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: 1.8993
Accuracy: 0.8047

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
0.8539	1.0	751	0.7969	0.6144
0.672	2.0	1502	0.7985	0.6194
0.6698	3.0	2253	0.6650	0.6962
0.6307	4.0	3004	0.6193	0.7379
0.6465	5.0	3755	0.8387	0.6244
0.6942	6.0	4506	0.5940	0.7329
0.5576	7.0	5257	0.5999	0.7312
0.5549	8.0	6008	0.6970	0.6795
0.5488	9.0	6759	0.5984	0.7229
0.5437	10.0	7510	0.5960	0.7412
0.5332	11.0	8261	0.5776	0.7362
0.5215	12.0	9012	0.5476	0.7880
0.5333	13.0	9763	0.5233	0.7713
0.4806	14.0	10514	0.5633	0.7629
0.4615	15.0	11265	0.5133	0.7830
0.535	16.0	12016	0.5400	0.7362
0.4592	17.0	12767	0.5660	0.7546
0.4186	18.0	13518	0.5267	0.7913
0.4986	19.0	14269	0.5193	0.7846
0.4777	20.0	15020	0.5053	0.7880
0.4305	21.0	15771	0.5032	0.7846
0.4268	22.0	16522	0.5151	0.7830
0.3969	23.0	17273	0.5356	0.7846
0.4042	24.0	18024	0.5574	0.7780
0.3889	25.0	18775	0.5262	0.7679
0.4127	26.0	19526	0.5352	0.7696
0.3984	27.0	20277	0.5102	0.7796
0.4155	28.0	21028	0.5170	0.8030
0.2889	29.0	21779	0.5194	0.8080
0.2798	30.0	22530	0.5140	0.8063
0.3345	31.0	23281	0.5542	0.8097
0.3266	32.0	24032	0.5467	0.7763
0.3139	33.0	24783	0.5408	0.8013
0.2727	34.0	25534	0.6157	0.7997
0.2326	35.0	26285	0.6453	0.8080
0.2862	36.0	27036	0.6184	0.8080
0.2315	37.0	27787	0.5988	0.8097
0.2292	38.0	28538	0.5807	0.8164
0.1347	39.0	29289	0.6678	0.8247
0.2469	40.0	30040	0.7259	0.8080
0.1108	41.0	30791	0.8996	0.7813
0.1298	42.0	31542	0.8857	0.8247
0.1746	43.0	32293	0.9061	0.8197
0.0873	44.0	33044	1.0379	0.8230
0.087	45.0	33795	1.2137	0.8130
0.1277	46.0	34546	1.3333	0.8197
0.069	47.0	35297	1.4653	0.8164
0.0206	48.0	36048	1.6979	0.8063
0.0279	49.0	36799	1.8534	0.8063
0.02	50.0	37550	1.8993	0.8047

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2

hkivancoral/smids_10x_deit_tiny_rms_001_fold1

smids_10x_deit_tiny_rms_001_fold1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hkivancoral/
smids_10x_deit_tiny_rms_001_fold1