
# 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:_

![Sample Images](https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/raw/main/Computer%20Vision/Image%20Classification/Multiclass%20Classification/Intel%20Image%20Classification/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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 0.2305        | 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
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "f1", "recall", "precision"], "pipeline_tag": "image-classification", "model-index": [{"name": "vit-base-patch16-224-in21k-Intel_Images", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9486666666666667, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer en dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-base-patch16-224-in21k-Intel\_Images
========================================


This model is a fine-tuned version of 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: URL


Intended uses & limitations
---------------------------


This model is intended to demonstrate my ability to solve a complex problem using technology.


Training and evaluation data
----------------------------


Dataset Source: URL


*Sample Images From Dataset:*


!Sample Images


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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #en #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### 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### Framework versions


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