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# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_10

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: 0.2175
- Accuracy: 0.9375
- F1 Score: 0.9383
- Precision: 0.9451
- Sensitivity: 0.9381
- Specificity: 0.9843

## 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 | Validation Loss | Accuracy | F1 Score | Precision | Sensitivity | Specificity |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:-----------:|:-----------:|
| 1.3428        | 0.99  | 19   | 0.7059          | 0.7467   | 0.7535   | 0.7951    | 0.7464      | 0.9332      |
| 0.3308        | 1.97  | 38   | 0.2314          | 0.9183   | 0.9194   | 0.9239    | 0.9191      | 0.9792      |
| 0.1601        | 2.96  | 57   | 0.2024          | 0.9305   | 0.9314   | 0.9349    | 0.9306      | 0.9824      |
| 0.0976        | 4.0   | 77   | 0.3376          | 0.8904   | 0.8943   | 0.9126    | 0.8930      | 0.9724      |
| 0.0585        | 4.99  | 96   | 0.3893          | 0.8830   | 0.8853   | 0.9115    | 0.8854      | 0.9706      |
| 0.0432        | 5.97  | 115  | 0.2559          | 0.9214   | 0.9239   | 0.9330    | 0.9237      | 0.9802      |
| 0.0313        | 6.96  | 134  | 0.2175          | 0.9375   | 0.9383   | 0.9451    | 0.9381      | 0.9843      |
| 0.0176        | 8.0   | 154  | 0.2309          | 0.9313   | 0.9326   | 0.9386    | 0.9320      | 0.9827      |
| 0.0152        | 8.99  | 173  | 0.2358          | 0.9328   | 0.9339   | 0.9416    | 0.9336      | 0.9831      |
| 0.0089        | 9.87  | 190  | 0.2116          | 0.9360   | 0.9374   | 0.9437    | 0.9372      | 0.9839      |


### Framework versions

- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- 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-final_10", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9375490966221524, "name": "Accuracy"}, {"type": "precision", "value": 0.9451238954076366, "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-final\_10
==================================================================


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: 0.2175
* Accuracy: 0.9375
* F1 Score: 0.9383
* Precision: 0.9451
* Sensitivity: 0.9381
* Specificity: 0.9843


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 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* 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 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* 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 4.29.2
* Pytorch 2.0.1+cu117
* Datasets 2.12.0
* Tokenizers 0.13.3