README.md

metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_rms_0001_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: 0.9065108514190318

smids_3x_deit_small_rms_0001_fold1

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

• Loss: 0.8635
• Accuracy: 0.9065

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
0.3954	1.0	226	0.3893	0.8280
0.2465	2.0	452	0.3428	0.8881
0.1728	3.0	678	0.3745	0.8831
0.1347	4.0	904	0.4194	0.8848
0.0709	5.0	1130	0.4431	0.8898
0.0807	6.0	1356	0.4957	0.8781
0.0722	7.0	1582	0.4748	0.8898
0.036	8.0	1808	0.6178	0.8831
0.0808	9.0	2034	0.5850	0.8831
0.0404	10.0	2260	0.5350	0.9015
0.016	11.0	2486	0.5574	0.8831
0.0147	12.0	2712	0.5709	0.8865
0.0113	13.0	2938	0.6888	0.8815
0.0209	14.0	3164	0.4757	0.9149
0.0245	15.0	3390	0.6913	0.8815
0.0203	16.0	3616	0.6653	0.8865
0.0109	17.0	3842	0.7353	0.8898
0.0341	18.0	4068	0.7660	0.8865
0.0053	19.0	4294	0.6013	0.8965
0.0015	20.0	4520	0.6073	0.8965
0.0003	21.0	4746	0.7366	0.8965
0.0274	22.0	4972	0.7587	0.8798
0.0019	23.0	5198	0.6702	0.8998
0.0001	24.0	5424	0.7767	0.8815
0.0008	25.0	5650	0.6634	0.8998
0.023	26.0	5876	0.7380	0.8915
0.0	27.0	6102	0.8025	0.8898
0.0797	28.0	6328	0.7171	0.8948
0.0492	29.0	6554	0.6827	0.8982
0.0	30.0	6780	0.7690	0.9048
0.0	31.0	7006	0.7411	0.9048
0.0	32.0	7232	0.7425	0.8965
0.0032	33.0	7458	0.7178	0.9115
0.0006	34.0	7684	0.7893	0.9082
0.0	35.0	7910	0.8185	0.8932
0.0181	36.0	8136	0.8745	0.8932
0.0003	37.0	8362	0.8672	0.8932
0.0	38.0	8588	0.8314	0.8982
0.0	39.0	8814	0.8333	0.8965
0.0	40.0	9040	0.7854	0.9065
0.0036	41.0	9266	0.8828	0.9015
0.0028	42.0	9492	0.8402	0.9065
0.0	43.0	9718	0.8689	0.8982
0.0	44.0	9944	0.8390	0.9065
0.0	45.0	10170	0.8434	0.9082
0.0	46.0	10396	0.8531	0.9132
0.0	47.0	10622	0.8589	0.9098
0.0	48.0	10848	0.8632	0.9065
0.0	49.0	11074	0.8630	0.9065
0.0	50.0	11300	0.8635	0.9065

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.0+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2