hushem_5x_deit_small_rms_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: 1.6694
• Accuracy: 0.5952

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
2.1638	1.0	28	1.7503	0.2381
1.4446	2.0	56	1.5611	0.2619
1.4481	3.0	84	1.4312	0.2381
1.3982	4.0	112	1.3919	0.2619
1.3867	5.0	140	1.4053	0.2619
1.382	6.0	168	1.3617	0.2619
1.2911	7.0	196	1.5439	0.4048
1.1486	8.0	224	1.1564	0.4286
1.0554	9.0	252	1.0568	0.4762
1.0402	10.0	280	0.8946	0.6190
0.9192	11.0	308	0.7214	0.7381
1.0116	12.0	336	0.8931	0.6905
0.9735	13.0	364	0.8359	0.6905
0.9105	14.0	392	0.6761	0.7619
0.8218	15.0	420	0.6339	0.7857
0.8745	16.0	448	0.7396	0.7619
0.8355	17.0	476	0.7738	0.7381
0.8644	18.0	504	0.6532	0.7619
0.8014	19.0	532	0.7016	0.7381
0.8685	20.0	560	0.7175	0.7381
0.7709	21.0	588	0.6588	0.7619
0.778	22.0	616	0.8635	0.7381
0.8232	23.0	644	0.6385	0.7143
0.891	24.0	672	0.7133	0.6667
0.714	25.0	700	0.6807	0.6905
0.6766	26.0	728	0.9128	0.6429
0.734	27.0	756	0.7515	0.6905
0.7087	28.0	784	0.6378	0.6905
0.6295	29.0	812	0.9113	0.6667
0.6414	30.0	840	0.9201	0.6190
0.6359	31.0	868	0.7354	0.7143
0.6485	32.0	896	0.6558	0.6429
0.6242	33.0	924	0.7790	0.6429
0.647	34.0	952	1.0490	0.5952
0.6524	35.0	980	0.7508	0.6667
0.5325	36.0	1008	0.9344	0.6667
0.476	37.0	1036	1.0580	0.5952
0.4941	38.0	1064	0.9380	0.7143
0.4232	39.0	1092	1.0384	0.5476
0.4302	40.0	1120	1.0844	0.6190
0.4057	41.0	1148	1.3995	0.5952
0.3483	42.0	1176	1.4823	0.5476
0.3043	43.0	1204	1.2186	0.6667
0.2598	44.0	1232	1.3028	0.5952
0.2113	45.0	1260	1.5042	0.6190
0.2104	46.0	1288	1.6174	0.5952
0.1769	47.0	1316	1.5011	0.6429
0.1341	48.0	1344	1.6784	0.5714
0.1239	49.0	1372	1.6694	0.5952
0.1545	50.0	1400	1.6694	0.5952

Framework versions

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