smids_10x_deit_small_rms_00001_fold1

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.8415
• Accuracy: 0.9132

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
0.2066	1.0	751	0.2884	0.8948
0.1058	2.0	1502	0.3397	0.8898
0.0676	3.0	2253	0.3801	0.9098
0.0667	4.0	3004	0.5510	0.8998
0.0253	5.0	3755	0.5205	0.9048
0.0216	6.0	4506	0.6002	0.9082
0.0362	7.0	5257	0.8486	0.8815
0.0357	8.0	6008	0.6658	0.9082
0.035	9.0	6759	0.5803	0.9065
0.001	10.0	7510	0.6719	0.9082
0.0231	11.0	8261	0.7324	0.9098
0.0001	12.0	9012	0.6703	0.9082
0.0008	13.0	9763	0.8412	0.8965
0.0582	14.0	10514	0.7418	0.9115
0.0077	15.0	11265	0.8736	0.8998
0.0	16.0	12016	0.7725	0.9115
0.0019	17.0	12767	0.7981	0.9065
0.0064	18.0	13518	1.0317	0.8848
0.0	19.0	14269	0.8686	0.9048
0.0	20.0	15020	0.8083	0.9082
0.0	21.0	15771	0.6862	0.9115
0.0	22.0	16522	0.7317	0.9132
0.0	23.0	17273	0.6937	0.9182
0.0	24.0	18024	0.7497	0.9199
0.0	25.0	18775	0.8180	0.9098
0.0001	26.0	19526	0.8680	0.9098
0.0	27.0	20277	0.8268	0.9115
0.0	28.0	21028	0.8126	0.9082
0.0042	29.0	21779	0.8397	0.9048
0.024	30.0	22530	0.8418	0.9098
0.0	31.0	23281	0.8800	0.9065
0.0	32.0	24032	0.8577	0.9065
0.0	33.0	24783	0.7988	0.9048
0.0	34.0	25534	0.8415	0.9032
0.0	35.0	26285	0.8311	0.9132
0.0	36.0	27036	0.8203	0.9149
0.0	37.0	27787	0.8136	0.9165
0.0	38.0	28538	0.8129	0.9199
0.0	39.0	29289	0.8165	0.9199
0.0	40.0	30040	0.8151	0.9149
0.0	41.0	30791	0.8242	0.9115
0.0	42.0	31542	0.8225	0.9115
0.0	43.0	32293	0.8294	0.9132
0.0	44.0	33044	0.8299	0.9132
0.0	45.0	33795	0.8337	0.9132
0.0	46.0	34546	0.8327	0.9132
0.0	47.0	35297	0.8358	0.9132
0.0	48.0	36048	0.8374	0.9132
0.0	49.0	36799	0.8388	0.9115
0.0	50.0	37550	0.8415	0.9132

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2