Zero-Shot Image Classification
Transformers
PyTorch
English
clip
geolocalization
geolocation
geographic
street
climate
urban
rural
multi-modal
geoguessr
Instructions to use geolocal/StreetCLIP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use geolocal/StreetCLIP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="geolocal/StreetCLIP") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("geolocal/StreetCLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("geolocal/StreetCLIP", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from geolocal/StreetCLIP: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/geolocal/StreetCLIP/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://geolocal/StreetCLIP@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/geolocal/StreetCLIP/resolve/refs%2Fpr%2F1/tokenizer.json
2.22 MB
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