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hushem_40x_deit_tiny_rms_0001_fold4

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
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hushem_40x_deit_tiny_rms_0001_fold4 / README.md
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metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_40x_deit_tiny_rms_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: 0.9523809523809523

hushem_40x_deit_tiny_rms_0001_fold4

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: 0.4037
  • Accuracy: 0.9524

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
0.1838 1.0 219 0.4926 0.8571
0.0446 2.0 438 0.2754 0.9286
0.0295 3.0 657 0.9751 0.8810
0.0096 4.0 876 0.1123 0.9762
0.0055 5.0 1095 0.3687 0.9048
0.0033 6.0 1314 0.3076 0.9524
0.0283 7.0 1533 0.8089 0.8571
0.0044 8.0 1752 0.2435 0.9286
0.0018 9.0 1971 0.7038 0.8571
0.0191 10.0 2190 0.5242 0.9048
0.0001 11.0 2409 0.8130 0.9286
0.0007 12.0 2628 0.6030 0.9048
0.0189 13.0 2847 0.5406 0.9048
0.0002 14.0 3066 0.6774 0.8571
0.0018 15.0 3285 0.6982 0.9286
0.0001 16.0 3504 0.3877 0.9524
0.0008 17.0 3723 0.6996 0.8810
0.0 18.0 3942 0.5507 0.9286
0.0 19.0 4161 0.3796 0.9524
0.0001 20.0 4380 0.3967 0.9286
0.0 21.0 4599 0.4081 0.9286
0.0 22.0 4818 0.3898 0.9286
0.0 23.0 5037 0.3709 0.9286
0.0 24.0 5256 0.3640 0.9524
0.0 25.0 5475 0.3789 0.9524
0.0 26.0 5694 0.3987 0.9286
0.0 27.0 5913 0.4326 0.9286
0.0 28.0 6132 0.4566 0.9286
0.0 29.0 6351 0.4673 0.9286
0.0 30.0 6570 0.4642 0.9286
0.0 31.0 6789 0.4534 0.9286
0.0 32.0 7008 0.4388 0.9286
0.0 33.0 7227 0.4268 0.9286
0.0 34.0 7446 0.4182 0.9286
0.0 35.0 7665 0.4134 0.9286
0.0 36.0 7884 0.4102 0.9286
0.0 37.0 8103 0.4079 0.9286
0.0 38.0 8322 0.4066 0.9286
0.0 39.0 8541 0.4041 0.9286
0.0 40.0 8760 0.4048 0.9286
0.0 41.0 8979 0.4034 0.9524
0.0 42.0 9198 0.4032 0.9524
0.0 43.0 9417 0.4038 0.9524
0.0 44.0 9636 0.4040 0.9524
0.0 45.0 9855 0.4040 0.9524
0.0 46.0 10074 0.4038 0.9524
0.0 47.0 10293 0.4038 0.9524
0.0 48.0 10512 0.4039 0.9524
0.0 49.0 10731 0.4037 0.9524
0.0 50.0 10950 0.4037 0.9524

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.1+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2