Instructions to use thelabel/image-labeling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thelabel/image-labeling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="thelabel/image-labeling") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("thelabel/image-labeling") model = AutoModelForZeroShotImageClassification.from_pretrained("thelabel/image-labeling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download label_mapping.json from thelabel/image-labeling: direct link, hf CLI and curl.
- Browser
- Download file 512 Bytes
-
https://huggingface.co/thelabel/image-labeling/resolve/main/label_mapping.json
- Command line
-
hf download hf://thelabel/image-labeling/label_mapping.json
-
curl -L -o label_mapping.json https://huggingface.co/thelabel/image-labeling/resolve/main/label_mapping.json
512 Bytes
| {"Human > Studio > zoomed > side": 0, "Human > Studio > zoomed > front": 1, "Human > Studio > half-body > side": 2, "Human > Studio > half-body > front": 3, "Human > Studio > half-body > back": 4, "Human > Studio > full-body > side": 5, "Human > Studio > full-body > front": 6, "Human > Studio > full-body > back": 7, "Human > Indoor > half-body > front": 8, "Human > Indoor > half-body > back": 9, "Ghost > Studio > zoomed > front": 10, "Ghost > Studio > top > front": 11, "Ghost > Studio > bottom > front": 12} |