readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: resnet-18-feature-extraction
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.95
      - name: Precision
        type: precision
        value: 0.9652777777777778
      - name: Recall
        type: recall
        value: 0.9788732394366197
      - name: F1
        type: f1
        value: 0.972027972027972
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # resnet-18-feature-extraction
  
  This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1485
  - Accuracy: 0.95
  - Precision: 0.9653
  - Recall: 0.9789
  - F1: 0.9720
  - Roc Auc: 0.8505
  
  ## 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: 2e-05
  - train_batch_size: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - 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 | Precision | Recall | F1     | Roc Auc |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
  | No log        | 0.8   | 2    | 0.6232          | 0.75     | 0.9636    | 0.7465 | 0.8413 | 0.7621  |
  | No log        | 1.8   | 4    | 0.6971          | 0.4875   | 1.0       | 0.4225 | 0.5941 | 0.7113  |
  | No log        | 2.8   | 6    | 0.7915          | 0.2875   | 1.0       | 0.1972 | 0.3294 | 0.5986  |
  | No log        | 3.8   | 8    | 0.8480          | 0.2875   | 1.0       | 0.1972 | 0.3294 | 0.5986  |
  | 0.8651        | 4.8   | 10   | 0.9094          | 0.2562   | 1.0       | 0.1620 | 0.2788 | 0.5810  |
  | 0.8651        | 5.8   | 12   | 0.7470          | 0.5625   | 1.0       | 0.5070 | 0.6729 | 0.7535  |
  | 0.8651        | 6.8   | 14   | 0.5915          | 0.85     | 1.0       | 0.8310 | 0.9077 | 0.9155  |
  | 0.8651        | 7.8   | 16   | 0.4817          | 0.8875   | 0.9844    | 0.8873 | 0.9333 | 0.8881  |
  | 0.8651        | 8.8   | 18   | 0.3455          | 0.9187   | 0.9778    | 0.9296 | 0.9531 | 0.8815  |
  | 0.5349        | 9.8   | 20   | 0.2966          | 0.9187   | 0.9708    | 0.9366 | 0.9534 | 0.8572  |
  | 0.5349        | 10.8  | 22   | 0.2347          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
  | 0.5349        | 11.8  | 24   | 0.2468          | 0.9313   | 0.9645    | 0.9577 | 0.9611 | 0.8400  |
  | 0.5349        | 12.8  | 26   | 0.2310          | 0.9563   | 0.9720    | 0.9789 | 0.9754 | 0.8783  |
  | 0.5349        | 13.8  | 28   | 0.2083          | 0.9313   | 0.9580    | 0.9648 | 0.9614 | 0.8157  |
  | 0.3593        | 14.8  | 30   | 0.1840          | 0.9375   | 0.9521    | 0.9789 | 0.9653 | 0.7950  |
  | 0.3593        | 15.8  | 32   | 0.1947          | 0.9375   | 0.9648    | 0.9648 | 0.9648 | 0.8435  |
  | 0.3593        | 16.8  | 34   | 0.1837          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
  | 0.3593        | 17.8  | 36   | 0.1819          | 0.9437   | 0.9524    | 0.9859 | 0.9689 | 0.7985  |
  | 0.3593        | 18.8  | 38   | 0.1924          | 0.9437   | 0.9650    | 0.9718 | 0.9684 | 0.8470  |
  | 0.2737        | 19.8  | 40   | 0.1990          | 0.95     | 0.9653    | 0.9789 | 0.9720 | 0.8505  |
  | 0.2737        | 20.8  | 42   | 0.1759          | 0.95     | 0.9718    | 0.9718 | 0.9718 | 0.8748  |
  | 0.2737        | 21.8  | 44   | 0.1804          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
  | 0.2737        | 22.8  | 46   | 0.1666          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
  | 0.2737        | 23.8  | 48   | 0.1534          | 0.9437   | 0.9524    | 0.9859 | 0.9689 | 0.7985  |
  | 0.2278        | 24.8  | 50   | 0.1612          | 0.9375   | 0.9521    | 0.9789 | 0.9653 | 0.7950  |
  | 0.2278        | 25.8  | 52   | 0.1535          | 0.9437   | 0.9586    | 0.9789 | 0.9686 | 0.8228  |
  | 0.2278        | 26.8  | 54   | 0.1568          | 0.9437   | 0.9716    | 0.9648 | 0.9682 | 0.8713  |
  | 0.2278        | 27.8  | 56   | 0.2107          | 0.9375   | 0.9714    | 0.9577 | 0.9645 | 0.8678  |
  | 0.2278        | 28.8  | 58   | 0.1592          | 0.9313   | 0.9517    | 0.9718 | 0.9617 | 0.7915  |
  | 0.2057        | 29.8  | 60   | 0.1557          | 0.9375   | 0.9648    | 0.9648 | 0.