metadata
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-eurosat
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: 0.9828042328042328
swin-tiny-patch4-window7-224-eurosat
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: 0.0684
- Accuracy: 0.9828
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 |
|---|---|---|---|---|
| 1.2075 | 0.98 | 33 | 0.5666 | 0.8519 |
| 0.2022 | 1.98 | 66 | 0.2523 | 0.9127 |
| 0.1206 | 2.98 | 99 | 0.1576 | 0.9497 |
| 0.0897 | 3.98 | 132 | 0.1421 | 0.9563 |
| 0.0564 | 4.98 | 165 | 0.1114 | 0.9656 |
| 0.0475 | 5.98 | 198 | 0.0678 | 0.9815 |
| 0.0332 | 6.98 | 231 | 0.0819 | 0.9775 |
| 0.0234 | 7.98 | 264 | 0.0679 | 0.9802 |
| 0.0126 | 8.98 | 297 | 0.0684 | 0.9828 |
| 0.0306 | 9.98 | 330 | 0.0719 | 0.9815 |
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Datasets 2.7.1
- Tokenizers 0.13.2