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smids_10x_deit_small_sgd_0001_fold4

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_fold4
    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: 0.8416666666666667

smids_10x_deit_small_sgd_0001_fold4

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: 0.4063
  • Accuracy: 0.8417

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
0.9715 1.0 750 1.0172 0.455
0.9076 2.0 1500 0.9524 0.5267
0.8403 3.0 2250 0.8812 0.625
0.7987 4.0 3000 0.8125 0.6817
0.7256 5.0 3750 0.7521 0.7183
0.6364 6.0 4500 0.7018 0.7483
0.5752 7.0 5250 0.6571 0.775
0.63 8.0 6000 0.6211 0.7817
0.6197 9.0 6750 0.5901 0.79
0.5118 10.0 7500 0.5651 0.7983
0.5006 11.0 8250 0.5449 0.8017
0.5617 12.0 9000 0.5276 0.8033
0.4842 13.0 9750 0.5134 0.8083
0.5031 14.0 10500 0.5016 0.81
0.4417 15.0 11250 0.4908 0.8083
0.4457 16.0 12000 0.4818 0.8083
0.3768 17.0 12750 0.4743 0.8117
0.4232 18.0 13500 0.4671 0.8167
0.4491 19.0 14250 0.4614 0.8167
0.4472 20.0 15000 0.4557 0.8233
0.3954 21.0 15750 0.4506 0.8267
0.405 22.0 16500 0.4463 0.83
0.4169 23.0 17250 0.4425 0.8317
0.4563 24.0 18000 0.4389 0.8333
0.3987 25.0 18750 0.4356 0.8333
0.39 26.0 19500 0.4325 0.8317
0.4056 27.0 20250 0.4297 0.8317
0.3872 28.0 21000 0.4272 0.8317
0.3817 29.0 21750 0.4249 0.835
0.4035 30.0 22500 0.4229 0.8367
0.3636 31.0 23250 0.4211 0.835
0.4122 32.0 24000 0.4193 0.8367
0.3917 33.0 24750 0.4176 0.8383
0.3839 34.0 25500 0.4161 0.84
0.3217 35.0 26250 0.4147 0.84
0.3641 36.0 27000 0.4136 0.84
0.3379 37.0 27750 0.4124 0.84
0.3959 38.0 28500 0.4115 0.84
0.3972 39.0 29250 0.4106 0.84
0.3899 40.0 30000 0.4098 0.84
0.3662 41.0 30750 0.4090 0.84
0.3473 42.0 31500 0.4084 0.8417
0.3905 43.0 32250 0.4078 0.8417
0.3794 44.0 33000 0.4074 0.8417
0.3783 45.0 33750 0.4070 0.8417
0.3309 46.0 34500 0.4067 0.8417
0.3086 47.0 35250 0.4065 0.8417
0.3454 48.0 36000 0.4063 0.8417
0.3559 49.0 36750 0.4063 0.8417
0.323 50.0 37500 0.4063 0.8417

Framework versions

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