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-swin-tiny-patch4-window7-224-finetuned-leukemia.v2.1
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: 0.954
swin-tiny-patch4-window7-224-swin-tiny-patch4-window7-224-finetuned-leukemia.v2.1
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: 0.1379
- Accuracy: 0.954
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 |
|---|---|---|---|---|
| 0.4215 | 0.9991 | 281 | 0.3880 | 0.8293 |
| 0.3137 | 1.9982 | 562 | 0.2898 | 0.8788 |
| 0.2631 | 2.9973 | 843 | 0.2382 | 0.907 |
| 0.2338 | 4.0 | 1125 | 0.4090 | 0.8575 |
| 0.1834 | 4.9991 | 1406 | 0.2477 | 0.8985 |
| 0.2065 | 5.9982 | 1687 | 0.1331 | 0.9513 |
| 0.1555 | 6.9973 | 1968 | 0.1304 | 0.9473 |
| 0.1521 | 8.0 | 2250 | 0.1837 | 0.9293 |
| 0.1512 | 8.9991 | 2531 | 0.1708 | 0.9405 |
| 0.119 | 9.9911 | 2810 | 0.1379 | 0.954 |
Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1