Downloads · 30 days
7
1% of all-time downloads
swin-tiny-patch4-window7-224-finetuned-eurosat
ShinjiRay/swin-tiny-patch4-window7-224-finetuned-eurosat
swin-tiny-patch4-window7-224-finetuned-eurosat is a image classification model from ShinjiRay. Use it when you need a label for an image. It is set up for transformers. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
All-time downloads
564
Public
Parameters
27.6M
442 MB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin110 MB · 50%
Parameter types
How the weights are stored.
F3227.5M · 100%
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 2.0695
- Accuracy: 0.22
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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 0.8 | 3 | 2.1547 | 0.24 |
| No log | 1.87 | 7 | 2.0737 | 0.24 |
| No log | 2.4 | 9 | 2.0695 | 0.22 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
- Task
- Image Classification
- Library
- transformers
- Type
- swin
- License
- apache-2.0
- Created
- Nov 19, 2023
- Updated
- Nov 22, 2023