metadata
license: apache-2.0
base_model: microsoft/swinv2-small-patch4-window8-256
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swinv2-small-patch4-window8-256-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: 0.6818181818181818
swinv2-small-patch4-window8-256-finetuned-eurosat
This model is a fine-tuned version of microsoft/swinv2-small-patch4-window8-256 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6481
- Accuracy: 0.6818
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.91 | 5 | 0.6724 | 0.5649 |
| 0.6041 | 2.0 | 11 | 0.5980 | 0.7273 |
| 0.6041 | 2.91 | 16 | 0.6070 | 0.6883 |
| 0.4131 | 4.0 | 22 | 0.6315 | 0.6753 |
| 0.4131 | 4.55 | 25 | 0.6481 | 0.6818 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2