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End of training
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metadata
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
model-index:
  - name: Rice-Plant-Disease-Detection-Model
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.8958333333333334
          - name: F1
            type: f1
            value: 0.8965189410560187

Rice-Plant-Disease-Detection-Model

This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2929
  • Accuracy: 0.8958
  • F1: 0.8965

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.5517 1.0 18 0.5222 0.875 0.8754
0.2996 2.0 36 0.3833 0.8542 0.8564
0.1529 3.0 54 0.3152 0.875 0.8763
0.0843 4.0 72 0.2929 0.8958 0.8965
0.0549 5.0 90 0.2756 0.875 0.8754
0.0402 6.0 108 0.2765 0.875 0.8754
0.0327 7.0 126 0.2875 0.875 0.8754
0.0277 8.0 144 0.2938 0.875 0.8754
0.0244 9.0 162 0.2992 0.875 0.8754
0.0222 10.0 180 0.2996 0.8958 0.8960
0.0203 11.0 198 0.3052 0.8958 0.8960
0.019 12.0 216 0.3087 0.8958 0.8960
0.018 13.0 234 0.3143 0.8958 0.8960
0.0171 14.0 252 0.3206 0.8958 0.8960
0.0164 15.0 270 0.3227 0.8958 0.8960
0.0158 16.0 288 0.3250 0.8958 0.8960
0.0155 17.0 306 0.3257 0.8958 0.8960
0.0152 18.0 324 0.3264 0.8958 0.8960
0.015 19.0 342 0.3276 0.8958 0.8960
0.0149 20.0 360 0.3275 0.8958 0.8960

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.1.0+cpu
  • Datasets 2.14.5
  • Tokenizers 0.14.0