hushem_1x_deit_tiny_rms_lr001_fold2

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: 1.2600
• Accuracy: 0.3556

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
No log	1.0	6	2.0874	0.2444
4.6196	2.0	12	2.3422	0.2444
4.6196	3.0	18	1.7914	0.2444
1.8086	4.0	24	1.6082	0.2667
1.5901	5.0	30	1.5144	0.2444
1.5901	6.0	36	1.6190	0.2444
1.5211	7.0	42	1.5231	0.2444
1.5211	8.0	48	1.5027	0.2444
1.4477	9.0	54	1.4266	0.2444
1.4394	10.0	60	1.4345	0.2444
1.4394	11.0	66	1.3152	0.4444
1.3604	12.0	72	1.3376	0.2444
1.3604	13.0	78	1.3260	0.2667
1.3864	14.0	84	1.5120	0.2444
1.3555	15.0	90	1.2685	0.3556
1.3555	16.0	96	1.1751	0.4444
1.3501	17.0	102	1.2648	0.4444
1.3501	18.0	108	1.3992	0.3778
1.2496	19.0	114	1.4208	0.2889
1.2587	20.0	120	1.1782	0.4444
1.2587	21.0	126	1.2882	0.4444
1.2321	22.0	132	1.3142	0.4444
1.2321	23.0	138	1.1784	0.4222
1.1985	24.0	144	1.2247	0.3778
1.234	25.0	150	1.2329	0.3778
1.234	26.0	156	1.2482	0.3556
1.1913	27.0	162	1.4153	0.3111
1.1913	28.0	168	1.2994	0.3333
1.1911	29.0	174	1.1400	0.4667
1.1955	30.0	180	1.2156	0.3778
1.1955	31.0	186	1.2232	0.4
1.1751	32.0	192	1.3853	0.2889
1.1751	33.0	198	1.2309	0.3333
1.1328	34.0	204	1.2338	0.3333
1.195	35.0	210	1.2383	0.3333
1.195	36.0	216	1.2991	0.3778
1.1661	37.0	222	1.3228	0.3556
1.1661	38.0	228	1.2550	0.3778
1.0748	39.0	234	1.2591	0.3556
1.1122	40.0	240	1.2234	0.3778
1.1122	41.0	246	1.2608	0.3556
1.102	42.0	252	1.2600	0.3556
1.102	43.0	258	1.2600	0.3556
1.0792	44.0	264	1.2600	0.3556
1.1126	45.0	270	1.2600	0.3556
1.1126	46.0	276	1.2600	0.3556
1.0704	47.0	282	1.2600	0.3556
1.0704	48.0	288	1.2600	0.3556
1.1302	49.0	294	1.2600	0.3556
1.0797	50.0	300	1.2600	0.3556

Framework versions

• Transformers 4.35.0
• Pytorch 2.1.0+cu118
• Datasets 2.14.6
• Tokenizers 0.14.1