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# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final_02

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: 0.5751
- Accuracy: 0.7637
- F1 Score: 0.7582
- Precision: 0.8412

## 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|
| 1.2461        | 0.96  | 20   | 1.2674          | 0.4083   | 0.3093   | 0.4583    |
| 0.7271        | 1.98  | 41   | 0.9923          | 0.5666   | 0.5074   | 0.6374    |
| 0.3593        | 2.99  | 62   | 0.7205          | 0.7084   | 0.6803   | 0.7511    |
| 0.2162        | 4.0   | 83   | 0.7662          | 0.6925   | 0.6572   | 0.7751    |
| 0.1575        | 4.96  | 103  | 0.6434          | 0.7335   | 0.7161   | 0.8072    |
| 0.1174        | 5.98  | 124  | 0.6836          | 0.7141   | 0.6923   | 0.8004    |
| 0.0988        | 6.99  | 145  | 0.5644          | 0.7665   | 0.7572   | 0.8313    |
| 0.079         | 8.0   | 166  | 0.5964          | 0.7506   | 0.7376   | 0.8172    |
| 0.0786        | 8.96  | 186  | 0.5741          | 0.7574   | 0.7503   | 0.8315    |
| 0.0677        | 9.98  | 207  | 0.5306          | 0.7802   | 0.7697   | 0.8363    |
| 0.0518        | 10.99 | 228  | 0.5828          | 0.7625   | 0.7525   | 0.8331    |
| 0.0466        | 12.0  | 249  | 0.6159          | 0.7460   | 0.7370   | 0.8328    |
| 0.0344        | 12.96 | 269  | 0.6178          | 0.7489   | 0.7418   | 0.8339    |
| 0.0296        | 13.98 | 290  | 0.6194          | 0.7483   | 0.7389   | 0.8298    |
| 0.0246        | 14.99 | 311  | 0.6697          | 0.7329   | 0.7209   | 0.8281    |
| 0.0202        | 16.0  | 332  | 0.5769          | 0.7642   | 0.7570   | 0.8382    |
| 0.0261        | 16.96 | 352  | 0.5960          | 0.7551   | 0.7485   | 0.8363    |
| 0.0183        | 17.98 | 373  | 0.5145          | 0.7904   | 0.7856   | 0.8488    |
| 0.0169        | 18.99 | 394  | 0.5720          | 0.7648   | 0.7594   | 0.8412    |
| 0.0172        | 19.28 | 400  | 0.5751          | 0.7637   | 0.7582   | 0.8412    |


### 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_02", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7636674259681093, "name": "Accuracy"}, {"type": "precision", "value": 0.8412462220936494, "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\_02
==================================================================


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: 0.5751
* Accuracy: 0.7637
* F1 Score: 0.7582
* Precision: 0.8412


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: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20


### 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: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### 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: 1e-05
* train\_batch\_size: 64
* eval\_batch\_size: 64
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 256
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.1
* num\_epochs: 20 ### Training results ### Framework versions


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