readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper2_mesum5
    results: []
  ---
  
  <!-- 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. -->
  
  # exper2_mesum5
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the sudo-s/herbier_mesuem5 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.4589
  - Accuracy: 0.1308
  
  ## 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: 0.002
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 4.4265        | 0.23  | 100  | 4.3676          | 0.0296   |
  | 4.1144        | 0.47  | 200  | 4.1606          | 0.0544   |
  | 4.0912        | 0.7   | 300  | 4.1071          | 0.0509   |
  | 4.0361        | 0.93  | 400  | 4.0625          | 0.0669   |
  | 4.0257        | 1.16  | 500  | 3.9682          | 0.0822   |
  | 3.8846        | 1.4   | 600  | 3.9311          | 0.0834   |
  | 3.9504        | 1.63  | 700  | 3.9255          | 0.0698   |
  | 3.9884        | 1.86  | 800  | 3.9404          | 0.0722   |
  | 3.7191        | 2.09  | 900  | 3.8262          | 0.0935   |
  | 3.7952        | 2.33  | 1000 | 3.8236          | 0.0734   |
  | 3.8085        | 2.56  | 1100 | 3.7694          | 0.0964   |
  | 3.7535        | 2.79  | 1200 | 3.6757          | 0.1059   |
  | 3.4218        | 3.02  | 1300 | 3.6474          | 0.1095   |
  | 3.5172        | 3.26  | 1400 | 3.5621          | 0.1166   |
  | 3.5173        | 3.49  | 1500 | 3.5579          | 0.1207   |
  | 3.4346        | 3.72  | 1600 | 3.4817          | 0.1249   |
  | 3.3995        | 3.95  | 1700 | 3.4589          | 0.1308   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  