metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
base_model: microsoft/swin-base-patch4-window7-224-in22k
model-index:
- name: Brain_Tumor_Classification_using_swin_transformer
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- type: accuracy
value: 0.9949179046129789
name: Accuracy
- type: f1
value: 0.9949179046129789
name: F1
- type: recall
value: 0.9949179046129789
name: Recall
- type: precision
value: 0.9949179046129789
name: Precision
Brain_Tumor_Classification_using_swin_transformer
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: 0.0118
- Accuracy: 0.9949
- F1: 0.9949
- Recall: 0.9949
- Precision: 0.9949
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|---|---|---|---|---|---|---|---|
| 0.081 | 1.0 | 180 | 0.0557 | 0.9832 | 0.9832 | 0.9832 | 0.9832 |
| 0.0816 | 2.0 | 360 | 0.0187 | 0.9937 | 0.9937 | 0.9937 | 0.9937 |
| 0.0543 | 3.0 | 540 | 0.0118 | 0.9949 | 0.9949 | 0.9949 | 0.9949 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1