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: 0.8405797101449275
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: 0.4560
- Accuracy: 0.8406
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 |
---|---|---|---|---|
1.7738 | 1.0 | 13 | 1.3796 | 0.5845 |
1.2319 | 2.0 | 26 | 1.1322 | 0.6039 |
1.0355 | 3.0 | 39 | 0.9885 | 0.6232 |
0.7439 | 4.0 | 52 | 1.2022 | 0.6232 |
0.6792 | 5.0 | 65 | 0.7238 | 0.7246 |
0.6195 | 6.0 | 78 | 0.7041 | 0.7536 |
0.5151 | 7.0 | 91 | 0.6132 | 0.7826 |
0.556 | 8.0 | 104 | 0.6381 | 0.7488 |
0.4727 | 9.0 | 117 | 0.6127 | 0.7923 |
0.4879 | 10.0 | 130 | 0.4921 | 0.8551 |
0.436 | 11.0 | 143 | 0.5578 | 0.7923 |
0.3781 | 12.0 | 156 | 0.5095 | 0.8261 |
0.4201 | 13.0 | 169 | 0.5151 | 0.8454 |
0.3773 | 14.0 | 182 | 0.4612 | 0.8261 |
0.3611 | 15.0 | 195 | 0.5384 | 0.7971 |
0.3855 | 16.0 | 208 | 0.5267 | 0.8261 |
0.3926 | 17.0 | 221 | 0.4100 | 0.8647 |
0.3513 | 18.0 | 234 | 0.4508 | 0.8454 |
0.3389 | 19.0 | 247 | 0.4420 | 0.8502 |
0.3232 | 20.0 | 260 | 0.4560 | 0.8406 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1