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: 0.8111298482293423
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: 0.4435
- Accuracy: 0.8111
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: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5077 | 0.98 | 41 | 0.6378 | 0.6796 |
| 0.5111 | 1.99 | 83 | 0.7097 | 0.6577 |
| 0.5395 | 2.99 | 125 | 0.5374 | 0.7470 |
| 0.5498 | 4.0 | 167 | 0.5524 | 0.7420 |
| 0.4754 | 4.98 | 208 | 0.5324 | 0.7639 |
| 0.4662 | 5.99 | 250 | 0.4962 | 0.7639 |
| 0.4677 | 6.99 | 292 | 0.5070 | 0.7774 |
| 0.4525 | 8.0 | 334 | 0.5144 | 0.7673 |
| 0.4635 | 8.98 | 375 | 0.4978 | 0.7757 |
| 0.4309 | 9.99 | 417 | 0.5388 | 0.7774 |
| 0.4292 | 10.99 | 459 | 0.4937 | 0.7825 |
| 0.4182 | 12.0 | 501 | 0.5234 | 0.7808 |
| 0.4242 | 12.98 | 542 | 0.4539 | 0.7960 |
| 0.4053 | 13.99 | 584 | 0.5089 | 0.7858 |
| 0.4135 | 14.99 | 626 | 0.4655 | 0.8044 |
| 0.3888 | 16.0 | 668 | 0.4398 | 0.8212 |
| 0.3701 | 16.98 | 709 | 0.4258 | 0.8145 |
| 0.3641 | 17.99 | 751 | 0.4339 | 0.8196 |
| 0.3547 | 18.99 | 793 | 0.4556 | 0.7993 |
| 0.3623 | 19.64 | 820 | 0.4435 | 0.8111 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0