Instructions to use watersplash/waste-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use watersplash/waste-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="watersplash/waste-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("watersplash/waste-classification") model = AutoModelForImageClassification.from_pretrained("watersplash/waste-classification") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
An Image Classifier model fine-tuned on ViT. This model can classify garbage images.
Model Details
Model Description
- Finetuned from model : ViT
Model Sources [optional]
Uses
- Target classes: Battery, Biological, Brown-grass, Cardboard, Clothes, Green-Glass, Metal, Paper, Plastic, Shoes, Trash, White-Glass
Training Details
Training Data
https://www.kaggle.com/datasets/mostafaabla/garbage-classification
Metrics
Accuracy
Results
Accuracy: 98%
Summary
- Hours used: 1 hour 30 minutes
- References: Based on the model yangy50/garbage-classification
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