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license: apache-2.0
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9774436090225563
---
<!-- 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_model
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: 0.0821
- Accuracy: 0.9774
## Model description
This model distinguishes between healthy and diseased bean leaves. It can also categorize between two diseases: bean rust and angular leaf spot. Just upload a photo and the model will tell you the probability of these three categories.
# Healty
# Bean Rust
# Angular Leaf Spot
## Intended uses & limitations
Just classifies bean leaves
## Training and evaluation data
The model was trained with the dataset beans: https://huggingface.co/datasets/beans
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1435 | 3.85 | 500 | 0.0821 | 0.9774 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
|