smids_1x_deit_tiny_sgd_001_fold3

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: 0.3753
• Accuracy: 0.8433

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
1.1	1.0	75	1.0685	0.46
0.9367	2.0	150	0.9646	0.5417
0.8514	3.0	225	0.8770	0.6067
0.8379	4.0	300	0.8018	0.6683
0.6855	5.0	375	0.7375	0.715
0.6563	6.0	450	0.6796	0.7433
0.6233	7.0	525	0.6334	0.7633
0.5422	8.0	600	0.5966	0.7817
0.5439	9.0	675	0.5698	0.795
0.5302	10.0	750	0.5521	0.795
0.503	11.0	825	0.5277	0.8083
0.4486	12.0	900	0.5133	0.8083
0.4811	13.0	975	0.4988	0.8133
0.4014	14.0	1050	0.4857	0.815
0.3711	15.0	1125	0.4759	0.8217
0.4033	16.0	1200	0.4684	0.8167
0.4414	17.0	1275	0.4593	0.8233
0.3659	18.0	1350	0.4514	0.8233
0.3656	19.0	1425	0.4448	0.8233
0.4021	20.0	1500	0.4388	0.825
0.4189	21.0	1575	0.4325	0.8267
0.3524	22.0	1650	0.4276	0.8283
0.356	23.0	1725	0.4217	0.8283
0.3419	24.0	1800	0.4174	0.835
0.3528	25.0	1875	0.4128	0.83
0.3819	26.0	1950	0.4110	0.8233
0.3203	27.0	2025	0.4057	0.835
0.3243	28.0	2100	0.4027	0.8333
0.3594	29.0	2175	0.3998	0.835
0.3291	30.0	2250	0.3976	0.84
0.3185	31.0	2325	0.3939	0.84
0.3337	32.0	2400	0.3916	0.8383
0.3127	33.0	2475	0.3899	0.8367
0.3111	34.0	2550	0.3887	0.835
0.3206	35.0	2625	0.3862	0.8383
0.3237	36.0	2700	0.3848	0.845
0.2644	37.0	2775	0.3830	0.85
0.3198	38.0	2850	0.3820	0.845
0.3031	39.0	2925	0.3811	0.8417
0.2823	40.0	3000	0.3801	0.8433
0.2812	41.0	3075	0.3793	0.8417
0.3147	42.0	3150	0.3783	0.8417
0.2862	43.0	3225	0.3776	0.845
0.2684	44.0	3300	0.3773	0.8433
0.306	45.0	3375	0.3765	0.8467
0.2738	46.0	3450	0.3761	0.845
0.2493	47.0	3525	0.3758	0.8433
0.2906	48.0	3600	0.3756	0.8433
0.2971	49.0	3675	0.3754	0.8433
0.2597	50.0	3750	0.3753	0.8433

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0