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smids_5x_deit_small_adamax_0001_fold5

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_5x_deit_small_adamax_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: 0.925

smids_5x_deit_small_adamax_0001_fold5

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: 0.8104
  • Accuracy: 0.925

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
0.1649 1.0 750 0.2981 0.8767
0.1219 2.0 1500 0.3351 0.9117
0.088 3.0 2250 0.4113 0.9117
0.0191 4.0 3000 0.5041 0.91
0.0316 5.0 3750 0.6431 0.905
0.0519 6.0 4500 0.6485 0.9017
0.0145 7.0 5250 0.6712 0.91
0.001 8.0 6000 0.7180 0.91
0.0005 9.0 6750 0.6201 0.9067
0.0001 10.0 7500 0.7375 0.92
0.009 11.0 8250 0.7397 0.9133
0.0 12.0 9000 0.7531 0.9233
0.0005 13.0 9750 0.7094 0.9167
0.0 14.0 10500 0.6906 0.9217
0.0 15.0 11250 0.7622 0.92
0.0 16.0 12000 0.7690 0.9167
0.0099 17.0 12750 0.7093 0.925
0.0049 18.0 13500 0.7817 0.9167
0.0 19.0 14250 0.7714 0.9183
0.0 20.0 15000 0.7423 0.92
0.0 21.0 15750 0.7472 0.9283
0.0 22.0 16500 0.8201 0.9217
0.0003 23.0 17250 0.7230 0.925
0.0 24.0 18000 0.7873 0.9233
0.0 25.0 18750 0.7903 0.9233
0.0 26.0 19500 0.7611 0.9233
0.0 27.0 20250 0.7662 0.9267
0.0 28.0 21000 0.7601 0.9267
0.0 29.0 21750 0.7659 0.925
0.0054 30.0 22500 0.7697 0.9217
0.0 31.0 23250 0.7755 0.9217
0.0 32.0 24000 0.7712 0.9217
0.0 33.0 24750 0.7599 0.9267
0.0 34.0 25500 0.7735 0.9267
0.0 35.0 26250 0.7806 0.925
0.0 36.0 27000 0.7835 0.9217
0.0039 37.0 27750 0.7879 0.925
0.0 38.0 28500 0.7885 0.9267
0.0 39.0 29250 0.7918 0.925
0.0 40.0 30000 0.7945 0.9267
0.0 41.0 30750 0.7955 0.9267
0.0 42.0 31500 0.7991 0.9233
0.0 43.0 32250 0.8003 0.925
0.0 44.0 33000 0.8023 0.925
0.0 45.0 33750 0.8041 0.925
0.0 46.0 34500 0.8060 0.925
0.0 47.0 35250 0.8084 0.925
0.0 48.0 36000 0.8088 0.925
0.0 49.0 36750 0.8102 0.9267
0.0 50.0 37500 0.8104 0.925

Framework versions

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