smids_3x_beit_base_adamax_00001_fold2

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

• Loss: 0.8293
• Accuracy: 0.8952

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
0.3036	1.0	375	0.2703	0.8918
0.2118	2.0	750	0.2674	0.8968
0.1557	3.0	1125	0.2889	0.8918
0.074	4.0	1500	0.2842	0.9002
0.0616	5.0	1875	0.3403	0.8935
0.036	6.0	2250	0.3534	0.9101
0.0382	7.0	2625	0.4309	0.8985
0.0686	8.0	3000	0.4834	0.8985
0.022	9.0	3375	0.5298	0.8935
0.0159	10.0	3750	0.5866	0.8985
0.0173	11.0	4125	0.5611	0.8968
0.0241	12.0	4500	0.6961	0.8869
0.0125	13.0	4875	0.6251	0.8952
0.0052	14.0	5250	0.6175	0.9002
0.0252	15.0	5625	0.6443	0.8935
0.0003	16.0	6000	0.6752	0.8952
0.0517	17.0	6375	0.6928	0.8985
0.0082	18.0	6750	0.6809	0.8985
0.0008	19.0	7125	0.7189	0.8935
0.012	20.0	7500	0.7838	0.9002
0.0206	21.0	7875	0.7183	0.8968
0.0006	22.0	8250	0.7126	0.9085
0.0002	23.0	8625	0.7379	0.8985
0.0002	24.0	9000	0.7747	0.8952
0.0001	25.0	9375	0.7907	0.8869
0.0001	26.0	9750	0.7652	0.8985
0.0153	27.0	10125	0.8239	0.8935
0.002	28.0	10500	0.7554	0.9018
0.0228	29.0	10875	0.8026	0.9002
0.0049	30.0	11250	0.7927	0.9052
0.003	31.0	11625	0.8114	0.8968
0.0086	32.0	12000	0.8422	0.8935
0.0066	33.0	12375	0.8193	0.8935
0.0014	34.0	12750	0.8462	0.9002
0.0005	35.0	13125	0.8418	0.8902
0.0031	36.0	13500	0.8633	0.8918
0.0051	37.0	13875	0.8436	0.8918
0.0003	38.0	14250	0.8576	0.8902
0.0037	39.0	14625	0.8301	0.8902
0.0238	40.0	15000	0.8339	0.8952
0.0147	41.0	15375	0.8449	0.8968
0.011	42.0	15750	0.8207	0.8968
0.0179	43.0	16125	0.8212	0.8968
0.0045	44.0	16500	0.8067	0.9018
0.023	45.0	16875	0.8396	0.8918
0.027	46.0	17250	0.8319	0.8935
0.0247	47.0	17625	0.8325	0.8935
0.019	48.0	18000	0.8318	0.8952
0.0136	49.0	18375	0.8279	0.8952
0.0009	50.0	18750	0.8293	0.8952

Framework versions

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