readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper6_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. -->
  
  # exper6_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: 0.8241
  - Accuracy: 0.8036
  
  ## 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: 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: 16
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.9276        | 0.23  | 100  | 3.8550          | 0.2089   |
  | 3.0853        | 0.47  | 200  | 3.1106          | 0.3414   |
  | 2.604         | 0.7   | 300  | 2.5732          | 0.4379   |
  | 2.3183        | 0.93  | 400  | 2.2308          | 0.4882   |
  | 1.5326        | 1.16  | 500  | 1.7903          | 0.5828   |
  | 1.3367        | 1.4   | 600  | 1.5524          | 0.6349   |
  | 1.1544        | 1.63  | 700  | 1.3167          | 0.6645   |
  | 1.0788        | 1.86  | 800  | 1.3423          | 0.6385   |
  | 0.6762        | 2.09  | 900  | 1.0780          | 0.7124   |
  | 0.6483        | 2.33  | 1000 | 1.0090          | 0.7284   |
  | 0.6321        | 2.56  | 1100 | 1.0861          | 0.7024   |
  | 0.5558        | 2.79  | 1200 | 0.9933          | 0.7183   |
  | 0.342         | 3.02  | 1300 | 0.8871          | 0.7462   |
  | 0.2964        | 3.26  | 1400 | 0.9330          | 0.7408   |
  | 0.1959        | 3.49  | 1500 | 0.9367          | 0.7343   |
  | 0.368         | 3.72  | 1600 | 0.8472          | 0.7550   |
  | 0.1821        | 3.95  | 1700 | 0.8937          | 0.7568   |
  | 0.1851        | 4.19  | 1800 | 0.9546          | 0.7485   |
  | 0.1648        | 4.42  | 1900 | 0.9790          | 0.7355   |
  | 0.172         | 4.65  | 2000 | 0.8947          | 0.7627   |
  | 0.0928        | 4.88  | 2100 | 1.0093          | 0.7462   |
  | 0.0699        | 5.12  | 2200 | 0.8374          | 0.7639   |
  | 0.0988        | 5.35  | 2300 | 0.9189          | 0.7645   |
  | 0.0822        | 5.58  | 2400 | 0.9512          | 0.7580   |
  | 0.1223        | 5.81  | 2500 | 1.0809          | 0.7349   |
  | 0.0509        | 6.05  | 2600 | 0.9297          | 0.7769   |
  | 0.0511        | 6.28  | 2700 | 0.8981          | 0.7822   |
  | 0.0596        | 6.51  | 2800 | 0.9468          | 0.7704   |
  | 0.0494        | 6.74  | 2900 | 0.9045          | 0.7870   |
  | 0.0643        | 6.98  | 3000 | 1.1559          | 0.7391   |
  | 0.0158        | 7.21  | 3100 | 0.8450          | 0.7899   |
  | 0.0129        | 7.44  | 3200 | 0.8241          | 0.8036   |
  | 0.0441        | 7.67  | 3300 | 0.9679          | 0.7751   |
  | 0.0697        | 7.91  | 3400 | 1.0387          | 0.7751   |
  | 0.0084        | 8.14  | 3500 | 0.9441          | 0.7947   |
  | 0.0182        | 8.37  | 3600 | 0.8967          | 0.7994   |
  | 0.0042        | 8.6   | 3700 | 0.8750          | 0.8041   |
  | 0.0028        | 8.84  | 3800 | 0.9349          | 0.8041   |
  | 0.0053        | 9.07  | 3900 | 0.9403          | 0.7982   |
  | 0.0266        | 9.3   | 4000 | 0.9966          | 0.7959   |
  | 0.0022        | 9.53  | 4100 | 0.9472          | 0.8018   |
  | 0.0018        | 9.77  | 4200 | 0.8717          | 0.8136   |
  | 0.0018        | 10.0  | 4300 | 0.8964          | 0.8083   |
  | 0.0046        | 10.23 | 4400 | 0.8623          | 0.8160   |
  | 0.0037        | 10.47 | 4500 | 0.8762          | 0.8172   |
  | 0.0013        | 10.7  | 4600 | 0.9028          | 0.8142   |
  | 0.0013        | 10.93 | 4700 | 0.9084          | 0.8178   |
  | 0.0013        | 11.16 | 4800 | 0.8733          | 0.8213   |
  | 0.001         | 11.4  | 4900 | 0.8823          | 0.8207   |
  | 0.0009        | 11.63 | 5000 | 0.8769          | 0.8213   |
  | 0.0282        | 11.86 | 5100 | 0.8791          | 0.8219   |
  | 0.001         | 12.09 | 5200 | 0.8673          | 0.8249   |
  | 0.0016        | 12.33 | 5300 | 0.8633          | 0.8225   |
  | 0.0008        | 12.56 | 5400 | 0.8766          | 0.8195   |
  | 0.0008        | 12.79 | 5500 | 0.8743          | 0.8225   |
  | 0.0008        | 13.02 | 5600 | 0.8752          | 0.8231   |
  | 0.0008        | 13.26 | 5700 | 0.8676          | 0.8237   |
  | 0.0007        | 13.49 | 5800 | 0.8677          | 0.8237   |
  | 0.0008        | 13.72 | 5900 | 0.8703          | 0.8237   |
  | 0.0007        | 13.95 | 6000 | 0.8725          | 0.8237   |
  | 0.0006        | 1