smids_10x_deit_tiny_rms_001_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: 2.8355
• Accuracy: 0.8117

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
0.7719	1.0	750	0.7663	0.6417
0.7164	2.0	1500	0.6422	0.7167
0.6166	3.0	2250	0.5992	0.7233
0.5878	4.0	3000	0.6512	0.6783
0.5907	5.0	3750	0.5898	0.7183
0.5259	6.0	4500	0.5668	0.7617
0.4934	7.0	5250	0.5092	0.7817
0.5606	8.0	6000	0.5230	0.76
0.5352	9.0	6750	0.5089	0.7733
0.4475	10.0	7500	0.5880	0.745
0.4921	11.0	8250	0.5315	0.765
0.5457	12.0	9000	0.5773	0.7583
0.4353	13.0	9750	0.5700	0.7533
0.4266	14.0	10500	0.5929	0.7633
0.4011	15.0	11250	0.5510	0.7883
0.4243	16.0	12000	0.5772	0.7633
0.2788	17.0	12750	0.5913	0.7817
0.36	18.0	13500	0.5472	0.7767
0.3727	19.0	14250	0.5501	0.7867
0.2873	20.0	15000	0.6706	0.77
0.3441	21.0	15750	0.5563	0.8083
0.3741	22.0	16500	0.5905	0.7767
0.2914	23.0	17250	0.6313	0.785
0.3593	24.0	18000	0.5992	0.7983
0.2548	25.0	18750	0.6167	0.8
0.2172	26.0	19500	0.6453	0.785
0.1957	27.0	20250	0.6311	0.815
0.2482	28.0	21000	0.7520	0.8067
0.1858	29.0	21750	0.7460	0.7917
0.1724	30.0	22500	0.6735	0.8183
0.1536	31.0	23250	0.8260	0.7933
0.1432	32.0	24000	0.9327	0.765
0.1489	33.0	24750	0.8695	0.7967
0.1215	34.0	25500	0.8392	0.8167
0.1302	35.0	26250	1.0000	0.8133
0.0677	36.0	27000	1.0715	0.8083
0.0727	37.0	27750	1.1501	0.7983
0.1221	38.0	28500	1.3342	0.7883
0.0469	39.0	29250	1.3213	0.8
0.068	40.0	30000	1.4945	0.8
0.0607	41.0	30750	1.4763	0.8133
0.0293	42.0	31500	1.8072	0.79
0.0304	43.0	32250	2.0290	0.7817
0.0187	44.0	33000	2.2554	0.7867
0.0136	45.0	33750	2.3220	0.8
0.0034	46.0	34500	2.4619	0.8033
0.0045	47.0	35250	2.5490	0.8
0.0159	48.0	36000	2.5993	0.825
0.0004	49.0	36750	2.7895	0.8083
0.0001	50.0	37500	2.8355	0.8117

Framework versions

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