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smids_10x_deit_small_sgd_0001_fold2

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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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_fold2
    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: 0.826955074875208

smids_10x_deit_small_sgd_0001_fold2

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: 0.4097
  • Accuracy: 0.8270

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
0.9968 1.0 750 1.0169 0.4659
0.9174 2.0 1500 0.9543 0.5308
0.8121 3.0 2250 0.8838 0.6273
0.7871 4.0 3000 0.8228 0.6522
0.691 5.0 3750 0.7665 0.6922
0.6733 6.0 4500 0.7184 0.7271
0.611 7.0 5250 0.6739 0.7488
0.5495 8.0 6000 0.6348 0.7537
0.5871 9.0 6750 0.6046 0.7587
0.5362 10.0 7500 0.5781 0.7754
0.5478 11.0 8250 0.5567 0.7754
0.5521 12.0 9000 0.5409 0.7804
0.475 13.0 9750 0.5265 0.7787
0.4124 14.0 10500 0.5147 0.7887
0.4689 15.0 11250 0.5048 0.7870
0.4042 16.0 12000 0.4956 0.7903
0.3787 17.0 12750 0.4873 0.7937
0.4203 18.0 13500 0.4799 0.7937
0.4173 19.0 14250 0.4729 0.7987
0.4444 20.0 15000 0.4676 0.8020
0.4225 21.0 15750 0.4619 0.8020
0.3886 22.0 16500 0.4572 0.8070
0.3882 23.0 17250 0.4523 0.8120
0.3793 24.0 18000 0.4484 0.8103
0.4027 25.0 18750 0.4443 0.8136
0.4864 26.0 19500 0.4411 0.8136
0.4229 27.0 20250 0.4378 0.8153
0.4258 28.0 21000 0.4349 0.8153
0.3905 29.0 21750 0.4322 0.8170
0.4099 30.0 22500 0.4297 0.8170
0.3721 31.0 23250 0.4276 0.8186
0.4104 32.0 24000 0.4255 0.8203
0.3815 33.0 24750 0.4237 0.8220
0.3966 34.0 25500 0.4218 0.8220
0.4057 35.0 26250 0.4202 0.8220
0.4004 36.0 27000 0.4187 0.8220
0.3921 37.0 27750 0.4174 0.8220
0.4046 38.0 28500 0.4161 0.8220
0.3819 39.0 29250 0.4149 0.8220
0.4626 40.0 30000 0.4139 0.8236
0.4062 41.0 30750 0.4130 0.8236
0.3793 42.0 31500 0.4123 0.8253
0.3246 43.0 32250 0.4116 0.8253
0.3382 44.0 33000 0.4110 0.8270
0.3636 45.0 33750 0.4106 0.8270
0.4008 46.0 34500 0.4102 0.8270
0.3708 47.0 35250 0.4099 0.8270
0.3436 48.0 36000 0.4098 0.8270
0.3738 49.0 36750 0.4097 0.8270
0.373 50.0 37500 0.4097 0.8270

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2