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vit-base-beans

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vit-base-beans / README.md
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metadata
license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - beans
metrics:
  - accuracy
widget:
  - src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg
    example_title: Healthy
  - src: >-
      https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg
    example_title: Angular Leaf Spot
  - src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg
    example_title: Bean Rust
base_model: google/vit-base-patch16-224-in21k
model-index:
  - name: vit-base-beans
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: beans
          type: beans
          args: default
        metrics:
          - type: accuracy
            value: 0.9849624060150376
            name: Accuracy
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: beans
          type: beans
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.96875
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9716312056737588
            name: Precision Macro
            verified: true
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          - type: precision
            value: 0.96875
            name: Precision Micro
            verified: true
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          - type: precision
            value: 0.9714095744680851
            name: Precision Weighted
            verified: true
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          - type: recall
            value: 0.9689922480620154
            name: Recall Macro
            verified: true
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          - type: recall
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            verified: true
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          - type: recall
            value: 0.96875
            name: Recall Weighted
            verified: true
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          - type: f1
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          - type: f1
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vit-base-beans

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

  • Loss: 0.0505
  • Accuracy: 0.9850

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
0.1166 1.54 100 0.0764 0.9850
0.1607 3.08 200 0.2114 0.9398
0.0067 4.62 300 0.0692 0.9774
0.005 6.15 400 0.0944 0.9624
0.0043 7.69 500 0.0505 0.9850

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0