metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_1x_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: 0.765

smids_1x_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: 1.8052
Accuracy: 0.765

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
1.2535	1.0	75	0.9734	0.4717
1.0172	2.0	150	0.8857	0.5217
0.9205	3.0	225	0.8219	0.5633
0.8404	4.0	300	0.8833	0.54
0.8125	5.0	375	0.7752	0.615
0.8375	6.0	450	0.7791	0.6133
0.7706	7.0	525	0.7651	0.6433
0.6843	8.0	600	0.7674	0.6083
0.717	9.0	675	0.7318	0.655
0.6266	10.0	750	0.7160	0.6867
0.674	11.0	825	0.6761	0.69
0.6618	12.0	900	0.7236	0.6433
0.6204	13.0	975	0.7093	0.6733
0.6403	14.0	1050	0.6526	0.7133
0.5728	15.0	1125	0.7313	0.6617
0.5566	16.0	1200	0.6152	0.7317
0.5735	17.0	1275	0.6901	0.7083
0.6111	18.0	1350	0.6429	0.7317
0.6075	19.0	1425	0.6044	0.7533
0.5675	20.0	1500	0.5922	0.7633
0.4747	21.0	1575	0.6118	0.7483
0.5157	22.0	1650	0.6322	0.7383
0.4995	23.0	1725	0.6300	0.745
0.4632	24.0	1800	0.6076	0.74
0.4596	25.0	1875	0.6047	0.7733
0.4702	26.0	1950	0.6096	0.7633
0.5043	27.0	2025	0.6045	0.7567
0.5051	28.0	2100	0.5905	0.75
0.4664	29.0	2175	0.6085	0.7567
0.3949	30.0	2250	0.6634	0.76
0.3708	31.0	2325	0.6461	0.7667
0.3964	32.0	2400	0.6482	0.7617
0.3827	33.0	2475	0.6696	0.76
0.3422	34.0	2550	0.6799	0.765
0.3716	35.0	2625	0.7307	0.7767
0.3007	36.0	2700	0.7490	0.7583
0.2019	37.0	2775	0.8838	0.7533
0.232	38.0	2850	0.8738	0.76
0.221	39.0	2925	0.8842	0.7733
0.1875	40.0	3000	1.0078	0.7383
0.203	41.0	3075	1.0476	0.7567
0.1699	42.0	3150	1.0739	0.7567
0.171	43.0	3225	1.1644	0.7417
0.1205	44.0	3300	1.2501	0.7533
0.0811	45.0	3375	1.2967	0.755
0.0202	46.0	3450	1.5619	0.745
0.0237	47.0	3525	1.5862	0.7617
0.0127	48.0	3600	1.6631	0.7667
0.0204	49.0	3675	1.7536	0.7667
0.0042	50.0	3750	1.8052	0.765

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0