metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - beans
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
metrics:
  - accuracy
model-index:
  - name: vit-base-beans
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: beans
          type: beans
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9849624060150376

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