readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: modeversion1_m7_e4
    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. -->
  
  # modeversion1_m7_e4
  
  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: 0.0902
  - Accuracy: 0.9731
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 4.073         | 0.06  | 100  | 3.9370          | 0.1768   |
  | 3.4186        | 0.12  | 200  | 3.2721          | 0.2590   |
  | 2.6745        | 0.18  | 300  | 2.6465          | 0.3856   |
  | 2.2806        | 0.23  | 400  | 2.2600          | 0.4523   |
  | 1.9275        | 0.29  | 500  | 1.9653          | 0.5109   |
  | 1.6958        | 0.35  | 600  | 1.6815          | 0.6078   |
  | 1.2797        | 0.41  | 700  | 1.4514          | 0.6419   |
  | 1.3772        | 0.47  | 800  | 1.3212          | 0.6762   |
  | 1.1765        | 0.53  | 900  | 1.1476          | 0.7028   |
  | 1.0152        | 0.59  | 1000 | 1.0357          | 0.7313   |
  | 0.7861        | 0.64  | 1100 | 1.0230          | 0.7184   |
  | 1.0262        | 0.7   | 1200 | 0.9469          | 0.7386   |
  | 0.8905        | 0.76  | 1300 | 0.8184          | 0.7756   |
  | 0.6919        | 0.82  | 1400 | 0.8083          | 0.7711   |
  | 0.7494        | 0.88  | 1500 | 0.7601          | 0.7825   |
  | 0.5078        | 0.94  | 1600 | 0.6884          | 0.8056   |
  | 0.7134        | 1.0   | 1700 | 0.6311          | 0.8160   |
  | 0.4328        | 1.06  | 1800 | 0.5740          | 0.8252   |
  | 0.4971        | 1.11  | 1900 | 0.5856          | 0.8290   |
  | 0.5207        | 1.17  | 2000 | 0.6219          | 0.8167   |
  | 0.4027        | 1.23  | 2100 | 0.5703          | 0.8266   |
  | 0.5605        | 1.29  | 2200 | 0.5217          | 0.8372   |
  | 0.2723        | 1.35  | 2300 | 0.4805          | 0.8565   |
  | 0.401         | 1.41  | 2400 | 0.4811          | 0.8490   |
  | 0.3419        | 1.47  | 2500 | 0.4619          | 0.8608   |
  | 0.301         | 1.52  | 2600 | 0.4318          | 0.8712   |
  | 0.2872        | 1.58  | 2700 | 0.4698          | 0.8573   |
  | 0.2451        | 1.64  | 2800 | 0.4210          | 0.8729   |
  | 0.2211        | 1.7   | 2900 | 0.3645          | 0.8851   |
  | 0.3145        | 1.76  | 3000 | 0.4139          | 0.8715   |
  | 0.2001        | 1.82  | 3100 | 0.3605          | 0.8864   |
  | 0.3095        | 1.88  | 3200 | 0.4274          | 0.8675   |
  | 0.1915        | 1.93  | 3300 | 0.2910          | 0.9101   |
  | 0.2465        | 1.99  | 3400 | 0.2726          | 0.9103   |
  | 0.1218        | 2.05  | 3500 | 0.2742          | 0.9129   |
  | 0.0752        | 2.11  | 3600 | 0.2572          | 0.9183   |
  | 0.1067        | 2.17  | 3700 | 0.2584          | 0.9203   |
  | 0.0838        | 2.23  | 3800 | 0.2458          | 0.9212   |
  | 0.1106        | 2.29  | 3900 | 0.2412          | 0.9237   |
  | 0.092         | 2.34  | 4000 | 0.2232          | 0.9277   |
  | 0.1056        | 2.4   | 4100 | 0.2817          | 0.9077   |
  | 0.0696        | 2.46  | 4200 | 0.2334          | 0.9285   |
  | 0.0444        | 2.52  | 4300 | 0.2142          | 0.9363   |
  | 0.1046        | 2.58  | 4400 | 0.2036          | 0.9352   |
  | 0.066         | 2.64  | 4500 | 0.2115          | 0.9365   |
  | 0.0649        | 2.7   | 4600 | 0.1730          | 0.9448   |
  | 0.0513        | 2.75  | 4700 | 0.2148          | 0.9339   |
  | 0.0917        | 2.81  | 4800 | 0.1810          | 0.9438   |
  | 0.0879        | 2.87  | 4900 | 0.1971          | 0.9388   |
  | 0.1052        | 2.93  | 5000 | 0.1602          | 0.9508   |
  | 0.0362        | 2.99  | 5100 | 0.1475          | 0.9556   |
  | 0.041         | 3.05  | 5200 | 0.1328          | 0.9585   |
  | 0.0156        | 3.11  | 5300 | 0.1389          | 0.9571   |
  | 0.0047        | 3.17  | 5400 | 0.1224          | 0.9638   |
  | 0.0174        | 3.22  | 5500 | 0.1193          | 0.9651   |
  | 0.0087        | 3.28  | 5600 | 0.1276          | 0.9622   |
  | 0.0084        | 3.34  | 5700 | 0.1134          | 0.9662   |
  | 0.0141        | 3.4   | 5800 | 0.1239          | 0.9631   |
  | 0.0291        | 3.46  | 5900 | 0.1199          | 0.9645   |
  | 0.0049        | 3.52  | 6000 | 0.1103          | 0.9679   |
  | 0.0055  