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# vit-pneumonia

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 chest-xray-classification dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1086
- Accuracy: 0.9768

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0357        | 1.0   | 192  | 0.0955          | 0.9691   |
| 0.0404        | 2.0   | 384  | 0.0720          | 0.9751   |
| 0.0546        | 3.0   | 576  | 0.2275          | 0.9468   |
| 0.0113        | 4.0   | 768  | 0.1386          | 0.9648   |
| 0.0101        | 5.0   | 960  | 0.1212          | 0.9708   |
| 0.0003        | 6.0   | 1152 | 0.0929          | 0.9777   |
| 0.0002        | 7.0   | 1344 | 0.1051          | 0.9777   |
| 0.0002        | 8.0   | 1536 | 0.1075          | 0.9777   |
| 0.0002        | 9.0   | 1728 | 0.1084          | 0.9768   |
| 0.0002        | 10.0  | 1920 | 0.1086          | 0.9768   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["chest-xray-classification"], "metrics": ["accuracy"], "model-index": [{"name": "vit-pneumonia", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "chest-xray-classification", "type": "chest-xray-classification", "config": "full", "split": "validation", "args": "full"}, "metrics": [{"type": "accuracy", "value": 0.976824034334764, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:chest-xray-classification license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-chest-xray-classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

vit-pneumonia
=============


This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the chest-xray-classification dataset.
It achieves the following results on the evaluation set:


* Loss: 0.1086
* Accuracy: 0.9768


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: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.25
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.25
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-chest-xray-classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.25
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-chest-xray-classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0002
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_ratio: 0.25
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.26.1
* Pytorch 1.13.1+cu116
* Datasets 2.10.1
* Tokenizers 0.13.2