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_sgd_0001_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: 0.5916666666666667

smids_1x_deit_tiny_sgd_0001_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: 0.8882
Accuracy: 0.5917

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: 0.0001
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
1.2279	1.0	75	1.2828	0.36
1.1801	2.0	150	1.2204	0.3717
1.1425	3.0	225	1.1764	0.3733
1.1496	4.0	300	1.1467	0.3817
1.0862	5.0	375	1.1259	0.39
1.1317	6.0	450	1.1103	0.3983
1.0711	7.0	525	1.0972	0.4083
1.0717	8.0	600	1.0858	0.4167
1.0458	9.0	675	1.0754	0.42
1.0711	10.0	750	1.0656	0.425
1.0389	11.0	825	1.0563	0.4383
1.0272	12.0	900	1.0476	0.4467
1.0495	13.0	975	1.0393	0.4517
1.0448	14.0	1050	1.0308	0.4533
1.0339	15.0	1125	1.0229	0.4583
0.9744	16.0	1200	1.0150	0.4617
0.9857	17.0	1275	1.0069	0.47
1.0108	18.0	1350	0.9993	0.4717
0.9584	19.0	1425	0.9919	0.4717
0.9977	20.0	1500	0.9844	0.485
0.9787	21.0	1575	0.9775	0.49
0.9724	22.0	1650	0.9707	0.5067
0.9219	23.0	1725	0.9645	0.515
0.923	24.0	1800	0.9585	0.525
0.9224	25.0	1875	0.9527	0.5317
0.9312	26.0	1950	0.9470	0.5417
0.9161	27.0	2025	0.9417	0.5433
0.9574	28.0	2100	0.9369	0.5467
0.9255	29.0	2175	0.9322	0.5517
0.9146	30.0	2250	0.9278	0.555
0.9155	31.0	2325	0.9238	0.5617
0.856	32.0	2400	0.9200	0.565
0.9504	33.0	2475	0.9164	0.5717
0.9096	34.0	2550	0.9130	0.5783
0.8983	35.0	2625	0.9100	0.5817
0.8589	36.0	2700	0.9071	0.585
0.8916	37.0	2775	0.9044	0.5817
0.8984	38.0	2850	0.9020	0.585
0.8824	39.0	2925	0.8998	0.5867
0.8736	40.0	3000	0.8977	0.5867
0.8723	41.0	3075	0.8958	0.5883
0.8965	42.0	3150	0.8942	0.59
0.8854	43.0	3225	0.8928	0.59
0.8622	44.0	3300	0.8915	0.5917
0.8601	45.0	3375	0.8905	0.5917
0.8904	46.0	3450	0.8896	0.5917
0.8654	47.0	3525	0.8890	0.5917
0.8638	48.0	3600	0.8885	0.5917
0.8282	49.0	3675	0.8883	0.5917
0.8485	50.0	3750	0.8882	0.5917

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0