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_rms_0001_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: 0.855

smids_5x_beit_base_rms_0001_fold3

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: 1.3488
Accuracy: 0.855

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: 0.0001
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
0.5467	1.0	375	0.5969	0.7483
0.4112	2.0	750	0.6522	0.7967
0.3586	3.0	1125	0.4558	0.83
0.3318	4.0	1500	0.3669	0.8567
0.318	5.0	1875	0.4227	0.8267
0.2611	6.0	2250	0.4142	0.8467
0.2866	7.0	2625	0.4534	0.83
0.2297	8.0	3000	0.4296	0.8517
0.1623	9.0	3375	0.5359	0.835
0.1313	10.0	3750	0.5677	0.8433
0.1856	11.0	4125	0.5198	0.8667
0.087	12.0	4500	0.6463	0.8417
0.0974	13.0	4875	0.5874	0.8417
0.0478	14.0	5250	0.7058	0.84
0.0326	15.0	5625	0.7427	0.8283
0.0198	16.0	6000	0.8945	0.84
0.0746	17.0	6375	0.8489	0.8333
0.1024	18.0	6750	0.7564	0.8383
0.0499	19.0	7125	0.8028	0.8483
0.0808	20.0	7500	1.0400	0.8267
0.0495	21.0	7875	1.0596	0.83
0.0441	22.0	8250	0.9512	0.85
0.0385	23.0	8625	0.8380	0.8483
0.0162	24.0	9000	1.0671	0.8517
0.0061	25.0	9375	0.8747	0.86
0.0284	26.0	9750	1.0398	0.815
0.0446	27.0	10125	0.9748	0.845
0.0208	28.0	10500	1.0700	0.8517
0.0357	29.0	10875	1.0579	0.845
0.0301	30.0	11250	0.9043	0.8583
0.0099	31.0	11625	0.9420	0.8533
0.0327	32.0	12000	1.0192	0.8467
0.0502	33.0	12375	0.8952	0.8517
0.0352	34.0	12750	0.9041	0.8667
0.0188	35.0	13125	1.2059	0.8433
0.0229	36.0	13500	1.2761	0.84
0.0123	37.0	13875	1.1077	0.8583
0.0002	38.0	14250	1.1468	0.85
0.0009	39.0	14625	1.1590	0.8617
0.0211	40.0	15000	1.3901	0.8683
0.001	41.0	15375	1.2933	0.8533
0.0077	42.0	15750	1.1576	0.8583
0.0369	43.0	16125	1.3070	0.8433
0.0132	44.0	16500	1.0120	0.8633
0.0003	45.0	16875	1.2641	0.8633
0.0001	46.0	17250	1.2268	0.8633
0.0004	47.0	17625	1.1854	0.8583
0.0001	48.0	18000	1.3326	0.865
0.0187	49.0	18375	1.3505	0.8567
0.0011	50.0	18750	1.3488	0.855

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2