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
File size: 512 Bytes
ede2500 | 1 | {"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} |