metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - f1
  - recall
  - precision
model-index:
  - name: Brain_Tumor_Classification_using_swin
    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: 0.9960906958561376
          - name: F1
            type: f1
            value: 0.9960906958561376
          - name: Recall
            type: recall
            value: 0.9960906958561376
          - name: Precision
            type: precision
            value: 0.9960906958561376

Brain_Tumor_Classification_using_swin

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: 0.0123
Accuracy: 0.9961
F1: 0.9961
Recall: 0.9961
Precision: 0.9961

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
0.1234	1.0	180	0.0450	0.9840	0.9840	0.9840	0.9840
0.0837	2.0	360	0.0198	0.9926	0.9926	0.9926	0.9926
0.0373	3.0	540	0.0123	0.9961	0.9961	0.9961	0.9961

Framework versions

Transformers 4.23.1
Pytorch 1.13.0
Datasets 2.6.1
Tokenizers 0.13.1