|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
- Landscape |
|
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 |
|
- F1 |
|
- Weighted: 0.8694 |
|
- Micro: 0.8687 |
|
- Macro: 0.8694 |
|
- Recall |
|
- Weighted: 0.8687 |
|
- Micro: 0.8687 |
|
- Macro: 0.8687 |
|
- Precision |
|
- Weighted: 0.8714 |
|
- Micro: 0.8687 |
|
- Macro: 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:_ |
|
|
|
![Sample Images](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Landscape%20Recognition/Images/Sample%20Images.png) |
|
|
|
## 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 |