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: 0.7674418604651163
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: 0.6503
- Accuracy: 0.7674
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 |
|---|---|---|---|---|
| 1.0764 | 0.99 | 18 | 0.8915 | 0.6318 |
| 0.7888 | 1.97 | 36 | 0.7533 | 0.6860 |
| 0.707 | 2.96 | 54 | 0.7525 | 0.6977 |
| 0.6115 | 4.0 | 73 | 0.6723 | 0.7403 |
| 0.5552 | 4.99 | 91 | 0.7092 | 0.7093 |
| 0.5057 | 5.97 | 109 | 0.6733 | 0.7326 |
| 0.4761 | 6.96 | 127 | 0.6893 | 0.7326 |
| 0.4393 | 8.0 | 146 | 0.6933 | 0.7287 |
| 0.4284 | 8.99 | 164 | 0.6348 | 0.7519 |
| 0.4084 | 9.86 | 180 | 0.6503 | 0.7674 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0