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: 0.9879154078549849
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: 0.0367
- Accuracy: 0.9879
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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 |
|---|---|---|---|---|
| 0.1993 | 0.9904 | 77 | 0.0961 | 0.9617 |
| 0.1729 | 1.9936 | 155 | 0.1151 | 0.9486 |
| 0.1509 | 2.9968 | 233 | 0.0603 | 0.9748 |
| 0.1081 | 4.0 | 311 | 0.0367 | 0.9879 |
| 0.1195 | 4.9904 | 388 | 0.0936 | 0.9627 |
| 0.0674 | 5.9936 | 466 | 0.0370 | 0.9849 |
| 0.0629 | 6.9968 | 544 | 0.0400 | 0.9839 |
| 0.0718 | 8.0 | 622 | 0.0496 | 0.9839 |
| 0.0335 | 8.9904 | 699 | 0.0533 | 0.9819 |
| 0.0843 | 9.9035 | 770 | 0.0550 | 0.9809 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1