metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_tiny_sgd_001_fold1
    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: 0.8914858096828047

smids_5x_deit_tiny_sgd_001_fold1

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: 0.2974
Accuracy: 0.8915

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
0.7214	1.0	376	0.7354	0.7112
0.5305	2.0	752	0.5484	0.7780
0.423	3.0	1128	0.4775	0.8063
0.4098	4.0	1504	0.4302	0.8364
0.4286	5.0	1880	0.4059	0.8497
0.3605	6.0	2256	0.3872	0.8548
0.3093	7.0	2632	0.3738	0.8648
0.3348	8.0	3008	0.3632	0.8664
0.3284	9.0	3384	0.3510	0.8765
0.3008	10.0	3760	0.3447	0.8748
0.289	11.0	4136	0.3398	0.8798
0.2542	12.0	4512	0.3320	0.8848
0.245	13.0	4888	0.3263	0.8865
0.2258	14.0	5264	0.3225	0.8865
0.3082	15.0	5640	0.3188	0.8848
0.2685	16.0	6016	0.3171	0.8848
0.2379	17.0	6392	0.3137	0.8865
0.2778	18.0	6768	0.3111	0.8848
0.2374	19.0	7144	0.3083	0.8848
0.1845	20.0	7520	0.3061	0.8848
0.2126	21.0	7896	0.3049	0.8865
0.2068	22.0	8272	0.3078	0.8831
0.2364	23.0	8648	0.3060	0.8798
0.1851	24.0	9024	0.3035	0.8881
0.2035	25.0	9400	0.3013	0.8848
0.2146	26.0	9776	0.3016	0.8881
0.1495	27.0	10152	0.2986	0.8915
0.1962	28.0	10528	0.2989	0.8898
0.2019	29.0	10904	0.2993	0.8881
0.1531	30.0	11280	0.2975	0.8932
0.1643	31.0	11656	0.2990	0.8898
0.2082	32.0	12032	0.2991	0.8881
0.1845	33.0	12408	0.2980	0.8915
0.1333	34.0	12784	0.2976	0.8932
0.1524	35.0	13160	0.3000	0.8865
0.1908	36.0	13536	0.2977	0.8915
0.1391	37.0	13912	0.2964	0.8948
0.1756	38.0	14288	0.2975	0.8915
0.2131	39.0	14664	0.2969	0.8932
0.1588	40.0	15040	0.2977	0.8898
0.1631	41.0	15416	0.2962	0.8932
0.1431	42.0	15792	0.2974	0.8915
0.1556	43.0	16168	0.2976	0.8898
0.1705	44.0	16544	0.2978	0.8915
0.1792	45.0	16920	0.2986	0.8898
0.1949	46.0	17296	0.2975	0.8915
0.1472	47.0	17672	0.2972	0.8915
0.139	48.0	18048	0.2974	0.8915
0.1452	49.0	18424	0.2974	0.8915
0.1388	50.0	18800	0.2974	0.8915

Framework versions

Transformers 4.32.1
Pytorch 2.1.1+cu121
Datasets 2.12.0
Tokenizers 0.13.2