hushem_40x_deit_small_sgd_001_fold4

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: 0.2365
• Accuracy: 0.8810

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
1.2757	1.0	219	1.3298	0.2619
1.0766	2.0	438	1.1919	0.4048
0.9095	3.0	657	1.0786	0.5476
0.7507	4.0	876	0.9821	0.5476
0.6994	5.0	1095	0.8850	0.5952
0.5864	6.0	1314	0.8204	0.6429
0.4328	7.0	1533	0.7576	0.6905
0.4293	8.0	1752	0.6999	0.7143
0.3464	9.0	1971	0.6320	0.7143
0.3175	10.0	2190	0.5956	0.7381
0.2382	11.0	2409	0.5588	0.7381
0.2672	12.0	2628	0.5195	0.7381
0.2016	13.0	2847	0.4850	0.8095
0.1832	14.0	3066	0.4528	0.8095
0.1406	15.0	3285	0.4338	0.8333
0.1305	16.0	3504	0.3948	0.8571
0.1504	17.0	3723	0.3785	0.8571
0.1139	18.0	3942	0.3689	0.8571
0.096	19.0	4161	0.3548	0.8571
0.0869	20.0	4380	0.3393	0.8571
0.0874	21.0	4599	0.3057	0.8571
0.0797	22.0	4818	0.2990	0.8571
0.0596	23.0	5037	0.2862	0.8571
0.053	24.0	5256	0.3012	0.8810
0.0562	25.0	5475	0.2885	0.8810
0.0463	26.0	5694	0.2676	0.8810
0.0374	27.0	5913	0.2870	0.8810
0.037	28.0	6132	0.2638	0.8810
0.0341	29.0	6351	0.2690	0.8810
0.0327	30.0	6570	0.2566	0.8810
0.0238	31.0	6789	0.2611	0.8810
0.0256	32.0	7008	0.2643	0.8810
0.0284	33.0	7227	0.2717	0.8810
0.0213	34.0	7446	0.2627	0.8810
0.0191	35.0	7665	0.2395	0.8810
0.0246	36.0	7884	0.2517	0.8810
0.0207	37.0	8103	0.2515	0.8810
0.0134	38.0	8322	0.2484	0.8810
0.0162	39.0	8541	0.2279	0.8810
0.0165	40.0	8760	0.2516	0.8810
0.0146	41.0	8979	0.2253	0.8810
0.0168	42.0	9198	0.2425	0.8810
0.0155	43.0	9417	0.2370	0.8810
0.0145	44.0	9636	0.2352	0.8810
0.0118	45.0	9855	0.2414	0.8810
0.0107	46.0	10074	0.2338	0.8810
0.0124	47.0	10293	0.2350	0.8810
0.0125	48.0	10512	0.2352	0.8810
0.0138	49.0	10731	0.2367	0.8810
0.0183	50.0	10950	0.2365	0.8810

Framework versions

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