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: 0.8338171262699564
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: 0.4653
- Accuracy: 0.8338
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 | Accuracy | Validation Loss |
|---|---|---|---|---|
| 0.8849 | 1.0 | 97 | 0.6836 | 0.8586 |
| 0.7461 | 2.0 | 194 | 0.7678 | 0.6177 |
| 0.6429 | 3.0 | 291 | 0.7881 | 0.5690 |
| 0.6522 | 4.0 | 388 | 0.7968 | 0.5325 |
| 0.6189 | 5.0 | 485 | 0.8084 | 0.5213 |
| 0.6045 | 6.0 | 582 | 0.5242 | 0.8099 |
| 0.5851 | 7.0 | 679 | 0.5081 | 0.8157 |
| 0.5268 | 8.0 | 776 | 0.4941 | 0.8193 |
| 0.5269 | 9.0 | 873 | 0.4589 | 0.8295 |
| 0.556 | 10.0 | 970 | 0.4653 | 0.8338 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0