README.md

metadata

license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_beit_base_adamax_00001_fold1
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9031719532554258

smids_1x_beit_base_adamax_00001_fold1

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: 0.6618
• Accuracy: 0.9032

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
0.4246	1.0	76	0.3687	0.8598
0.2552	2.0	152	0.2999	0.8798
0.1978	3.0	228	0.2886	0.8731
0.1972	4.0	304	0.2763	0.8865
0.1608	5.0	380	0.2799	0.8865
0.1346	6.0	456	0.3048	0.8815
0.0943	7.0	532	0.3402	0.8898
0.0622	8.0	608	0.3287	0.8915
0.0613	9.0	684	0.3634	0.8865
0.0585	10.0	760	0.3905	0.8881
0.0328	11.0	836	0.3830	0.8948
0.0344	12.0	912	0.4094	0.8915
0.053	13.0	988	0.4103	0.8932
0.0261	14.0	1064	0.4498	0.8932
0.0261	15.0	1140	0.4936	0.8915
0.0343	16.0	1216	0.4859	0.8932
0.0153	17.0	1292	0.5143	0.8815
0.0038	18.0	1368	0.5271	0.8865
0.0046	19.0	1444	0.5417	0.8898
0.0282	20.0	1520	0.5283	0.8948
0.0048	21.0	1596	0.5421	0.8965
0.0018	22.0	1672	0.5503	0.8898
0.0064	23.0	1748	0.5860	0.8848
0.0241	24.0	1824	0.5762	0.8948
0.0207	25.0	1900	0.5869	0.8915
0.0293	26.0	1976	0.5842	0.8948
0.0029	27.0	2052	0.6141	0.8932
0.0198	28.0	2128	0.6046	0.8982
0.0329	29.0	2204	0.6286	0.8948
0.0036	30.0	2280	0.6053	0.8948
0.0339	31.0	2356	0.6159	0.8881
0.0211	32.0	2432	0.6253	0.8932
0.0315	33.0	2508	0.6357	0.8915
0.0135	34.0	2584	0.6365	0.8932
0.0361	35.0	2660	0.6309	0.8965
0.0313	36.0	2736	0.6365	0.8965
0.0198	37.0	2812	0.6348	0.8965
0.0132	38.0	2888	0.6243	0.8948
0.0085	39.0	2964	0.6351	0.8948
0.001	40.0	3040	0.6372	0.8948
0.0149	41.0	3116	0.6607	0.8998
0.0056	42.0	3192	0.6570	0.9065
0.0011	43.0	3268	0.6635	0.8998
0.003	44.0	3344	0.6527	0.8982
0.041	45.0	3420	0.6537	0.8982
0.0011	46.0	3496	0.6576	0.8982
0.0196	47.0	3572	0.6599	0.8998
0.0117	48.0	3648	0.6620	0.9032
0.0018	49.0	3724	0.6617	0.9032
0.0144	50.0	3800	0.6618	0.9032

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu118
• Datasets 2.15.0
• Tokenizers 0.15.0