README.md

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_adamax_001_fold2
    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: 0.8935108153078203

smids_3x_deit_tiny_adamax_001_fold2

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: 0.9522
• Accuracy: 0.8935

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
0.5261	1.0	225	0.3629	0.8552
0.3081	2.0	450	0.3850	0.8303
0.2433	3.0	675	0.4084	0.8486
0.2976	4.0	900	0.3348	0.8752
0.2892	5.0	1125	0.3154	0.8752
0.1338	6.0	1350	0.4213	0.8586
0.1811	7.0	1575	0.4568	0.8602
0.1262	8.0	1800	0.4156	0.8702
0.1405	9.0	2025	0.4962	0.8552
0.1378	10.0	2250	0.4880	0.8652
0.0783	11.0	2475	0.5529	0.8602
0.1156	12.0	2700	0.5059	0.8569
0.0435	13.0	2925	0.5510	0.8735
0.06	14.0	3150	0.5625	0.8669
0.0749	15.0	3375	0.6173	0.8719
0.0723	16.0	3600	0.5869	0.8785
0.0343	17.0	3825	0.6758	0.8852
0.0074	18.0	4050	0.7248	0.8686
0.0351	19.0	4275	0.6545	0.8785
0.0367	20.0	4500	0.7634	0.8785
0.0039	21.0	4725	0.8073	0.8752
0.0183	22.0	4950	0.6969	0.8869
0.015	23.0	5175	0.7193	0.8885
0.0003	24.0	5400	0.8406	0.8719
0.0461	25.0	5625	0.8687	0.8702
0.0004	26.0	5850	0.7424	0.8802
0.0001	27.0	6075	0.8481	0.8819
0.0001	28.0	6300	0.8060	0.8785
0.0003	29.0	6525	0.8316	0.8869
0.0012	30.0	6750	0.8183	0.8835
0.007	31.0	6975	0.7519	0.8802
0.0	32.0	7200	0.8429	0.8852
0.002	33.0	7425	0.8340	0.8885
0.0	34.0	7650	0.8626	0.8785
0.0	35.0	7875	0.8155	0.8935
0.0035	36.0	8100	0.8392	0.8918
0.0	37.0	8325	0.9154	0.8852
0.0	38.0	8550	0.9252	0.8885
0.0047	39.0	8775	0.9247	0.8852
0.0	40.0	9000	0.9286	0.8918
0.0	41.0	9225	0.9340	0.8902
0.0	42.0	9450	0.9212	0.8885
0.0	43.0	9675	0.9298	0.8902
0.0	44.0	9900	0.9334	0.8935
0.0	45.0	10125	0.9402	0.8952
0.0	46.0	10350	0.9378	0.8952
0.0	47.0	10575	0.9454	0.8918
0.0	48.0	10800	0.9493	0.8935
0.0024	49.0	11025	0.9513	0.8935
0.0024	50.0	11250	0.9522	0.8935

Framework versions

• Transformers 4.32.1
• Pytorch 2.1.1+cu121
• Datasets 2.12.0
• Tokenizers 0.13.2