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: 2.3474
Accuracy: 0.6889

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
0.0591	1.0	215	0.9478	0.7111
0.0016	2.0	430	1.0737	0.6889
0.0003	3.0	645	1.0732	0.7111
0.0001	4.0	860	1.2338	0.7111
0.0	5.0	1075	1.3886	0.7111
0.0	6.0	1290	1.5328	0.6889
0.0	7.0	1505	1.6761	0.6889
0.0	8.0	1720	1.8802	0.6889
0.0	9.0	1935	2.1375	0.6889
0.0	10.0	2150	2.2804	0.6889
0.0	11.0	2365	2.5018	0.6667
0.0	12.0	2580	2.6034	0.7111
0.0	13.0	2795	2.1119	0.7556
0.0	14.0	3010	2.5118	0.7111
0.0	15.0	3225	2.4215	0.6889
0.0	16.0	3440	2.4416	0.6889
0.0	17.0	3655	2.4789	0.6889
0.0	18.0	3870	2.5530	0.6889
0.0	19.0	4085	2.6223	0.6889
0.0	20.0	4300	2.7198	0.6889
0.0	21.0	4515	2.8171	0.7111
0.0	22.0	4730	2.8585	0.7111
0.0	23.0	4945	2.8584	0.7111
0.0	24.0	5160	2.7240	0.7111
0.0	25.0	5375	2.6522	0.7111
0.0	26.0	5590	2.6766	0.7111
0.0	27.0	5805	2.6051	0.7333
0.0	28.0	6020	2.4780	0.7333
0.0	29.0	6235	2.4371	0.7333
0.0	30.0	6450	2.3680	0.7333
0.0	31.0	6665	2.3696	0.7111
0.0	32.0	6880	2.3638	0.7333
0.0	33.0	7095	2.3261	0.7333
0.0	34.0	7310	2.3611	0.7333
0.0	35.0	7525	2.3737	0.7333
0.0	36.0	7740	2.3371	0.6889
0.0	37.0	7955	2.3450	0.7111
0.0	38.0	8170	2.3727	0.6889
0.0	39.0	8385	2.3620	0.6889
0.0	40.0	8600	2.3928	0.6889
0.0	41.0	8815	2.3547	0.6889
0.0	42.0	9030	2.3935	0.6889
0.0	43.0	9245	2.3835	0.6889
0.0	44.0	9460	2.3407	0.6889
0.0	45.0	9675	2.3628	0.6889
0.0	46.0	9890	2.3464	0.6889
0.0	47.0	10105	2.3571	0.6889
0.0	48.0	10320	2.3604	0.6889
0.0	49.0	10535	2.3495	0.6889
0.0	50.0	10750	2.3474	0.6889

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/hushem_40x_deit_tiny_rms_00001_fold2

hushem_40x_deit_tiny_rms_00001_fold2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
hushem_40x_deit_tiny_rms_00001_fold2