readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: modeversion2_m7_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. -->
  
  # modeversion2_m7_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_mesuem7 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1060
  - Accuracy: 0.9761
  
  ## 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: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 4.0231        | 0.06  | 100   | 3.8568          | 0.1883   |
  | 3.3863        | 0.12  | 200   | 3.2510          | 0.2596   |
  | 2.6187        | 0.18  | 300   | 2.6243          | 0.3882   |
  | 2.3097        | 0.23  | 400   | 2.2189          | 0.4527   |
  | 1.9016        | 0.29  | 500   | 1.9495          | 0.5244   |
  | 1.7478        | 0.35  | 600   | 1.6609          | 0.6091   |
  | 1.2345        | 0.41  | 700   | 1.4335          | 0.6426   |
  | 1.4129        | 0.47  | 800   | 1.3001          | 0.6752   |
  | 1.1722        | 0.53  | 900   | 1.2030          | 0.6785   |
  | 1.0808        | 0.59  | 1000  | 1.0051          | 0.7273   |
  | 0.8814        | 0.64  | 1100  | 1.0715          | 0.7063   |
  | 0.9831        | 0.7   | 1200  | 0.9283          | 0.7334   |
  | 0.8118        | 0.76  | 1300  | 0.8525          | 0.7631   |
  | 0.7203        | 0.82  | 1400  | 0.7849          | 0.7756   |
  | 0.8881        | 0.88  | 1500  | 0.8786          | 0.7487   |
  | 0.6407        | 0.94  | 1600  | 0.6896          | 0.8000   |
  | 0.7574        | 1.0   | 1700  | 0.7314          | 0.7754   |
  | 0.6063        | 1.06  | 1800  | 0.6312          | 0.8068   |
  | 0.4797        | 1.11  | 1900  | 0.5792          | 0.8296   |
  | 0.4973        | 1.17  | 2000  | 0.5846          | 0.8221   |
  | 0.4432        | 1.23  | 2100  | 0.7057          | 0.7905   |
  | 0.5518        | 1.29  | 2200  | 0.5621          | 0.8304   |
  | 0.3256        | 1.35  | 2300  | 0.5890          | 0.8143   |
  | 0.4284        | 1.41  | 2400  | 0.5204          | 0.8485   |
  | 0.3702        | 1.47  | 2500  | 0.5699          | 0.8256   |
  | 0.2858        | 1.52  | 2600  | 0.5815          | 0.8287   |
  | 0.3706        | 1.58  | 2700  | 0.4615          | 0.8571   |
  | 0.3484        | 1.64  | 2800  | 0.4812          | 0.8518   |
  | 0.2865        | 1.7   | 2900  | 0.4285          | 0.8638   |
  | 0.4474        | 1.76  | 3000  | 0.5217          | 0.8377   |
  | 0.2101        | 1.82  | 3100  | 0.4478          | 0.8589   |
  | 0.3545        | 1.88  | 3200  | 0.4444          | 0.8612   |
  | 0.2728        | 1.93  | 3300  | 0.4213          | 0.8645   |
  | 0.3525        | 1.99  | 3400  | 0.3551          | 0.8848   |
  | 0.0936        | 2.05  | 3500  | 0.4074          | 0.8748   |
  | 0.2118        | 2.11  | 3600  | 0.4089          | 0.8812   |
  | 0.2744        | 2.17  | 3700  | 0.3534          | 0.8894   |
  | 0.211         | 2.23  | 3800  | 0.4422          | 0.8599   |
  | 0.1684        | 2.29  | 3900  | 0.3705          | 0.8858   |
  | 0.1885        | 2.34  | 4000  | 0.3651          | 0.8862   |
  | 0.249         | 2.4   | 4100  | 0.4234          | 0.8687   |
  | 0.1485        | 2.46  | 4200  | 0.3784          | 0.8798   |
  | 0.1188        | 2.52  | 4300  | 0.3589          | 0.8873   |
  | 0.1274        | 2.58  | 4400  | 0.3570          | 0.8917   |
  | 0.2206        | 2.64  | 4500  | 0.3377          | 0.8920   |
  | 0.1287        | 2.7   | 4600  | 0.3170          | 0.9023   |
  | 0.1805        | 2.75  | 4700  | 0.3469          | 0.8934   |
  | 0.1505        | 2.81  | 4800  | 0.4258          | 0.8757   |
  | 0.1592        | 2.87  | 4900  | 0.3415          | 0.8948   |
  | 0.1297        | 2.93  | 5000  | 0.3168          | 0.9028   |
  | 0.1284        | 2.99  | 5100  | 0.3060          | 0.9089   |
  | 0.0833        | 3.05  | 5200  | 0.2610          | 0.9207   |
  | 0.0334        | 3.11  | 5300  | 0.2766          | 0.9197   |
  | 0.0847        | 3.17  | 5400  | 0.3366          | 0.9016   |
  | 0.1112        | 3.22  | 5500  | 0.3098          | 0.9079   |
  | 0.0477        | 3.28  | 5600  | 0.3385          | 0.9041   |
  | 0.0419        | 3.34  | 5700  | 0.2944          | 0.9139   |
  | 0.0827        | 3.4   | 5800  | 0.2715          | 0.9239   |
  | 0.0659        | 3.46  | 5900  | 0.2695          | 0.9230   |
  | 0.0244   