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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-Intel_Images
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: 0.9486666666666667
language:
- en
pipeline_tag: image-classification
---
# vit-base-patch16-224-in21k-Intel_Images
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: 0.1822
- Accuracy: 0.9487
- F1
- Weighted: 0.9485
- Micro: 0.9487
- Macro: 0.9497
- Recall
- Weighted: 0.9487
- Micro: 0.9487
- Macro: 0.9500
- Precision
- Weighted: 0.9485
- Micro: 0.9487
- Macro: 0.9496
## Model description
This is a multiclass image classification model of different scenery types.
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/Intel%20Image%20Classification/Intel_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/puneet6060/intel-image-classification
_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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.2305 | 1.0 | 878 | 0.2362 | 0.9153 | 0.9144 | 0.9153 | 0.9152 | 0.9153 | 0.9153 | 0.9148 | 0.9208 | 0.9153 | 0.9231 |
| 0.1136 | 2.0 | 1756 | 0.1785 | 0.9393 | 0.9391 | 0.9393 | 0.9405 | 0.9393 | 0.9393 | 0.9405 | 0.9391 | 0.9393 | 0.9407 |
| 0.0435 | 3.0 | 2634 | 0.1822 | 0.9487 | 0.9485 | 0.9487 | 0.9497 | 0.9487 | 0.9487 | 0.9500 | 0.9485 | 0.9487 | 0.9496 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0
- Datasets 2.11.0
- Tokenizers 0.13.3 |