metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_rms_001_fold5
    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: 0.8166666666666667

smids_3x_deit_tiny_rms_001_fold5

This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 1.6588
Accuracy: 0.8167

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
0.972	1.0	225	1.0247	0.3783
0.8568	2.0	450	0.8274	0.5533
0.8081	3.0	675	0.8040	0.5483
0.7625	4.0	900	0.7546	0.5933
0.7576	5.0	1125	0.7605	0.64
0.6884	6.0	1350	0.7873	0.5983
0.766	7.0	1575	0.7270	0.6583
0.6978	8.0	1800	0.7176	0.64
0.6732	9.0	2025	0.7347	0.645
0.6839	10.0	2250	0.7289	0.6267
0.6569	11.0	2475	0.6542	0.7183
0.6318	12.0	2700	0.6186	0.7283
0.6796	13.0	2925	0.6663	0.71
0.6092	14.0	3150	0.6155	0.7117
0.6242	15.0	3375	0.6625	0.6967
0.5314	16.0	3600	0.5775	0.7533
0.5564	17.0	3825	0.5848	0.7533
0.5755	18.0	4050	0.5751	0.7583
0.5677	19.0	4275	0.5731	0.7617
0.5761	20.0	4500	0.5204	0.785
0.4524	21.0	4725	0.5722	0.75
0.4782	22.0	4950	0.5385	0.7733
0.4908	23.0	5175	0.5176	0.7933
0.5195	24.0	5400	0.5242	0.7917
0.4871	25.0	5625	0.5298	0.7983
0.5293	26.0	5850	0.5066	0.8
0.504	27.0	6075	0.4969	0.81
0.4467	28.0	6300	0.5630	0.79
0.4177	29.0	6525	0.5247	0.8067
0.3722	30.0	6750	0.5359	0.8117
0.3286	31.0	6975	0.5623	0.795
0.3205	32.0	7200	0.5594	0.8017
0.3627	33.0	7425	0.5968	0.815
0.2799	34.0	7650	0.5562	0.825
0.2664	35.0	7875	0.6268	0.81
0.2603	36.0	8100	0.6102	0.82
0.2382	37.0	8325	0.6448	0.8083
0.1999	38.0	8550	0.7396	0.825
0.1413	39.0	8775	0.7329	0.8167
0.1906	40.0	9000	0.8804	0.81
0.1179	41.0	9225	0.7998	0.84
0.0965	42.0	9450	0.9317	0.8217
0.0987	43.0	9675	0.9015	0.825
0.1035	44.0	9900	1.1023	0.8083
0.0347	45.0	10125	1.2315	0.82
0.054	46.0	10350	1.2317	0.8083
0.014	47.0	10575	1.4229	0.82
0.0044	48.0	10800	1.5732	0.8217
0.0012	49.0	11025	1.6140	0.8183
0.0003	50.0	11250	1.6588	0.8167

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2