metadata

license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_beit_base_adamax_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: 0.7145242070116862

smids_5x_beit_base_adamax_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: 0.6198
Accuracy: 0.7145

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
0.9679	1.0	376	0.9413	0.5025
0.8566	2.0	752	0.8816	0.5259
0.8176	3.0	1128	0.7970	0.5710
0.859	4.0	1504	0.8016	0.5626
1.031	5.0	1880	0.8469	0.5576
0.741	6.0	2256	0.8899	0.5426
0.7405	7.0	2632	0.8021	0.6194
0.7314	8.0	3008	0.7493	0.6327
0.6987	9.0	3384	0.7283	0.6461
0.7152	10.0	3760	0.7824	0.6260
0.6328	11.0	4136	0.7526	0.6294
0.8425	12.0	4512	0.7315	0.6678
0.7019	13.0	4888	0.7450	0.6344
0.695	14.0	5264	0.7358	0.6611
0.6413	15.0	5640	0.7306	0.6361
0.6189	16.0	6016	0.6963	0.6644
0.6245	17.0	6392	0.6845	0.6611
0.7409	18.0	6768	0.7226	0.6845
0.7054	19.0	7144	0.7324	0.6694
0.6703	20.0	7520	0.7291	0.6427
0.6526	21.0	7896	0.7119	0.6678
0.6212	22.0	8272	0.7262	0.6628
0.681	23.0	8648	0.6972	0.6644
0.6987	24.0	9024	0.7456	0.6594
0.6922	25.0	9400	0.6847	0.6694
0.6394	26.0	9776	0.6840	0.6745
0.6161	27.0	10152	0.6631	0.6828
0.5613	28.0	10528	0.6637	0.6761
0.6083	29.0	10904	0.7192	0.6745
0.6653	30.0	11280	0.6777	0.7045
0.5903	31.0	11656	0.6722	0.7012
0.6548	32.0	12032	0.7012	0.6511
0.5854	33.0	12408	0.6634	0.6811
0.614	34.0	12784	0.6595	0.6878
0.5441	35.0	13160	0.6831	0.6878
0.6051	36.0	13536	0.6864	0.6895
0.5008	37.0	13912	0.6407	0.6962
0.6049	38.0	14288	0.6571	0.7028
0.5851	39.0	14664	0.6506	0.6928
0.6223	40.0	15040	0.6528	0.6912
0.5754	41.0	15416	0.6469	0.7028
0.5456	42.0	15792	0.6395	0.7112
0.5001	43.0	16168	0.6344	0.7162
0.547	44.0	16544	0.6296	0.7212
0.4783	45.0	16920	0.6245	0.7179
0.4972	46.0	17296	0.6191	0.7312
0.5348	47.0	17672	0.6318	0.7295
0.5739	48.0	18048	0.6151	0.7062
0.4956	49.0	18424	0.6143	0.7179
0.4645	50.0	18800	0.6198	0.7145

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2