Instructions to use Mirkat/Plant_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mirkat/Plant_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Mirkat/Plant_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("Mirkat/Plant_Classification") model = AutoModelForImageClassification.from_pretrained("Mirkat/Plant_Classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbc2afdf5dd0a1b873c8b689614c6b1fe601e3a6a4399bed5d8ff1eb56ce7790
- Size of remote file:
- 3.9 kB
- SHA256:
- aaa90b2bf99e43bd879b69acbb4d88ab9a0137685671e23de8df26be9573fb3d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.