metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_small_sgd_00001_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: 0.5933333333333334

smids_10x_deit_small_sgd_00001_fold5

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: 0.9331
Accuracy: 0.5933

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
1.0687	1.0	750	1.0724	0.425
1.0435	2.0	1500	1.0669	0.42
1.0439	3.0	2250	1.0614	0.4283
1.0595	4.0	3000	1.0559	0.435
1.0216	5.0	3750	1.0506	0.4383
1.0179	6.0	4500	1.0454	0.4467
1.0048	7.0	5250	1.0402	0.4517
1.0171	8.0	6000	1.0351	0.4533
1.0075	9.0	6750	1.0302	0.4567
0.9942	10.0	7500	1.0255	0.4683
0.9968	11.0	8250	1.0209	0.4783
0.9853	12.0	9000	1.0163	0.485
0.9829	13.0	9750	1.0118	0.4933
0.9676	14.0	10500	1.0075	0.4933
0.9869	15.0	11250	1.0033	0.5
0.9385	16.0	12000	0.9992	0.5117
0.9422	17.0	12750	0.9953	0.5167
0.9475	18.0	13500	0.9914	0.5233
0.9706	19.0	14250	0.9876	0.5267
0.9823	20.0	15000	0.9840	0.53
0.9281	21.0	15750	0.9805	0.535
0.9429	22.0	16500	0.9770	0.54
0.9545	23.0	17250	0.9738	0.545
0.9266	24.0	18000	0.9706	0.545
0.943	25.0	18750	0.9675	0.545
0.9362	26.0	19500	0.9646	0.55
0.9017	27.0	20250	0.9618	0.5517
0.9415	28.0	21000	0.9592	0.555
0.9141	29.0	21750	0.9566	0.555
0.9329	30.0	22500	0.9543	0.5567
0.931	31.0	23250	0.9520	0.5617
0.9115	32.0	24000	0.9498	0.5633
0.9251	33.0	24750	0.9478	0.565
0.8996	34.0	25500	0.9460	0.5717
0.9232	35.0	26250	0.9442	0.5717
0.8817	36.0	27000	0.9427	0.5717
0.8794	37.0	27750	0.9412	0.575
0.8813	38.0	28500	0.9398	0.5767
0.8952	39.0	29250	0.9386	0.58
0.8846	40.0	30000	0.9375	0.5817
0.8967	41.0	30750	0.9366	0.5867
0.9065	42.0	31500	0.9358	0.5883
0.9123	43.0	32250	0.9351	0.59
0.8878	44.0	33000	0.9345	0.59
0.8772	45.0	33750	0.9340	0.59
0.9035	46.0	34500	0.9336	0.5933
0.9152	47.0	35250	0.9334	0.5933
0.8837	48.0	36000	0.9332	0.5933
0.8879	49.0	36750	0.9331	0.5933
0.8918	50.0	37500	0.9331	0.5933

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2