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: 0.8571428571428571
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: 0.4634
- Accuracy: 0.8571
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 |
---|---|---|---|---|
2.0639 | 0.99 | 18 | 1.1575 | 0.6873 |
1.0962 | 1.97 | 36 | 0.8400 | 0.7336 |
0.8715 | 2.96 | 54 | 0.7163 | 0.7915 |
0.7241 | 4.0 | 73 | 0.6331 | 0.7992 |
0.6171 | 4.99 | 91 | 0.5140 | 0.8340 |
0.5467 | 5.97 | 109 | 0.4848 | 0.8610 |
0.518 | 6.96 | 127 | 0.4585 | 0.8456 |
0.4543 | 8.0 | 146 | 0.4549 | 0.8726 |
0.4378 | 8.99 | 164 | 0.4448 | 0.8571 |
0.431 | 9.86 | 180 | 0.4634 | 0.8571 |
Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2