	11.0	4125	0.6268	0.8802
0.068	12.0	4500	0.5720	0.8869
0.0342	13.0	4875	0.7774	0.8652
0.007	14.0	5250	0.7661	0.8769
0.0136	15.0	5625	0.7670	0.8885
0.025	16.0	6000	0.9672	0.8752
0.0117	17.0	6375	0.6723	0.9018
0.01	18.0	6750	0.7201	0.8835
0.1057	19.0	7125	0.7988	0.8686
0.0079	20.0	7500	0.8529	0.8735
0.0114	21.0	7875	0.9574	0.8835
0.0141	22.0	8250	0.8344	0.8819
0.0006	23.0	8625	0.9308	0.8769
0.0008	24.0	9000	0.8418	0.8752
0.0001	25.0	9375	0.7076	0.8869
0.0123	26.0	9750	0.9006	0.8686
0.0003	27.0	10125	0.9386	0.8702
0.0081	28.0	10500	1.0332	0.8735
0.0	29.0	10875	0.9316	0.8752
0.0272	30.0	11250	0.9157	0.8835
0.0042	31.0	11625	0.9011	0.8752
0.0038	32.0	12000	0.9259	0.8769
0.0044	33.0	12375	0.9290	0.8869
0.0	34.0	12750	0.9423	0.8852
0.0	35.0	13125	0.8933	0.8902
0.0	36.0	13500	0.8976	0.8885
0.0	37.0	13875	0.8889	0.8835
0.0004	38.0	14250	1.0859	0.8802
0.0	39.0	14625	1.0992	0.8869
0.0031	40.0	15000	1.0003	0.8952
0.0	41.0	15375	1.0009	0.8985
0.0027	42.0	15750	1.0542	0.8885
0.0026	43.0	16125	1.0230	0.8852
0.0026	44.0	16500	1.0414	0.8885
0.0027	45.0	16875	1.0121	0.8885
0.0	46.0	17250	1.0455	0.8885
0.006	47.0	17625	1.0427	0.8918
0.0	48.0	18000	1.0481	0.8918
0.0024	49.0	18375	1.0499	0.8918
0.0022	50.0	18750	1.0513	0.8902

Framework versions

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