Instructions to use SeyedAli/Remote-Sensing-UAV-image-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SeyedAli/Remote-Sensing-UAV-image-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="SeyedAli/Remote-Sensing-UAV-image-classification") 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("SeyedAli/Remote-Sensing-UAV-image-classification") model = AutoModelForImageClassification.from_pretrained("SeyedAli/Remote-Sensing-UAV-image-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6ad3d38583f2a7ad41a4ed82150161f9de04fa690caf9841dc71fb3281cf461
- Size of remote file:
- 343 MB
- SHA256:
- 2645713d76c85eeb70bd052d962df17e6692b35246219ddf8872b10ccbea02d9
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