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vit-base-patch16-224-finetuned-for-agricultural

Image Classification
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vit-base-patch16-224-finetuned-for-agricultural
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---
license: apache-2.0
base_model: xiaopch/vit-base-patch16-224-finetuned
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned-for-agricultural
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7309236947791165
---

<!-- 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-patch16-224-finetuned-for-agricultural

This model is a fine-tuned version of [xiaopch/vit-base-patch16-224-finetuned](https://huggingface.co/xiaopch/vit-base-patch16-224-finetuned) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9246
- Accuracy: 0.7309

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9131        | 1.0   | 35   | 1.0878          | 0.6847   |
| 0.8066        | 2.0   | 70   | 0.9933          | 0.7189   |
| 0.7259        | 3.0   | 105  | 0.9445          | 0.7249   |
| 0.6719        | 4.0   | 140  | 0.9246          | 0.7309   |
| 0.6056        | 5.0   | 175  | 0.9258          | 0.7229   |
| 0.5576        | 6.0   | 210  | 0.9230          | 0.7309   |
| 0.5113        | 7.0   | 245  | 0.9152          | 0.7169   |
| 0.488         | 8.0   | 280  | 0.9119          | 0.7209   |
| 0.4822        | 9.0   | 315  | 0.9061          | 0.7269   |
| 0.4163        | 10.0  | 350  | 0.9039          | 0.7289   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0