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: 0.7713178294573644
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: 0.5588
- Accuracy: 0.7713
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 |
|---|---|---|---|---|
| 1.047 | 0.99 | 18 | 0.8238 | 0.6667 |
| 0.7685 | 1.97 | 36 | 0.7295 | 0.6705 |
| 0.7036 | 2.96 | 54 | 0.6794 | 0.6977 |
| 0.6215 | 4.0 | 73 | 0.6780 | 0.7093 |
| 0.5409 | 4.99 | 91 | 0.5864 | 0.7519 |
| 0.5165 | 5.97 | 109 | 0.5343 | 0.7713 |
| 0.4803 | 6.96 | 127 | 0.6034 | 0.7326 |
| 0.4328 | 8.0 | 146 | 0.6063 | 0.7326 |
| 0.4125 | 8.99 | 164 | 0.5815 | 0.7558 |
| 0.4237 | 9.86 | 180 | 0.5588 | 0.7713 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0