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: 0.9896390374331551
- name: Precision
type: precision
value: 0.9897531473312668
- name: Recall
type: recall
value: 0.9896390374331551
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: 0.0355
- Accuracy: 0.9896
- Precision: 0.9898
- Recall: 0.9896
- Confusion Matrix: [[1508, 4], [27, 1453]]
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 |
|---|---|---|---|---|---|---|---|
| 0.0585 | 1.0 | 374 | 0.0224 | 0.9940 | 0.9940 | 0.9940 | [[1506, 6], [12, 1468]] |
| 0.0792 | 2.0 | 748 | 0.0346 | 0.9910 | 0.9911 | 0.9910 | [[1509, 3], [24, 1456]] |
| 0.0634 | 3.0 | 1122 | 0.0355 | 0.9896 | 0.9898 | 0.9896 | [[1508, 4], [27, 1453]] |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0