hushem_1x_deit_tiny_sgd_lr00001_fold4

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: 1.5565
• Accuracy: 0.1905

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: 1e-05
• 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	1.5632	0.1905
1.6067	2.0	12	1.5628	0.1905
1.6067	3.0	18	1.5625	0.1905
1.6234	4.0	24	1.5621	0.1905
1.6412	5.0	30	1.5618	0.1905
1.6412	6.0	36	1.5615	0.1905
1.6304	7.0	42	1.5612	0.1905
1.6304	8.0	48	1.5609	0.1905
1.6339	9.0	54	1.5606	0.1905
1.6208	10.0	60	1.5604	0.1905
1.6208	11.0	66	1.5601	0.1905
1.599	12.0	72	1.5598	0.1905
1.599	13.0	78	1.5596	0.1905
1.6454	14.0	84	1.5594	0.1905
1.5993	15.0	90	1.5591	0.1905
1.5993	16.0	96	1.5589	0.1905
1.6104	17.0	102	1.5587	0.1905
1.6104	18.0	108	1.5585	0.1905
1.5995	19.0	114	1.5584	0.1905
1.6359	20.0	120	1.5582	0.1905
1.6359	21.0	126	1.5580	0.1905
1.5868	22.0	132	1.5579	0.1905
1.5868	23.0	138	1.5577	0.1905
1.67	24.0	144	1.5576	0.1905
1.5662	25.0	150	1.5575	0.1905
1.5662	26.0	156	1.5573	0.1905
1.6118	27.0	162	1.5572	0.1905
1.6118	28.0	168	1.5571	0.1905
1.6163	29.0	174	1.5570	0.1905
1.6392	30.0	180	1.5569	0.1905
1.6392	31.0	186	1.5568	0.1905
1.6602	32.0	192	1.5568	0.1905
1.6602	33.0	198	1.5567	0.1905
1.5354	34.0	204	1.5567	0.1905
1.6205	35.0	210	1.5566	0.1905
1.6205	36.0	216	1.5566	0.1905
1.6201	37.0	222	1.5565	0.1905
1.6201	38.0	228	1.5565	0.1905
1.5923	39.0	234	1.5565	0.1905
1.6521	40.0	240	1.5565	0.1905
1.6521	41.0	246	1.5565	0.1905
1.6177	42.0	252	1.5565	0.1905
1.6177	43.0	258	1.5565	0.1905
1.6437	44.0	264	1.5565	0.1905
1.5948	45.0	270	1.5565	0.1905
1.5948	46.0	276	1.5565	0.1905
1.6236	47.0	282	1.5565	0.1905
1.6236	48.0	288	1.5565	0.1905
1.6168	49.0	294	1.5565	0.1905
1.6032	50.0	300	1.5565	0.1905

Framework versions

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