Hugging Face's logo

hkivancoral
/
smids_3x_deit_tiny_rms_00001_fold4

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
smids_3x_deit_tiny_rms_00001_fold4 / README.md
hkivancoral's picture
hkivancoral
End of training
4e4167e
preview code
|
raw
history blame contribute delete
4.87 kB
metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_rms_00001_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: 0.8783333333333333

smids_3x_deit_tiny_rms_00001_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: 1.3270
  • Accuracy: 0.8783

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: 1e-05
  • 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
0.3199 1.0 225 0.3628 0.8533
0.1542 2.0 450 0.3942 0.8633
0.1186 3.0 675 0.4019 0.8783
0.1068 4.0 900 0.4521 0.8667
0.0629 5.0 1125 0.5068 0.8733
0.0347 6.0 1350 0.6175 0.8733
0.0294 7.0 1575 0.7043 0.8683
0.0692 8.0 1800 0.7322 0.8683
0.0709 9.0 2025 0.8957 0.8683
0.0668 10.0 2250 0.9618 0.8633
0.0043 11.0 2475 1.0931 0.855
0.0053 12.0 2700 1.0669 0.8617
0.0043 13.0 2925 1.2308 0.8533
0.0002 14.0 3150 1.1863 0.86
0.0021 15.0 3375 1.1504 0.8633
0.0 16.0 3600 1.1831 0.8683
0.0542 17.0 3825 1.2418 0.8633
0.0002 18.0 4050 1.2334 0.8567
0.0 19.0 4275 1.2760 0.8717
0.0018 20.0 4500 1.3796 0.8567
0.0 21.0 4725 1.2762 0.8683
0.0 22.0 4950 1.3847 0.8583
0.0001 23.0 5175 1.2718 0.8733
0.0 24.0 5400 1.2359 0.8717
0.0 25.0 5625 1.1935 0.8783
0.0 26.0 5850 1.2054 0.8783
0.0 27.0 6075 1.3706 0.86
0.0 28.0 6300 1.3639 0.8683
0.0 29.0 6525 1.2958 0.865
0.0 30.0 6750 1.3600 0.8617
0.0001 31.0 6975 1.4078 0.865
0.0 32.0 7200 1.3542 0.8667
0.0 33.0 7425 1.3120 0.865
0.0 34.0 7650 1.2757 0.8717
0.0 35.0 7875 1.2928 0.8667
0.0 36.0 8100 1.3460 0.8667
0.0002 37.0 8325 1.3775 0.8633
0.0 38.0 8550 1.3704 0.8617
0.0 39.0 8775 1.3338 0.87
0.0 40.0 9000 1.3261 0.8717
0.0 41.0 9225 1.3217 0.8717
0.0068 42.0 9450 1.3066 0.8733
0.0 43.0 9675 1.3141 0.875
0.0059 44.0 9900 1.3176 0.8767
0.0 45.0 10125 1.3464 0.865
0.0 46.0 10350 1.3263 0.8733
0.0 47.0 10575 1.3244 0.875
0.0 48.0 10800 1.3245 0.8767
0.0 49.0 11025 1.3277 0.8783
0.0 50.0 11250 1.3270 0.8783

Framework versions

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