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---
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
- task:
type: image-classification
name: Image Classification
dataset:
name: beans
type: beans
config: default
split: test
metrics:
- name: Accuracy
type: accuracy
value: 0.96875
verified: true
- name: Precision Macro
type: precision
value: 0.9716312056737588
verified: true
- name: Precision Micro
type: precision
value: 0.96875
verified: true
- name: Precision Weighted
type: precision
value: 0.9714095744680851
verified: true
- name: Recall Macro
type: recall
value: 0.9689922480620154
verified: true
- name: Recall Micro
type: recall
value: 0.96875
verified: true
- name: Recall Weighted
type: recall
value: 0.96875
verified: true
- name: F1 Macro
type: f1
value: 0.9689250225835592
verified: true
- name: F1 Micro
type: f1
value: 0.96875
verified: true
- name: F1 Weighted
type: f1
value: 0.9686822493224932
verified: true
- name: loss
type: loss
value: 0.1282731592655182
verified: true
---
<!-- 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. -->
# vit-base-beans
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 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
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