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-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: 0.9764705882352941
swin-tiny-patch4-window7-224-finetuned-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: 0.0625
- Accuracy: 0.9765
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 |
|---|---|---|---|---|
| 1.506 | 1.0 | 30 | 1.1397 | 0.5976 |
| 0.5645 | 2.0 | 60 | 0.3396 | 0.88 |
| 0.4507 | 3.0 | 90 | 0.1972 | 0.9247 |
| 0.418 | 4.0 | 120 | 0.1484 | 0.9506 |
| 0.3169 | 5.0 | 150 | 0.1866 | 0.92 |
| 0.3346 | 6.0 | 180 | 0.0973 | 0.9718 |
| 0.2823 | 7.0 | 210 | 0.0973 | 0.9694 |
| 0.2711 | 8.0 | 240 | 0.0805 | 0.9671 |
| 0.2638 | 9.0 | 270 | 0.0749 | 0.9718 |
| 0.2755 | 10.0 | 300 | 0.0625 | 0.9765 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2