|
--- |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
datasets: |
|
- imagefolder |
|
metrics: |
|
- accuracy |
|
- f1 |
|
- recall |
|
- precision |
|
model-index: |
|
- name: Brain_Tumor_Classification |
|
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: 0.9613120269133726 |
|
- name: F1 |
|
type: f1 |
|
value: 0.9613120269133726 |
|
- name: Recall |
|
type: recall |
|
value: 0.9613120269133726 |
|
- name: Precision |
|
type: precision |
|
value: 0.9613120269133726 |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# Brain_Tumor_Classification |
|
|
|
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.1093 |
|
- Accuracy: 0.9613 |
|
- F1: 0.9613 |
|
- Recall: 0.9613 |
|
- Precision: 0.9613 |
|
|
|
## 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 | |
|
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
|
| 0.4369 | 0.99 | 83 | 0.2500 | 0.9092 | 0.9092 | 0.9092 | 0.9092 | |
|
| 0.3777 | 1.99 | 166 | 0.1763 | 0.9302 | 0.9302 | 0.9302 | 0.9302 | |
|
| 0.2684 | 2.99 | 249 | 0.1215 | 0.9512 | 0.9512 | 0.9512 | 0.9512 | |
|
| 0.2363 | 3.99 | 332 | 0.1093 | 0.9613 | 0.9613 | 0.9613 | 0.9613 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.23.1 |
|
- Pytorch 1.12.1 |
|
- Datasets 2.6.1 |
|
- Tokenizers 0.13.1 |
|
|
