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: hushem_5x_beit_base_adamax_0001_fold2
    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: 0.8666666666666667

hushem_5x_beit_base_adamax_0001_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: 1.1073
• Accuracy: 0.8667

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: 0.0001
• 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
0.56	1.0	27	0.5803	0.8444
0.1296	2.0	54	0.5439	0.8444
0.0323	3.0	81	1.1689	0.7556
0.051	4.0	108	0.6969	0.8444
0.0058	5.0	135	0.7561	0.8444
0.0281	6.0	162	0.6767	0.8444
0.0031	7.0	189	0.9959	0.8
0.006	8.0	216	1.0852	0.8222
0.0042	9.0	243	0.9557	0.8667
0.0002	10.0	270	0.9792	0.8444
0.0001	11.0	297	1.0071	0.8667
0.0001	12.0	324	1.1614	0.8
0.0001	13.0	351	1.0429	0.8667
0.0001	14.0	378	1.0442	0.8667
0.0001	15.0	405	1.1430	0.8222
0.0001	16.0	432	1.1457	0.8222
0.0027	17.0	459	1.3728	0.8222
0.0001	18.0	486	1.0448	0.8667
0.0001	19.0	513	1.0357	0.8667
0.0	20.0	540	1.2604	0.8
0.0026	21.0	567	1.0654	0.8667
0.0004	22.0	594	1.1414	0.8444
0.0001	23.0	621	1.1523	0.8444
0.0001	24.0	648	1.1307	0.8444
0.0002	25.0	675	1.1816	0.8444
0.0	26.0	702	1.1278	0.8444
0.0	27.0	729	1.0224	0.8667
0.0001	28.0	756	1.4430	0.7333
0.0001	29.0	783	1.3228	0.7556
0.0	30.0	810	1.2875	0.8
0.0001	31.0	837	1.2284	0.8222
0.0003	32.0	864	1.3104	0.8444
0.0001	33.0	891	1.1798	0.8667
0.0001	34.0	918	1.1586	0.8667
0.0001	35.0	945	1.1617	0.8667
0.0	36.0	972	1.1634	0.8667
0.0	37.0	999	1.1712	0.8667
0.0	38.0	1026	1.1767	0.8667
0.0	39.0	1053	1.1666	0.8667
0.0	40.0	1080	1.1693	0.8667
0.0	41.0	1107	1.1706	0.8667
0.0	42.0	1134	1.0988	0.8667
0.001	43.0	1161	1.0945	0.8667
0.0	44.0	1188	1.0895	0.8667
0.0	45.0	1215	1.0977	0.8667
0.0004	46.0	1242	1.0978	0.8667
0.0	47.0	1269	1.1013	0.8667
0.0001	48.0	1296	1.1072	0.8667
0.0001	49.0	1323	1.1073	0.8667
0.0	50.0	1350	1.1073	0.8667

Framework versions

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