readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: resnet-50-finetuned-eurosat
    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: 0.8239812959251837
  ---
  
  <!-- 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-finetuned-eurosat
  
  This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9095
  - Accuracy: 0.8240
  
  ## 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
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.78          | 0.96  | 17   | 1.7432          | 0.4321   |
  | 1.7105        | 1.96  | 34   | 1.6596          | 0.6307   |
  | 1.6045        | 2.96  | 51   | 1.5369          | 0.6758   |
  | 1.6526        | 3.96  | 68   | 1.4111          | 0.7139   |
  | 1.4018        | 4.96  | 85   | 1.2686          | 0.7602   |
  | 1.2812        | 5.96  | 102  | 1.1433          | 0.7714   |
  | 1.3282        | 6.96  | 119  | 1.0643          | 0.7910   |
  | 1.1246        | 7.96  | 136  | 0.9794          | 0.8133   |
  | 1.0731        | 8.96  | 153  | 0.9279          | 0.8087   |
  | 1.0531        | 9.96  | 170  | 0.9095          | 0.8240   |
  
  
  ### Framework versions
  
  - Transformers 4.24.0
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  