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smids_3x_deit_tiny_rms_0001_fold1

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-tiny-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_3x_deit_tiny_rms_0001_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: 0.8981636060100167

smids_3x_deit_tiny_rms_0001_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: 0.8923
  • Accuracy: 0.8982

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
0.4584 1.0 226 0.3564 0.8414
0.3047 2.0 452 0.3557 0.8614
0.2416 3.0 678 0.4578 0.8097
0.1428 4.0 904 0.4233 0.8631
0.1227 5.0 1130 0.4440 0.8698
0.1524 6.0 1356 0.3981 0.8915
0.0691 7.0 1582 0.5148 0.8932
0.0615 8.0 1808 0.6305 0.8581
0.1111 9.0 2034 0.5927 0.8848
0.0229 10.0 2260 0.6397 0.8932
0.0429 11.0 2486 0.8816 0.8497
0.0206 12.0 2712 0.6220 0.8815
0.0864 13.0 2938 0.9519 0.8481
0.0084 14.0 3164 0.6931 0.8898
0.0377 15.0 3390 0.7392 0.8815
0.0362 16.0 3616 0.8551 0.8581
0.0572 17.0 3842 0.7247 0.8698
0.1009 18.0 4068 0.6228 0.8715
0.0504 19.0 4294 0.8902 0.8648
0.0251 20.0 4520 0.7786 0.8831
0.0065 21.0 4746 0.9795 0.8781
0.0845 22.0 4972 0.7973 0.8815
0.0036 23.0 5198 0.8709 0.8648
0.0477 24.0 5424 0.8349 0.8781
0.0002 25.0 5650 0.8199 0.8781
0.0014 26.0 5876 1.0541 0.8648
0.0123 27.0 6102 0.9016 0.8798
0.0001 28.0 6328 0.7666 0.8881
0.0 29.0 6554 0.7915 0.8881
0.0004 30.0 6780 0.9036 0.8881
0.0001 31.0 7006 0.8349 0.8932
0.0 32.0 7232 0.8309 0.8848
0.0025 33.0 7458 0.8690 0.8932
0.0 34.0 7684 0.9464 0.8848
0.0312 35.0 7910 0.9721 0.8881
0.0 36.0 8136 0.9282 0.8848
0.0 37.0 8362 0.8805 0.8915
0.0 38.0 8588 0.8709 0.8948
0.0 39.0 8814 0.8888 0.8915
0.0 40.0 9040 0.8303 0.9015
0.0028 41.0 9266 0.8497 0.8965
0.0031 42.0 9492 0.8601 0.8932
0.0 43.0 9718 0.8400 0.8932
0.0 44.0 9944 0.8634 0.8965
0.0 45.0 10170 0.8516 0.9015
0.0 46.0 10396 0.9026 0.8948
0.0 47.0 10622 0.8950 0.9015
0.0 48.0 10848 0.8925 0.8998
0.0 49.0 11074 0.8907 0.8982
0.0 50.0 11300 0.8923 0.8982

Framework versions

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