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_0001_fold3
    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: 0.855

smids_10x_deit_small_sgd_0001_fold3

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: 0.3901
Accuracy: 0.855

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
0.9824	1.0	750	1.0331	0.435
0.9063	2.0	1500	0.9735	0.5233
0.8503	3.0	2250	0.9109	0.59
0.7679	4.0	3000	0.8466	0.645
0.7248	5.0	3750	0.7860	0.69
0.6585	6.0	4500	0.7311	0.7167
0.6129	7.0	5250	0.6856	0.7283
0.6082	8.0	6000	0.6417	0.7617
0.581	9.0	6750	0.6068	0.7683
0.5231	10.0	7500	0.5777	0.7767
0.5113	11.0	8250	0.5554	0.7833
0.4834	12.0	9000	0.5347	0.8
0.5002	13.0	9750	0.5194	0.8067
0.5244	14.0	10500	0.5049	0.8117
0.478	15.0	11250	0.4926	0.8183
0.4573	16.0	12000	0.4823	0.8183
0.4332	17.0	12750	0.4737	0.8233
0.4552	18.0	13500	0.4642	0.8283
0.4717	19.0	14250	0.4573	0.8283
0.4284	20.0	15000	0.4511	0.8283
0.418	21.0	15750	0.4442	0.835
0.4355	22.0	16500	0.4394	0.8417
0.442	23.0	17250	0.4349	0.84
0.4592	24.0	18000	0.4307	0.845
0.4174	25.0	18750	0.4266	0.8483
0.4133	26.0	19500	0.4227	0.8483
0.3538	27.0	20250	0.4190	0.8517
0.4061	28.0	21000	0.4159	0.8533
0.4077	29.0	21750	0.4132	0.8517
0.4051	30.0	22500	0.4109	0.8533
0.3404	31.0	23250	0.4086	0.8517
0.353	32.0	24000	0.4061	0.855
0.3864	33.0	24750	0.4039	0.8567
0.3572	34.0	25500	0.4020	0.8567
0.3431	35.0	26250	0.4002	0.8567
0.3693	36.0	27000	0.3992	0.8567
0.3706	37.0	27750	0.3978	0.8567
0.423	38.0	28500	0.3964	0.855
0.3909	39.0	29250	0.3953	0.855
0.41	40.0	30000	0.3943	0.855
0.3387	41.0	30750	0.3933	0.855
0.3698	42.0	31500	0.3927	0.855
0.3644	43.0	32250	0.3919	0.855
0.3722	44.0	33000	0.3914	0.8567
0.3269	45.0	33750	0.3910	0.8567
0.3532	46.0	34500	0.3906	0.8567
0.3899	47.0	35250	0.3904	0.8567
0.3783	48.0	36000	0.3902	0.855
0.3767	49.0	36750	0.3901	0.855
0.3232	50.0	37500	0.3901	0.855

Framework versions

Transformers 4.32.1
Pytorch 2.1.0+cu121
Datasets 2.12.0
Tokenizers 0.13.2