smids_10x_deit_small_rms_00001_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: 0.9311
• Accuracy: 0.9167

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
0.1779	1.0	750	0.2473	0.905
0.139	2.0	1500	0.3262	0.8817
0.0937	3.0	2250	0.2997	0.9133
0.0255	4.0	3000	0.4034	0.9033
0.0426	5.0	3750	0.4840	0.9133
0.0099	6.0	4500	0.7148	0.9017
0.0272	7.0	5250	0.7135	0.9
0.0013	8.0	6000	0.7156	0.91
0.0252	9.0	6750	0.7066	0.9
0.0	10.0	7500	0.7258	0.91
0.0281	11.0	8250	0.8120	0.8967
0.0	12.0	9000	0.7428	0.91
0.0001	13.0	9750	0.7455	0.9183
0.0272	14.0	10500	0.7891	0.92
0.0	15.0	11250	0.8803	0.8967
0.0	16.0	12000	0.8867	0.9
0.0025	17.0	12750	0.8600	0.9067
0.0	18.0	13500	0.7993	0.9183
0.0	19.0	14250	0.8779	0.9133
0.0	20.0	15000	0.8996	0.9117
0.0004	21.0	15750	0.9765	0.8917
0.0157	22.0	16500	0.7715	0.92
0.0	23.0	17250	0.7227	0.91
0.0	24.0	18000	0.7725	0.9167
0.0	25.0	18750	0.8320	0.9117
0.0004	26.0	19500	0.9795	0.8967
0.0	27.0	20250	0.8537	0.9183
0.0	28.0	21000	0.8796	0.9033
0.0	29.0	21750	0.8896	0.9067
0.0035	30.0	22500	0.9700	0.9033
0.0	31.0	23250	0.8273	0.9117
0.0	32.0	24000	0.8778	0.91
0.0	33.0	24750	0.8576	0.9117
0.0	34.0	25500	0.8235	0.9167
0.0	35.0	26250	0.8389	0.9133
0.0	36.0	27000	0.8611	0.9133
0.0052	37.0	27750	0.9201	0.91
0.0	38.0	28500	0.9394	0.9117
0.0	39.0	29250	0.9985	0.91
0.0	40.0	30000	0.9682	0.9133
0.0	41.0	30750	0.9333	0.915
0.0	42.0	31500	0.9270	0.9167
0.0	43.0	32250	0.9299	0.915
0.0	44.0	33000	0.9241	0.9133
0.0	45.0	33750	0.9269	0.9133
0.0	46.0	34500	0.9286	0.915
0.0	47.0	35250	0.9293	0.915
0.0	48.0	36000	0.9293	0.915
0.0	49.0	36750	0.9307	0.915
0.0	50.0	37500	0.9311	0.9167

Framework versions

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