metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: swin-tiny-patch4-window7-224-finetuned-brainTumorData
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9911764705882353
          - name: F1
            type: f1
            value: 0.9923273657289001
          - name: Recall
            type: recall
            value: 0.9948717948717949
          - name: Precision
            type: precision
            value: 0.9897959183673469

swin-tiny-patch4-window7-224-finetuned-brainTumorData

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

Loss: 0.0484
Accuracy: 0.9912
F1: 0.9923
Recall: 0.9949
Precision: 0.9898

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: 4

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.3405	1.0	24	0.1137	0.9647	0.9688	0.9538	0.9841
0.2449	2.0	48	0.0811	0.9735	0.9766	0.9641	0.9895
0.1713	3.0	72	0.0613	0.9794	0.9818	0.9692	0.9947
0.1615	4.0	96	0.0484	0.9912	0.9923	0.9949	0.9898

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1