uoft-cs/cifar10
Viewer • Updated • 60k • 480k • 116
How to use sck/swin-tiny-patch4-window7-224-finetuned-eurosat with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-classification", model="sck/swin-tiny-patch4-window7-224-finetuned-eurosat")
pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png") # Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("sck/swin-tiny-patch4-window7-224-finetuned-eurosat")
model = AutoModelForImageClassification.from_pretrained("sck/swin-tiny-patch4-window7-224-finetuned-eurosat", device_map="auto")This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the cifar10 dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5329 | 1.0 | 176 | 0.1501 | 0.9512 |
| 0.3966 | 2.0 | 352 | 0.1093 | 0.9636 |
| 0.3778 | 3.0 | 528 | 0.0944 | 0.9674 |