metadata
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024.v1.1
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: 0.8892018779342723
swin-tiny-patch4-window7-224-finetuned-leukemia-08-2024.v1.1
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.3631
- Accuracy: 0.8892
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.32 | 0.9984 | 312 | 0.9902 | 0.6911 |
| 0.2625 | 2.0 | 625 | 0.5526 | 0.7690 |
| 0.1584 | 2.9984 | 937 | 0.5605 | 0.8019 |
| 0.1382 | 4.0 | 1250 | 0.4291 | 0.8498 |
| 0.1058 | 4.9984 | 1562 | 0.3911 | 0.8667 |
| 0.0703 | 6.0 | 1875 | 0.8593 | 0.8028 |
| 0.0671 | 6.9984 | 2187 | 0.3631 | 0.8892 |
| 0.0457 | 8.0 | 2500 | 0.7271 | 0.8338 |
| 0.0396 | 8.9984 | 2812 | 0.4655 | 0.8826 |
| 0.0293 | 9.984 | 3120 | 0.7581 | 0.8610 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1