Instructions to use anggtpd/snacks_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anggtpd/snacks_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="anggtpd/snacks_classifier") 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("anggtpd/snacks_classifier") model = AutoModelForImageClassification.from_pretrained("anggtpd/snacks_classifier") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
datasets:
- Matthijs/snacks
metrics:
- accuracy
snacks_classifier
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.0090
- Accuracy: 0.8908