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: 0.5641
Accuracy: 0.7746

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
0.9582	1.0	751	0.8489	0.5409
0.6559	2.0	1502	0.7002	0.6861
0.7114	3.0	2253	0.6777	0.6761
0.6817	4.0	3004	0.6465	0.6928
0.7123	5.0	3755	0.7746	0.6277
0.7345	6.0	4506	0.6493	0.7062
0.5686	7.0	5257	0.6822	0.7145
0.6113	8.0	6008	0.7258	0.6594
0.5841	9.0	6759	0.7856	0.6694
0.5988	10.0	7510	0.5594	0.7746
0.5433	11.0	8261	0.5624	0.7696
0.5465	12.0	9012	0.6273	0.7396
0.5648	13.0	9763	0.5471	0.7646
0.5274	14.0	10514	0.5762	0.7596
0.5506	15.0	11265	0.6408	0.7279
0.5782	16.0	12016	0.6203	0.7346
0.6505	17.0	12767	0.6410	0.7429
0.5965	18.0	13518	0.6045	0.7245
0.5759	19.0	14269	0.6441	0.7229
0.6397	20.0	15020	0.5941	0.7529
0.5964	21.0	15771	0.5925	0.7496
0.5874	22.0	16522	0.5857	0.7462
0.4809	23.0	17273	0.5684	0.7596
0.5688	24.0	18024	0.5817	0.7663
0.5465	25.0	18775	0.5474	0.7663
0.5665	26.0	19526	0.7830	0.6611
0.5896	27.0	20277	0.5680	0.7513
0.6236	28.0	21028	0.6568	0.6811
0.5166	29.0	21779	0.6086	0.7446
0.452	30.0	22530	0.5634	0.7579
0.4919	31.0	23281	0.5616	0.7780
0.5586	32.0	24032	0.5217	0.7679
0.4558	33.0	24783	0.5662	0.7513
0.4855	34.0	25534	0.5721	0.7613
0.4522	35.0	26285	0.5872	0.7663
0.527	36.0	27036	0.5260	0.7696
0.5143	37.0	27787	0.5855	0.7429
0.4104	38.0	28538	0.5552	0.7713
0.3918	39.0	29289	0.5587	0.7763
0.4719	40.0	30040	0.5354	0.7863
0.4591	41.0	30791	0.5453	0.7629
0.3895	42.0	31542	0.5592	0.7746
0.4622	43.0	32293	0.5533	0.7796
0.3949	44.0	33044	0.5492	0.7947
0.4199	45.0	33795	0.5523	0.7780
0.4316	46.0	34546	0.5332	0.7896
0.3892	47.0	35297	0.5380	0.7846
0.3721	48.0	36048	0.5481	0.7880
0.3964	49.0	36799	0.5559	0.7846
0.3446	50.0	37550	0.5641	0.7746

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_10x_deit_tiny_rms_0001_fold1

smids_10x_deit_tiny_rms_0001_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_10x_deit_tiny_rms_0001_fold1