metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_40x_beit_large_adamax_001_fold4
    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: 0.9761904761904762

hushem_40x_beit_large_adamax_001_fold4

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: 0.0452
Accuracy: 0.9762

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: 0.001
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
0.4622	1.0	219	0.6192	0.7143
0.1812	2.0	438	0.3334	0.8810
0.1061	3.0	657	0.3100	0.8810
0.061	4.0	876	0.3909	0.9048
0.07	5.0	1095	0.5029	0.8095
0.0116	6.0	1314	0.1841	0.9286
0.0286	7.0	1533	0.1625	0.9524
0.0589	8.0	1752	0.3628	0.9286
0.0111	9.0	1971	0.1004	0.9762
0.0199	10.0	2190	0.2149	0.9524
0.0026	11.0	2409	0.2299	0.9524
0.003	12.0	2628	0.0798	0.9524
0.0002	13.0	2847	0.3767	0.9524
0.0	14.0	3066	0.3423	0.9524
0.0	15.0	3285	0.3097	0.9524
0.0	16.0	3504	0.3620	0.9524
0.0	17.0	3723	0.3599	0.9524
0.0109	18.0	3942	1.0112	0.8810
0.0058	19.0	4161	0.3536	0.9286
0.0	20.0	4380	0.1749	0.9524
0.0	21.0	4599	0.1549	0.9762
0.0	22.0	4818	0.1579	0.9762
0.0001	23.0	5037	0.2020	0.9762
0.0	24.0	5256	0.1981	0.9524
0.0	25.0	5475	0.2004	0.9524
0.0	26.0	5694	0.2385	0.9524
0.0	27.0	5913	0.2312	0.9762
0.0	28.0	6132	0.2326	0.9524
0.0	29.0	6351	0.2329	0.9762
0.0	30.0	6570	0.2354	0.9762
0.0	31.0	6789	0.2406	0.9762
0.0	32.0	7008	0.1614	0.9524
0.0	33.0	7227	0.7242	0.8810
0.0	34.0	7446	0.6237	0.9048
0.0	35.0	7665	0.2046	0.9762
0.0	36.0	7884	0.3311	0.9524
0.0	37.0	8103	0.0102	1.0
0.0	38.0	8322	0.0205	0.9762
0.0	39.0	8541	0.4064	0.9286
0.0	40.0	8760	0.2152	0.9524
0.0	41.0	8979	0.0320	0.9762
0.0	42.0	9198	0.0414	0.9762
0.0	43.0	9417	0.0410	0.9762
0.0	44.0	9636	0.0475	0.9762
0.0	45.0	9855	0.0475	0.9762
0.0	46.0	10074	0.0463	0.9762
0.0	47.0	10293	0.0463	0.9762
0.0	48.0	10512	0.0476	0.9762
0.0	49.0	10731	0.0481	0.9762
0.0	50.0	10950	0.0452	0.9762

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2