metadata

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
    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: 0.9396355353075171
          - name: Precision
            type: precision
            value: 0.9408448811333167

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 imagefolder dataset. It achieves the following results on the evaluation set:

Loss: 0.1577
Accuracy: 0.9396
F1 Score: 0.9385
Precision: 0.9408

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: 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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Precision
1.1562	0.99	41	1.1378	0.6378	0.6191	0.6537
0.4878	1.99	82	0.6477	0.7591	0.7499	0.7874
0.2623	2.98	123	0.4410	0.8337	0.8311	0.8488
0.1985	4.0	165	0.4660	0.8144	0.8115	0.8455
0.1736	4.99	206	0.3230	0.8776	0.8760	0.8894
0.124	5.99	247	0.2684	0.9026	0.9014	0.9090
0.1278	6.98	288	0.2210	0.9180	0.9166	0.9210
0.0959	8.0	330	0.2151	0.9208	0.9195	0.9260
0.0849	8.99	371	0.2154	0.9220	0.9205	0.9291
0.0805	9.99	412	0.2112	0.9191	0.9179	0.9251
0.0682	10.98	453	0.1563	0.9385	0.9369	0.9402
0.0624	12.0	495	0.1577	0.9396	0.9385	0.9408
0.0415	12.99	536	0.1836	0.9305	0.9294	0.9332
0.0465	13.99	577	0.2145	0.9203	0.9192	0.9252
0.056	14.98	618	0.1710	0.9339	0.9325	0.9369
0.0545	16.0	660	0.2094	0.9248	0.9236	0.9298
0.0591	16.99	701	0.1752	0.9317	0.9303	0.9341
0.0512	17.99	742	0.1781	0.9311	0.9297	0.9342
0.0424	18.98	783	0.1873	0.9305	0.9293	0.9338
0.0438	19.88	820	0.1955	0.9265	0.9252	0.9307

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu117
Datasets 2.12.0
Tokenizers 0.13.3