|
--- |
|
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-skullStrippded_04 |
|
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: 0.9842906234658811 |
|
- name: Precision |
|
type: precision |
|
value: 0.9845888529063952 |
|
--- |
|
|
|
<!-- 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. --> |
|
|
|
# swin-base-patch4-window7-224-in22k-finetuned-brain-tumor-skullStrippded_04 |
|
|
|
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0470 |
|
- Accuracy: 0.9843 |
|
- F1 Score: 0.9844 |
|
- Precision: 0.9846 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 100 |
|
- eval_batch_size: 100 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 400 |
|
- 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 | Accuracy | F1 Score | Validation Loss | Precision | |
|
|:-------------:|:-----:|:----:|:--------:|:--------:|:---------------:|:---------:| |
|
| 1.2662 | 1.0 | 16 | 0.8370 | 0.8309 | 0.4424 | 0.8464 | |
|
| 0.3778 | 0.98 | 20 | 0.2700 | 0.9062 | 0.9067 | 0.9072 | |
|
| 0.2377 | 2.0 | 41 | 0.2035 | 0.9229 | 0.9234 | 0.9269 | |
|
| 0.1201 | 2.98 | 61 | 0.1345 | 0.9465 | 0.9467 | 0.9512 | |
|
| 0.0774 | 4.0 | 82 | 0.1229 | 0.9612 | 0.9618 | 0.9643 | |
|
| 0.0495 | 4.98 | 102 | 0.0562 | 0.9813 | 0.9815 | 0.9816 | |
|
| 0.0358 | 6.0 | 123 | 0.0470 | 0.9843 | 0.9844 | 0.9846 | |
|
| 0.0228 | 6.98 | 143 | 0.0447 | 0.9833 | 0.9834 | 0.9836 | |
|
| 0.0181 | 8.0 | 164 | 0.0465 | 0.9828 | 0.9830 | 0.9831 | |
|
| 0.0132 | 8.98 | 184 | 0.0436 | 0.9833 | 0.9835 | 0.9836 | |
|
| 0.0126 | 9.76 | 200 | 0.0461 | 0.9838 | 0.9840 | 0.9840 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.27.4 |
|
- Pytorch 2.0.0+cu117 |
|
- Datasets 2.11.0 |
|
- Tokenizers 0.13.3 |
|
|
