metadata

license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_10x_deit_tiny_sgd_001_fold4
    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: 0.8666666666666667

smids_10x_deit_tiny_sgd_001_fold4

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: 0.3938
Accuracy: 0.8667

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
0.5453	1.0	750	0.5623	0.7633
0.3882	2.0	1500	0.4483	0.8183
0.3799	3.0	2250	0.4088	0.8317
0.3643	4.0	3000	0.3893	0.8383
0.2628	5.0	3750	0.3770	0.8467
0.2344	6.0	4500	0.3757	0.8467
0.2158	7.0	5250	0.3640	0.8583
0.2518	8.0	6000	0.3700	0.86
0.2784	9.0	6750	0.3645	0.8617
0.2124	10.0	7500	0.3619	0.86
0.2508	11.0	8250	0.3628	0.8583
0.2963	12.0	9000	0.3717	0.86
0.2464	13.0	9750	0.3675	0.86
0.2153	14.0	10500	0.3661	0.8633
0.1783	15.0	11250	0.3637	0.8633
0.1889	16.0	12000	0.3675	0.865
0.1615	17.0	12750	0.3615	0.8633
0.1602	18.0	13500	0.3665	0.8683
0.2382	19.0	14250	0.3640	0.8633
0.1431	20.0	15000	0.3640	0.8667
0.1246	21.0	15750	0.3698	0.865
0.1642	22.0	16500	0.3698	0.8617
0.1435	23.0	17250	0.3719	0.8617
0.184	24.0	18000	0.3745	0.865
0.1543	25.0	18750	0.3749	0.8617
0.1463	26.0	19500	0.3762	0.8633
0.1225	27.0	20250	0.3737	0.8667
0.1542	28.0	21000	0.3785	0.865
0.1065	29.0	21750	0.3788	0.87
0.1351	30.0	22500	0.3799	0.8667
0.1281	31.0	23250	0.3825	0.8667
0.1337	32.0	24000	0.3866	0.8633
0.1066	33.0	24750	0.3848	0.8667
0.1503	34.0	25500	0.3856	0.87
0.0933	35.0	26250	0.3837	0.8717
0.1119	36.0	27000	0.3871	0.87
0.0916	37.0	27750	0.3845	0.87
0.1419	38.0	28500	0.3888	0.8683
0.1831	39.0	29250	0.3865	0.87
0.1443	40.0	30000	0.3886	0.8683
0.1089	41.0	30750	0.3938	0.865
0.0931	42.0	31500	0.3903	0.8683
0.1349	43.0	32250	0.3917	0.8683
0.1005	44.0	33000	0.3917	0.8667
0.12	45.0	33750	0.3918	0.8667
0.1354	46.0	34500	0.3924	0.8667
0.0817	47.0	35250	0.3922	0.8667
0.0828	48.0	36000	0.3931	0.8667
0.0941	49.0	36750	0.3938	0.8667
0.0837	50.0	37500	0.3938	0.8667

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2