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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Rename images/no garbage.jpg to images/no_garbage.jpg
7c7a2fa -
Rename images/trz (197).jpg to images/garbage.jpg
a1ef6d6 -
Rename images/wtz (243).jpg to images/no garbage.jpg
92a932e -
Delete images/no_garbage.jpg
ee6459c -
Delete images/garbage.jpg
2eeebd5 -
Upload 2 files
0782323 -
commit files to HF hub
2a81bb5