hkivancoral's picture
End of training
9121cb5
metadata
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: hushem_1x_deit_tiny_sgd_lr001_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: 0.3902439024390244

hushem_1x_deit_tiny_sgd_lr001_fold5

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: 1.3032
  • Accuracy: 0.3902

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: 0.001
  • 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
No log 1.0 6 1.6123 0.1220
1.5785 2.0 12 1.5813 0.1463
1.5785 3.0 18 1.5542 0.1463
1.5395 4.0 24 1.5297 0.1463
1.4749 5.0 30 1.5083 0.1951
1.4749 6.0 36 1.4884 0.1951
1.4296 7.0 42 1.4718 0.1707
1.4296 8.0 48 1.4578 0.1463
1.4059 9.0 54 1.4447 0.1463
1.3876 10.0 60 1.4316 0.2195
1.3876 11.0 66 1.4209 0.2195
1.3523 12.0 72 1.4102 0.2195
1.3523 13.0 78 1.4009 0.2439
1.3412 14.0 84 1.3926 0.2439
1.3216 15.0 90 1.3847 0.2927
1.3216 16.0 96 1.3782 0.3171
1.2923 17.0 102 1.3713 0.3415
1.2923 18.0 108 1.3652 0.3415
1.305 19.0 114 1.3592 0.3415
1.2722 20.0 120 1.3536 0.3415
1.2722 21.0 126 1.3490 0.3659
1.2479 22.0 132 1.3441 0.3659
1.2479 23.0 138 1.3399 0.3659
1.2818 24.0 144 1.3360 0.3659
1.2363 25.0 150 1.3318 0.3659
1.2363 26.0 156 1.3281 0.3659
1.2375 27.0 162 1.3249 0.3659
1.2375 28.0 168 1.3220 0.3659
1.2164 29.0 174 1.3194 0.3659
1.2359 30.0 180 1.3171 0.3902
1.2359 31.0 186 1.3148 0.3902
1.2121 32.0 192 1.3127 0.3902
1.2121 33.0 198 1.3110 0.3902
1.2131 34.0 204 1.3092 0.3902
1.1973 35.0 210 1.3077 0.3902
1.1973 36.0 216 1.3064 0.3902
1.1836 37.0 222 1.3054 0.3902
1.1836 38.0 228 1.3046 0.3902
1.2087 39.0 234 1.3039 0.3902
1.2019 40.0 240 1.3035 0.3902
1.2019 41.0 246 1.3033 0.3902
1.2033 42.0 252 1.3032 0.3902
1.2033 43.0 258 1.3032 0.3902
1.1754 44.0 264 1.3032 0.3902
1.1907 45.0 270 1.3032 0.3902
1.1907 46.0 276 1.3032 0.3902
1.2082 47.0 282 1.3032 0.3902
1.2082 48.0 288 1.3032 0.3902
1.1699 49.0 294 1.3032 0.3902
1.2038 50.0 300 1.3032 0.3902

Framework versions

  • Transformers 4.35.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.14.6
  • Tokenizers 0.14.1