metadata

license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_small_adamax_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: 0.8831385642737897

smids_3x_deit_small_adamax_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: 0.9284
Accuracy: 0.8831

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
0.5541	1.0	226	0.6212	0.7379
0.2991	2.0	452	0.3577	0.8748
0.3025	3.0	678	0.3328	0.8765
0.2535	4.0	904	0.3762	0.8631
0.2002	5.0	1130	0.3237	0.8581
0.2266	6.0	1356	0.2994	0.8898
0.1986	7.0	1582	0.4068	0.8831
0.102	8.0	1808	0.5862	0.8564
0.1677	9.0	2034	0.4842	0.8431
0.0849	10.0	2260	0.4657	0.8848
0.0597	11.0	2486	0.4867	0.8698
0.0739	12.0	2712	0.4601	0.8881
0.057	13.0	2938	0.5719	0.8614
0.0476	14.0	3164	0.5741	0.8898
0.0028	15.0	3390	0.5632	0.8932
0.0442	16.0	3616	0.6974	0.8748
0.0232	17.0	3842	0.7080	0.8731
0.0139	18.0	4068	0.6478	0.8848
0.0142	19.0	4294	0.6320	0.8881
0.0278	20.0	4520	0.7359	0.8698
0.0017	21.0	4746	0.5842	0.8948
0.0064	22.0	4972	0.8360	0.8681
0.0257	23.0	5198	0.6566	0.8865
0.0027	24.0	5424	0.6515	0.8948
0.0001	25.0	5650	0.6767	0.8998
0.0009	26.0	5876	0.7783	0.8865
0.0001	27.0	6102	0.7799	0.8815
0.0004	28.0	6328	0.8472	0.8831
0.0002	29.0	6554	0.6998	0.8848
0.0	30.0	6780	0.8253	0.8815
0.0	31.0	7006	0.7884	0.8898
0.0001	32.0	7232	0.8156	0.8798
0.0032	33.0	7458	0.8222	0.8881
0.0	34.0	7684	0.8187	0.8848
0.0	35.0	7910	0.8080	0.8848
0.0	36.0	8136	0.8285	0.8798
0.0	37.0	8362	0.8277	0.8848
0.0	38.0	8588	0.8358	0.8831
0.0	39.0	8814	0.8499	0.8831
0.0	40.0	9040	0.8554	0.8798
0.0034	41.0	9266	0.8762	0.8798
0.0035	42.0	9492	0.8827	0.8798
0.0	43.0	9718	0.8772	0.8798
0.0	44.0	9944	0.8964	0.8831
0.0	45.0	10170	0.9072	0.8831
0.0	46.0	10396	0.9164	0.8815
0.0	47.0	10622	0.9219	0.8815
0.0	48.0	10848	0.9247	0.8831
0.0	49.0	11074	0.9274	0.8831
0.0	50.0	11300	0.9284	0.8831

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2