smids_1x_deit_tiny_rms_00001_fold5

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: 0.8945
• Accuracy: 0.8683

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: 1e-05
• 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
0.4595	1.0	75	0.3430	0.875
0.3075	2.0	150	0.3392	0.8667
0.1885	3.0	225	0.2939	0.8833
0.2401	4.0	300	0.3047	0.88
0.1913	5.0	375	0.2948	0.8833
0.0779	6.0	450	0.3840	0.8733
0.0659	7.0	525	0.3753	0.8867
0.0683	8.0	600	0.4413	0.88
0.0365	9.0	675	0.5012	0.875
0.0284	10.0	750	0.5436	0.8783
0.0339	11.0	825	0.6040	0.8883
0.0022	12.0	900	0.6211	0.8767
0.0421	13.0	975	0.7125	0.87
0.0295	14.0	1050	0.8588	0.8567
0.0522	15.0	1125	0.7259	0.875
0.0002	16.0	1200	0.7288	0.87
0.0239	17.0	1275	0.7745	0.865
0.0349	18.0	1350	0.7880	0.8667
0.0099	19.0	1425	0.7942	0.8683
0.0002	20.0	1500	0.8016	0.88
0.0181	21.0	1575	0.8187	0.87
0.0067	22.0	1650	0.8802	0.87
0.0135	23.0	1725	0.8554	0.865
0.0001	24.0	1800	0.8527	0.8733
0.0254	25.0	1875	0.7951	0.87
0.0001	26.0	1950	0.8033	0.87
0.0127	27.0	2025	0.8239	0.8783
0.0152	28.0	2100	0.9169	0.8683
0.019	29.0	2175	0.8220	0.8767
0.0038	30.0	2250	0.8836	0.8683
0.0001	31.0	2325	0.8686	0.875
0.0	32.0	2400	0.8778	0.8717
0.0121	33.0	2475	1.0279	0.8583
0.0001	34.0	2550	0.9813	0.8583
0.0072	35.0	2625	0.8647	0.8767
0.0	36.0	2700	0.8869	0.8683
0.0027	37.0	2775	0.9010	0.865
0.0	38.0	2850	0.9248	0.8633
0.0	39.0	2925	0.9337	0.8617
0.0	40.0	3000	0.8918	0.865
0.0054	41.0	3075	0.9181	0.865
0.0	42.0	3150	0.8813	0.8717
0.0078	43.0	3225	0.9054	0.8633
0.0	44.0	3300	0.8996	0.8667
0.0016	45.0	3375	0.9082	0.8633
0.0	46.0	3450	0.9083	0.865
0.0068	47.0	3525	0.9001	0.8683
0.0048	48.0	3600	0.8991	0.8683
0.0	49.0	3675	0.8976	0.8683
0.0029	50.0	3750	0.8945	0.8683

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0