metadata
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-200k
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: 0.796086508753862
swin-tiny-patch4-window7-224-finetuned-200k
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: 0.4347
- Accuracy: 0.7961
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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.634 | 0.99 | 36 | 0.6243 | 0.6262 |
| 0.5551 | 1.99 | 72 | 0.5186 | 0.7250 |
| 0.5183 | 2.98 | 108 | 0.4826 | 0.7673 |
| 0.4854 | 4.0 | 145 | 0.5640 | 0.7261 |
| 0.4645 | 4.99 | 181 | 0.4598 | 0.7817 |
| 0.4655 | 5.99 | 217 | 0.4787 | 0.7786 |
| 0.4582 | 6.98 | 253 | 0.4483 | 0.7899 |
| 0.4415 | 8.0 | 290 | 0.4709 | 0.7765 |
| 0.4546 | 8.99 | 326 | 0.4717 | 0.7817 |
| 0.4566 | 9.99 | 362 | 0.4538 | 0.7951 |
| 0.4675 | 10.98 | 398 | 0.4491 | 0.7817 |
| 0.4449 | 12.0 | 435 | 0.4992 | 0.7652 |
| 0.4349 | 12.99 | 471 | 0.4627 | 0.7817 |
| 0.4253 | 13.99 | 507 | 0.4492 | 0.7858 |
| 0.4278 | 14.98 | 543 | 0.4442 | 0.7951 |
| 0.4567 | 16.0 | 580 | 0.4362 | 0.7899 |
| 0.4205 | 16.99 | 616 | 0.4550 | 0.7889 |
| 0.4233 | 17.99 | 652 | 0.4336 | 0.7909 |
| 0.4014 | 18.98 | 688 | 0.4565 | 0.7889 |
| 0.4176 | 20.0 | 725 | 0.4323 | 0.7940 |
| 0.411 | 20.99 | 761 | 0.4348 | 0.7951 |
| 0.4128 | 21.99 | 797 | 0.4378 | 0.7971 |
| 0.4045 | 22.98 | 833 | 0.4317 | 0.7951 |
| 0.4001 | 24.0 | 870 | 0.4452 | 0.7868 |
| 0.4061 | 24.99 | 906 | 0.4286 | 0.7920 |
| 0.4033 | 25.99 | 942 | 0.4306 | 0.7951 |
| 0.3953 | 26.98 | 978 | 0.4320 | 0.7920 |
| 0.3924 | 28.0 | 1015 | 0.4338 | 0.7940 |
| 0.4056 | 28.99 | 1051 | 0.4329 | 0.7930 |
| 0.4032 | 29.79 | 1080 | 0.4347 | 0.7961 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3