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_sgd_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: 0.7142857142857143

hushem_5x_beit_base_sgd_001_fold4

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: 0.9066
Accuracy: 0.7143

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
1.529	1.0	28	1.4303	0.2857
1.4725	2.0	56	1.3788	0.2857
1.3888	3.0	84	1.3402	0.3571
1.357	4.0	112	1.3238	0.3333
1.2619	5.0	140	1.3109	0.3571
1.2354	6.0	168	1.2864	0.4286
1.2209	7.0	196	1.2694	0.4524
1.2033	8.0	224	1.2439	0.4524
1.1737	9.0	252	1.2291	0.4762
1.1593	10.0	280	1.2131	0.4762
1.1467	11.0	308	1.1977	0.4762
1.1374	12.0	336	1.1819	0.5
1.1253	13.0	364	1.1622	0.4762
1.1026	14.0	392	1.1551	0.4762
1.0893	15.0	420	1.1365	0.4762
1.0476	16.0	448	1.1177	0.4762
1.0789	17.0	476	1.1065	0.4762
1.0455	18.0	504	1.0907	0.4762
1.0028	19.0	532	1.0816	0.4762
1.004	20.0	560	1.0638	0.4762
0.967	21.0	588	1.0579	0.5
0.9933	22.0	616	1.0403	0.5
0.9551	23.0	644	1.0323	0.5714
0.9924	24.0	672	1.0183	0.5952
0.9236	25.0	700	1.0095	0.6190
0.9232	26.0	728	0.9951	0.6190
0.9574	27.0	756	1.0017	0.6190
0.9076	28.0	784	0.9866	0.6429
0.9034	29.0	812	0.9711	0.6429
0.8865	30.0	840	0.9696	0.6667
0.9168	31.0	868	0.9618	0.6667
0.8917	32.0	896	0.9532	0.6905
0.901	33.0	924	0.9560	0.6667
0.8911	34.0	952	0.9475	0.6667
0.9166	35.0	980	0.9435	0.7143
0.9177	36.0	1008	0.9323	0.7143
0.8498	37.0	1036	0.9279	0.7143
0.8848	38.0	1064	0.9240	0.7143
0.8498	39.0	1092	0.9206	0.7143
0.8193	40.0	1120	0.9192	0.7143
0.8443	41.0	1148	0.9102	0.7143
0.8576	42.0	1176	0.9108	0.7143
0.8916	43.0	1204	0.9094	0.7143
0.8299	44.0	1232	0.9082	0.7143
0.8298	45.0	1260	0.9062	0.7143
0.8683	46.0	1288	0.9063	0.7143
0.8298	47.0	1316	0.9065	0.7143
0.8412	48.0	1344	0.9065	0.7143
0.8333	49.0	1372	0.9066	0.7143
0.8603	50.0	1400	0.9066	0.7143

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0