Hugging Face's logo

hkivancoral
/
smids_3x_deit_tiny_sgd_001_fold1

Image Classification
Transformers
PyTorch
vit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
smids_3x_deit_tiny_sgd_001_fold1 / README.md
hkivancoral's picture
hkivancoral
End of training
1e85bb5
preview code
|
raw
history blame contribute delete
4.86 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_sgd_001_fold1
    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: 0.8731218697829716

smids_3x_deit_tiny_sgd_001_fold1

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: 0.3078
  • Accuracy: 0.8731

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
0.9352 1.0 226 0.9208 0.5376
0.6928 2.0 452 0.7389 0.6861
0.5623 3.0 678 0.6105 0.7429
0.5246 4.0 904 0.5485 0.7563
0.5426 5.0 1130 0.4979 0.7880
0.4977 6.0 1356 0.4581 0.8080
0.3766 7.0 1582 0.4327 0.8130
0.4038 8.0 1808 0.4167 0.8097
0.3541 9.0 2034 0.3987 0.8431
0.3195 10.0 2260 0.3857 0.8247
0.3215 11.0 2486 0.3815 0.8297
0.2707 12.0 2712 0.3604 0.8414
0.2756 13.0 2938 0.3575 0.8364
0.2853 14.0 3164 0.3492 0.8414
0.3202 15.0 3390 0.3434 0.8447
0.3213 16.0 3616 0.3398 0.8497
0.246 17.0 3842 0.3305 0.8581
0.2485 18.0 4068 0.3288 0.8564
0.2691 19.0 4294 0.3315 0.8598
0.2123 20.0 4520 0.3213 0.8648
0.2607 21.0 4746 0.3252 0.8564
0.2646 22.0 4972 0.3186 0.8664
0.2851 23.0 5198 0.3202 0.8631
0.2373 24.0 5424 0.3144 0.8748
0.1908 25.0 5650 0.3143 0.8698
0.2924 26.0 5876 0.3120 0.8698
0.1662 27.0 6102 0.3113 0.8748
0.2215 28.0 6328 0.3120 0.8681
0.1838 29.0 6554 0.3136 0.8698
0.2131 30.0 6780 0.3140 0.8731
0.2074 31.0 7006 0.3100 0.8715
0.194 32.0 7232 0.3083 0.8748
0.1635 33.0 7458 0.3091 0.8748
0.1521 34.0 7684 0.3083 0.8748
0.2333 35.0 7910 0.3078 0.8748
0.1942 36.0 8136 0.3076 0.8731
0.242 37.0 8362 0.3062 0.8748
0.2131 38.0 8588 0.3090 0.8748
0.2044 39.0 8814 0.3079 0.8748
0.1565 40.0 9040 0.3082 0.8731
0.1709 41.0 9266 0.3089 0.8748
0.2023 42.0 9492 0.3080 0.8748
0.2299 43.0 9718 0.3077 0.8731
0.1365 44.0 9944 0.3081 0.8765
0.1955 45.0 10170 0.3078 0.8748
0.2025 46.0 10396 0.3089 0.8781
0.1982 47.0 10622 0.3076 0.8731
0.1881 48.0 10848 0.3078 0.8731
0.1389 49.0 11074 0.3077 0.8731
0.1646 50.0 11300 0.3078 0.8731

Framework versions

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