metadata
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swinv2-small-patch4-window16-256-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: 0.9892592592592593
- name: F1
type: f1
value: 0.9892542163878574
- name: Precision
type: precision
value: 0.9892896521886161
- name: Recall
type: recall
value: 0.9892592592592593
swinv2-small-patch4-window16-256-finetuned-eurosat
This model is a fine-tuned version of microsoft/swinv2-small-patch4-window16-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0328
- Accuracy: 0.9893
- F1: 0.9893
- Precision: 0.9893
- Recall: 0.9893
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: 0.0001
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.2326 | 1.0 | 253 | 0.0870 | 0.9715 | 0.9716 | 0.9720 | 0.9715 |
| 0.1955 | 2.0 | 506 | 0.0576 | 0.9789 | 0.9788 | 0.9794 | 0.9789 |
| 0.1229 | 3.0 | 759 | 0.0450 | 0.9837 | 0.9837 | 0.9839 | 0.9837 |
| 0.0797 | 4.0 | 1012 | 0.0332 | 0.9889 | 0.9889 | 0.9889 | 0.9889 |
| 0.0826 | 5.0 | 1265 | 0.0328 | 0.9893 | 0.9893 | 0.9893 | 0.9893 |
Framework versions
- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1