readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: exper_batch_8_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_8_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: 0.4608
  - Accuracy: 0.9052
  
  ## 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: 8
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 4.2202        | 0.08  | 100   | 4.1245          | 0.1237   |
  | 3.467         | 0.16  | 200   | 3.5622          | 0.2143   |
  | 3.3469        | 0.23  | 300   | 3.1688          | 0.2675   |
  | 2.8086        | 0.31  | 400   | 2.8965          | 0.3034   |
  | 2.6291        | 0.39  | 500   | 2.5858          | 0.4025   |
  | 2.2382        | 0.47  | 600   | 2.2908          | 0.4133   |
  | 1.9259        | 0.55  | 700   | 2.2007          | 0.4676   |
  | 1.8088        | 0.63  | 800   | 2.0419          | 0.4742   |
  | 1.9462        | 0.7   | 900   | 1.6793          | 0.5578   |
  | 1.5392        | 0.78  | 1000  | 1.5460          | 0.6079   |
  | 1.561         | 0.86  | 1100  | 1.5793          | 0.5690   |
  | 1.2135        | 0.94  | 1200  | 1.4663          | 0.5929   |
  | 1.0725        | 1.02  | 1300  | 1.2974          | 0.6534   |
  | 0.8696        | 1.1   | 1400  | 1.2406          | 0.6569   |
  | 0.8758        | 1.17  | 1500  | 1.2127          | 0.6623   |
  | 1.1737        | 1.25  | 1600  | 1.2243          | 0.6550   |
  | 0.8242        | 1.33  | 1700  | 1.1371          | 0.6735   |
  | 1.0141        | 1.41  | 1800  | 1.0536          | 0.7024   |
  | 0.9855        | 1.49  | 1900  | 0.9885          | 0.7205   |
  | 0.805         | 1.57  | 2000  | 0.9048          | 0.7479   |
  | 0.7207        | 1.64  | 2100  | 0.8842          | 0.7490   |
  | 0.7101        | 1.72  | 2200  | 0.8954          | 0.7436   |
  | 0.5946        | 1.8   | 2300  | 0.9174          | 0.7386   |
  | 0.6937        | 1.88  | 2400  | 0.7818          | 0.7760   |
  | 0.5593        | 1.96  | 2500  | 0.7449          | 0.7934   |
  | 0.4139        | 2.04  | 2600  | 0.7787          | 0.7830   |
  | 0.2929        | 2.11  | 2700  | 0.7122          | 0.7945   |
  | 0.4159        | 2.19  | 2800  | 0.7446          | 0.7907   |
  | 0.4079        | 2.27  | 2900  | 0.7354          | 0.7938   |
  | 0.516         | 2.35  | 3000  | 0.7499          | 0.8007   |
  | 0.2728        | 2.43  | 3100  | 0.6851          | 0.8061   |
  | 0.4159        | 2.51  | 3200  | 0.7258          | 0.7999   |
  | 0.3396        | 2.58  | 3300  | 0.7455          | 0.7972   |
  | 0.1918        | 2.66  | 3400  | 0.6793          | 0.8119   |
  | 0.1228        | 2.74  | 3500  | 0.6696          | 0.8134   |
  | 0.2671        | 2.82  | 3600  | 0.6306          | 0.8285   |
  | 0.4986        | 2.9   | 3700  | 0.6111          | 0.8296   |
  | 0.3699        | 2.98  | 3800  | 0.5600          | 0.8508   |
  | 0.0444        | 3.05  | 3900  | 0.6021          | 0.8331   |
  | 0.1489        | 3.13  | 4000  | 0.5599          | 0.8516   |
  | 0.15          | 3.21  | 4100  | 0.6377          | 0.8365   |
  | 0.2535        | 3.29  | 4200  | 0.5752          | 0.8543   |
  | 0.2679        | 3.37  | 4300  | 0.5677          | 0.8608   |
  | 0.0989        | 3.45  | 4400  | 0.6325          | 0.8396   |
  | 0.0825        | 3.52  | 4500  | 0.5979          | 0.8524   |
  | 0.0427        | 3.6   | 4600  | 0.5903          | 0.8516   |
  | 0.1806        | 3.68  | 4700  | 0.5323          | 0.8628   |
  | 0.2672        | 3.76  | 4800  | 0.5688          | 0.8604   |
  | 0.2674        | 3.84  | 4900  | 0.5369          | 0.8635   |
  | 0.2185        | 3.92  | 5000  | 0.4743          | 0.8820   |
  | 0.2195        | 3.99  | 5100  | 0.5340          | 0.8709   |
  | 0.0049        | 4.07  | 5200  | 0.5883          | 0.8608   |
  | 0.0204        | 4.15  | 5300  | 0.6102          | 0.8539   |
  | 0.0652        | 4.23  | 5400  | 0.5659          | 0.8670   |
  | 0.028         | 4.31  | 5500  | 0.4916          | 0.8840   |
  | 0.0423        | 4.39  | 5600  | 0.5706          | 0.8736   |
  | 0.0087        | 4.46  | 5700  | 0.5653          | 0.8697   |
  | 0.0964        | 4.54  | 5800  | 0.5423          | 0.8755   |
  | 0.0841        | 4.62  | 5900  | 0.5160          | 0.8743   |
  | 0.0945