---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-Landscape_Recognition
  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: 0.866
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-in21k-Landscape_Recognition

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5122
- Accuracy: 0.866
- Weighted f1: 0.8678
- Micro f1: 0.866
- Macro f1: 0.8678
- Weighted recall: 0.866
- Micro recall: 0.866
- Macro recall: 0.866
- Weighted precision: 0.8710
- Micro precision: 0.866
- Macro precision: 0.8710

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.2866        | 1.0   | 625  | 0.4308          | 0.8487   | 0.8538      | 0.8487   | 0.8538   | 0.8487          | 0.8487       | 0.8487       | 0.8700             | 0.8487          | 0.8700          |
| 0.1522        | 2.0   | 1250 | 0.4648          | 0.8687   | 0.8694      | 0.8687   | 0.8694   | 0.8687          | 0.8687       | 0.8687       | 0.8714             | 0.8687          | 0.8714          |
| 0.0609        | 3.0   | 1875 | 0.5122          | 0.866    | 0.8678      | 0.866    | 0.8678   | 0.866           | 0.866        | 0.866        | 0.8710             | 0.866           | 0.8710          |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3