readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  model-index:
  - name: resnet_weather_model
    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: 0.6735537190082644
      - name: F1
        type: f1
        value: 0.6654635943888922
  ---
  
  <!-- 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_weather_model
  
  This model was trained from scratch on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.7452
  - Accuracy: 0.6736
  - F1: 0.6655
  
  ## 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
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | 2.3598        | 1.0   | 91   | 2.1983          | 0.5165   | 0.5146 |
  | 2.0319        | 2.0   | 182  | 1.8708          | 0.6446   | 0.6433 |
  | 1.7971        | 3.0   | 273  | 1.7452          | 0.6736   | 0.6655 |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  