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: 0.7289719626168224
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: 0.6419
- Accuracy: 0.7290
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 |
|---|---|---|---|---|
| 1.0627 | 1.0 | 15 | 0.9620 | 0.5467 |
| 0.8137 | 2.0 | 30 | 0.7780 | 0.6589 |
| 0.7516 | 3.0 | 45 | 0.7737 | 0.6822 |
| 0.6395 | 4.0 | 60 | 0.7195 | 0.6869 |
| 0.579 | 5.0 | 75 | 0.6742 | 0.7150 |
| 0.5505 | 6.0 | 90 | 0.6526 | 0.7243 |
| 0.5312 | 7.0 | 105 | 0.6616 | 0.7290 |
| 0.4793 | 8.0 | 120 | 0.6470 | 0.7430 |
| 0.4443 | 9.0 | 135 | 0.6375 | 0.7383 |
| 0.4685 | 10.0 | 150 | 0.6419 | 0.7290 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0