smids_10x_beit_large_adamax_00001_fold1

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

• Loss: 0.8887
• Accuracy: 0.9282

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
0.1288	1.0	751	0.2785	0.9065
0.0676	2.0	1502	0.3146	0.9149
0.0264	3.0	2253	0.4181	0.9115
0.025	4.0	3004	0.5488	0.9199
0.0069	5.0	3755	0.5526	0.9182
0.0049	6.0	4506	0.6296	0.9165
0.0005	7.0	5257	0.7054	0.9149
0.0001	8.0	6008	0.7404	0.9182
0.0362	9.0	6759	0.7520	0.9132
0.0001	10.0	7510	0.8011	0.9149
0.0001	11.0	8261	0.7591	0.9199
0.0002	12.0	9012	0.7216	0.9215
0.0024	13.0	9763	0.8101	0.9132
0.0	14.0	10514	0.8382	0.9249
0.0	15.0	11265	0.8571	0.9165
0.0	16.0	12016	0.8307	0.9249
0.0002	17.0	12767	0.8135	0.9098
0.0	18.0	13518	0.9070	0.9132
0.0	19.0	14269	0.8650	0.9115
0.0	20.0	15020	0.8297	0.9265
0.0	21.0	15771	0.8359	0.9282
0.0	22.0	16522	0.8827	0.9265
0.0	23.0	17273	0.8484	0.9215
0.0	24.0	18024	0.8739	0.9182
0.0004	25.0	18775	0.8728	0.9232
0.0	26.0	19526	0.8742	0.9149
0.0	27.0	20277	0.9029	0.9199
0.0	28.0	21028	0.8812	0.9232
0.0109	29.0	21779	0.9326	0.9215
0.0	30.0	22530	0.9197	0.9115
0.0001	31.0	23281	0.8910	0.9215
0.0	32.0	24032	0.8659	0.9215
0.0	33.0	24783	0.8759	0.9232
0.0	34.0	25534	0.9176	0.9199
0.0	35.0	26285	0.8674	0.9249
0.0	36.0	27036	0.8364	0.9249
0.0	37.0	27787	0.8518	0.9265
0.0	38.0	28538	0.8614	0.9232
0.0	39.0	29289	0.8789	0.9215
0.0	40.0	30040	0.8979	0.9215
0.0	41.0	30791	0.9262	0.9199
0.0107	42.0	31542	0.8969	0.9232
0.0	43.0	32293	0.9021	0.9265
0.0	44.0	33044	0.8921	0.9282
0.0	45.0	33795	0.9002	0.9249
0.0007	46.0	34546	0.9147	0.9199
0.0	47.0	35297	0.8904	0.9249
0.0	48.0	36048	0.8842	0.9282
0.0	49.0	36799	0.8899	0.9265
0.0	50.0	37550	0.8887	0.9282

Framework versions

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