smids_1x_deit_small_sgd_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: 1.0422
• Accuracy: 0.4983

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
1.0773	1.0	75	1.0761	0.4267
1.0923	2.0	150	1.0743	0.43
1.0827	3.0	225	1.0725	0.4317
1.0592	4.0	300	1.0709	0.4333
1.0688	5.0	375	1.0693	0.43
1.0722	6.0	450	1.0678	0.4317
1.0759	7.0	525	1.0664	0.4317
1.0583	8.0	600	1.0650	0.4333
1.0565	9.0	675	1.0637	0.4383
1.0589	10.0	750	1.0624	0.44
1.0608	11.0	825	1.0612	0.4483
1.0706	12.0	900	1.0600	0.4517
1.0517	13.0	975	1.0589	0.4567
1.0525	14.0	1050	1.0579	0.4567
1.0257	15.0	1125	1.0569	0.4583
1.0608	16.0	1200	1.0559	0.4617
1.0548	17.0	1275	1.0550	0.46
1.0482	18.0	1350	1.0541	0.4617
1.0606	19.0	1425	1.0533	0.4633
1.0832	20.0	1500	1.0524	0.4667
1.0387	21.0	1575	1.0517	0.4717
1.0524	22.0	1650	1.0510	0.4733
1.043	23.0	1725	1.0503	0.4733
1.0404	24.0	1800	1.0496	0.475
1.0507	25.0	1875	1.0490	0.4767
1.026	26.0	1950	1.0484	0.48
1.0409	27.0	2025	1.0478	0.48
1.0569	28.0	2100	1.0473	0.4867
1.0416	29.0	2175	1.0468	0.4867
1.0319	30.0	2250	1.0463	0.4867
1.0368	31.0	2325	1.0459	0.49
1.0498	32.0	2400	1.0455	0.4933
1.0315	33.0	2475	1.0451	0.4933
1.0281	34.0	2550	1.0447	0.49
1.0165	35.0	2625	1.0444	0.4917
1.0233	36.0	2700	1.0441	0.4933
1.0217	37.0	2775	1.0438	0.4967
1.0413	38.0	2850	1.0435	0.4967
1.0419	39.0	2925	1.0433	0.4967
1.0408	40.0	3000	1.0431	0.4967
1.0269	41.0	3075	1.0429	0.4983
1.0155	42.0	3150	1.0428	0.4983
1.0319	43.0	3225	1.0426	0.4983
1.015	44.0	3300	1.0425	0.4983
1.0304	45.0	3375	1.0424	0.4983
1.037	46.0	3450	1.0424	0.4983
1.0444	47.0	3525	1.0423	0.4983
1.0465	48.0	3600	1.0423	0.4983
1.0337	49.0	3675	1.0423	0.4983
1.0221	50.0	3750	1.0422	0.4983

Framework versions

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