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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-weather-images-classification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: data
split: train
args: data
metrics:
- name: Accuracy
type: accuracy
value: 0.9339762611275965
language:
- en
pipeline_tag: image-classification
---
# vit-base-patch16-224-in21k-weather-images-classification
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: 0.2255
- Accuracy: 0.9340
- Weighted f1: 0.9341
- Micro f1: 0.9340
- Macro f1: 0.9372
- Weighted recall: 0.9340
- Micro recall: 0.9340
- Macro recall: 0.9354
- Weighted precision: 0.9347
- Micro precision: 0.9340
- Macro precision: 0.9398
## Model description
This is a classification model of weather images.
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/Weather%20Images/Weather_Images_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/jehanbhathena/weather-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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 2.4333 | 1.0 | 337 | 0.3374 | 0.9036 | 0.9028 | 0.9036 | 0.9080 | 0.9036 | 0.9036 | 0.9002 | 0.9088 | 0.9036 | 0.9234 |
| 0.4422 | 2.0 | 674 | 0.2504 | 0.9228 | 0.9226 | 0.9228 | 0.9285 | 0.9228 | 0.9228 | 0.9273 | 0.9248 | 0.9228 | 0.9318 |
| 0.1051 | 3.0 | 1011 | 0.2255 | 0.9340 | 0.9341 | 0.9340 | 0.9372 | 0.9340 | 0.9340 | 0.9354 | 0.9347 | 0.9340 | 0.9398 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1
- Datasets 2.8.0
- Tokenizers 0.12.1 |