readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper7_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. -->
  
  # exper7_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: 0.5889
  - Accuracy: 0.8538
  
  ## 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: 0.0001
  - 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 4.2072        | 0.23  | 100  | 4.1532          | 0.1923   |
  | 3.5433        | 0.47  | 200  | 3.5680          | 0.2888   |
  | 3.1388        | 0.7   | 300  | 3.1202          | 0.3911   |
  | 2.7924        | 0.93  | 400  | 2.7434          | 0.4787   |
  | 2.1269        | 1.16  | 500  | 2.3262          | 0.5781   |
  | 1.8589        | 1.4   | 600  | 1.9754          | 0.6272   |
  | 1.7155        | 1.63  | 700  | 1.7627          | 0.6840   |
  | 1.4689        | 1.86  | 800  | 1.5937          | 0.6994   |
  | 1.0149        | 2.09  | 900  | 1.3168          | 0.7497   |
  | 0.8148        | 2.33  | 1000 | 1.1630          | 0.7615   |
  | 0.7159        | 2.56  | 1100 | 1.0869          | 0.7675   |
  | 0.7257        | 2.79  | 1200 | 0.9607          | 0.7893   |
  | 0.4171        | 3.02  | 1300 | 0.8835          | 0.7935   |
  | 0.2969        | 3.26  | 1400 | 0.8259          | 0.8130   |
  | 0.2405        | 3.49  | 1500 | 0.7711          | 0.8142   |
  | 0.2948        | 3.72  | 1600 | 0.7629          | 0.8112   |
  | 0.1765        | 3.95  | 1700 | 0.7117          | 0.8124   |
  | 0.1603        | 4.19  | 1800 | 0.6946          | 0.8237   |
  | 0.0955        | 4.42  | 1900 | 0.6597          | 0.8349   |
  | 0.0769        | 4.65  | 2000 | 0.6531          | 0.8266   |
  | 0.0816        | 4.88  | 2100 | 0.6335          | 0.8337   |
  | 0.0315        | 5.12  | 2200 | 0.6087          | 0.8402   |
  | 0.0368        | 5.35  | 2300 | 0.6026          | 0.8444   |
  | 0.0377        | 5.58  | 2400 | 0.6450          | 0.8278   |
  | 0.0603        | 5.81  | 2500 | 0.6564          | 0.8343   |
  | 0.0205        | 6.05  | 2600 | 0.6119          | 0.8467   |
  | 0.019         | 6.28  | 2700 | 0.6070          | 0.8479   |
  | 0.0249        | 6.51  | 2800 | 0.6002          | 0.8538   |
  | 0.0145        | 6.74  | 2900 | 0.6012          | 0.8497   |
  | 0.0134        | 6.98  | 3000 | 0.5991          | 0.8521   |
  | 0.0271        | 7.21  | 3100 | 0.5972          | 0.8503   |
  | 0.0128        | 7.44  | 3200 | 0.5911          | 0.8521   |
  | 0.0123        | 7.67  | 3300 | 0.5889          | 0.8538   |
  | 0.0278        | 7.91  | 3400 | 0.6135          | 0.8491   |
  | 0.0106        | 8.14  | 3500 | 0.5934          | 0.8533   |
  | 0.0109        | 8.37  | 3600 | 0.5929          | 0.8533   |
  | 0.0095        | 8.6   | 3700 | 0.5953          | 0.8550   |
  | 0.009         | 8.84  | 3800 | 0.5933          | 0.8574   |
  | 0.009         | 9.07  | 3900 | 0.5948          | 0.8550   |
  | 0.0089        | 9.3   | 4000 | 0.5953          | 0.8556   |
  | 0.0086        | 9.53  | 4100 | 0.5956          | 0.8544   |
  | 0.0085        | 9.77  | 4200 | 0.5955          | 0.8556   |
  | 0.0087        | 10.0  | 4300 | 0.5954          | 0.8538   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  