readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - fashion_mnist_quality_drift
  metrics:
  - accuracy
  - f1
  model-index:
  - name: resnet-50-fashion-mnist-quality-drift
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: fashion_mnist_quality_drift
        type: fashion_mnist_quality_drift
        config: default
        split: training
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.73
      - name: F1
        type: f1
        value: 0.7289360255705818
  ---
  
  <!-- 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-50-fashion-mnist-quality-drift
  
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the fashion_mnist_quality_drift dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7473
  - Accuracy: 0.73
  - F1: 0.7289
  
  ## 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: 0.0002
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 1.5138        | 1.0   | 750  | 0.9237          | 0.684    | 0.6826 |
  | 0.9377        | 2.0   | 1500 | 0.7861          | 0.722    | 0.7253 |
  | 0.8276        | 3.0   | 2250 | 0.7473          | 0.73     | 0.7289 |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  