hushem_5x_deit_tiny_adamax_001_fold5

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: 2.7115
• Accuracy: 0.6585

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
1.4348	1.0	28	1.3734	0.2439
1.3624	2.0	56	1.4229	0.2683
1.1348	3.0	84	1.1315	0.3902
0.9863	4.0	112	1.0099	0.6829
0.9002	5.0	140	0.8153	0.7317
0.8747	6.0	168	0.8078	0.7317
0.7431	7.0	196	0.8202	0.7073
0.7236	8.0	224	0.6730	0.7073
0.7214	9.0	252	0.7811	0.6829
0.7661	10.0	280	0.8373	0.6341
0.6997	11.0	308	0.7829	0.7073
0.5964	12.0	336	0.9580	0.5366
0.601	13.0	364	0.8593	0.6341
0.4989	14.0	392	0.8291	0.7317
0.484	15.0	420	0.8268	0.7317
0.3579	16.0	448	0.8735	0.6585
0.3201	17.0	476	1.3019	0.6341
0.2054	18.0	504	1.2022	0.6829
0.2162	19.0	532	1.3723	0.6098
0.2359	20.0	560	2.1538	0.5854
0.1213	21.0	588	1.4495	0.6829
0.1657	22.0	616	1.5861	0.6341
0.2091	23.0	644	1.3652	0.6585
0.0692	24.0	672	1.7622	0.6585
0.1092	25.0	700	2.0505	0.6585
0.0584	26.0	728	2.2675	0.5610
0.0661	27.0	756	1.7051	0.7073
0.0353	28.0	784	1.9468	0.6585
0.0164	29.0	812	2.4092	0.6341
0.0019	30.0	840	2.7744	0.6585
0.0033	31.0	868	3.2900	0.5610
0.0105	32.0	896	2.4900	0.5854
0.0008	33.0	924	2.5105	0.6341
0.0047	34.0	952	2.0758	0.7073
0.0004	35.0	980	2.7140	0.6585
0.0	36.0	1008	2.9025	0.6585
0.0013	37.0	1036	2.6654	0.6585
0.0	38.0	1064	2.6558	0.6829
0.0	39.0	1092	2.6667	0.6585
0.0	40.0	1120	2.6779	0.6585
0.0	41.0	1148	2.6850	0.6585
0.0	42.0	1176	2.6917	0.6585
0.0	43.0	1204	2.6986	0.6585
0.0	44.0	1232	2.7032	0.6585
0.0	45.0	1260	2.7065	0.6585
0.0	46.0	1288	2.7090	0.6585
0.0	47.0	1316	2.7107	0.6585
0.0	48.0	1344	2.7115	0.6585
0.0	49.0	1372	2.7115	0.6585
0.0	50.0	1400	2.7115	0.6585

Framework versions

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