readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: convnext-large-224-22k-1k-FV2-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: 0.866306027820711
      - name: Precision
        type: precision
        value: 0.8617341777601428
      - name: Recall
        type: recall
        value: 0.866306027820711
      - name: F1
        type: f1
        value: 0.8629450778711495
  ---
  
  <!-- 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. -->
  
  # convnext-large-224-22k-1k-FV2-finetuned-memes
  
  This model is a fine-tuned version of [facebook/convnext-large-224-22k-1k](https://huggingface.co/facebook/convnext-large-224-22k-1k) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4290
  - Accuracy: 0.8663
  - Precision: 0.8617
  - Recall: 0.8663
  - F1: 0.8629
  
  ## 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: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 256
  - 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 | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 0.8992        | 0.99  | 20   | 0.6455          | 0.7658   | 0.7512    | 0.7658 | 0.7534 |
  | 0.4245        | 1.99  | 40   | 0.4008          | 0.8539   | 0.8680    | 0.8539 | 0.8541 |
  | 0.2054        | 2.99  | 60   | 0.3245          | 0.8694   | 0.8631    | 0.8694 | 0.8650 |
  | 0.1102        | 3.99  | 80   | 0.3231          | 0.8671   | 0.8624    | 0.8671 | 0.8645 |
  | 0.0765        | 4.99  | 100  | 0.3882          | 0.8563   | 0.8603    | 0.8563 | 0.8556 |
  | 0.0642        | 5.99  | 120  | 0.4133          | 0.8601   | 0.8604    | 0.8601 | 0.8598 |
  | 0.0574        | 6.99  | 140  | 0.3889          | 0.8694   | 0.8657    | 0.8694 | 0.8667 |
  | 0.0526        | 7.99  | 160  | 0.4145          | 0.8655   | 0.8705    | 0.8655 | 0.8670 |
  | 0.0468        | 8.99  | 180  | 0.4256          | 0.8679   | 0.8642    | 0.8679 | 0.8650 |
  | 0.0472        | 9.99  | 200  | 0.4290          | 0.8663   | 0.8617    | 0.8663 | 0.8629 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  