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smids_5x_deit_small_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-small-patch16-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
model-index:
  - name: smids_5x_deit_small_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: 0.8901830282861897

smids_5x_deit_small_rms_0001_fold2

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: 1.0513
  • Accuracy: 0.8902

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
0.2658 1.0 375 0.3126 0.8819
0.1908 2.0 750 0.3283 0.8918
0.0825 3.0 1125 0.4361 0.8702
0.0963 4.0 1500 0.5286 0.8619
0.0865 5.0 1875 0.5193 0.8885
0.0848 6.0 2250 0.5531 0.8835
0.072 7.0 2625 0.6299 0.8669
0.0017 8.0 3000 0.6200 0.8819
0.0259 9.0 3375 0.6078 0.9002
0.0609 10.0 3750 0.5800 0.8918
0.045 11.0 4125 0.6268 0.8802
0.068 12.0 4500 0.5720 0.8869
0.0342 13.0 4875 0.7774 0.8652
0.007 14.0 5250 0.7661 0.8769
0.0136 15.0 5625 0.7670 0.8885
0.025 16.0 6000 0.9672 0.8752
0.0117 17.0 6375 0.6723 0.9018
0.01 18.0 6750 0.7201 0.8835
0.1057 19.0 7125 0.7988 0.8686
0.0079 20.0 7500 0.8529 0.8735
0.0114 21.0 7875 0.9574 0.8835
0.0141 22.0 8250 0.8344 0.8819
0.0006 23.0 8625 0.9308 0.8769
0.0008 24.0 9000 0.8418 0.8752
0.0001 25.0 9375 0.7076 0.8869
0.0123 26.0 9750 0.9006 0.8686
0.0003 27.0 10125 0.9386 0.8702
0.0081 28.0 10500 1.0332 0.8735
0.0 29.0 10875 0.9316 0.8752
0.0272 30.0 11250 0.9157 0.8835
0.0042 31.0 11625 0.9011 0.8752
0.0038 32.0 12000 0.9259 0.8769
0.0044 33.0 12375 0.9290 0.8869
0.0 34.0 12750 0.9423 0.8852
0.0 35.0 13125 0.8933 0.8902
0.0 36.0 13500 0.8976 0.8885
0.0 37.0 13875 0.8889 0.8835
0.0004 38.0 14250 1.0859 0.8802
0.0 39.0 14625 1.0992 0.8869
0.0031 40.0 15000 1.0003 0.8952
0.0 41.0 15375 1.0009 0.8985
0.0027 42.0 15750 1.0542 0.8885
0.0026 43.0 16125 1.0230 0.8852
0.0026 44.0 16500 1.0414 0.8885
0.0027 45.0 16875 1.0121 0.8885
0.0 46.0 17250 1.0455 0.8885
0.006 47.0 17625 1.0427 0.8918
0.0 48.0 18000 1.0481 0.8918
0.0024 49.0 18375 1.0499 0.8918
0.0022 50.0 18750 1.0513 0.8902

Framework versions

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