readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - precision
  - recall
  - f1
  model-index:
  - name: resnet-152-fv-finetuned-memess
    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: 0.767387944358578
      - name: Precision
        type: precision
        value: 0.7651125602674349
      - name: Recall
        type: recall
        value: 0.767387944358578
      - name: F1
        type: f1
        value: 0.7646848616766787
  ---
  
  <!-- 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-152-fv-finetuned-memess
  
  This model is a fine-tuned version of [microsoft/resnet-152](https://huggingface.co/microsoft/resnet-152) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6281
  - Accuracy: 0.7674
  - Precision: 0.7651
  - Recall: 0.7674
  - F1: 0.7647
  
  ## 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: 20
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
  | 1.5902        | 0.99  | 20   | 1.5519          | 0.4938   | 0.3491    | 0.4938 | 0.3529 |
  | 1.4694        | 1.99  | 40   | 1.3730          | 0.4892   | 0.4095    | 0.4892 | 0.3222 |
  | 1.3129        | 2.99  | 60   | 1.2052          | 0.5301   | 0.3504    | 0.5301 | 0.4005 |
  | 1.1831        | 3.99  | 80   | 1.1142          | 0.5587   | 0.4077    | 0.5587 | 0.4444 |
  | 1.0581        | 4.99  | 100  | 0.9930          | 0.6012   | 0.5680    | 0.6012 | 0.5108 |
  | 0.9464        | 5.99  | 120  | 0.9263          | 0.6507   | 0.6200    | 0.6507 | 0.6029 |
  | 0.8581        | 6.99  | 140  | 0.8400          | 0.6917   | 0.6645    | 0.6917 | 0.6638 |
  | 0.7739        | 7.99  | 160  | 0.7829          | 0.7087   | 0.6918    | 0.7087 | 0.6845 |
  | 0.6762        | 8.99  | 180  | 0.7512          | 0.7318   | 0.7206    | 0.7318 | 0.7189 |
  | 0.6162        | 9.99  | 200  | 0.7409          | 0.7264   | 0.7244    | 0.7264 | 0.7241 |
  | 0.5546        | 10.99 | 220  | 0.6936          | 0.7465   | 0.7429    | 0.7465 | 0.7395 |
  | 0.4633        | 11.99 | 240  | 0.6779          | 0.7473   | 0.7393    | 0.7473 | 0.7412 |
  | 0.4373        | 12.99 | 260  | 0.6736          | 0.7573   | 0.7492    | 0.7573 | 0.7523 |
  | 0.4074        | 13.99 | 280  | 0.6534          | 0.7566   | 0.7516    | 0.7566 | 0.7528 |
  | 0.39          | 14.99 | 300  | 0.6521          | 0.7651   | 0.7603    | 0.7651 | 0.7608 |
  | 0.3766        | 15.99 | 320  | 0.6499          | 0.7682   | 0.7607    | 0.7682 | 0.7630 |
  | 0.3507        | 16.99 | 340  | 0.6497          | 0.7697   | 0.7686    | 0.7697 | 0.7686 |
  | 0.3589        | 17.99 | 360  | 0.6519          | 0.7535   | 0.7485    | 0.7535 | 0.7502 |
  | 0.3261        | 18.99 | 380  | 0.6449          | 0.7589   | 0.7597    | 0.7589 | 0.7585 |
  | 0.3234        | 19.99 | 400  | 0.6281          | 0.7674   | 0.7651    | 0.7674 | 0.7647 |
  
  
  ### Framework versions
  
  - Transformers 4.24.0.dev0
  - Pytorch 1.11.0+cu102
  - Datasets 2.6.1.dev0
  - Tokenizers 0.13.1
  