hushem_40x_beit_large_adamax_00001_fold3

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: 1.0094
• Accuracy: 0.8837

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
0.0088	1.0	217	0.5009	0.8605
0.0048	2.0	434	0.5720	0.8837
0.0002	3.0	651	0.6684	0.8605
0.0005	4.0	868	0.6185	0.8605
0.0001	5.0	1085	0.7115	0.8837
0.0002	6.0	1302	0.7630	0.8837
0.0001	7.0	1519	0.6588	0.8837
0.0	8.0	1736	0.6227	0.8837
0.0001	9.0	1953	0.5468	0.9070
0.0	10.0	2170	0.7021	0.8837
0.0	11.0	2387	0.7605	0.8605
0.0002	12.0	2604	0.7994	0.8837
0.0	13.0	2821	1.0881	0.8372
0.0002	14.0	3038	0.8413	0.8605
0.0002	15.0	3255	0.9237	0.8837
0.0	16.0	3472	0.9623	0.8605
0.0	17.0	3689	0.9912	0.8605
0.0001	18.0	3906	0.7287	0.9070
0.0	19.0	4123	0.9687	0.8372
0.0	20.0	4340	0.6790	0.9070
0.0	21.0	4557	0.8424	0.9070
0.0	22.0	4774	0.7674	0.9070
0.0	23.0	4991	0.8450	0.9070
0.0	24.0	5208	0.8947	0.8837
0.0	25.0	5425	0.8485	0.8837
0.0	26.0	5642	0.9138	0.8837
0.0	27.0	5859	0.9516	0.8837
0.0	28.0	6076	0.8628	0.9070
0.0	29.0	6293	0.9458	0.8837
0.0	30.0	6510	0.9582	0.8837
0.0	31.0	6727	1.1730	0.8837
0.0	32.0	6944	1.0331	0.8837
0.0	33.0	7161	1.1055	0.8605
0.0	34.0	7378	0.9893	0.8837
0.0	35.0	7595	1.0353	0.8837
0.0	36.0	7812	1.0373	0.8837
0.0	37.0	8029	1.0358	0.8837
0.0	38.0	8246	1.0426	0.8837
0.0	39.0	8463	1.1391	0.8837
0.0	40.0	8680	1.0647	0.8837
0.0	41.0	8897	1.0082	0.8837
0.0	42.0	9114	1.0681	0.8837
0.0	43.0	9331	1.0189	0.8837
0.0	44.0	9548	1.0129	0.8837
0.0	45.0	9765	1.0237	0.8837
0.0	46.0	9982	1.0239	0.8837
0.0	47.0	10199	1.0008	0.8837
0.0	48.0	10416	1.0075	0.8837
0.0001	49.0	10633	1.0115	0.8837
0.0	50.0	10850	1.0094	0.8837

Framework versions

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