Instructions to use smanna/clip-ship-inspection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smanna/clip-ship-inspection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="smanna/clip-ship-inspection") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("smanna/clip-ship-inspection") model = AutoModelForZeroShotImageClassification.from_pretrained("smanna/clip-ship-inspection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from smanna/clip-ship-inspection: direct link, hf CLI and curl.
- Browser
- Download file 3.64 MB
-
https://huggingface.co/smanna/clip-ship-inspection/resolve/main/tokenizer.json
- Command line
-
hf download hf://smanna/clip-ship-inspection/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/smanna/clip-ship-inspection/resolve/main/tokenizer.json
3.64 MB
File too large to display, you can check the raw version instead.