hushem_5x_deit_base_adamax_001_fold5

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: 2.0054
• Accuracy: 0.7317

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
1.4493	1.0	28	1.4115	0.2439
1.4085	2.0	56	1.2905	0.2927
1.0193	3.0	84	1.4163	0.5366
1.1237	4.0	112	0.8304	0.6585
0.9964	5.0	140	0.7827	0.6585
0.9566	6.0	168	0.6329	0.7317
0.8689	7.0	196	0.6958	0.5854
0.8599	8.0	224	0.5797	0.7805
0.7599	9.0	252	0.9330	0.7805
0.7635	10.0	280	0.7011	0.6829
0.6603	11.0	308	0.9167	0.6829
0.7173	12.0	336	0.9009	0.5854
0.6949	13.0	364	0.5844	0.8049
0.5978	14.0	392	0.9237	0.7805
0.6548	15.0	420	0.5173	0.8049
0.5794	16.0	448	0.9750	0.7073
0.5927	17.0	476	0.8636	0.8049
0.4408	18.0	504	0.5076	0.8537
0.5047	19.0	532	0.9978	0.7073
0.5155	20.0	560	0.8993	0.7805
0.3022	21.0	588	1.0654	0.7805
0.3634	22.0	616	1.0189	0.8049
0.3346	23.0	644	0.9586	0.7805
0.2995	24.0	672	0.9302	0.7317
0.345	25.0	700	1.2111	0.7561
0.2746	26.0	728	1.7821	0.6585
0.1747	27.0	756	2.2030	0.6585
0.214	28.0	784	1.2078	0.6585
0.0609	29.0	812	1.3388	0.8049
0.0765	30.0	840	1.4109	0.7561
0.0654	31.0	868	1.4789	0.7561
0.0843	32.0	896	1.4884	0.7073
0.0165	33.0	924	2.0871	0.6341
0.0138	34.0	952	2.0174	0.6341
0.0253	35.0	980	2.0599	0.6098
0.0093	36.0	1008	1.7213	0.7317
0.0034	37.0	1036	1.8852	0.7561
0.0001	38.0	1064	1.8415	0.7561
0.0014	39.0	1092	1.8486	0.7073
0.001	40.0	1120	1.8899	0.7561
0.0001	41.0	1148	1.9569	0.7317
0.0001	42.0	1176	1.9763	0.7317
0.0001	43.0	1204	1.9852	0.7317
0.0001	44.0	1232	1.9927	0.7317
0.0001	45.0	1260	1.9966	0.7317
0.0	46.0	1288	2.0017	0.7317
0.0	47.0	1316	2.0041	0.7317
0.0001	48.0	1344	2.0053	0.7317
0.0	49.0	1372	2.0054	0.7317
0.0	50.0	1400	2.0054	0.7317

Framework versions

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