metadata

license: apache-2.0
tags:
  - image-classification
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - precision
model-index:
  - name: swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: Brain Tumor
          type: imagefolder
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.9265375854214123
          - name: Precision
            type: precision
            value: 0.9269521372101541

swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-final

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the Brain Tumor dataset. It achieves the following results on the evaluation set:

Loss: 0.1925
Accuracy: 0.9265
F1 Score: 0.9252
Precision: 0.9270

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Precision
1.2212	0.96	20	1.1407	0.6429	0.6225	0.6601
0.565	1.98	41	0.5162	0.8326	0.8311	0.8428
0.3245	2.99	62	0.3265	0.8804	0.8784	0.8843
0.2618	4.0	83	0.2713	0.9066	0.9054	0.9105
0.2164	4.96	103	0.2812	0.8946	0.8929	0.8994
0.1814	5.98	124	0.2411	0.9060	0.9043	0.9091
0.1481	6.99	145	0.2345	0.9100	0.9084	0.9130
0.1468	8.0	166	0.2340	0.9072	0.9055	0.9108
0.1336	8.96	186	0.1925	0.9265	0.9252	0.9270
0.133	9.64	200	0.2021	0.9220	0.9207	0.9235

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3