metadata

license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_beit_base_rms_001_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: 0.7579298831385642

smids_3x_beit_base_rms_001_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: 0.7807
Accuracy: 0.7579

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
1.1042	1.0	226	1.0981	0.3456
0.942	2.0	452	0.9022	0.5275
0.8328	3.0	678	0.9600	0.4691
0.8702	4.0	904	0.9083	0.5543
0.8313	5.0	1130	0.8171	0.5760
0.8558	6.0	1356	0.8467	0.5342
0.7514	7.0	1582	0.7612	0.6277
0.7839	8.0	1808	0.7968	0.5659
0.7602	9.0	2034	0.7655	0.6210
0.7694	10.0	2260	0.7429	0.6060
0.7166	11.0	2486	0.7968	0.5626
0.7048	12.0	2712	0.8272	0.6077
0.6745	13.0	2938	0.8054	0.5993
0.7185	14.0	3164	0.7867	0.6194
0.7264	15.0	3390	0.7701	0.6377
0.6767	16.0	3616	0.7383	0.6144
0.6006	17.0	3842	0.8677	0.6077
0.6721	18.0	4068	0.7460	0.6361
0.6352	19.0	4294	0.7492	0.6127
0.642	20.0	4520	0.7712	0.6160
0.6647	21.0	4746	0.7257	0.6544
0.6408	22.0	4972	0.7629	0.6611
0.7655	23.0	5198	0.7723	0.6127
0.7074	24.0	5424	0.6879	0.6928
0.6919	25.0	5650	0.6962	0.6828
0.698	26.0	5876	0.7479	0.6361
0.641	27.0	6102	0.7653	0.6644
0.6417	28.0	6328	0.7791	0.6594
0.6123	29.0	6554	0.7195	0.6761
0.5918	30.0	6780	0.6991	0.6995
0.5562	31.0	7006	0.6938	0.6978
0.6293	32.0	7232	0.6564	0.7145
0.5615	33.0	7458	0.7421	0.6878
0.5411	34.0	7684	0.6688	0.7145
0.4483	35.0	7910	0.7701	0.6962
0.4776	36.0	8136	0.6349	0.7412
0.4775	37.0	8362	0.6430	0.7262
0.4854	38.0	8588	0.7095	0.7078
0.4208	39.0	8814	0.6254	0.7412
0.3846	40.0	9040	0.6645	0.7396
0.3663	41.0	9266	0.6430	0.7563
0.3616	42.0	9492	0.6767	0.7462
0.3863	43.0	9718	0.6432	0.7596
0.2608	44.0	9944	0.6472	0.7563
0.4159	45.0	10170	0.6400	0.7663
0.333	46.0	10396	0.6911	0.7613
0.2718	47.0	10622	0.7119	0.7696
0.2639	48.0	10848	0.7421	0.7596
0.236	49.0	11074	0.7543	0.7613
0.2672	50.0	11300	0.7807	0.7579

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2