README.md

metadata

language: en
license: apache-2.0
tags:
  - generated_from_trainer
  - image-classification
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
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: 0.9774436090225563
            name: Accuracy
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: beans
          type: beans
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.9453125
            name: Accuracy
            verified: true
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          - type: precision
            value: 0.9453325082933705
            name: Precision Macro
            verified: true
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          - type: precision
            value: 0.9453125
            name: Precision Micro
            verified: true
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          - type: precision
            value: 0.9452605321507761
            name: Precision Weighted
            verified: true
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          - type: recall
            value: 0.945736434108527
            name: Recall Macro
            verified: true
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          - type: recall
            value: 0.9453125
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          - type: recall
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          - type: f1
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            name: loss
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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: 0.0942
• Accuracy: 0.9774

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: 8
• eval_batch_size: 8
• seed: 1337
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 5.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2809	1.0	130	0.2287	0.9699
0.1097	2.0	260	0.1676	0.9624
0.1027	3.0	390	0.0942	0.9774
0.0923	4.0	520	0.1104	0.9699
0.1726	5.0	650	0.1030	0.9699

Framework versions

• Transformers 4.10.0.dev0
• Pytorch 1.9.0+cu102
• Datasets 1.11.1.dev0
• Tokenizers 0.10.3