hushem_5x_deit_base_adamax_001_fold1

This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 5.3716
• Accuracy: 0.4444

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
1.4051	1.0	27	1.4119	0.2667
1.4631	2.0	54	1.4906	0.2444
1.1049	3.0	81	1.5988	0.2889
1.1893	4.0	108	1.5499	0.3778
0.8095	5.0	135	1.4187	0.3778
0.7867	6.0	162	1.5747	0.4444
0.8062	7.0	189	1.7621	0.3778
0.6185	8.0	216	1.6443	0.3778
0.6622	9.0	243	2.0501	0.4
0.7761	10.0	270	1.5776	0.3778
0.7478	11.0	297	1.4437	0.3556
0.6339	12.0	324	1.4821	0.4
0.6965	13.0	351	1.6662	0.3556
0.5829	14.0	378	1.5844	0.3778
0.4908	15.0	405	2.2476	0.3778
0.5234	16.0	432	1.3445	0.4222
0.4872	17.0	459	2.4054	0.3556
0.466	18.0	486	1.6395	0.4
0.4427	19.0	513	1.8006	0.4
0.3436	20.0	540	1.6496	0.4
0.3213	21.0	567	2.4490	0.3778
0.3024	22.0	594	1.9317	0.4222
0.3831	23.0	621	2.1520	0.3556
0.2575	24.0	648	2.1832	0.4
0.3022	25.0	675	2.7719	0.4222
0.2889	26.0	702	2.2904	0.3778
0.1794	27.0	729	2.3541	0.3778
0.1256	28.0	756	3.3430	0.4444
0.1586	29.0	783	2.5549	0.4
0.1387	30.0	810	2.5659	0.3778
0.0828	31.0	837	3.1241	0.4
0.0717	32.0	864	3.9424	0.4222
0.0424	33.0	891	4.2735	0.4444
0.0451	34.0	918	4.8532	0.4222
0.0483	35.0	945	5.1314	0.4444
0.0395	36.0	972	4.8009	0.4
0.0349	37.0	999	4.7979	0.4222
0.003	38.0	1026	5.0139	0.4222
0.0007	39.0	1053	5.1670	0.4222
0.0004	40.0	1080	5.3634	0.4444
0.0028	41.0	1107	5.3467	0.4667
0.0002	42.0	1134	5.3534	0.4667
0.0001	43.0	1161	5.3434	0.4667
0.0001	44.0	1188	5.3497	0.4444
0.0001	45.0	1215	5.3599	0.4444
0.0001	46.0	1242	5.3641	0.4444
0.0001	47.0	1269	5.3694	0.4444
0.0001	48.0	1296	5.3716	0.4444
0.0001	49.0	1323	5.3716	0.4444
0.0001	50.0	1350	5.3716	0.4444

Framework versions

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