smids_5x_deit_small_adamax_0001_fold5

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: 0.8104
• Accuracy: 0.925

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
0.1649	1.0	750	0.2981	0.8767
0.1219	2.0	1500	0.3351	0.9117
0.088	3.0	2250	0.4113	0.9117
0.0191	4.0	3000	0.5041	0.91
0.0316	5.0	3750	0.6431	0.905
0.0519	6.0	4500	0.6485	0.9017
0.0145	7.0	5250	0.6712	0.91
0.001	8.0	6000	0.7180	0.91
0.0005	9.0	6750	0.6201	0.9067
0.0001	10.0	7500	0.7375	0.92
0.009	11.0	8250	0.7397	0.9133
0.0	12.0	9000	0.7531	0.9233
0.0005	13.0	9750	0.7094	0.9167
0.0	14.0	10500	0.6906	0.9217
0.0	15.0	11250	0.7622	0.92
0.0	16.0	12000	0.7690	0.9167
0.0099	17.0	12750	0.7093	0.925
0.0049	18.0	13500	0.7817	0.9167
0.0	19.0	14250	0.7714	0.9183
0.0	20.0	15000	0.7423	0.92
0.0	21.0	15750	0.7472	0.9283
0.0	22.0	16500	0.8201	0.9217
0.0003	23.0	17250	0.7230	0.925
0.0	24.0	18000	0.7873	0.9233
0.0	25.0	18750	0.7903	0.9233
0.0	26.0	19500	0.7611	0.9233
0.0	27.0	20250	0.7662	0.9267
0.0	28.0	21000	0.7601	0.9267
0.0	29.0	21750	0.7659	0.925
0.0054	30.0	22500	0.7697	0.9217
0.0	31.0	23250	0.7755	0.9217
0.0	32.0	24000	0.7712	0.9217
0.0	33.0	24750	0.7599	0.9267
0.0	34.0	25500	0.7735	0.9267
0.0	35.0	26250	0.7806	0.925
0.0	36.0	27000	0.7835	0.9217
0.0039	37.0	27750	0.7879	0.925
0.0	38.0	28500	0.7885	0.9267
0.0	39.0	29250	0.7918	0.925
0.0	40.0	30000	0.7945	0.9267
0.0	41.0	30750	0.7955	0.9267
0.0	42.0	31500	0.7991	0.9233
0.0	43.0	32250	0.8003	0.925
0.0	44.0	33000	0.8023	0.925
0.0	45.0	33750	0.8041	0.925
0.0	46.0	34500	0.8060	0.925
0.0	47.0	35250	0.8084	0.925
0.0	48.0	36000	0.8088	0.925
0.0	49.0	36750	0.8102	0.9267
0.0	50.0	37500	0.8104	0.925

Framework versions

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