Image Classification
Transformers
PyTorch
TensorBoard
swin
Generated from Trainer
Eval Results (legacy)
Instructions to use nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia") 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("nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia") model = AutoModelForImageClassification.from_pretrained("nielsr/swin-tiny-patch4-window7-224-finetuned-eurosat-kornia") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
#1
by librarian-bot - opened
This pull request aims to enrich the metadata of your model by adding microsoft/swin-tiny-patch4-window7-224 as a base_model field, situated in the YAML block of your model's README.md.
How did we find this information? We performed a regular expression match on your README.md file to determine the connection.
Why add this? Enhancing your model's metadata in this way:
- Boosts Discoverability - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- Highlights Impact - It showcases the contributions and influences different models have within the community.
For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at librarian-bots/base_model_explorer.
This PR was requested via the Librarian Bot metadata request service by request of davanstrien
nielsr changed pull request status to merged