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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
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: 0.8686666666666667
language:
- en
pipeline_tag: image-classification
---
# 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).
It achieves the following results on the evaluation set:
- Loss: 0.4648
- Accuracy: 0.8687
- Weighted f1: 0.8694
- Micro f1: 0.8687
- Macro f1: 0.8694
- Weighted recall: 0.8687
- Micro recall: 0.8687
- Macro recall: 0.8687
- Weighted precision: 0.8714
- Micro precision: 0.8687
- Macro precision: 0.8714
## Model description
This is a multiclass image classification model of different types of landscaping.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/blob/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Landscape%20Recognition/Landscape_Recognition_ViT.ipynb
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/utkarshsaxenadn/landscape-recognition-image-dataset-12k-images
_Sample Images From Dataset:_
## 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 |