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---
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: 0.9966577540106952
- name: Precision
type: precision
value: 0.9966586895563994
- name: Recall
type: recall
value: 0.9966577540106952
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0154
- Accuracy: 0.9967
- Precision: 0.9967
- Recall: 0.9967
- Confusion Matrix: [[1506, 6], [4, 1476]]
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----------------------:|
| 0.0568 | 1.0 | 374 | 0.0186 | 0.9940 | 0.9940 | 0.9940 | [[1500, 12], [6, 1474]] |
| 0.0516 | 2.0 | 748 | 0.0191 | 0.9960 | 0.9960 | 0.9960 | [[1504, 8], [4, 1476]] |
| 0.0719 | 3.0 | 1122 | 0.0154 | 0.9967 | 0.9967 | 0.9967 | [[1506, 6], [4, 1476]] |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|