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_rms_00001_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: 0.9033333333333333

smids_3x_deit_tiny_rms_00001_fold3

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: 0.9705
• Accuracy: 0.9033

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: 1e-05
• 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
0.3413	1.0	225	0.2808	0.8933
0.2287	2.0	450	0.2742	0.9
0.1591	3.0	675	0.3006	0.89
0.0862	4.0	900	0.3381	0.8883
0.087	5.0	1125	0.4066	0.8783
0.0686	6.0	1350	0.4702	0.885
0.0747	7.0	1575	0.5496	0.8933
0.0256	8.0	1800	0.6539	0.885
0.0458	9.0	2025	0.6793	0.8883
0.0397	10.0	2250	0.7901	0.88
0.0252	11.0	2475	0.7599	0.8967
0.0212	12.0	2700	0.8640	0.89
0.033	13.0	2925	0.8965	0.88
0.0005	14.0	3150	0.8332	0.895
0.0068	15.0	3375	0.9483	0.8717
0.0008	16.0	3600	1.0157	0.8783
0.0016	17.0	3825	0.8948	0.8867
0.0002	18.0	4050	0.8418	0.895
0.0	19.0	4275	0.8994	0.8983
0.0044	20.0	4500	0.8798	0.9083
0.0	21.0	4725	1.0795	0.8717
0.0009	22.0	4950	1.0744	0.885
0.0066	23.0	5175	0.9462	0.8883
0.0	24.0	5400	0.8715	0.9017
0.0088	25.0	5625	0.9553	0.8983
0.0	26.0	5850	0.9300	0.9
0.0271	27.0	6075	0.9362	0.8967
0.0	28.0	6300	0.9453	0.8967
0.0056	29.0	6525	1.0241	0.8917
0.0134	30.0	6750	0.9852	0.9017
0.0062	31.0	6975	0.9946	0.9017
0.0	32.0	7200	1.0433	0.89
0.0	33.0	7425	0.9693	0.8983
0.0	34.0	7650	0.9977	0.8983
0.0	35.0	7875	0.9577	0.9033
0.0	36.0	8100	0.9692	0.8967
0.0	37.0	8325	0.9675	0.8967
0.0	38.0	8550	0.9775	0.9
0.0	39.0	8775	0.9600	0.9
0.0	40.0	9000	0.9503	0.8983
0.0	41.0	9225	0.9634	0.895
0.0	42.0	9450	0.9624	0.9
0.0	43.0	9675	0.9737	0.9
0.0	44.0	9900	0.9743	0.9017
0.0	45.0	10125	0.9706	0.9
0.0	46.0	10350	0.9706	0.9
0.0019	47.0	10575	0.9708	0.9017
0.0	48.0	10800	0.9707	0.9017
0.0	49.0	11025	0.9710	0.9017
0.0	50.0	11250	0.9705	0.9033

Framework versions

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