Image Classification
Transformers
PyTorch
TensorBoard
Safetensors
vit
huggingpics
Eval Results (legacy)
Instructions to use Bazaar/cv_corridor_garbage_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bazaar/cv_corridor_garbage_detection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Bazaar/cv_corridor_garbage_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("Bazaar/cv_corridor_garbage_detection") model = AutoModelForImageClassification.from_pretrained("Bazaar/cv_corridor_garbage_detection") - Notebooks
- Google Colab
- Kaggle
cv_corridor_garbage_detection
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
Example Images
garbage
no garbage
- Downloads last month
- 9
Evaluation results
- Accuracyself-reported0.973

