readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: resnet50-finetuned-memes
    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: 0.5741885625965997
    - task:
        type: image-classification
        name: Image Classification
      dataset:
        type: custom
        name: custom
        split: test
      metrics:
      - type: f1
        value: 0.47811617701687364
        name: F1
      - type: precision
        value: 0.43689216537139497
        name: Precision
      - type: recall
        value: 0.5695517774343122
        name: Recall
  ---
  
  <!-- 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. -->
  
  # resnet50-finetuned-memes
  
  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: 1.0625
  - Accuracy: 0.5742
  
  ## 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: 0.00012
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.4795        | 0.99  | 40   | 1.4641          | 0.4382   |
  | 1.3455        | 1.99  | 80   | 1.3281          | 0.4389   |
  | 1.262         | 2.99  | 120  | 1.2583          | 0.4583   |
  | 1.1975        | 3.99  | 160  | 1.1978          | 0.4876   |
  | 1.1358        | 4.99  | 200  | 1.1614          | 0.5139   |
  | 1.1273        | 5.99  | 240  | 1.1316          | 0.5379   |
  | 1.0379        | 6.99  | 280  | 1.1024          | 0.5464   |
  | 1.041         | 7.99  | 320  | 1.0927          | 0.5580   |
  | 0.9952        | 8.99  | 360  | 1.0790          | 0.5541   |
  | 1.0146        | 9.99  | 400  | 1.0625          | 0.5742   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  