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smids_5x_deit_small_rms_00001_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-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_rms_00001_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: 0.9232053422370617

smids_5x_deit_small_rms_00001_fold1

This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7417
  • Accuracy: 0.9232

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
0.2432 1.0 376 0.2964 0.8731
0.1614 2.0 752 0.2849 0.8932
0.1382 3.0 1128 0.2988 0.9048
0.0674 4.0 1504 0.3782 0.9082
0.0238 5.0 1880 0.4767 0.9132
0.0038 6.0 2256 0.4683 0.9215
0.0081 7.0 2632 0.5149 0.9015
0.0311 8.0 3008 0.6215 0.9082
0.0258 9.0 3384 0.6420 0.9015
0.0003 10.0 3760 0.7389 0.8982
0.0016 11.0 4136 0.7097 0.9032
0.0237 12.0 4512 0.7322 0.8965
0.0 13.0 4888 0.6330 0.9065
0.0121 14.0 5264 0.6713 0.8998
0.0128 15.0 5640 0.6959 0.9032
0.0 16.0 6016 0.5921 0.9165
0.0147 17.0 6392 0.7286 0.9032
0.0096 18.0 6768 0.6654 0.9115
0.0001 19.0 7144 0.7241 0.9065
0.026 20.0 7520 0.7595 0.9115
0.0004 21.0 7896 0.7089 0.9132
0.0001 22.0 8272 0.7020 0.9132
0.0189 23.0 8648 0.7064 0.9032
0.0 24.0 9024 0.6953 0.9182
0.0008 25.0 9400 0.6754 0.9048
0.0029 26.0 9776 0.6682 0.9149
0.0039 27.0 10152 0.7036 0.9115
0.0 28.0 10528 0.7901 0.9098
0.0047 29.0 10904 0.7958 0.9165
0.0042 30.0 11280 0.7246 0.9115
0.0 31.0 11656 0.7694 0.9132
0.0 32.0 12032 0.7581 0.9082
0.0 33.0 12408 0.7146 0.9149
0.0 34.0 12784 0.7034 0.9165
0.0 35.0 13160 0.7688 0.9115
0.0 36.0 13536 0.7638 0.9132
0.0 37.0 13912 0.8028 0.9115
0.0 38.0 14288 0.7323 0.9215
0.0 39.0 14664 0.7555 0.9199
0.0 40.0 15040 0.7506 0.9215
0.0 41.0 15416 0.7416 0.9215
0.0 42.0 15792 0.7376 0.9199
0.0 43.0 16168 0.7280 0.9215
0.0 44.0 16544 0.7390 0.9215
0.0 45.0 16920 0.7365 0.9232
0.0028 46.0 17296 0.7367 0.9232
0.0 47.0 17672 0.7395 0.9232
0.0 48.0 18048 0.7408 0.9232
0.0 49.0 18424 0.7418 0.9232
0.0024 50.0 18800 0.7417 0.9232

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.12.0
  • Tokenizers 0.13.2