Instructions to use hilmiatha/resnet18-flower-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hilmiatha/resnet18-flower-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="hilmiatha/resnet18-flower-classifier") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hilmiatha/resnet18-flower-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5c12c5a68fd1bf288a4a0471d42c7d1ac9c7e7c09d8dc06e25ab39a094f43e6
- Size of remote file:
- 44.8 MB
- SHA256:
- 716e1d839aba5a49d9d1a95aa8e5b08de1c13bb71914da71a70763aa513b17c6
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