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# vit-base-breast-cancer

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 breast-invasive-ductal-carcinoma-cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3578
- Accuracy: 0.8731

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.206         | 0.23  | 100  | 0.3991          | 0.8342   |
| 0.2959        | 0.46  | 200  | 0.3608          | 0.8691   |
| 0.2492        | 0.69  | 300  | 0.3624          | 0.8681   |
| 0.237         | 0.92  | 400  | 0.3578          | 0.8731   |
| 0.2277        | 1.14  | 500  | 0.4044          | 0.8721   |
| 0.1821        | 1.37  | 600  | 0.4185          | 0.8661   |
| 0.1995        | 1.6   | 700  | 0.4412          | 0.8721   |
| 0.1288        | 1.83  | 800  | 0.4424          | 0.8272   |
| 0.1546        | 2.06  | 900  | 0.4491          | 0.8641   |
| 0.0319        | 2.29  | 1000 | 0.5021          | 0.8641   |
| 0.0537        | 2.52  | 1100 | 0.5292          | 0.8472   |
| 0.1493        | 2.75  | 1200 | 0.4235          | 0.8821   |
| 0.0774        | 2.97  | 1300 | 0.4753          | 0.8631   |
| 0.0823        | 3.2   | 1400 | 0.5120          | 0.8561   |
| 0.0264        | 3.43  | 1500 | 0.4868          | 0.8701   |
| 0.0386        | 3.66  | 1600 | 0.4909          | 0.8651   |
| 0.008         | 3.89  | 1700 | 0.5035          | 0.8651   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-breast-cancer", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "breast-invasive-ductal-carcinoma-cancer", "type": "imagefolder", "config": "dataset", "split": "validation", "args": "dataset"}, "metrics": [{"type": "accuracy", "value": 0.8731268731268731, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

vit-base-breast-cancer
======================


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the breast-invasive-ductal-carcinoma-cancer dataset.
It achieves the following results on the evaluation set:


* Loss: 0.3578
* Accuracy: 0.8731


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


More information needed


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


More information needed


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


More information needed


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: 4
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* 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: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #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: 4
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.27.1
* Pytorch 2.0.1+cu118
* Datasets 2.9.0
* Tokenizers 0.13.3