readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper4_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. -->
  
  # exper4_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: 3.4389
  - Accuracy: 0.1331
  
  ## 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: 2e-05
  - 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.3793        | 0.23  | 100  | 3.4527          | 0.1308   |
  | 3.2492        | 0.47  | 200  | 3.4501          | 0.1331   |
  | 3.3847        | 0.7   | 300  | 3.4500          | 0.1272   |
  | 3.3739        | 0.93  | 400  | 3.4504          | 0.1320   |
  | 3.4181        | 1.16  | 500  | 3.4452          | 0.1320   |
  | 3.214         | 1.4   | 600  | 3.4503          | 0.1320   |
  | 3.282         | 1.63  | 700  | 3.4444          | 0.1325   |
  | 3.5308        | 1.86  | 800  | 3.4473          | 0.1337   |
  | 3.2251        | 2.09  | 900  | 3.4415          | 0.1361   |
  | 3.4385        | 2.33  | 1000 | 3.4408          | 0.1343   |
  | 3.3702        | 2.56  | 1100 | 3.4406          | 0.1325   |
  | 3.366         | 2.79  | 1200 | 3.4411          | 0.1355   |
  | 3.2022        | 3.02  | 1300 | 3.4403          | 0.1308   |
  | 3.2768        | 3.26  | 1400 | 3.4394          | 0.1320   |
  | 3.3444        | 3.49  | 1500 | 3.4394          | 0.1314   |
  | 3.2981        | 3.72  | 1600 | 3.4391          | 0.1331   |
  | 3.3349        | 3.95  | 1700 | 3.4389          | 0.1331   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  