hushem_5x_deit_tiny_adamax_00001_fold1

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: 1.3516
• Accuracy: 0.6444

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
1.3422	1.0	27	1.4246	0.2444
1.0091	2.0	54	1.3182	0.3333
0.8493	3.0	81	1.2550	0.3556
0.7491	4.0	108	1.2490	0.4
0.5567	5.0	135	1.2577	0.4444
0.4888	6.0	162	1.1346	0.4444
0.3998	7.0	189	1.0755	0.4889
0.3183	8.0	216	1.0806	0.5111
0.2348	9.0	243	1.0626	0.4667
0.1921	10.0	270	1.0210	0.5556
0.1327	11.0	297	1.0080	0.5556
0.0957	12.0	324	1.0134	0.5111
0.0834	13.0	351	1.0055	0.5556
0.0584	14.0	378	0.9707	0.5778
0.0407	15.0	405	0.9642	0.6
0.0299	16.0	432	0.9671	0.6222
0.0236	17.0	459	1.0493	0.6
0.0189	18.0	486	1.0428	0.6
0.0123	19.0	513	1.0847	0.6222
0.0089	20.0	540	1.0847	0.6667
0.0061	21.0	567	1.1226	0.6
0.0054	22.0	594	1.1899	0.6222
0.0037	23.0	621	1.2113	0.6444
0.0029	24.0	648	1.2131	0.6222
0.0025	25.0	675	1.2220	0.6222
0.0023	26.0	702	1.2400	0.6444
0.0022	27.0	729	1.2553	0.6444
0.0017	28.0	756	1.2656	0.6667
0.0018	29.0	783	1.2717	0.6444
0.0016	30.0	810	1.2861	0.6444
0.0014	31.0	837	1.2858	0.6444
0.0015	32.0	864	1.2948	0.6444
0.0012	33.0	891	1.3044	0.6444
0.0012	34.0	918	1.3113	0.6444
0.0011	35.0	945	1.3140	0.6444
0.0011	36.0	972	1.3218	0.6444
0.001	37.0	999	1.3254	0.6444
0.001	38.0	1026	1.3310	0.6444
0.0009	39.0	1053	1.3375	0.6444
0.0009	40.0	1080	1.3355	0.6444
0.0009	41.0	1107	1.3390	0.6444
0.0009	42.0	1134	1.3441	0.6444
0.0008	43.0	1161	1.3451	0.6444
0.0008	44.0	1188	1.3481	0.6444
0.0009	45.0	1215	1.3501	0.6444
0.0008	46.0	1242	1.3500	0.6444
0.0008	47.0	1269	1.3513	0.6444
0.0008	48.0	1296	1.3516	0.6444
0.0008	49.0	1323	1.3516	0.6444
0.0008	50.0	1350	1.3516	0.6444

Framework versions

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