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smids_5x_deit_tiny_rms_0001_fold2

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_5x_deit_tiny_rms_0001_fold2
    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: 0.8735440931780366

smids_5x_deit_tiny_rms_0001_fold2

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: 1.1259
  • Accuracy: 0.8735

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
0.3139 1.0 375 0.2920 0.8835
0.213 2.0 750 0.3450 0.8785
0.2004 3.0 1125 0.4306 0.8719
0.1151 4.0 1500 0.4856 0.8702
0.1363 5.0 1875 0.5483 0.8752
0.0415 6.0 2250 0.6014 0.8719
0.0888 7.0 2625 0.6594 0.8636
0.0129 8.0 3000 0.7394 0.8702
0.0606 9.0 3375 0.7551 0.8619
0.0273 10.0 3750 0.7977 0.8536
0.0575 11.0 4125 0.7927 0.8702
0.0142 12.0 4500 0.8285 0.8619
0.006 13.0 4875 0.8594 0.8819
0.0339 14.0 5250 0.8600 0.8686
0.0029 15.0 5625 0.9289 0.8719
0.0348 16.0 6000 0.7828 0.8819
0.0273 17.0 6375 0.7381 0.8885
0.029 18.0 6750 0.9087 0.8686
0.0306 19.0 7125 0.9194 0.8785
0.0034 20.0 7500 1.0978 0.8619
0.0052 21.0 7875 0.9530 0.8785
0.0001 22.0 8250 0.9575 0.8752
0.0447 23.0 8625 0.9869 0.8819
0.0122 24.0 9000 0.8869 0.8785
0.0018 25.0 9375 1.0324 0.8669
0.0117 26.0 9750 0.9387 0.8852
0.0206 27.0 10125 1.0468 0.8719
0.0002 28.0 10500 0.9421 0.8785
0.0001 29.0 10875 0.8621 0.8968
0.0027 30.0 11250 0.9653 0.8769
0.0116 31.0 11625 0.9958 0.8785
0.0019 32.0 12000 1.1300 0.8752
0.0084 33.0 12375 1.0346 0.8802
0.0 34.0 12750 1.0458 0.8719
0.0 35.0 13125 1.0740 0.8719
0.0001 36.0 13500 1.0706 0.8719
0.0 37.0 13875 1.2116 0.8735
0.0 38.0 14250 1.1598 0.8735
0.0 39.0 14625 1.1682 0.8785
0.0029 40.0 15000 1.0573 0.8835
0.0 41.0 15375 1.1307 0.8735
0.0028 42.0 15750 1.1484 0.8702
0.0032 43.0 16125 1.1289 0.8752
0.0031 44.0 16500 1.1224 0.8769
0.0027 45.0 16875 1.1287 0.8719
0.0 46.0 17250 1.1176 0.8752
0.006 47.0 17625 1.1207 0.8752
0.0 48.0 18000 1.1234 0.8752
0.0024 49.0 18375 1.1256 0.8752
0.0022 50.0 18750 1.1259 0.8735

Framework versions

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