Instructions to use dima806/fruit_vegetable_image_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dima806/fruit_vegetable_image_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/fruit_vegetable_image_detection") 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("dima806/fruit_vegetable_image_detection") model = AutoModelForImageClassification.from_pretrained("dima806/fruit_vegetable_image_detection") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoImageProcessor, AutoModelForImageClassification
processor = AutoImageProcessor.from_pretrained("dima806/fruit_vegetable_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/fruit_vegetable_image_detection")Quick Links
See https://www.kaggle.com/code/dima806/fruit-and-vegetable-image-detection-vit for more details.
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Model tree for dima806/fruit_vegetable_image_detection
Base model
google/vit-base-patch16-224-in21k
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="dima806/fruit_vegetable_image_detection") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")