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# Guldeniz/vit-base-patch16-224-in21k-lung_and_colon

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on Lung and Colon Histopathological Images dataset. This dataset can be reach via [Kaggle](https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images).
It achieves the following results on the evaluation set:
- Train Loss: 0.0088
- Train Accuracy: 1.0
- Train Top-3-accuracy: 1.0
- Validation Loss: 0.0084
- Validation Accuracy: 0.9997
- Validation Top-3-accuracy: 1.0
- Epoch: 3

## Model description

The vision transformer model, trained by Google, has been fine-tuned using a lung and colon cancer image dataset consisting of a total of 25,000 images across 5 labels. The obtained results are highly promising, and the model demonstrates the ability to predict the following listed labels.

- colon_aca

- colon_n

- lung_aca

- lung_n

- lung_scc

### Training hyperparameters

The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 3325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32

### Training results

| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 0.1870     | 0.9784         | 0.9985               | 0.0455          | 0.9987              | 1.0                       | 0     |
| 0.0345     | 0.9972         | 1.0                  | 0.0189          | 0.9995              | 1.0                       | 1     |
| 0.0134     | 1.0            | 1.0                  | 0.0110          | 0.9997              | 1.0                       | 2     |
| 0.0088     | 1.0            | 1.0                  | 0.0084          | 0.9997              | 1.0                       | 3     |


### Framework versions

- Transformers 4.26.1
- TensorFlow 2.12.0
- Datasets 2.10.1
- Tokenizers 0.13.3
{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["generated_from_keras_callback", "vision_transformer"], "metrics": ["accuracy"], "pipeline_tag": "image-classification", "model-index": [{"name": "Guldeniz/vit-base-patch16-224-in21k-lung_and_colon", "results": []}]}
transformers tf tensorboard vit image-classification generated_from_keras_callback vision_transformer en license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #vision_transformer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

Guldeniz/vit-base-patch16-224-in21k-lung\_and\_colon
====================================================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on Lung and Colon Histopathological Images dataset. This dataset can be reach via Kaggle.
It achieves the following results on the evaluation set:


* Train Loss: 0.0088
* Train Accuracy: 1.0
* Train Top-3-accuracy: 1.0
* Validation Loss: 0.0084
* Validation Accuracy: 0.9997
* Validation Top-3-accuracy: 1.0
* Epoch: 3


Model description
-----------------


The vision transformer model, trained by Google, has been fine-tuned using a lung and colon cancer image dataset consisting of a total of 25,000 images across 5 labels. The obtained results are highly promising, and the model demonstrates the ability to predict the following listed labels.


* colon\_aca
* colon\_n
* lung\_aca
* lung\_n
* lung\_scc


### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 3325, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32


### Training results



### Framework versions


* Transformers 4.26.1
* TensorFlow 2.12.0
* Datasets 2.10.1
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 3325, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.1
* TensorFlow 2.12.0
* Datasets 2.10.1
* Tokenizers 0.13.3
TAGS
#transformers #tf #tensorboard #vit #image-classification #generated_from_keras_callback #vision_transformer #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 3325, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01}
* training\_precision: float32 ### Training results ### Framework versions


* Transformers 4.26.1
* TensorFlow 2.12.0
* Datasets 2.10.1
* Tokenizers 0.13.3