readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper_batch_32_e8
    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. -->
  
  # exper_batch_32_e8
  
  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_mesuem1 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3520
  - Accuracy: 0.9113
  
  ## 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: 0.0002
  - train_batch_size: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  - mixed_precision_training: Apex, opt level O1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.3787        | 0.31  | 100  | 3.3100          | 0.3566   |
  | 2.3975        | 0.62  | 200  | 2.3196          | 0.5717   |
  | 1.5578        | 0.94  | 300  | 1.6764          | 0.6461   |
  | 1.0291        | 1.25  | 400  | 1.1713          | 0.7463   |
  | 0.8185        | 1.56  | 500  | 0.9292          | 0.7953   |
  | 0.6181        | 1.88  | 600  | 0.7732          | 0.8169   |
  | 0.3873        | 2.19  | 700  | 0.6877          | 0.8277   |
  | 0.2979        | 2.5   | 800  | 0.6250          | 0.8404   |
  | 0.2967        | 2.81  | 900  | 0.6151          | 0.8365   |
  | 0.1874        | 3.12  | 1000 | 0.5401          | 0.8608   |
  | 0.2232        | 3.44  | 1100 | 0.5032          | 0.8712   |
  | 0.1109        | 3.75  | 1200 | 0.4635          | 0.8774   |
  | 0.0539        | 4.06  | 1300 | 0.4495          | 0.8843   |
  | 0.0668        | 4.38  | 1400 | 0.4273          | 0.8951   |
  | 0.0567        | 4.69  | 1500 | 0.4427          | 0.8867   |
  | 0.0285        | 5.0   | 1600 | 0.4092          | 0.8955   |
  | 0.0473        | 5.31  | 1700 | 0.3720          | 0.9071   |
  | 0.0225        | 5.62  | 1800 | 0.3691          | 0.9063   |
  | 0.0196        | 5.94  | 1900 | 0.3775          | 0.9048   |
  | 0.0173        | 6.25  | 2000 | 0.3641          | 0.9040   |
  | 0.0092        | 6.56  | 2100 | 0.3551          | 0.9090   |
  | 0.008         | 6.88  | 2200 | 0.3591          | 0.9125   |
  | 0.0072        | 7.19  | 2300 | 0.3542          | 0.9121   |
  | 0.007         | 7.5   | 2400 | 0.3532          | 0.9106   |
  | 0.007         | 7.81  | 2500 | 0.3520          | 0.9113   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.5.1
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  