smids_5x_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: 0.8603
• Accuracy: 0.905

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
0.2338	1.0	375	0.3930	0.8433
0.1865	2.0	750	0.3259	0.8733
0.1356	3.0	1125	0.2805	0.9033
0.0896	4.0	1500	0.3878	0.88
0.0385	5.0	1875	0.4177	0.8883
0.0314	6.0	2250	0.4802	0.8967
0.0588	7.0	2625	0.6345	0.895
0.0141	8.0	3000	0.7091	0.9033
0.0524	9.0	3375	0.8142	0.8817
0.0425	10.0	3750	0.7582	0.8983
0.0006	11.0	4125	0.7258	0.9
0.0097	12.0	4500	0.7403	0.9
0.0104	13.0	4875	0.9310	0.89
0.0001	14.0	5250	0.7672	0.9
0.0	15.0	5625	0.9240	0.8917
0.0003	16.0	6000	0.8712	0.8983
0.0135	17.0	6375	0.7633	0.9033
0.0335	18.0	6750	1.0118	0.8917
0.0155	19.0	7125	0.8189	0.905
0.0	20.0	7500	0.8004	0.8983
0.0	21.0	7875	1.0772	0.88
0.0255	22.0	8250	0.7694	0.91
0.0019	23.0	8625	0.8682	0.8983
0.0	24.0	9000	0.8775	0.8933
0.0	25.0	9375	0.9259	0.9017
0.0	26.0	9750	0.8433	0.895
0.0119	27.0	10125	0.9223	0.8983
0.0	28.0	10500	0.7870	0.91
0.0	29.0	10875	0.9279	0.895
0.0131	30.0	11250	0.9531	0.8933
0.0	31.0	11625	0.8850	0.8967
0.0	32.0	12000	0.8772	0.8983
0.0	33.0	12375	0.8996	0.8917
0.0	34.0	12750	0.9022	0.8983
0.0	35.0	13125	0.8990	0.8933
0.0	36.0	13500	0.8690	0.9033
0.0	37.0	13875	0.8890	0.9
0.0071	38.0	14250	0.8769	0.9017
0.0	39.0	14625	0.8323	0.9067
0.0	40.0	15000	0.8920	0.9033
0.0	41.0	15375	0.8465	0.9083
0.0	42.0	15750	0.8536	0.905
0.0	43.0	16125	0.8497	0.905
0.0	44.0	16500	0.8492	0.905
0.0	45.0	16875	0.8481	0.9067
0.0	46.0	17250	0.8573	0.9067
0.0029	47.0	17625	0.8575	0.9067
0.0	48.0	18000	0.8605	0.905
0.0	49.0	18375	0.8627	0.905
0.0013	50.0	18750	0.8603	0.905

Framework versions

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