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"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("geolocal/StreetCLIP") model = AutoModelForZeroShotImageClassification.from_pretrained("geolocal/StreetCLIP") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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**IM2GPS**
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| PlaNet (2016) | 24.5 | 37.6 | 53.6 | 71.3 |
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| ISNs (2018) | 43.0 | 51.9 | 66.7 | 80.2 |
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**IM2GPS**
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Metric: Percentage at Kilometer (% @ KM)
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| Model | 25km | 200km | 750km | 2,500km |
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| PlaNet (2016) | 24.5 | 37.6 | 53.6 | 71.3 |
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| ISNs (2018) | 43.0 | 51.9 | 66.7 | 80.2 |
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