smids_5x_deit_tiny_rms_0001_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: 0.9895
• Accuracy: 0.9067

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
0.3055	1.0	375	0.4440	0.825
0.2222	2.0	750	0.3224	0.8867
0.2186	3.0	1125	0.3702	0.8883
0.1592	4.0	1500	0.4759	0.85
0.0922	5.0	1875	0.4560	0.8767
0.0977	6.0	2250	0.5531	0.875
0.0567	7.0	2625	0.5054	0.8883
0.0612	8.0	3000	0.5016	0.9067
0.0471	9.0	3375	0.6558	0.895
0.0783	10.0	3750	0.7144	0.89
0.0337	11.0	4125	0.7483	0.8833
0.0522	12.0	4500	0.6408	0.8967
0.0122	13.0	4875	0.5578	0.8917
0.0456	14.0	5250	0.6886	0.9
0.0505	15.0	5625	0.6222	0.9067
0.0186	16.0	6000	0.7341	0.8867
0.0232	17.0	6375	0.6650	0.9083
0.0384	18.0	6750	0.6731	0.9133
0.0134	19.0	7125	0.7917	0.8883
0.0197	20.0	7500	0.7544	0.9033
0.006	21.0	7875	0.7694	0.8983
0.084	22.0	8250	0.7873	0.8917
0.0405	23.0	8625	0.7521	0.8967
0.0002	24.0	9000	0.9409	0.8883
0.0009	25.0	9375	0.8364	0.8967
0.0273	26.0	9750	0.7668	0.8933
0.001	27.0	10125	0.7995	0.88
0.0262	28.0	10500	0.8060	0.8883
0.0003	29.0	10875	0.7588	0.9083
0.0189	30.0	11250	0.9019	0.8867
0.0003	31.0	11625	1.0397	0.8867
0.0	32.0	12000	0.9253	0.895
0.0002	33.0	12375	0.8619	0.905
0.0003	34.0	12750	0.9328	0.9
0.0	35.0	13125	0.9364	0.905
0.0002	36.0	13500	0.9470	0.8967
0.0001	37.0	13875	0.9360	0.9033
0.0033	38.0	14250	1.0063	0.9033
0.0	39.0	14625	0.9618	0.9017
0.0	40.0	15000	0.9713	0.9083
0.0	41.0	15375	0.9440	0.9083
0.0	42.0	15750	0.9330	0.91
0.0	43.0	16125	0.9519	0.9083
0.0	44.0	16500	0.9407	0.905
0.0	45.0	16875	0.9804	0.9033
0.0	46.0	17250	0.9891	0.9033
0.0031	47.0	17625	0.9794	0.9033
0.0	48.0	18000	0.9842	0.9033
0.0	49.0	18375	0.9888	0.9067
0.0021	50.0	18750	0.9895	0.9067

Framework versions

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