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# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-skullStrippded_04

This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0470
- Accuracy: 0.9843
- F1 Score: 0.9844
- Precision: 0.9846

## 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: 0.0001
- train_batch_size: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Accuracy | F1 Score | Validation Loss | Precision |
|:-------------:|:-----:|:----:|:--------:|:--------:|:---------------:|:---------:|
| 1.2662        | 1.0   | 16   | 0.8370   | 0.8309   | 0.4424          | 0.8464    |
| 0.3778        | 0.98  | 20   | 0.2700   | 0.9062   | 0.9067          | 0.9072    |
| 0.2377        | 2.0   | 41   | 0.2035   | 0.9229   | 0.9234          | 0.9269    |
| 0.1201        | 2.98  | 61   | 0.1345   | 0.9465   | 0.9467          | 0.9512    |
| 0.0774        | 4.0   | 82   | 0.1229   | 0.9612   | 0.9618          | 0.9643    |
| 0.0495        | 4.98  | 102  | 0.0562   | 0.9813   | 0.9815          | 0.9816    |
| 0.0358        | 6.0   | 123  | 0.0470   | 0.9843   | 0.9844          | 0.9846    |
| 0.0228        | 6.98  | 143  | 0.0447   | 0.9833   | 0.9834          | 0.9836    |
| 0.0181        | 8.0   | 164  | 0.0465   | 0.9828   | 0.9830          | 0.9831    |
| 0.0132        | 8.98  | 184  | 0.0436   | 0.9833   | 0.9835          | 0.9836    |
| 0.0126        | 9.76  | 200  | 0.0461   | 0.9838   | 0.9840          | 0.9840    |


### Framework versions

- Transformers 4.27.4
- Pytorch 2.0.0+cu117
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "precision"], "model-index": [{"name": "swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-skullStrippded_04", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9842906234658811, "name": "Accuracy"}, {"type": "precision", "value": 0.9845888529063952, "name": "Precision"}]}]}]}
transformers pytorch tensorboard swin image-classification generated_from_trainer dataset:imagefolder license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #swin #image-classification #generated_from_trainer #dataset-imagefolder #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-skullStrippded\_04
===========================================================================


This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset.
It achieves the following results on the evaluation set:


* Loss: 0.0470
* Accuracy: 0.9843
* F1 Score: 0.9844
* Precision: 0.9846


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: 0.0001
* train\_batch\_size: 100
* eval\_batch\_size: 100
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 400
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10


### Training results



### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 100
* eval\_batch\_size: 100
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 400
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #swin #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: 0.0001
* train\_batch\_size: 100
* eval\_batch\_size: 100
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 400
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10 ### Training results ### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #swin #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: 0.0001
* train\_batch\_size: 100
* eval\_batch\_size: 100
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 400
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 10### Training results### Framework versions


* Transformers 4.27.4
* Pytorch 2.0.0+cu117
* Datasets 2.11.0
* Tokenizers 0.13.3