metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: Brain_Tumor_Detector_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: 0.9981308411214953
- name: F1
type: f1
value: 0.9985111662531018
- name: Recall
type: recall
value: 0.9990069513406157
- name: Precision
type: precision
value: 0.998015873015873
Brain_Tumor_Detector_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: 0.0054
- Accuracy: 0.9981
- F1: 0.9985
- Recall: 0.9990
- Precision: 0.9980
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 |
|---|---|---|---|---|---|---|---|
| 0.079 | 1.0 | 113 | 0.0283 | 0.9882 | 0.9906 | 0.9930 | 0.9881 |
| 0.0575 | 2.0 | 226 | 0.0121 | 0.9956 | 0.9965 | 0.9950 | 0.9980 |
| 0.0312 | 3.0 | 339 | 0.0054 | 0.9981 | 0.9985 | 0.9990 | 0.9980 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.13.1