metadata
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
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: 0.9883021390374331
- name: Precision
type: precision
value: 0.9883071765108582
- name: Recall
type: recall
value: 0.9883021390374331
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: 0.0394
- Accuracy: 0.9883
- Precision: 0.9883
- Recall: 0.9883
- Confusion Matrix: [[1497, 15], [20, 1460]]
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | Confusion Matrix |
|---|---|---|---|---|---|---|---|
| 0.1107 | 1.0 | 374 | 0.0641 | 0.9786 | 0.9787 | 0.9786 | [[1488, 24], [40, 1440]] |
| 0.1079 | 2.0 | 748 | 0.0560 | 0.9773 | 0.9776 | 0.9773 | [[1498, 14], [54, 1426]] |
| 0.0624 | 3.0 | 1122 | 0.0394 | 0.9883 | 0.9883 | 0.9883 | [[1497, 15], [20, 1460]] |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0