hushem_5x_deit_tiny_rms_001_fold3

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: 0.9822
• Accuracy: 0.6977

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
1.9654	1.0	28	2.6283	0.2558
1.5373	2.0	56	2.1986	0.2558
1.4995	3.0	84	1.6042	0.2558
1.4355	4.0	112	1.6838	0.2558
1.4504	5.0	140	1.5958	0.2326
1.4097	6.0	168	1.4017	0.2791
1.4297	7.0	196	1.5664	0.2326
1.4137	8.0	224	1.4485	0.2558
1.3745	9.0	252	1.2801	0.4186
1.2697	10.0	280	1.2764	0.3256
1.1321	11.0	308	1.5227	0.3256
1.1096	12.0	336	1.2384	0.3953
1.0727	13.0	364	1.1395	0.4884
1.0037	14.0	392	1.3856	0.3953
1.0402	15.0	420	1.1134	0.5116
1.0378	16.0	448	1.2506	0.4419
0.958	17.0	476	1.1080	0.4651
0.9953	18.0	504	1.2467	0.4884
0.9958	19.0	532	1.0807	0.5814
0.9467	20.0	560	1.1055	0.4186
0.9535	21.0	588	1.1974	0.5116
0.9184	22.0	616	1.1307	0.4186
0.9252	23.0	644	1.0833	0.5116
0.8662	24.0	672	1.1623	0.5349
0.8421	25.0	700	0.9575	0.5814
0.8602	26.0	728	1.1189	0.5581
0.923	27.0	756	1.3369	0.5116
0.8226	28.0	784	1.0806	0.6279
0.8183	29.0	812	1.2385	0.4186
0.801	30.0	840	0.8599	0.6744
0.7516	31.0	868	1.3471	0.4884
0.7555	32.0	896	1.0726	0.5814
0.7219	33.0	924	0.8253	0.6977
0.7341	34.0	952	0.9501	0.6744
0.7645	35.0	980	0.9024	0.6512
0.6775	36.0	1008	0.6982	0.6977
0.6942	37.0	1036	0.8138	0.6744
0.6421	38.0	1064	1.1443	0.6279
0.6108	39.0	1092	0.7170	0.6977
0.7595	40.0	1120	0.7538	0.6744
0.6409	41.0	1148	1.2761	0.5581
0.6168	42.0	1176	1.0481	0.6279
0.5155	43.0	1204	0.7647	0.7209
0.5674	44.0	1232	0.9942	0.6744
0.5763	45.0	1260	0.8142	0.6977
0.4817	46.0	1288	0.8614	0.6977
0.4723	47.0	1316	1.0386	0.6512
0.4863	48.0	1344	0.9689	0.7209
0.4909	49.0	1372	0.9822	0.6977
0.4786	50.0	1400	0.9822	0.6977

Framework versions

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