metadata

license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_beit_large_adamax_001_fold3
    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: 0.9016666666666666

smids_10x_beit_large_adamax_001_fold3

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: 1.0324
Accuracy: 0.9017

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
0.3438	1.0	750	0.3826	0.8517
0.2931	2.0	1500	0.3034	0.89
0.2025	3.0	2250	0.3971	0.8783
0.2582	4.0	3000	0.3086	0.8867
0.2483	5.0	3750	0.3346	0.8917
0.1606	6.0	4500	0.3908	0.8717
0.1236	7.0	5250	0.4286	0.8783
0.1197	8.0	6000	0.3887	0.9
0.0412	9.0	6750	0.4924	0.885
0.0384	10.0	7500	0.5551	0.89
0.0583	11.0	8250	0.4882	0.9017
0.0806	12.0	9000	0.5902	0.88
0.0489	13.0	9750	0.5212	0.88
0.0353	14.0	10500	0.5171	0.9
0.0094	15.0	11250	0.6341	0.895
0.0154	16.0	12000	0.5409	0.9133
0.0118	17.0	12750	0.6110	0.8833
0.0159	18.0	13500	0.6873	0.9033
0.0026	19.0	14250	0.7871	0.8983
0.0163	20.0	15000	0.6341	0.895
0.0002	21.0	15750	0.7139	0.9017
0.0006	22.0	16500	0.6717	0.9033
0.0266	23.0	17250	0.6268	0.895
0.0051	24.0	18000	0.6425	0.905
0.0	25.0	18750	0.7506	0.91
0.0004	26.0	19500	0.6864	0.9017
0.0002	27.0	20250	0.6111	0.9117
0.0163	28.0	21000	0.6875	0.9017
0.0001	29.0	21750	0.8050	0.8967
0.0002	30.0	22500	0.7397	0.8967
0.0004	31.0	23250	0.8218	0.8983
0.0	32.0	24000	0.8725	0.8983
0.0	33.0	24750	0.9662	0.8967
0.0	34.0	25500	0.9148	0.9083
0.0	35.0	26250	0.8492	0.9083
0.0001	36.0	27000	0.8264	0.9067
0.0	37.0	27750	0.8650	0.895
0.0004	38.0	28500	0.9030	0.91
0.0	39.0	29250	0.9540	0.9
0.0	40.0	30000	1.0292	0.8883
0.0	41.0	30750	1.0282	0.8917
0.0	42.0	31500	1.0128	0.8933
0.0	43.0	32250	1.0147	0.8983
0.0	44.0	33000	0.9709	0.8983
0.0	45.0	33750	0.9643	0.9067
0.0	46.0	34500	0.9770	0.9017
0.0	47.0	35250	1.0000	0.8983
0.0	48.0	36000	1.0223	0.9017
0.0	49.0	36750	1.0291	0.9017
0.0	50.0	37500	1.0324	0.9017

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2