readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: resnet-50-finetuned-resnet50_0831
    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: 0.976407675369613
  ---
  
  <!-- 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-resnet50_0831
  
  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: 0.0862
  - Accuracy: 0.9764
  
  ## 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: 5e-05
  - 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.9066        | 1.0   | 223  | 0.8770          | 0.6659   |
  | 0.5407        | 2.0   | 446  | 0.4251          | 0.7867   |
  | 0.3614        | 3.0   | 669  | 0.2009          | 0.9390   |
  | 0.3016        | 4.0   | 892  | 0.1362          | 0.9582   |
  | 0.2358        | 5.0   | 1115 | 0.1139          | 0.9676   |
  | 0.247         | 6.0   | 1338 | 0.1081          | 0.9698   |
  | 0.2135        | 7.0   | 1561 | 0.1027          | 0.9720   |
  | 0.2043        | 8.0   | 1784 | 0.1026          | 0.9695   |
  | 0.2165        | 9.0   | 2007 | 0.0957          | 0.9733   |
  | 0.1983        | 10.0  | 2230 | 0.0936          | 0.9736   |
  | 0.2116        | 11.0  | 2453 | 0.0949          | 0.9736   |
  | 0.2341        | 12.0  | 2676 | 0.0905          | 0.9755   |
  | 0.2004        | 13.0  | 2899 | 0.0901          | 0.9739   |
  | 0.1956        | 14.0  | 3122 | 0.0877          | 0.9755   |
  | 0.1668        | 15.0  | 3345 | 0.0847          | 0.9764   |
  | 0.1855        | 16.0  | 3568 | 0.0850          | 0.9755   |
  | 0.18          | 17.0  | 3791 | 0.0897          | 0.9745   |
  | 0.1772        | 18.0  | 4014 | 0.0852          | 0.9755   |
  | 0.1881        | 19.0  | 4237 | 0.0845          | 0.9764   |
  | 0.2145        | 20.0  | 4460 | 0.0862          | 0.9764   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  